# Best Big Data Integration Platforms

## How Many Big Data Integration Platforms Products Does G2 Track?

**Total Products under this Category:** 130

### Category Stats (Aug 2026)

- **Average Rating:** 4.51/5 The average rating of products in this category, based on all submitted ratings
- **Top Trending Product:** Control-M (+0.41%) - Among all products in this category, Control-M recorded the largest rating increase compared to last month

_Last updated: August 04, 2026_

## How Does G2 Rank Big Data Integration Platforms Products?

**Why You Can Trust G2's Software Rankings:**

- 30 Analysts and Data Experts
- 10,300+ Authentic Reviews
- 130+ Products
- Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.

## G2 Grid® for Big Data Integration Platforms
 ![G2 Grid® for Big Data Integration Platforms plotting products by satisfaction and market presence](https://www.g2.com/categories/big-data-integration-platforms/grids.png?focus%5B%5D=6073&focus%5B%5D=989&focus%5B%5D=10938&focus%5B%5D=23920&focus%5B%5D=15884&focus%5B%5D=52204&focus%5B%5D=2975&focus%5B%5D=10898)

Highlighted products: Google Cloud BigQuery, Alteryx, Snowflake, Fivetran, Workato, Azure Data Factory, SnapLogic Intelligent Integration Platform (IIP), and Amazon Redshift.

Underlying data: [Grid® JSON](https://www.g2.com/categories/big-data-integration-platforms/grids.json?focus%5B%5D=google-cloud-bigquery&focus%5B%5D=alteryx&focus%5B%5D=snowflake&focus%5B%5D=fivetran&focus%5B%5D=workato&focus%5B%5D=azure-data-factory&focus%5B%5D=snaplogic-intelligent-integration-platform-iip&focus%5B%5D=amazon-redshift)

**Sponsored**

### Amazon Redshift

Tens of thousands of customers use Amazon Redshift, a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. It is optimized for datasets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

[Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list_llm&secure%5Bcategory_id%5D=1186&secure%5Bchosen_at%5D=2026-08-04T23%3A19%3A44Z&secure%5Bdisplayable_resource_id%5D=1186&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=1186&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=10898&secure%5Bresource_id%5D=1186&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fbig-data-integration-platforms&secure%5Btoken%5D=06d44938abb5999869e8f926944b5b126d373dd9c2f1f64734e497238f21923c&secure%5Burl%5D=https%3A%2F%2Faws.amazon.com%2Fredshift%2F%3Ftrk%3Dde302eb2-ad94-4a9b-8ef9-3610f836bf6a%26sc_channel%3Ddisplay%2Bads&secure%5Burl_type%5D=custom_url)

### [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews)

BigQuery is an AI-ready, petabyte-scale, and cost-effective data warehouse that lets you run analytics over vast amounts of data in near real time. Store 10 GiB of data and run up to 1 TiB of queries for free per month.

**Average Rating:** 4.5/5.0

**Total Reviews:** 1,144

#### How Do G2 Users Rate Google Cloud BigQuery?

- **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 8.9/10)
- **Quality of Support:** 8.3/10 (Category avg: 8.9/10)
- **Ease of Use:** 8.7/10 (Category avg: 8.9/10)
- **Ease of Admin:** 8.5/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Google Cloud BigQuery?

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google  
31,899,995 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=fe4a5936665c9702418dd53c477fef5a7baea08078bb117ed67e966fc581b9ec&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1441%2F&secure%5Burl_type%5D=linkedin_company_website)  
341,888 employees on LinkedIn®
- **Ownership:** NASDAQ:GOOG

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Data Analyst
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 38% Large, 35% Medium

#### What Do G2 Reviewers Say About Google Cloud BigQuery?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** of Google Cloud BigQuery, enabling quick analysis of massive datasets without hassle.
- Users appreciate the **exceptional speed** of BigQuery, allowing for quick processing of large datasets seamlessly.
- Users love the **easy integration** with Google Cloud services, enabling smooth data analysis and management.
- Users appreciate the **fast querying capabilities** of Google Cloud BigQuery, allowing effortless analysis of massive datasets.
- Users appreciate the **query efficiency** of BigQuery, effortlessly processing complex queries on massive datasets with speed.

##### Cons

- Users find the **costs can escalate quickly** with Google Cloud BigQuery, requiring careful query optimization to manage expenses.
- Users struggle with **query issues** in BigQuery, facing rising costs and challenges in query optimization and troubleshooting.
- Users find **cost management challenging** with Google Cloud BigQuery due to unpredictable pricing and incidents of unexpected charges.
- Users experience **cost issues** with Google Cloud BigQuery, struggling with unexpected high bills and limited pricing visibility.
- Users find the **steep learning curve** of Google Cloud BigQuery challenging, particularly for advanced features and optimization techniques.

#### What Are Recent G2 Reviews of Google Cloud BigQuery?

**["Easy-to-Use Cloud Tool with Shareable, Saved Queries"](https://www.g2.com/survey_responses/google-cloud-bigquery-review-12958418)**

**Rating:** 4.0/5.0 stars

_— Reetika P._

[Read full review](https://www.g2.com/survey_responses/google-cloud-bigquery-review-12958418)

**["Scalable, Secure BigQuery That Connects Seamlessly Across Services"](https://www.g2.com/survey_responses/google-cloud-bigquery-review-12638747)**

**Rating:** 5.0/5.0 stars

_— Aayush M._

[Read full review](https://www.g2.com/survey_responses/google-cloud-bigquery-review-12638747)

#### What Are G2 Users Discussing About Google Cloud BigQuery?

- [Is Big Query free?](https://www.g2.com/discussions/is-big-query-free) - 3 comments, 1 upvote
- [Is BigQuery part of Google Cloud Platform?](https://www.g2.com/discussions/is-bigquery-part-of-google-cloud-platform) - 2 comments, 2 upvotes
- [What is Google BigQuery based on?](https://www.g2.com/discussions/what-is-google-bigquery-based-on) - 1 comment
- [What is Google BigQuery used for?](https://www.g2.com/discussions/what-is-google-bigquery-used-for) - 1 comment

### [Alteryx](https://www.g2.com/products/alteryx/reviews)

Alteryx, through it's Alteryx One platform, helps enterprises transform complex, disconnected data into a clean, AI-ready state. Whether you’re creating financial forecasts, analyzing supplier performance, segmenting customer data, analyzing employee retention, or building competitive AI applications from your proprietary data, Alteryx One makes it easy to cleanse, blend, and analyze data to unlock the unique insights that drive impactful decisions. AI-Guided Analytics Alteryx automates and simplifies every stage of data preparation and analysis, from validation and enrichment to predictive analytics and automated insights. Incorporate generative AI directly into your workflows to streamline complex data tasks and generate insights faster. Unmatched flexibility, whether you prefer code-free workflows, natural language commands, or low-code options, Alteryx adapts to your needs. Trusted. Secure. Enterprise-Ready. Alteryx is trusted by over half of the Global 2000 and 19 of the top 20 global banks. With built-in automation, governance, and security, your workflows can scale and maintain compliance while delivering consistent results. And it doesn’t matter if your systems are on-premises, hybrid, or in the cloud; Alteryx fits effortlessly into your infrastructure. Easy to Use. Deeply Connected. What truly sets Alteryx apart is our focus on efficiency and ease of use for analysts and our active community of 700,000 Alteryx users to support you at every step of your journey. With seamless integration to data everywhere including platforms like Databricks, Snowflake, AWS, Google, SAP, and Salesforce, our platform helps unify siloed data and accelerate getting to insights. Visit Alteryx.com for more information, and to start your free trial.

**Average Rating:** 4.6/5.0

**Total Reviews:** 856

#### How Do G2 Users Rate Alteryx?

- **Has the product been a good partner in doing business?:** 8.8/10 (Category avg: 8.9/10)
- **Quality of Support:** 8.5/10 (Category avg: 8.9/10)
- **Ease of Use:** 8.7/10 (Category avg: 8.9/10)
- **Ease of Admin:** 8.3/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Alteryx?

- **Seller:** [Alteryx](https://www.g2.com/sellers/alteryx)
- **Company Website:** www.alteryx.com
- **Year Founded:** 1997
- **HQ Location:** Irvine, CA
- **Twitter:** @alteryx  
26,149 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=ae8a7629c5a6d593caff29361a6ee3fb670df11992dd94a9656c66461078b340&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F903031%2F&secure%5Burl_type%5D=linkedin_company_website)  
2,304 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Analyst, Analyst
- **Top Industries:** Financial Services, Accounting
- **Company Size:** 63% Large, 21% Medium

#### What Do G2 Reviewers Say About Alteryx?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** of Alteryx, finding it user-friendly and efficient for non-technical users.
- Users appreciate the **automation capabilities** of Alteryx, enhancing speed and efficiency in data preparation and analysis.
- Users love the **intuitive design** of Alteryx, making data management and workflow creation effortless and efficient.
- Users find Alteryx to be **very easy to learn and use** , enhancing their data workflow and automation experience.
- Users appreciate the **efficiency** of Alteryx, enabling quick data processing and streamlined workflows without complex coding.

##### Cons

- Users mention that Alteryx has a **high cost** which can be challenging for small teams and startups.
- Users find a **steep learning curve** for advanced features, making it challenging for beginners to master Alteryx quickly.
- Users point out the **missing features** in Alteryx, such as limited connectors and issues with output flexibility.
- Users find **learning difficulty** in Alteryx due to confusing tools and troubleshooting errors, especially for beginners.
- Users encounter **slow performance** when processing large datasets, impacting efficiency and usability in Alteryx.

#### What Are Recent G2 Reviews of Alteryx?

**["Scales Operations and Saves Time with Automated Data Workflows"](https://www.g2.com/survey_responses/alteryx-review-13047637)**

**Rating:** 4.5/5.0 stars

_— Ihor B._

[Read full review](https://www.g2.com/survey_responses/alteryx-review-13047637)

**["Makes Data Prep Faster with an Intuitive Drag-and-Drop Workflow"](https://www.g2.com/survey_responses/alteryx-review-13190742)**

**Rating:** 4.5/5.0 stars

_— Anushka S._

[Read full review](https://www.g2.com/survey_responses/alteryx-review-13190742)

### [Snowflake](https://www.g2.com/products/snowflake/reviews)

Snowflake makes enterprise AI easy, efficient and trusted. Thousands of companies around the globe, including hundreds of the world’s largest, use Snowflake’s AI Data Cloud to share data, build applications, and power their business with AI. The era of enterprise AI is here. Learn more at snowflake.com (NYSE: SNOW).

**Average Rating:** 4.5/5.0

**Total Reviews:** 712

#### How Do G2 Users Rate Snowflake?

- **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 8.9/10)
- **Quality of Support:** 8.7/10 (Category avg: 8.9/10)
- **Ease of Use:** 9.0/10 (Category avg: 8.9/10)
- **Ease of Admin:** 8.7/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Snowflake?

- **Seller:** [Snowflake, Inc.](https://www.g2.com/sellers/snowflake-inc)
- **Company Website:** www.snowflake.com
- **Year Founded:** 2012
- **HQ Location:** 135 Constitution Drive, Menlo Park CA
- **Twitter:** @SnowflakeDB  
278 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=ad18ff73a9b8bb34dd1b98a6ba1c6be57f7364939ad352612ecc483aba05d2b2&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fsnowflake-computing%2F&secure%5Burl_type%5D=linkedin_company_website)  
11,308 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Data Analyst
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 45% Medium, 43% Large

#### What Do G2 Reviewers Say About Snowflake?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** of Snowflake, finding it fast and effective for data sharing and analytics.
- Users value the **reliable features** of Snowflake, enjoying its intuitive interface and seamless data integration for analytics.
- Users find Snowflake's **data management capabilities** excellent for efficiently aggregating and querying across multiple datasets.
- Users admire the **seamless scalability** of Snowflake, effortlessly accommodating work demands and ensuring optimal performance.
- Users appreciate the **fast data analysis** of Snowflake, enabling quick insights without infrastructure worries.

##### Cons

- Users find Snowflake's **high costs** burdensome, especially for small businesses with limited budgets.
- Users find **feature limitations** in Snowflake, such as lack of code blocks and challenge in permissions management.
- Users find that **cost management** requires discipline, as unexpected charges can accumulate quickly without careful monitoring.
- Users find the **cost structure difficult to optimize** , leading to unexpectedly high initial expenses during implementation.
- Users find Snowflake's **limited features** in dynamic scripts and monitoring hinder flexibility and usability.

#### What Are Recent G2 Reviews of Snowflake?

**["Elastic Scaling and Fast Analytics with Snowflake"](https://www.g2.com/survey_responses/snowflake-review-13129003)**

**Rating:** 4.5/5.0 stars

_— Ravindra N._

[Read full review](https://www.g2.com/survey_responses/snowflake-review-13129003)

**["Snowflake Simplifies Data Management at Scale"](https://www.g2.com/survey_responses/snowflake-review-12898129)**

**Rating:** 4.0/5.0 stars

_— Harshil A._

[Read full review](https://www.g2.com/survey_responses/snowflake-review-12898129)

#### What Are G2 Users Discussing About Snowflake?

- [What is Snowflake used for?](https://www.g2.com/discussions/what-is-snowflake-used-for) - 2 comments, 1 upvote

### [Fivetran](https://www.g2.com/products/fivetran/reviews)

Fivetran is the data foundation for AI. One platform moves, manages, and transforms data from every application, database, event stream, and file your business runs on into a governed foundation that analytics, operations, and AI can act on. Connectors deploy in minutes, run themselves, and adjust automatically when a source changes, so your data team spends its time building, not maintaining pipelines.

**Average Rating:** 4.3/5.0

**Total Reviews:** 805

#### How Do G2 Users Rate Fivetran?

- **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 8.9/10)
- **Quality of Support:** 8.5/10 (Category avg: 8.9/10)
- **Ease of Use:** 9.0/10 (Category avg: 8.9/10)
- **Ease of Admin:** 8.8/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Fivetran?

- **Seller:** [Fivetran](https://www.g2.com/sellers/fivetran)
- **Company Website:** www.fivetran.com
- **Year Founded:** 2012
- **HQ Location:** Oakland, CA
- **Twitter:** @fivetran  
5,767 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=7f1ca0a984cd3b7fe678e6cf023235ee7978629644faf3910a5df7f92b63eea3&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Ffivetran%2F&secure%5Burl_type%5D=linkedin_company_website)  
1,848 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Data Analyst
- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 59% Medium, 27% Small

#### What Do G2 Reviewers Say About Fivetran?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** of Fivetran, enjoying seamless integration and effortless maintenance post-setup.
- Users appreciate the **easy setup** of Fivetran, making it straightforward to integrate with existing applications.
- Users value the **easy integration** of Fivetran, enabling seamless connectivity with various applications and tools.
- Users value the **responsive customer support** from Fivetran, enhancing their overall experience and satisfaction.
- Users appreciate the **intuitive and simple layout** of Fivetran, making data management effortless and enjoyable.

##### Cons

- Users experience **sync issues** with sporadic failures and confusion over usage quotas, complicating their workflow.
- Users note that Fivetran's pricing is **quite expensive** , which limits accessibility for many potential users.
- Users face **integration issues** with Fivetran, especially regarding schema ownership and modifying connections efficiently.
- Users find the **learning curve steep** , requiring significant technical knowledge to effectively navigate the system initially.
- Users consider Fivetran's **pricing issues** a barrier, wishing for lower costs and more flexible options for accessibility.

#### What Are Recent G2 Reviews of Fivetran?

**["No-Code, Self-Monitoring Pipelines with Connectors That Just Work"](https://www.g2.com/survey_responses/fivetran-review-13137995)**

**Rating:** 5.0/5.0 stars

_— Umesh ._

[Read full review](https://www.g2.com/survey_responses/fivetran-review-13137995)

**["Fast, Reliable No-Code Data Integrations with Huge ROI"](https://www.g2.com/survey_responses/fivetran-review-13138238)**

**Rating:** 4.5/5.0 stars

_— Jose Maria P._

[Read full review](https://www.g2.com/survey_responses/fivetran-review-13138238)

#### What Are G2 Users Discussing About Fivetran?

- [What is Fivetran used for?](https://www.g2.com/discussions/fivetran-what-is-fivetran-used-for) - 1 comment
- [What is Census used for?](https://www.g2.com/discussions/what-is-census-used-for)
- [Who owns Fivetran?](https://www.g2.com/discussions/who-owns-fivetran)
- [How much does Fivetran cost?](https://www.g2.com/discussions/how-much-does-fivetran-cost) - 1 comment
- [Is Fivetran an ETL tool?](https://www.g2.com/discussions/is-fivetran-an-etl-tool) - 2 comments

### [Workato](https://www.g2.com/es/products/workato/reviews)

Workato es el iPaaS mejor valorado y el líder en MCP Empresarial: la plataforma en la que las empresas confían para unificar integración, automatización e IA en un entorno seguro y nativo en la nube. Con la confianza de más de 12,000 clientes, incluyendo la mitad de las empresas Fortune 500, Workato conecta cada sistema, proceso y fuente de datos con más de 14,000 conectores preconstruidos. Lo que distingue a Workato: MCP Empresarial convierte procesos empresariales probados en habilidades gobernadas y listas para agentes que cualquier agente de IA — Claude, ChatGPT, Cursor o personalizado — puede ejecutar de manera segura y predecible. No se requiere reemplazo total. Ya sea modernizando integraciones heredadas o implementando IA agentiva a gran escala, Workato ofrece la orquestación, gobernanza y confianza necesarias en la empresa.

**Average Rating:** 4.7/5.0

**Total Reviews:** 747

#### How Do G2 Users Rate Workato?

- **¿Ha sido the product un buen socio para hacer negocios?:** 9.4/10 (Category avg: 8.9/10)
- **Calidad del soporte:** 9.2/10 (Category avg: 8.9/10)
- **Facilidad de uso:** 9.0/10 (Category avg: 8.9/10)
- **Facilidad de administración:** 9.0/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Workato?

- **Vendedor:** [Workato](https://www.g2.com/es/sellers/workato)
- **Sitio web de la empresa:** www.workato.com
- **Año de fundación:** 2013
- **Ubicación de la sede:** Mountain View, California
- **Twitter:** @Workato  
3,641 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=229b7e77382a8d2c3a0aeebe68dfc2316ea2caa6dd5d95ed0ee0d08884e6fc88&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F3675685&secure%5Burl_type%5D=linkedin_company_website)  
1,401 empleados en LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Ingeniero de software, Ingeniero de Software Senior
- **Top Industries:** Software de Computadora, Tecnología de la información y servicios
- **Company Size:** 43% Medium, 33% Large

#### What Do G2 Reviewers Say About Workato?

_AI-generated summary from verified user reviews_

##### Pros

- Los usuarios encuentran que Workato es **fácil de usar y eficiente** , permitiendo una automatización sencilla sin necesidad de conocimientos técnicos.
- A los usuarios les encantan las **integraciones fáciles** que ofrece Workato, haciendo que la automatización entre herramientas sea simple y eficiente.
- Los usuarios valoran la **facilidad de las integraciones** con Workato, apreciando su interfaz fácil de usar y sus extensos conectores preconstruidos.
- Los usuarios aprecian el **diseño fácil de usar y las capacidades de automatización** de Workato, mejorando la productividad y simplificando flujos de trabajo complejos.
- A los usuarios les encanta la **facilidad de automatización** en Workato, ahorrando horas al integrar sin esfuerzo varias herramientas y sistemas.

##### Cons

- Los usuarios encuentran la **complejidad** de Workato abrumadora, especialmente en lo que respecta a la terminología y las estructuras de precios que confunden a los recién llegados.
- Los usuarios encuentran la **curva de aprendizaje empinada** , con flujos de trabajo complejos y una incorporación abrumadora que complica el uso inicial.
- Los usuarios expresan frustración por las **limitaciones de datos** en Workato, lo que dificulta el envío de correos electrónicos y la transferencia de archivos para tareas más grandes.
- Los usuarios encuentran la **biblioteca de aplicaciones limitada** de Workato restrictiva, requiriendo configuración manual para integraciones menos comunes.
- Los usuarios enfrentan una **curva de aprendizaje pronunciada** con Workato, encontrando la incorporación y la configuración inicial bastante abrumadoras y complejas.

#### What Are Recent G2 Reviews of Workato?

**["Workato nos ayuda a construir integraciones complejas a una velocidad vertiginosa."](https://www.g2.com/es/survey_responses/workato-review-10305521)**

**Rating:** 5.0/5.0 stars

_— Sreenath B._

[Read full review](https://www.g2.com/es/survey_responses/workato-review-10305521)

**["La plataforma que creció con nosotros"](https://www.g2.com/es/survey_responses/workato-review-12941177)**

**Rating:** 5.0/5.0 stars

_— Anshu b._

[Read full review](https://www.g2.com/es/survey_responses/workato-review-12941177)

#### What Are G2 Users Discussing About Workato?

- [What does Workato do?](https://www.g2.com/es/discussions/what-does-workato-do)
- [¿Cuánto cuesta Workato?](https://www.g2.com/es/discussions/how-much-does-workato-cost) - 1 comment
- [¿Qué es una receta de Workato?](https://www.g2.com/es/discussions/what-is-a-workato-recipe) - 3 comments
- [What is Workato used for?](https://www.g2.com/es/discussions/what-is-workato-used-for)

### [Azure Data Factory](https://www.g2.com/es/products/azure-data-factory/reviews)

Azure Data Factory (ADF) es un servicio de integración de datos completamente gestionado y sin servidor, diseñado para simplificar el proceso de ingestión, preparación y transformación de datos de diversas fuentes. Permite a las organizaciones construir y orquestar flujos de trabajo de Extracción, Transformación y Carga (ETL) y Extracción, Carga y Transformación (ELT) en un entorno sin código, facilitando el movimiento y la transformación de datos de manera fluida entre sistemas locales y basados en la nube. Características y Funcionalidades Clave: - Conectividad Extensa: ADF ofrece más de 90 conectores integrados, permitiendo la integración con una amplia gama de fuentes de datos, incluyendo bases de datos relacionales, sistemas NoSQL, aplicaciones SaaS, APIs y servicios de almacenamiento en la nube. - Transformación de Datos Sin Código: Utilizando flujos de datos de mapeo impulsados por Apache Spark™, ADF permite a los usuarios realizar transformaciones de datos complejas sin escribir código, agilizando el proceso de preparación de datos. - Reubicación de Paquetes SSIS: Las organizaciones pueden migrar y extender fácilmente sus paquetes existentes de SQL Server Integration Services (SSIS) a la nube, logrando ahorros significativos en costos y una escalabilidad mejorada. - Escalable y Rentable: Como un servicio sin servidor, ADF escala automáticamente para satisfacer las demandas de integración de datos, ofreciendo un modelo de precios de pago por uso que elimina la necesidad de inversiones iniciales en infraestructura. - Monitoreo y Gestión Integral: ADF proporciona herramientas de monitoreo robustas, permitiendo a los usuarios rastrear el rendimiento de las canalizaciones, configurar alertas y asegurar el funcionamiento eficiente de los flujos de trabajo de datos. Valor Principal y Soluciones para el Usuario: Azure Data Factory aborda las complejidades de la integración de datos moderna proporcionando una plataforma unificada que conecta fuentes de datos dispares, automatiza flujos de trabajo de datos y facilita transformaciones de datos avanzadas. Esto empodera a las organizaciones para derivar conocimientos accionables de sus datos, mejorar los procesos de toma de decisiones y acelerar las iniciativas de transformación digital. Al ofrecer un entorno escalable, rentable y sin código, ADF reduce la carga operativa en los equipos de TI y permite a los ingenieros de datos y analistas de negocios centrarse en entregar valor a través de estrategias basadas en datos.

**Average Rating:** 4.6/5.0

**Total Reviews:** 95

#### How Do G2 Users Rate Azure Data Factory?

- **¿Ha sido the product un buen socio para hacer negocios?:** 9.1/10 (Category avg: 8.9/10)
- **Calidad del soporte:** 8.8/10 (Category avg: 8.9/10)
- **Facilidad de uso:** 8.9/10 (Category avg: 8.9/10)
- **Facilidad de administración:** 8.7/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Azure Data Factory?

- **Vendedor:** [Microsoft](https://www.g2.com/es/sellers/microsoft)
- **Año de fundación:** 1975
- **Ubicación de la sede:** Redmond, Washington
- **Twitter:** @microsoft  
13,091,739 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=9458f51bd6ded48ad432a804f19ad736469f007787569b63827154231c315630&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fmicrosoft%2F&secure%5Burl_type%5D=linkedin_company_website)  
231,632 empleados en LinkedIn®
- **Propiedad:** MSFT

#### Who Uses This Product?

- **Who Uses This:** Ingeniero de Datos, Ingeniero de software
- **Top Industries:** Tecnología de la información y servicios, Software de Computadora
- **Company Size:** 60% Large, 30% Medium

#### What Do G2 Reviewers Say About Azure Data Factory?

_AI-generated summary from verified user reviews_

##### Pros

- A los usuarios les encantan las **capacidades de integración de datos sin problemas** de Azure Data Factory, que simplifican los flujos de trabajo complejos y mejoran la productividad.
- Los usuarios valoran la **facilidad de uso** de Azure Data Factory, simplificando la integración de datos con su interfaz visual de bajo código.
- Los usuarios aprecian la **conectividad sin interrupciones** de Azure Data Factory para integrar diversas fuentes de datos con una codificación mínima.
- Los usuarios valoran las **capacidades de integración sin problemas** de Azure Data Factory, simplificando flujos de trabajo de datos complejos a través de diversas fuentes.
- Los usuarios valoran la **escalabilidad** de Azure Data Factory, lo que les permite gestionar e integrar sin esfuerzo vastas fuentes de datos.

##### Cons

- Los usuarios encuentran la **dificultad de depuración** en Azure Data Factory frustrante, especialmente para tuberías complejas y la resolución de fallos.
- Los usuarios encuentran **difícil depurar** con Azure Data Factory, a menudo enfrentando desafíos para solucionar problemas en tuberías complejas de manera efectiva.
- Los usuarios encuentran que Azure Data Factory es **caro** debido a los costos impredecibles vinculados a las ejecuciones de canalizaciones y movimientos de datos.
- Los usuarios encuentran que las **limitaciones de características** de Azure Data Factory obstaculizan su capacidad para monitorear e integrar eficientemente con Power BI.
- Los usuarios encuentran **la complejidad y las limitaciones** de Azure Data Factory desafiantes, especialmente al depurar y gestionar flujos de trabajo complejos.

#### What Are Recent G2 Reviews of Azure Data Factory?

**["Integración de datos intuitiva y escalable con Azure Data Factory"](https://www.g2.com/es/survey_responses/azure-data-factory-review-12454264)**

**Rating:** 4.5/5.0 stars

_— Alan R._

[Read full review](https://www.g2.com/es/survey_responses/azure-data-factory-review-12454264)

**["Arrastrar y soltar de bajo código que facilita el desarrollo para desarrolladores y usuarios empresariales."](https://www.g2.com/es/survey_responses/azure-data-factory-review-12746463)**

**Rating:** 4.5/5.0 stars

_— Shyam s._

[Read full review](https://www.g2.com/es/survey_responses/azure-data-factory-review-12746463)

#### What Are G2 Users Discussing About Azure Data Factory?

- [Is Azure data Factory an ETL tool?](https://www.g2.com/es/discussions/is-azure-data-factory-an-etl-tool) - 2 comments
- [What are the additional capabilities of data Factory?](https://www.g2.com/es/discussions/what-are-the-additional-capabilities-of-data-factory)
- [Which 3 types of activities can you run in Microsoft Azure data Factory?](https://www.g2.com/es/discussions/which-3-types-of-activities-can-you-run-in-microsoft-azure-data-factory)
- [What does Azure data/factory do?](https://www.g2.com/es/discussions/what-does-azure-data-factory-do)

### [SnapLogic Intelligent Integration Platform (IIP)](https://www.g2.com/es/products/snaplogic-intelligent-integration-platform-iip/reviews)

La Plataforma SnapLogic es una solución de integración y automatización agentica que ayuda a los equipos empresariales a conectar aplicaciones, fuentes de datos y APIs, y a orquestar flujos de trabajo potenciados por IA en entornos en la nube y locales. SnapLogic tiene su sede en San Mateo, California. Fundada en 2006, la empresa atiende a clientes de diversas industrias, incluyendo servicios financieros, farmacéuticos, manufactura, software y educación superior, con oficinas en América del Norte, Europa y Asia Pacífico. La plataforma está diseñada para equipos de TI, ingenieros de datos y especialistas en integración que necesitan mover datos entre sistemas, automatizar procesos de negocio y gobernar la actividad de agentes de IA a gran escala. Soporta casos de uso que incluyen integración de aplicaciones, gestión de pipelines de datos, gestión del ciclo de vida de APIs, modernización de sistemas heredados y orquestación de IA empresarial. \*\*Características y capacidades clave de la Plataforma SnapLogic incluyen:\*\* - Constructor de pipelines visual y de bajo código: Un diseñador de arrastrar y soltar que permite a los equipos construir, probar y desplegar integraciones sin escribir código personalizado, reduciendo la dependencia de recursos de desarrolladores. - Biblioteca de conectores Snaps preconstruidos: Más de 1,000 conectores reutilizables para aplicaciones empresariales, bases de datos, servicios en la nube y plataformas de datos, configurables para patrones de integración tanto simples como complejos. - SnapGPT: Un copiloto de IA integrado en la plataforma que genera pipelines de integración, sugiere mapeos de datos y asiste con la resolución de problemas de pipelines usando entradas de lenguaje natural. - Gestión de APIs: Herramientas para crear, publicar, asegurar y monitorear APIs, permitiendo a las organizaciones exponer y consumir servicios de datos a través de sistemas internos y externos. - Automatización de flujos de trabajo agentica: Capacidades para diseñar y orquestar agentes de IA que ejecutan procesos de negocio de múltiples pasos, con soporte nativo para el Protocolo de Contexto de Modelo (MCP) para gestionar interacciones de agentes a través de modelos y herramientas. - Integración y transformación de datos: Soporte para pipelines de datos por lotes, en tiempo real y en streaming con capacidades de transformación, mapeo y enriquecimiento integradas a través de datos estructurados y no estructurados. - Monitoreo y gobernanza centralizados: Un panel unificado para rastrear el rendimiento de pipelines, gestionar controles de acceso y mantener la auditabilidad en toda la actividad de integración y automatización. La Plataforma SnapLogic aborda desafíos comunes que enfrentan las organizaciones al escalar sus operaciones tecnológicas: datos fragmentados a través de sistemas desconectados, altos costos de desarrollo de integración y la complejidad de desplegar IA en entornos regulados o críticos para la misión. Al proporcionar una plataforma unificada tanto para la integración tradicional como para la automatización agentica, reduce la dependencia de conectores codificados a medida y permite a los equipos construir y gestionar integraciones sin requerir una profunda experiencia en ingeniería de software.

**Average Rating:** 4.4/5.0

**Total Reviews:** 375

#### How Do G2 Users Rate SnapLogic Intelligent Integration Platform (IIP)?

- **¿Ha sido the product un buen socio para hacer negocios?:** 8.8/10 (Category avg: 8.9/10)
- **Calidad del soporte:** 8.3/10 (Category avg: 8.9/10)
- **Facilidad de uso:** 8.8/10 (Category avg: 8.9/10)
- **Facilidad de administración:** 8.6/10 (Category avg: 8.5/10)

#### Who Is the Company Behind SnapLogic Intelligent Integration Platform (IIP)?

- **Vendedor:** [SnapLogic](https://www.g2.com/es/sellers/snaplogic)
- **Sitio web de la empresa:** www.snaplogic.com
- **Año de fundación:** 2006
- **Ubicación de la sede:** San Mateo, CA
- **Twitter:** @SnapLogic  
7,348 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=5261a75f1a744444604a852bca79d92e4b9b8336bca6cd31b6688370561a9346&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F210766%2F&secure%5Burl_type%5D=linkedin_company_website)  
307 empleados en LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Ingeniero de Datos, Consultor
- **Top Industries:** Tecnología de la información y servicios, Software de Computadora
- **Company Size:** 47% Large, 36% Medium

#### What Do G2 Reviewers Say About SnapLogic Intelligent Integration Platform (IIP)?

_AI-generated summary from verified user reviews_

##### Pros

- Los usuarios aprecian la **facilidad de uso** de SnapLogic, permitiendo una integración rápida y una configuración de canalización sencilla.
- Los usuarios aprecian las **fáciles integraciones** de SnapLogic, beneficiándose de una interfaz fácil de usar y varios conectores.
- Los usuarios aprecian las capacidades de **integración perfecta** de SnapLogic IIP, permitiendo conexiones eficientes a través de diversos sistemas.
- Los usuarios disfrutan de la **interfaz fácil de usar** de SnapLogic, simplificando tareas y mejorando la productividad general.
- A los usuarios les encanta la **simplicidad de la automatización** en SnapLogic IIP, lo que permite un desarrollo más rápido y procesos de integración eficientes.

##### Cons

- Los usuarios informan de **problemas de rendimiento** con SnapLogic, especialmente bajo cargas de trabajo pesadas y en la funcionalidad de balanceo de carga.
- Los usuarios experimentan **un rendimiento deficiente** bajo cargas de trabajo pesadas, lo que lleva a la frustración con la depuración y las integraciones complejas.
- Los usuarios luchan con **dificultades técnicas** , incluyendo desafíos de depuración y caídas de rendimiento durante cargas de trabajo pesadas.
- Los usuarios encuentran que la **complejidad de entender Snaps** y rastrear errores es bastante desafiante en SnapLogic IIP.
- Los usuarios informan de **una mala notificación de errores** y falta de claridad en la depuración, lo que complica el proceso de resolución de problemas.

#### What Are Recent G2 Reviews of SnapLogic Intelligent Integration Platform (IIP)?

**["Integraciones súper rápidas: ¡ahorra un montón de dolores de cabeza con la codificación!"](https://www.g2.com/es/survey_responses/snaplogic-intelligent-integration-platform-iip-review-13191310)**

**Rating:** 5.0/5.0 stars

_— Pavan Simhadri D._

[Read full review](https://www.g2.com/es/survey_responses/snaplogic-intelligent-integration-platform-iip-review-13191310)

**["Integraciones rápidas y flexibles impulsadas por Snaps preconstruidos, flujos de trabajo visuales y un gran soporte."](https://www.g2.com/es/survey_responses/snaplogic-intelligent-integration-platform-iip-review-13181168)**

**Rating:** 5.0/5.0 stars

_— aravind k._

[Read full review](https://www.g2.com/es/survey_responses/snaplogic-intelligent-integration-platform-iip-review-13181168)

#### What Are G2 Users Discussing About SnapLogic Intelligent Integration Platform (IIP)?

- [¿Para qué se utiliza la Plataforma de Integración Inteligente (IIP) de SnapLogic?](https://www.g2.com/es/discussions/what-is-snaplogic-intelligent-integration-platform-iip-used-for) - 1 comment

### [Maia](https://www.g2.com/es/products/matillion-maia/reviews)

Maia es una plataforma de automatización de datos impulsada por agentes de IA autónomos que construyen, mantienen y evolucionan productos de datos, eliminando así el trabajo manual de datos. Maia empodera a los CDAOs y a los equipos de datos empresariales para entregar productos de datos a escala de máquina mientras mantienen la gobernanza. Su plataforma integrada combina agentes especializados en Maia Team, basados en la inteligencia de datos organizacionales del Maia Context Engine y ejecutados a través de las herramientas de datos gobernadas de Maia Foundation. Organizaciones como EDF, St. James’ Place y Nature’s Touch utilizan Maia para automatizar el trabajo de datos a escala, modernizar plataformas y acelerar las hojas de ruta de IA sin aumentar el personal. Vea Maia por usted mismo.

**Average Rating:** 4.5/5.0

**Total Reviews:** 120

#### How Do G2 Users Rate Maia?

- **¿Ha sido the product un buen socio para hacer negocios?:** 8.4/10 (Category avg: 8.9/10)
- **Calidad del soporte:** 8.6/10 (Category avg: 8.9/10)
- **Facilidad de uso:** 9.0/10 (Category avg: 8.9/10)
- **Facilidad de administración:** 8.3/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Maia?

- **Vendedor:** [Matillion](https://www.g2.com/es/sellers/matillion)
- **Sitio web de la empresa:** www.matillion.com
- **Año de fundación:** 2011
- **Ubicación de la sede:** Salford, GB
- **Twitter:** @matillion  
7,362 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=9b553fae1e51bb2b58491b1c1d561a15b7dd370d485b1ff7affe8e7cbd75e1cf&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F2360297%2F&secure%5Burl_type%5D=linkedin_company_website)  
463 empleados en LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Ingeniero de Datos
- **Top Industries:** Software de Computadora, Tecnología de la información y servicios
- **Company Size:** 47% Medium, 31% Large

#### What Do G2 Reviewers Say About Maia?

_AI-generated summary from verified user reviews_

##### Pros

- Los usuarios encuentran la **facilidad de uso** en la interfaz de Maia invaluable para una configuración ETL fluida y comprensión.
- Los usuarios aprecian la **automatización sin problemas** de Matillion, haciendo que los procesos ETL sean sencillos y eficientes en todas las plataformas.
- Los usuarios aprecian la **interfaz simple** de Matillion, lo que hace que la configuración sea fácil y amigable para todos.
- A los usuarios les encanta la **interfaz intuitiva y simple** de Matillion, lo que facilita a los principiantes configurarla y navegar.
- Los usuarios elogian a Maia por su **eficiencia ETL** , haciendo que los flujos de trabajo complejos sean fáciles de usar y escalables para todos los niveles de analistas.

##### Cons

- Los usuarios experimentan **problemas de rendimiento laboral** debido a las limitaciones del intérprete de Jython y los flujos de trabajo de un solo hilo.
- Los usuarios encuentran el **modelo de precios caro** , especialmente a medida que aumenta el volumen de datos, lo que afecta la asequibilidad general.
- Los usuarios informan de **problemas de rendimiento laboral** con el intérprete de Jython y limitaciones del flujo de trabajo de un solo hilo que afectan la eficiencia.
- Los usuarios expresan preocupación por la **dependencia de la nube** , sintiéndose atrapados en el entorno con opciones limitadas de personalización.
- Los usuarios encuentran **limitaciones de la API** en Maia frustrantes, ya que las funciones administrativas carecen de opciones suficientes de acceso y usabilidad.

#### What Are Recent G2 Reviews of Maia?

**["Maia hace que la incorporación sea rápida con una interfaz intuitiva y flujos de trabajo de bajo código."](https://www.g2.com/es/survey_responses/maia-review-12942268)**

**Rating:** 4.5/5.0 stars

_— Anthony S._

[Read full review](https://www.g2.com/es/survey_responses/maia-review-12942268)

**["Maia escaló más de 800 migraciones de canalización sin aumentar la carga de trabajo"](https://www.g2.com/es/survey_responses/maia-review-12920298)**

**Rating:** 5.0/5.0 stars

_— Keith G._

[Read full review](https://www.g2.com/es/survey_responses/maia-review-12920298)

#### What Are G2 Users Discussing About Maia?

- [¿Para qué se utiliza Matillion ETL?](https://www.g2.com/es/discussions/what-is-matillion-etl-used-for) - 1 comment
- [¿Para qué se utiliza Matillion Data Loader?](https://www.g2.com/es/discussions/what-is-matillion-data-loader-used-for)
- [What are ETL tools used for?](https://www.g2.com/es/discussions/what-are-etl-tools-used-for)
- [Is Matillion open source?](https://www.g2.com/es/discussions/is-matillion-open-source)
- [What is ETL in software?](https://www.g2.com/es/discussions/what-is-etl-in-software)

### [Amazon Redshift](https://www.g2.com/products/amazon-redshift/reviews)

Tens of thousands of customers use Amazon Redshift, a fast, fully managed, petabyte-scale data warehouse service that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. It is optimized for datasets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

**Average Rating:** 4.3/5.0

**Total Reviews:** 371

#### How Do G2 Users Rate Amazon Redshift?

- **Has the product been a good partner in doing business?:** 8.7/10 (Category avg: 8.9/10)
- **Quality of Support:** 8.5/10 (Category avg: 8.9/10)
- **Ease of Use:** 8.7/10 (Category avg: 8.9/10)
- **Ease of Admin:** 8.4/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Amazon Redshift?

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud  
2,232,483 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=072881eee28a2afe24f8d1bda9f20e3e146b9fb4b214f216411ce2ed6898b31e&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Famazon-web-services%2F&secure%5Burl_type%5D=linkedin_company_website)  
147,094 employees on LinkedIn®
- **Ownership:** NASDAQ: AMZN

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Senior Data Engineer
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 40% Large, 39% Medium

#### What Do G2 Reviewers Say About Amazon Redshift?

_AI-generated summary from verified user reviews_

##### Pros

- Users value the **fast querying capabilities** of Amazon Redshift, enabling efficient analysis of large datasets seamlessly.
- Users praise the **easy integrations** with other software, enhancing data solutions within the Amazon Redshift ecosystem.
- Users find Amazon Redshift's **ease of use** exceptional, facilitating quick access and efficient data management.
- Users value the **easy integrations** of Amazon Redshift, enhancing their ability to build seamless data solutions.
- Users praise the **impressive speed and scalability** of Amazon Redshift, optimizing data management and query performance.

##### Cons

- Users find **feature limitations** in Redshift, especially regarding advanced analytics and multi-language support for coding.
- Users note significant **software limitations** with Redshift, particularly regarding cost and performance issues with complex queries.
- Users find the **complexity of optimizations** in Amazon Redshift burdensome, requiring significant management and maintenance effort.
- Users face **query issues** with Amazon Redshift, requiring extensive optimization and management to maintain performance.
- Users find the **query optimization process cumbersome** , as it requires significant effort and specialized knowledge for efficiency.

#### What Are Recent G2 Reviews of Amazon Redshift?

**["Scalable and Efficient Cloud Data Platform"](https://www.g2.com/survey_responses/amazon-redshift-review-12872150)**

**Rating:** 4.5/5.0 stars

_— Swaroop W._

[Read full review](https://www.g2.com/survey_responses/amazon-redshift-review-12872150)

**["Powerful Analytics Tool with Some Flexibility Limitations"](https://www.g2.com/survey_responses/amazon-redshift-review-12781722)**

**Rating:** 4.0/5.0 stars

_— Atharva P._

[Read full review](https://www.g2.com/survey_responses/amazon-redshift-review-12781722)

#### What Are G2 Users Discussing About Amazon Redshift?

- [What is Amazon Redshift used for?](https://www.g2.com/discussions/what-is-amazon-redshift-used-for)
- [Is AWS redshift a database?](https://www.g2.com/discussions/is-aws-redshift-a-database) - 1 comment
- [When can I use Amazon redshift?](https://www.g2.com/discussions/when-can-i-use-amazon-redshift) - 3 comments
- [What are the characteristics of redshift?](https://www.g2.com/discussions/what-are-the-characteristics-of-redshift) - 2 comments
- [What does Amazon redshift do?](https://www.g2.com/discussions/what-does-amazon-redshift-do) - 2 comments

### [5X](https://www.g2.com/es/products/5x/reviews)

5X es una plataforma de datos e inteligencia artificial de extremo a extremo. La plataforma organiza tus datos independientemente de la fuente o el formato. Ya sea que tengas un equipo de datos dedicado o no, nuestra plataforma transforma datos fragmentados en ideas y aplicaciones accionables. La retroalimentación que recibimos más a menudo de los clientes es: "Esto es autoexplicativo" y "Es súper fácil de usar". Y ese era exactamente nuestro objetivo: crear una plataforma poderosa, todo en uno, que sea increíblemente fácil de usar. La pila de datos moderna ha evolucionado. Ya no se trata de unir proveedores. La próxima generación de la pila de datos moderna es una plataforma todo en uno que ofrece velocidad, simplicidad y un costo de propiedad reducido. Eso es exactamente lo que hemos creado en 5X. Las empresas utilizan 5X por múltiples razones: 1) Velocidad y productividad. Las plataformas de datos todo en uno son increíblemente eficientes. Hemos visto a empresas construir casos de uso en el día 1. ¡Contáctanos para ver si calificas para un inicio rápido gratuito de 48 horas! 🚀 2) Disminuye tu costo total de propiedad en un 30% en comparación con construir tu propia plataforma. Esto no cuenta las horas de personal necesarias para apoyar la construcción de una plataforma 🤯 3) Usa nuestra consultoría de datos de pila completa para soporte en ingeniería de datos y análisis 👨‍💻 5X fue fundada en 2020 con presencia en EE.UU., Singapur, Reino Unido e India. Nuestro equipo global cuenta con más de 70 personas y está creciendo rápidamente. Recientemente hemos recaudado nuestra ronda semilla de Flybridge Capital y estamos respaldados por fundadores destacados de empresas como Datadog, Preset, Astronomer, Mode, Rudderstack y otros prominentes inversores ángeles. Para más información, visita 5X.co No solo hablamos de velocidad y simplicidad; lo respaldamos con pruebas. Habla con nosotros sobre nuestro inicio rápido de 48 horas donde podemos construir un caso de uso de extremo a extremo para ti en 48 horas de forma gratuita.

**Average Rating:** 4.9/5.0

**Total Reviews:** 81

#### How Do G2 Users Rate 5X?

- **¿Ha sido the product un buen socio para hacer negocios?:** 9.8/10 (Category avg: 8.9/10)
- **Calidad del soporte:** 9.8/10 (Category avg: 8.9/10)
- **Facilidad de uso:** 9.5/10 (Category avg: 8.9/10)
- **Facilidad de administración:** 9.6/10 (Category avg: 8.5/10)

#### Who Is the Company Behind 5X?

- **Vendedor:** [5X](https://www.g2.com/es/sellers/5x)
- **Año de fundación:** 2020
- **Ubicación de la sede:** San Francisco
- **Twitter:** @DataWith5x  
49 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=123d4698fdf0b6036b49dc7dd7e99f12fd6c91a432ee93cdc153ec13a4d274d7&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fdatawith5x%2F&secure%5Burl_type%5D=linkedin_company_website)  
111 empleados en LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Software de Computadora, Servicios Financieros
- **Company Size:** 56% Medium, 40% Small

#### What Do G2 Reviewers Say About 5X?

_AI-generated summary from verified user reviews_

##### Pros

- Los usuarios valoran la **facilidad de uso** de 5X, apreciando su interfaz intuitiva y la integración perfecta con las herramientas existentes.
- Los usuarios aprecian el **soporte al cliente receptivo** de 5X, que aborda rápidamente las consultas e implementa las características solicitadas.
- Los usuarios elogian a 5X por sus **capacidades de integración sin problemas** , mejorando la automatización del flujo de trabajo y simplificando la gestión de datos a través de plataformas.
- Los usuarios valoran las **fáciles integraciones** de 5X, mejorando su ingesta de datos y la eficiencia operativa general sin esfuerzo.
- Los usuarios elogian 5X por su **diseño intuitivo y capacidades integradas** , facilitando la gestión eficiente de datos y flujos de trabajo sin problemas.

##### Cons

- Los usuarios encuentran una **curva de aprendizaje pronunciada** con 5X inicialmente, pero la capacitación ayuda a facilitar la transición.
- Los usuarios encuentran el **configuración compleja** de 5X desafiante, a menudo requiriendo soporte adicional para una implementación exitosa.
- Los usuarios encuentran una **curva de aprendizaje pronunciada** inicialmente, requiriendo sesiones de habilitación para adaptarse a la complejidad de la plataforma.
- Los usuarios encuentran que la **configuración difícil** de 5X consume mucho tiempo, requiriendo un aprendizaje extenso y soporte para integraciones complejas.
- Los usuarios notan **limitaciones de características** ya que algunas herramientas avanzadas aún están en desarrollo, afectando la funcionalidad general y los flujos de trabajo.

#### What Are Recent G2 Reviews of 5X?

**["Un socio de datos confiable y escalable"](https://www.g2.com/es/survey_responses/5x-review-11889175)**

**Rating:** 5.0/5.0 stars

_— Varinderjit K._

[Read full review](https://www.g2.com/es/survey_responses/5x-review-11889175)

**["Soporte excepcional y plataforma fácil de usar impulsando nuestra transformación de datos"](https://www.g2.com/es/survey_responses/5x-review-11903408)**

**Rating:** 4.0/5.0 stars

_— Shuming F._

[Read full review](https://www.g2.com/es/survey_responses/5x-review-11903408)

### [Astro by Astronomer](https://www.g2.com/es/products/astro-by-astronomer/reviews)

Para los equipos de datos que buscan aumentar la disponibilidad de datos confiables, Astronomer ofrece Astro, la moderna plataforma de orquestación de datos, impulsada por Airflow. Astro permite a los ingenieros de datos, científicos de datos y analistas de datos construir, ejecutar y observar pipelines como código. Astronomer es la fuerza impulsora detrás de Apache Airflow™, el estándar de facto para expresar flujos de datos como código. Airflow se descarga más de 31 millones de veces cada mes y es utilizado por cientos de miles de equipos en todo el mundo.

**Average Rating:** 4.5/5.0

**Total Reviews:** 135

#### How Do G2 Users Rate Astro by Astronomer?

- **¿Ha sido the product un buen socio para hacer negocios?:** 9.0/10 (Category avg: 8.9/10)
- **Calidad del soporte:** 8.9/10 (Category avg: 8.9/10)
- **Facilidad de uso:** 9.0/10 (Category avg: 8.9/10)
- **Facilidad de administración:** 8.8/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Astro by Astronomer?

- **Vendedor:** [Astronomer](https://www.g2.com/es/sellers/astronomer)
- **Sitio web de la empresa:** www.astronomer.io
- **Año de fundación:** 2018
- **Ubicación de la sede:** New York, US
- **Twitter:** @astronomerio  
19,697 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=14de7865093cbe981bc53aa7236a5a16a9a7e76fb4d0a5b96f23ea594dfac03b&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F10019299&secure%5Burl_type%5D=linkedin_company_website)  
4,572 empleados en LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Ingeniero de Datos, Ingeniero de Datos Senior
- **Top Industries:** Tecnología de la información y servicios, Servicios Financieros
- **Company Size:** 47% Medium, 38% Large

#### What Do G2 Reviewers Say About Astro by Astronomer?

_AI-generated summary from verified user reviews_

##### Pros

- Los usuarios destacan la **facilidad de uso** de Astro, con una interfaz intuitiva e integración perfecta con Slack para el monitoreo.
- Los usuarios aprecian la **mejora de eficiencia** de Astro, mejorando la gestión del flujo de trabajo y ahorrando tiempo valioso.
- Los usuarios aprecian la **interfaz de usuario intuitiva** de Astro, que simplifica flujos de trabajo complejos y mejora la colaboración en equipo.
- A los usuarios les encantan las **capacidades de automatización** de Astro por Astronomer, mejorando la eficiencia en la orquestación y el monitoreo de datos.
- Los usuarios valoran la **facilidad de implementación** de Astro por Astronomer, que simplifica Airflow y garantiza una gestión confiable de las canalizaciones de datos.

##### Cons

- Los usuarios expresan preocupaciones sobre Astro de Astronomer debido a su **alto precio** y falta de transparencia, especialmente para equipos más pequeños.
- Los usuarios informan de una **curva de aprendizaje pronunciada** para los nuevos miembros del equipo, lo que requiere un tiempo significativo para la adaptación y la formación.
- Los usuarios tienen dificultades con una **curva de aprendizaje pronunciada** , lo que hace que la adaptación y el entrenamiento para Astro sean desafiantes para los nuevos miembros del equipo.
- Los usuarios a menudo encuentran una **curva de aprendizaje pronunciada** con Astro, lo que requiere tiempo adicional para la adaptación y el entrenamiento de nuevos usuarios.
- Los usuarios encuentran la **personalización limitada** de Astro restrictiva, especialmente en comparación con las opciones de Airflow autoalojadas.

#### What Are Recent G2 Reviews of Astro by Astronomer?

**["Excelente experiencia para desarrolladores y clientes"](https://www.g2.com/es/survey_responses/astro-by-astronomer-review-8428848)**

**Rating:** 5.0/5.0 stars

_— Juan Roberto H._

[Read full review](https://www.g2.com/es/survey_responses/astro-by-astronomer-review-8428848)

**["Asro literalmente ayuda en el trabajo de ingeniería de datos, haciéndolo más fácil y productivo."](https://www.g2.com/es/survey_responses/astro-by-astronomer-review-8519803)**

**Rating:** 5.0/5.0 stars

_— Lakshminarayanan K._

[Read full review](https://www.g2.com/es/survey_responses/astro-by-astronomer-review-8519803)

#### What Are G2 Users Discussing About Astro by Astronomer?

- [What is your experience with Astro by Astronomer for data orchestration, and what challenges have you faced?](https://www.g2.com/es/discussions/what-is-your-experience-with-astro-by-astronomer-for-data-orchestration-and-what-challenges-have-you-faced)
- [¿Para qué se utiliza Astro de Astronomer?](https://www.g2.com/es/discussions/what-is-astro-by-astronomer-used-for)

### [IBM webMethods B2B](https://www.g2.com/es/products/ibm-webmethods-b2b/reviews)

La integración B2B te permite compartir documentos—órdenes de compra, facturas, avisos de envío, contratos y más—en la nube y mantener todo sincronizado con APIs.

**Average Rating:** 4.5/5.0

**Total Reviews:** 56

#### How Do G2 Users Rate IBM webMethods B2B?

- **¿Ha sido the product un buen socio para hacer negocios?:** 8.4/10 (Category avg: 8.9/10)
- **Calidad del soporte:** 8.7/10 (Category avg: 8.9/10)
- **Facilidad de uso:** 8.9/10 (Category avg: 8.9/10)
- **Facilidad de administración:** 8.2/10 (Category avg: 8.5/10)

#### Who Is the Company Behind IBM webMethods B2B?

- **Vendedor:** [IBM](https://www.g2.com/es/sellers/ibm)
- **Año de fundación:** 1911
- **Ubicación de la sede:** Armonk, New York, United States
- **Twitter:** @IBMSecurity  
74,660 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=14b544adaece4fdbc987f1d7f7028048c22259946811200cc751263825586af9&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1009%2F&secure%5Burl_type%5D=linkedin_company_website)  
328,202 empleados en LinkedIn®
- **Propiedad:** SWX:IBM

#### Who Uses This Product?

- **Top Industries:** Contratación y Reclutamiento, Software de Computadora
- **Company Size:** 42% Medium, 35% Large

#### What Do G2 Reviewers Say About IBM webMethods B2B?

_AI-generated summary from verified user reviews_

##### Pros

- Los usuarios aprecian la **facilidad de uso** en IBM webMethods B2B, simplificando la comunicación y la gestión de procesos con socios comerciales.
- Los usuarios valoran el **intercambio de datos sin interrupciones** y las características robustas que simplifican la integración y automatización B2B.
- Los usuarios valoran mucho las **fuertes características de seguridad** de IBM webMethods B2B, asegurando transacciones seguras y confiables.
- Los usuarios valoran la **automatización de extremo a extremo** de los procesos B2B, mejorando la eficiencia y simplificando el intercambio de datos con los socios.
- Los usuarios valoran las **capacidades de integración** de IBM webMethods B2B, facilitando conexiones fluidas e intercambios de documentos con socios.

##### Cons

- Los usuarios encuentran que la **complejidad** de IBM webMethods B2B es desafiante, lo que afecta la experiencia del usuario y las opciones de personalización.
- Los usuarios encuentran que la estructura de precios de IBM webMethods B2B es **cara** y desafiante para las pequeñas empresas.
- Los usuarios encuentran que la **curva de aprendizaje difícil** de IBM webMethods B2B es un desafío, especialmente para los recién llegados a las integraciones B2B.
- Los usuarios expresan preocupaciones sobre la **estructura de precios compleja** de webMethods B2B, encontrándola difícil de ajustar a los presupuestos.
- Los usuarios pueden experimentar una **curva de aprendizaje** con webMethods.io B2B, especialmente aquellos que son nuevos en las herramientas de integración.

#### What Are Recent G2 Reviews of IBM webMethods B2B?

**["Herramienta eficiente para el procesamiento de documentos empresariales sobre infraestructura en la nube."](https://www.g2.com/es/survey_responses/ibm-webmethods-b2b-review-9536771)**

**Rating:** 4.0/5.0 stars

_— Mahesh B._

[Read full review](https://www.g2.com/es/survey_responses/ibm-webmethods-b2b-review-9536771)

**["Recomiendo encarecidamente usar."](https://www.g2.com/es/survey_responses/ibm-webmethods-b2b-review-10173432)**

**Rating:** 5.0/5.0 stars

_— Shilpa J._

[Read full review](https://www.g2.com/es/survey_responses/ibm-webmethods-b2b-review-10173432)

### [Azure Synapse Analytics](https://www.g2.com/products/azure-synapse-analytics/reviews)

Azure Synapse Analytics is a cloud-based Enterprise Data Warehouse (EDW) that leverages Massively Parallel Processing (MPP) to quickly run complex queries across petabytes of data.

**Average Rating:** 4.4/5.0

**Total Reviews:** 37

#### How Do G2 Users Rate Azure Synapse Analytics?

- **Has the product been a good partner in doing business?:** 8.3/10 (Category avg: 8.9/10)
- **Quality of Support:** 8.7/10 (Category avg: 8.9/10)
- **Ease of Use:** 8.8/10 (Category avg: 8.9/10)
- **Ease of Admin:** 8.9/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Azure Synapse Analytics?

- **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft)
- **Year Founded:** 1975
- **HQ Location:** Redmond, Washington
- **Twitter:** @microsoft  
13,091,739 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=9458f51bd6ded48ad432a804f19ad736469f007787569b63827154231c315630&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fmicrosoft%2F&secure%5Burl_type%5D=linkedin_company_website)  
231,632 employees on LinkedIn®
- **Ownership:** MSFT

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services
- **Company Size:** 45% Medium, 32% Large

#### What Do G2 Reviewers Say About Azure Synapse Analytics?

_AI-generated summary from verified user reviews_

##### Pros

- Users laud the **unified analytics experience** of Azure Synapse Analytics, enhancing efficiency and simplifying complex data processes.
- Users value the **automation capabilities** of Azure Synapse Analytics, enhancing efficiency in data analytics solutions.
- Users appreciate the **seamless cloud integration** of Azure Synapse Analytics, enhancing data workflows and overall efficiency.
- Users value the **cost-effective** capabilities of Azure Synapse Analytics, enjoying scalable solutions without high expenditures.
- Users appreciate the **seamless data integration** capabilities of Azure Synapse Analytics, enhancing efficiency and simplifying analytics solutions.

##### Cons

- Users find the **cost estimation process complex** due to difficulties in monitoring and optimizing various service components.
- Users face challenges with **cost management** , struggling with optimization and monitoring across various Azure Synapse components.
- Users face challenges with **debugging complex pipeline failures** due to a lack of detailed error transparency, increasing troubleshooting time.
- Users face **difficult debugging** due to a steep learning curve and lack of detailed error transparency during pipeline failures.
- Users find Azure Synapse Analytics **expensive** , especially when managing costs across multiple services and queries.

#### What Are Recent G2 Reviews of Azure Synapse Analytics?

**["Unified Data Warehousing and Big Data in One Powerful Platform"](https://www.g2.com/survey_responses/azure-synapse-analytics-review-12435130)**

**Rating:** 4.5/5.0 stars

_— Daniel H._

[Read full review](https://www.g2.com/survey_responses/azure-synapse-analytics-review-12435130)

**["Unified Analytics Platform with Seamless Azure Integration"](https://www.g2.com/survey_responses/azure-synapse-analytics-review-12353239)**

**Rating:** 4.0/5.0 stars

_— Ashish D._

[Read full review](https://www.g2.com/survey_responses/azure-synapse-analytics-review-12353239)

#### What Are G2 Users Discussing About Azure Synapse Analytics?

- [Does Azure Synapse include Analysis Services?](https://www.g2.com/discussions/does-azure-synapse-include-analysis-services)
- [When should use Azure synapse analytics?](https://www.g2.com/discussions/when-should-use-azure-synapse-analytics)
- [What are advantages of Azure synapse analytics?](https://www.g2.com/discussions/what-are-advantages-of-azure-synapse-analytics)
- [What is included in Azure synapse analytics?](https://www.g2.com/discussions/what-is-included-in-azure-synapse-analytics)

### [dbt](https://www.g2.com/products/dbt/reviews)

dbt is a transformation workflow that lets data teams quickly and collaboratively deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. Now anyone who knows SQL can build production-grade data pipelines.

**Average Rating:** 4.7/5.0

**Total Reviews:** 208

#### How Do G2 Users Rate dbt?

- **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 8.9/10)
- **Quality of Support:** 8.8/10 (Category avg: 8.9/10)
- **Ease of Use:** 9.0/10 (Category avg: 8.9/10)
- **Ease of Admin:** 8.5/10 (Category avg: 8.5/10)

#### Who Is the Company Behind dbt?

- **Seller:** [dbt Labs](https://www.g2.com/sellers/dbt-labs)
- **Year Founded:** 2016
- **HQ Location:** Philadelphia, US
- **Twitter:** @getdbt  
14,792 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=2528ac46bc91e3acc7ad9e4f602bdecf43b7a29cef8c7a88fcb0b299043e4e1a&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fdbtlabs%2F&secure%5Burl_type%5D=linkedin_company_website)  
874 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Analytics Engineer
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 56% Medium, 27% Small

#### What Do G2 Reviewers Say About dbt?

_AI-generated summary from verified user reviews_

##### Pros

- Users love the **ease of use** of dbt, thanks to its clear structure, intuitive documentation, and seamless integration.
- Users value dbt for its **integration of software engineering best practices** , enhancing maintainability and collaboration in SQL transformations.
- Users value the **automation** features of dbt, significantly enhancing SQL code maintainability and transforming data workflows.
- Users value the **transformative power** of dbt, efficiently organizing and modeling data for actionable insights.
- Users value dbt for its **high data quality** , ensuring integrity and enhancing analytics workflows through modularization and documentation.

##### Cons

- Users face challenges with **limited functionality** in dbt due to rigid models and debugging difficulties, affecting project progress.
- Users often face **dependency issues** with dbt, leading to time-consuming troubleshooting and disruption in workflows.
- Users find the **steep learning curve** of mastering concepts like Jinja and Git to be quite challenging.
- Users struggle with **unhelpful error messages** in dbt, making troubleshooting difficult and frustrating.
- Users face **confusing error reporting** that complicates troubleshooting and hinders quick identification of issues.

#### What Are Recent G2 Reviews of dbt?

**["Simple SQL-Driven Materializations with Powerful Lineage"](https://www.g2.com/survey_responses/dbt-review-12985641)**

**Rating:** 5.0/5.0 stars

_— Anish G._

[Read full review](https://www.g2.com/survey_responses/dbt-review-12985641)

**["dbt Streamlines Data Pipelines with Powerful Incremental and SCD2 Features"](https://www.g2.com/survey_responses/dbt-review-12712114)**

**Rating:** 5.0/5.0 stars

_— Hithesh P._

[Read full review](https://www.g2.com/survey_responses/dbt-review-12712114)

#### What Are G2 Users Discussing About dbt?

- [What is DBT data Modelling?](https://www.g2.com/discussions/what-is-dbt-data-modelling) - 2 comments
- [What is DBT technology?](https://www.g2.com/discussions/what-is-dbt-technology) - 2 comments
- [What is DBT database tool?](https://www.g2.com/discussions/what-is-dbt-database-tool) - 1 comment
- [What is DBT tool used for?](https://www.g2.com/discussions/what-is-dbt-tool-used-for) - 2 comments

### [IBM StreamSets](https://www.g2.com/es/products/ibm-streamsets/reviews)

StreamSets, una empresa de Software AG, elimina la fricción de integración de datos en entornos híbridos y multi-nube complejos para mantenerse al ritmo de las demandas empresariales de datos inmediatos. Nuestra plataforma permite a los equipos de datos desbloquear datos, sin ceder el control, para habilitar una empresa impulsada por datos. - Las canalizaciones resilientes detectan y se adaptan a cambios constantes en la estructura de datos, semántica e infraestructura. - Aprende una vez para crear muchas canalizaciones de integración diferentes con una experiencia de diseño única para todos los patrones: streaming, batch, CDC, ETL, ELT, ML. - Los fragmentos de canalización reutilizables permiten a cualquiera usar la funcionalidad que diseñan tus ingenieros de datos. - El SDK de Python te permite crear plantillas de canalizaciones a escala creando fácilmente cientos de canalizaciones con solo unas pocas líneas de código. - Simplifica las transformaciones de datos con procesadores predefinidos para cumplir con el 99% de tus requisitos analíticos desde el primer momento. - Las topologías proporcionan transparencia para ver cómo los sistemas están conectados y cómo fluyen los datos a través de la empresa. - Los SLA de datos y las reglas exponen problemas ocultos en tus flujos de datos, creando límites a lo largo de las canalizaciones de datos para la calidad de los datos, dimensionamiento, rendimiento de rendimiento, tasas de error, fuga de información privada/sensible, y más. StreamSets entrega datos listos para análisis, mejorando la toma de decisiones en tiempo real y reduciendo los costos y riesgos asociados con el flujo de datos a través de una organización. Por eso, las empresas más grandes del mundo confían en StreamSets para impulsar millones de canalizaciones de datos para análisis modernos, ciencia de datos, aplicaciones inteligentes e integración híbrida.

**Average Rating:** 4.0/5.0

**Total Reviews:** 114

#### How Do G2 Users Rate IBM StreamSets?

- **¿Ha sido the product un buen socio para hacer negocios?:** 8.2/10 (Category avg: 8.9/10)
- **Calidad del soporte:** 8.0/10 (Category avg: 8.9/10)
- **Facilidad de uso:** 8.4/10 (Category avg: 8.9/10)
- **Facilidad de administración:** 7.8/10 (Category avg: 8.5/10)

#### Who Is the Company Behind IBM StreamSets?

- **Vendedor:** [IBM](https://www.g2.com/es/sellers/ibm)
- **Año de fundación:** 1911
- **Ubicación de la sede:** Armonk, New York, United States
- **Twitter:** @IBMSecurity  
74,660 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=14b544adaece4fdbc987f1d7f7028048c22259946811200cc751263825586af9&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1009%2F&secure%5Burl_type%5D=linkedin_company_website)  
328,202 empleados en LinkedIn®
- **Propiedad:** SWX:IBM

#### Who Uses This Product?

- **Who Uses This:** Ingeniero de Datos, Ingeniero de software
- **Top Industries:** Tecnología de la información y servicios, Software de Computadora
- **Company Size:** 42% Large, 34% Medium

#### What Do G2 Reviewers Say About IBM StreamSets?

_AI-generated summary from verified user reviews_

##### Pros

- Los usuarios valoran la **facilidad de uso** en IBM StreamSets, destacando su interfaz intuitiva y características amigables para principiantes.
- Los usuarios elogian la **interfaz de arrastrar y soltar fácil de usar** de IBM StreamSets, mejorando la eficiencia de visualización y depuración.
- Los usuarios valoran las **capacidades efectivas de gestión de datos** de IBM StreamSets para una integración y automatización sin problemas en todos los entornos.
- Los usuarios aprecian la **simplificación de los flujos de trabajo de integración de datos** en IBM StreamSets, haciendo que la creación de canalizaciones sea intuitiva y eficiente.
- Los usuarios valoran la **amplia gama de integraciones** ofrecidas por IBM StreamSets, mejorando la conectividad de datos en la nube y en las instalaciones.

##### Cons

- Los usuarios experimentan una **curva de aprendizaje pronunciada** , especialmente al utilizar funciones avanzadas y gestionar eficazmente tuberías complejas.
- Los usuarios encuentran que el **precio es excesivo** , especialmente para equipos más pequeños, lo que afecta la satisfacción general con IBM StreamSets.
- Los usuarios encuentran que la **dificultad de aprendizaje** de las funciones avanzadas en StreamSets consume mucho tiempo y no está bien respaldada.
- Los usuarios a menudo experimentan **rendimiento lento** con IBM StreamSets, particularmente al procesar grandes volúmenes de datos o tuberías complejas.
- Los usuarios encuentran desafiante la **empinada curva de aprendizaje** de IBM StreamSets, especialmente con características avanzadas y configuraciones.

#### What Are Recent G2 Reviews of IBM StreamSets?

**["Integración de datos poderosa con IBM Stream sets."](https://www.g2.com/es/survey_responses/ibm-streamsets-review-11654909)**

**Rating:** 5.0/5.0 stars

_— Abhishek D._

[Read full review](https://www.g2.com/es/survey_responses/ibm-streamsets-review-11654909)

**["Simplifica las canalizaciones de datos en tiempo real con personalización mixta"](https://www.g2.com/es/survey_responses/ibm-streamsets-review-12240946)**

**Rating:** 4.0/5.0 stars

_— Sravya A._

[Read full review](https://www.g2.com/es/survey_responses/ibm-streamsets-review-12240946)

#### What Are G2 Users Discussing About IBM StreamSets?

- [¿Para qué se utiliza StreamSets?](https://www.g2.com/es/discussions/what-is-streamsets-used-for)
- [What is StreamSets data collector?](https://www.g2.com/es/discussions/what-is-streamsets-data-collector)
- [What is StreamSets control hub?](https://www.g2.com/es/discussions/what-is-streamsets-control-hub)
- [Are StreamSets free?](https://www.g2.com/es/discussions/are-streamsets-free)
- [¿Qué es la herramienta StreamSets?](https://www.g2.com/es/discussions/what-is-streamsets-tool) - 1 comment

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Similar Categories

- [Cloud Migration](/categories/cloud-migration)
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[Browse Big Data Integration Platforms Themes](/categories/big-data-integration-platforms/themes)

 ![Shalaka Joshi](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Shalaka Joshi")
SJ

Researched and written by [Shalaka Joshi](https://research.g2.com/insights/author/shalaka-joshi)

Updated April 9, 2026

Big data integration platforms facilitate the integration and analysis of large-scale data across cloud applications and databases, helping companies manage and utilize enormous volumes of data collected from IoT endpoints, applications, and communications by creating structured pipelines that connect big data processing outputs to downstream systems.

### Core Capabilities of Big Data Integration Platforms

To qualify for inclusion in the Big Data Integration category, a product must:

- Integrate big data processing data to external sources
- Ingest and distribute large sets of homogenous and heterogeneous data
- Create a structured pipeline for big data management processes

### Common Use Cases for Big Data Integration Platforms

Data engineering and IT teams use big data integration platforms to connect large-scale data environments with business applications and analytics systems. Common use cases include:

- Integrating processed big data clusters with cloud applications and databases for downstream use
- Simplifying the management of high-volume IoT and application data across distributed environments
- Building structured data pipelines that enable consistent, reliable access to big data insights across the organization

### How Big Data Integration Platforms Differ from Other Tools

Big data integration platforms typically require big data to have been processed prior to integration, working in conjunction with [big data processing and distribution software](https://www.g2.com/categories/big-data-processing-and-distribution) rather than replacing it. While some platforms provide [stream analytics](https://www.g2.com/categories/stream-analytics) capabilities, their primary focus is on data management and integration pipelines rather than real-time analytical processing.

### Insights from G2 on Big Data Integration Platforms

Based on category trends on G2, pipeline flexibility and broad connector support for cloud applications and databases as standout capabilities. Improved data accessibility across systems and reduced integration complexity stand out as primary outcomes of adoption.

Top Tools at a Glance

| Product | Best for | User Review |
| --- | --- | --- |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_96b275379465d759df5bffd0099d849a/google-cloud-bigquery.png "Product Avatar Image")](https://www.g2.com/products/google-cloud-bigquery/reviews)[BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews)[4.5/5(1,223)](https://www.g2.com/products/google-cloud-bigquery/reviews) | Serverless SQL analytics across Google-native data pipelines | "Easy-to-Use Cloud Tool with Shareable, Saved Queries" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_2c6c77d8284b2f6609c2f9dc0ba6b5a9/alteryx.png "Product Avatar Image")](https://www.g2.com/products/alteryx/reviews)[Alteryx](https://www.g2.com/products/alteryx/reviews)[4.6/5(889)](https://www.g2.com/products/alteryx/reviews) | No-code ETL and multi-source data blending | "Scales Operations and Saves Time with Automated Data Workflows" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_2b00e05c107c3273cea5264090c3c1d0/snowflake.jpg "Product Avatar Image")](https://www.g2.com/products/snowflake/reviews)[Snowflake](https://www.g2.com/products/snowflake/reviews)[4.5/5(761)](https://www.g2.com/products/snowflake/reviews) | Multi-workload analytics with compute-storage separation | "Elastic Scaling and Fast Analytics with Snowflake" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_50b25c72253e48a2b66e69f996196956/workato.png "Product Avatar Image")](https://www.g2.com/products/workato/reviews)[Workato](https://www.g2.com/products/workato/reviews)[4.7/5(776)](https://www.g2.com/products/workato/reviews) | Cross-application data orchestration with low-code recipes | "Workato helps us building complex integrations at lightning speed." |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_f176b4154a751d10150daa67a57b7dc5/azure-data-factory.jpg "Product Avatar Image")](https://www.g2.com/products/azure-data-factory/reviews)[Azure Data Factory](https://www.g2.com/products/azure-data-factory/reviews)[4.6/5(100)](https://www.g2.com/products/azure-data-factory/reviews) | Azure-native ETL orchestration across hybrid data sources | "Intuitive, Scalable Data Integration with Azure Data Factory" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_c476c9375398c3b68bbf5ff649015bba/snaplogic-intelligent-integration-platform-iip.png "Product Avatar Image")](https://www.g2.com/products/snaplogic-intelligent-integration-platform-iip/reviews)[SnapLogic](https://www.g2.com/products/snaplogic-intelligent-integration-platform-iip/reviews)[4.4/5(402)](https://www.g2.com/products/snaplogic-intelligent-integration-platform-iip/reviews) | Low-code ETL pipeline building across hybrid environments | "Super fast integrations—saves tons of coding headache!" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_b3390b4cc3d92e87d570895f7358c003/amazon-redshift.jpg "Product Avatar Image")](https://www.g2.com/products/amazon-redshift/reviews)[Amazon Redshift](https://www.g2.com/products/amazon-redshift/reviews)[4.3/5(404)](https://www.g2.com/products/amazon-redshift/reviews) | AWS-native analytical data warehousing at petabyte scale | "Scalable and Efficient Cloud Data Platform" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_14ba2a098676494a780e6f3ec9b76c8d/5x.png "Product Avatar Image")](https://www.g2.com/products/5x/reviews)[5X](https://www.g2.com/products/5x/reviews)[4.9/5(81)](https://www.g2.com/products/5x/reviews) | End-to-end data stack consolidation with managed dbt orchestration | "A reliable and scalable data partner" |

* * *

Show More

### Big Data Integration Platforms Topics

- [What are Big Data Integration Platforms?](#what-are-big-data-integration-platforms)
- [What are the Common Features of Big Data Integration Platforms?](#what-are-the-common-features-of-big-data-integration-platforms)
- [What are the Benefits of Big Data Integration Platforms?](#what-are-the-benefits-of-big-data-integration-platforms)
- [Who Uses Big Data Integration Platforms?](#who-uses-big-data-integration-platforms)
- [Challenges with Big Data Integration Platforms](#challenges-with-big-data-integration-platforms)
- [Which Companies Should Buy Big Data Integration Platforms?](#which-companies-should-buy-big-data-integration-platforms)
- [How to Buy Big Data Integration Platforms?](#how-to-buy-big-data-integration-platforms)
- [What Do Big Data Integration Platforms Cost?](#what-do-big-data-integration-platforms-cost)
- [Implementation of Big Data Integration Platforms](#implementation-of-big-data-integration-platforms)
- [Big Data Integration Platforms Trends](#big-data-integration-platforms-trends)
- [Big Data Integration Platforms FAQs](#big-data-integration-platforms-faqs)
- [Most Popular FAQs](#most-popular-faqs)
- [Small Business FAQs](#small-business-faqs)
- [Enterprise FAQs](#enterprise-faqs)

[
### Big Data Integration Platforms Topics
Expand/Collapse ](#)
- [What are Big Data Integration Platforms?](#what-are-big-data-integration-platforms)
- [What are the Common Features of Big Data Integration Platforms?](#what-are-the-common-features-of-big-data-integration-platforms)
- [What are the Benefits of Big Data Integration Platforms?](#what-are-the-benefits-of-big-data-integration-platforms)
- [Who Uses Big Data Integration Platforms?](#who-uses-big-data-integration-platforms)
- [Challenges with Big Data Integration Platforms](#challenges-with-big-data-integration-platforms)
- [Which Companies Should Buy Big Data Integration Platforms?](#which-companies-should-buy-big-data-integration-platforms)
- [How to Buy Big Data Integration Platforms?](#how-to-buy-big-data-integration-platforms)
- [What Do Big Data Integration Platforms Cost?](#what-do-big-data-integration-platforms-cost)
- [Implementation of Big Data Integration Platforms](#implementation-of-big-data-integration-platforms)
- [Big Data Integration Platforms Trends](#big-data-integration-platforms-trends)
- [Big Data Integration Platforms FAQs](#big-data-integration-platforms-faqs)
- [Most Popular FAQs](#most-popular-faqs)
- [Small Business FAQs](#small-business-faqs)
- [Enterprise FAQs](#enterprise-faqs)

## Learn More About Big Data Integration Platforms

### What are Big Data Integration Platforms?

Big data integration is defined as a process within the data lifecycle that involves extracting data from heterogeneous sources and combining it to obtain insightful unified information which can aid in better decision making. 

Big data integration platforms are the tools that allow data to be extracted from various data sources and then sort and process it. There is a huge volume of data generated from various sources daily. Organizations are trying to capture value out of this data, and fully managed big data integration platforms with no indexing or tuning required have made that easier for teams without a large data engineering staff. Most of the data comes in an unstructured format. Required data is often distributed across various sources like IoT endpoints, applications, communications, or provided by third parties.

#### What Types of Big Data Integration Platforms Exist?

The end goal of a big data integration platform is to transfer and unify data from disparate sources. Data managers can get a better understanding of various methods of achieving this goal by understanding the different types of data integration software. They can decide which type of platform suits them the most: 

**Middleware data integration**

Middleware is a software that acts as a binding material for two different systems. It connects various applications and transfers data from application to database. Middleware is widely in use for application integration and data management. When an organization is integrating legacy systems with modern ones, middleware is used. 

**Data consolidation**

This term is interchangeably used with data integration. Data consolidation means combining data from all disparate sources. It also removes any errors before storing it in a data warehouse or data lake. Data consolidation improves data quality.

**Extract, transform and load (ETL)**

ETL forms the core of data integration tools even today. ETL is the process of consolidation of data in a data warehouse. It involves extracting the data from source systems, transforming it into the required format, and loading it to the target system.

**Enterprise data integration**

While big data integration is a broader term, enterprise data integration refers to the centralization of data across multiple organizations. This is usually done when the organizations go through mergers and acquisitions. 

### What are the Common Features of Big Data Integration Platforms?

Based on G2 reviews, data analysts and data engineers evaluate big data integration platforms by comparing connector breadth, data transformation capabilities, and governance controls. Big data integration software is one way for any organization to make informed decisions. Below are key features of big data integration platforms:

**Big data connectors:** Many applications use more than one database nowadays. Data connectors make it possible to move data from one database to another. Organizations use big data connectors to filter and transform data in a proper structure for querying and analyzing purposes. Organizations can benefit from the scalability and real-time data transmissions unlike that of traditional batches. With cloud-based and data-driven businesses gaining popularity, advanced data integration in any big data integration platform helps with more agile integrations, without constant schema changes. IPaaS provides pre-built big data connectors, business rules, and maps, which help organize integration flows. 

**Data transformation:** Data transformation is the process of changing data from one format structure into another. Organizations use this tool to organize the data better by making it compatible with other data, joining data, and so on. The processes such as data integration, data migration, data warehousing/data storage, and data wrangling all may involve data transformation.

**Leverage data from unconventional sources of big data:** This is one of the key features of any efficient big data integration platform. Common file formats like PDFs are usually supported by data integration tools. The advanced feature of leveraging data from unconventional sources supports file formats like COBOL, email sources, and XML/JSON files. Organizations use this feature to obtain streamlined data analysis.

**Data virtualization:** Organizations benefit from this feature by getting access to a unified view of various disparate systems. There is no physical movement of data to and from databases. The feature gives organizations real-time access to their data without exposing the technical details of the source systems.

**Data quality:** This feature is central to all the big data integration platforms. When data is of excellent quality, it is easier to process and analyze, ultimately helping organizations to make better decisions.

**Database integration:** Database technology aids in data storage and has evolved over the years. Relational, NoSQL, hierarchical, and many more are types of databases. NoSQL database is also known as a non-relational database. Database integration is usually done in cases of mergers and acquisitions. Two individual databases are integrated for a better understanding of new business.

**Big data management:** It is the organization, administration, and governance of large volumes of structured and unstructured data. Data governance is a major part of data management. A big data governance strategy plays a key role in determining how the business will benefit from available resources. Organizations leverage this feature to ensure a high level of data quality. 

**Data processing:** The feature manipulates data by collecting and combining it to obtain usable information. With big data migrating to the cloud, the benefits of cloud data processing can be reaped by small and large organizations alike.

**Application programming interface (API):** This feature connects one system to another via APIs, allowing the data exchange between those two systems. It facilitates seamless connectivity between devices and programs.

**Data warehouse:** This is a part of the data integration process which deals with cleansing, formatting, and data storage. One of the important implementations of big data integration is building a data warehouse. It is done by merging systems to unify the data from disparate sources. Technically data warehouses perform queries and analysis.

### What are the Benefits of Big Data Integration Platforms?

Businesses today are data-driven. Hence, it is important to clean, process, and organize this data for better decision-making. Following are the benefits of implementing big data integration platforms at organizations: 

**Reducing the complexity of big data:** In any organization, the more the number of applications, the more are the number of interfaces. Big data can be difficult to manage at times. However, big data integration software helps in managing complexity, making easier delivery of data to any system, and streamlining the connections. It begins with defining business-critical data; data related to customers, products, sites, and suppliers. The overall process might involve updating, collating, and refining data to form a uniform understanding of the same. 

**Scalability:** Big data is primarily unstructured and requires real-time analysis. Advanced big data tools in association with cloud computing aid in connecting the data with real-time events and automate resource allocation based on integration activities. When organizations have scalable data platforms, they are also prepared for potential growth in their data needs.

**Better decision making:** Organizations often deal with a variety of data from disparate sources. Data integration helps managers understand the dynamics of their business and anticipate shifts in the market. Data entered manually can often have flaws and thus poor insights going further. Integration platforms help in obtaining up-to-date data, thus facilitating faster and higher quality decision making. When data is unified, it is available for everyone in the organization to access. This boosts transparency, collaboration, and ultimately maximizes data value. 

**Cost optimization:** Integration platforms create a centralized software architecture that connects to system and software and allows transporting data seamlessly. This focuses on eliminating inefficiencies caused due to using multiple software within an organization. This brings down the cost required for storing, processing, and analyzing large amounts of data.

**Data governance:** This system helps in understanding the executives in charge of data assets in an organization. 

### Who Uses Big Data Integration Platforms?

**Data analysts and data scientists:** These employees are generally the main users of big data integration tools. They use the software to gather a deeper understanding of business-critical data. These teams may be tasked with data preparation, cleansing, and data processing for further analysis.

**Marketing teams:** Marketing teams often run different types of campaigns, including email marketing, digital advertising, or even traditional advertising campaigns. The data that is error free and insightful helps the marketing team to execute successful campaigns and strategies. Big data integration helps the marketing teams promote the company or its product to the target audience.

**Finance teams:** Finance teams leverage data integration platforms to gain insight and understanding into the factors that impact an organization's business. Finance teams require real-time data for obtaining actionable insights which is possible using advanced data integration software. By integrating financial data with other operations data, accounting and finance teams pull actionable insights that might not have been uncovered through the use of traditional tools.

#### Software Related to Big Data Integration Platforms

Related solutions that can be used together with data integration include:

**Metadata-driven data integration software:** Big data integration software can handle a variety of data. However, when used with powerful metadata, it can streamline the creation and management of BI reporting. Metadata repository provides a view and analyses the movement of data around the organization.

[Data management platforms](https://www.g2.com/categories/data-management-platforms) **:** This category of software is used to gather, analyze, and store big data. Data management platforms help organizations leverage big data from various sources in real time leading to effective customer engagement.

[Data replication software](https://www.g2.com/categories/data-replication) **:** Data replication can be one-time or an ongoing process. This software aims at keeping all the members of the organization on the same page. Data replication involves copying data from one server to a database on another server.

[Big data analytics software](https://www.g2.com/categories/big-data-analytics) **:** Data Analytics platforms are a great aid to any organization with the need for timely data visualization of high-level analytics. Many industries target their customers using data analytics which helps the companies provide a customized experience and meet customer expectations.

**Application integration software:** Application integration, like data integration, works in batches; this leaves gaps in taking quick actions. Organizations can benefit from moving data in real time with application integration to easy access and quicker actions.

### Challenges with Big Data Integration Platforms

**Managing large data volume:** The exponential growth of data from various sources is one of the biggest challenges of big data integration. This further creates issues with the retention of this data. Sometimes data runs on multiple platforms—a combination of on-premises and cloud hosting. This gives rise to complexity and managing can become difficult.

**Manual data integration tasks:** In many organizations, data scientists are the employees finding and preparing the data, which leaves an equivalent to only a week’s time for actual data science tasks and analytical work. This has made enterprises look for tools to automate ingestion and integration.

**Growth of heterogeneous data:** Heterogeneous data is a group of data with non-similar data types. Data is collected in different formats—structured, unstructured, and semi-structured. Integrating all these disparate data types is a tedious process and would need a proper ETL tool. Data is mostly handled by various data handling systems and it may not be in the same format.

**Issues with data quality:** Incompatible or invalid data may be present in the data obtained from disparate sources. Businesses might not be aware of this, and the analytics might show insights with this incompatible data which could have severe repercussions. The insights provided by data analytics could potentially be misleading. The quality of gathered data is kept in check by appointing an executive for data management. This manual job can be time consuming for huge volumes of data.

### Which Companies Should Buy Big Data Integration Platforms?

**Retail:** This industry is the most common one to use big data software. They want to attract more customers to their business. For that, they need to correctly anticipate what the customers want. Accurate insights can help companies to identify their target customers as well as build on their competitive advantage.

**Logistics:** Data Integration brings different systems together by combining data and functions. Data in the transportation and logistics industry is stored in on-premises ERP and cloud-based CRM systems. Big data integration solutions help organizations overcome challenges like traffic congestion and mismanagement of capacity using automated fleet management and cloud-based analytics. Business processes are optimized and transcription errors are also reduced.

**Education:** Data privacy and security are of utmost importance in the education industry. Big data tools are changing the educational scenario altogether. Cutting-edge technology can help make better educational assessments. 

**Banking and finance:** Data integration helps banks in providing better customer experience, cross-selling, customer retention, and overall profitability. Big data integration helps in fraud detection and compliance.

**Construction:** Large infrastructure projects are huge in volume. While construction is one of the least digitized industries, organizations are now realizing the importance of the data that is generated and that it should be leveraged for obtaining better results. Using big data integration platforms, companies can combine design and construction data so that every department remains on the same page. This leads to better tracking of project design data being used at the construction site.

**Healthcare:** Big data platforms are critical to the healthcare industry. The data in healthcare is unstructured and data integration can prove useful in obtaining valuable insights. The ultimate goal of data integration solutions in this industry is to improve the quality and cost of healthcare for patients and researchers.

### How to Buy Big Data Integration Platforms?

#### Requirements Gathering (RFI/RFP) for Big Data Integration Platforms

If a company is just starting out and looking to purchase the first big data integration platform, or maybe an organization needs to update a legacy system--wherever a business is in its buying process, g2.com can help select the best big data integration software for the business.

The particular business pain points might be related to all of the manual work that must be completed. If the company has amassed a lot of data, the need is to look for a solution that can grow with the organization. Users should think about the pain points and jot them down; these should be used to help create a checklist of criteria. Additionally, the buyer must determine the number of employees who will need to use the big data integration tool, as this drives the number of licenses they are likely to buy.

Taking a holistic overview of the business and identifying pain points can help the team springboard into creating a checklist of criteria. The checklist serves as a detailed guide that includes both necessary and nice-to-have features including budget features, number of users, integrations, security requirements, cloud or on-premises solutions, and more.

Depending on the scope of the deployment, it might be helpful to produce an RFI, a one-page list with a few bullet points describing what is needed from a big data integration platform.

#### Compare Big Data Integration Platforms Products

**Create a long list**

From meeting the business functionality needs to implementation, vendor evaluations are an essential part of the software buying process. For ease of comparison after all demos are complete, it helps to prepare a consistent list of questions regarding specific needs and concerns to ask each vendor.

**Create a short list**

From the long list of vendors, it is helpful to narrow down the list of vendors and come up with a shorter list of contenders, preferably no more than three to five. With this list in hand, businesses can produce a matrix to compare the features and pricing of the various big data integration solutions.

**Conduct demos**

To ensure the comparison is thorough, the user should demo each solution on the shortlist with the same use case and datasets. This will allow the business to evaluate like for like and see how each vendor stacks up against the competition.

#### Selection of Big Data Integration Platforms

**Choose a selection team**

Before getting started, it's crucial to create a team that will work together throughout the entire process, from identifying pain points to implementation. The software selection team should consist of members of the organization who have the right interest, skills, and time to participate in this process. A team of three to five people with roles such as the main decision maker, project manager, process owner, system owner, or staffing subject matter expert, as well as a technical lead, IT administrator would suffice. In smaller companies, the vendor selection team may be smaller, with fewer participants multitasking and taking on more responsibilities.

**Negotiation**

As data integration platforms are all about the data, the user must make sure that the selection process is data driven as well. The selection team should compare important data like pricing metrics of a particular vendor, the stage that buyer organization is in, and also terms and conditions of the organization.

**Final decision**

It is imperative to open up a conversation regarding pricing and licensing. For example, the vendor may be willing to give a discount for multi-year contracts or for recommending the product to others.

### What Do Big Data Integration Platforms Cost?

Data Integration software is available both on-premises and on cloud. The cost per type changes given there are certain factors for each type to consider. The organizations that consider deploying on-premises software are liable for costs associated with server hardware, power consumption, and space. Whereas software using the cloud can be charged for the resources it uses and prices go up or down depending on how much of the software is consumed. 

#### Return on Investment (ROI)

Organizations buy big data integration platforms with an expectation of a certain ROI. Although there are ways to directly calculate ROIs, it could be a little daunting to use those here. It entirely depends on the intricacy of the project and ultimately the software itself. ROI can be further looked at from an IT perspective and a business perspective. The ROI on IT infrastructure, staffing, expertise-building, and services cost is calculated. Whereas, for business, time investments, outside investments (the cost related to external partners involved in the project), and opportunity costs are treated as important.

### Implementation of Big Data Integration Platforms

**How are Big Data Integration Platforms Implemented?**

It is necessary to define the goals to be achieved using a big data integration platform. This will help measure the success of target projects for which big data integration software will be used. Large organizations have data in large volumes from heterogeneous data sources, hence it is better to hire an external party for implementing the software. Connectivity between systems is ensured during the process. With a rich experience throughout the years, the specialists from these consultancy firms can guide the businesses in connecting and consolidating their data effectively by helping the company to identify the best vendors in the space that would suit their business needs and goals.

**Who is Responsible for Big Data Integration Platforms Implementation?**

Data integration implementation can be a tedious process. In such times, it is advisable to have vendor support throughout the implementation. The team size could range from moderate to large depending on the complexity of the software being implemented. With cross-functional teams, it is possible to streamline the implementation process. Before actual use, it is always a good practice to test sample data.

**What Does the Implementation Process Look Like for Big Data Integration Platforms?**

The overall implementation process can be done in the following steps:

- Identifying and defining the project is a step when organizations can figure out the format in which the consolidated data has to be in so that it can prove of maximum usefulness to the organization.
- Reviewing the systems becomes crucial at this point. Depending on the connectivity, the consultancy specialists may advise on data connectors and/or SFTP ports to facilitate data interchange.
- Defining data integration framework.
- Defining how data will be processed.

**When Should You Implement Big Data Integration Platforms?**

Big data integration software is usually required when the organization deals with loads of data coming from disparate sources.

### Big Data Integration Platforms Trends

**Hybrid integration platforms**

These platforms help business users to handle highly complex data. Hybrid integration platforms integrate on-premises and cloud-based data. These platforms help in reducing costs and risks.

**Integration using artificial intelligence and machine learning**

The disruptive nature of today’s digital transformation has paved the way for many new developments in integration platforms. With artificial intelligence, it is possible to obtain accurate insights about customer data and thus meet up to their expectations. Machine learning helps in providing the transparency to make better decisions.

**Adoption of software as a service (SaaS) and cloud**

SaaS is helping traditional on-premises software to migrate to the cloud. The ease of use of cloud and SaaS enables the organizations to use data from any place, at any time, and pay for how much is used. It also eliminates the use of hardware making the infrastructure flexible. 

**Blockchain for data and analytics**

Blockchain technology can help in more than one way: 

- Enhances security
- Provides transparency
- Streamlines the integration process
- Simplifies communications
- Eliminates the need for middlemen thus reducing the cost.

### Big Data Integration Platforms FAQs

### Most Popular FAQs

#### Which big data integration software has the best reviews?

Across the[](https://www.g2.com/categories/big-data-integration-platforms)[Big Data Integration Platforms](https://www.g2.com/categories/big-data-integration-platforms) category, where data engineers make up a notable share of the most trusted user reviews, the strongest ratings tend to go to platforms that turn a genuinely complex job, moving and governing data at scale, into something a team can operate without constant firefighting.

- [ILUM](https://www.g2.com/products/ilum-ilum/reviews): A newer name in this data set, though it turns Spark-on-Kubernetes delivery into a repeatable practice, with CI, secrets, RBAC, cost tags, and lineage set up the same way every time.
- [Orchestra](https://www.g2.com/products/orchestra-orchestra/reviews): A smaller sample so far, but its clean, modern interface and built-in agents come up repeatedly as reasons teams stick with it.
- [Workato](https://www.g2.com/products/workato/reviews): Carries solid review volume at a high rating, with a clean, accessible interface even for non-technical users building a flow.

#### What are the top big data processing platforms for integration?

The platforms with both the largest review base and the broadest processing footprint tend to be the ones built to sit at the center of a company's entire data stack.

- [Alteryx](https://www.g2.com/products/alteryx/reviews): Carries the largest review volume in this category, with a fully visual workflow that lets you watch data move through each processing step.
- [Snowflake](https://www.g2.com/products/snowflake/reviews): Separates storage from compute, letting multiple teams query the same data simultaneously without one workload slowing down another.
- [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews): Runs entirely in the cloud with no hardware to manage, and lets teams save and share queries directly inside the platform.

#### What are the top big data integration tools for hybrid environments?

The strongest fits here are built specifically to bridge on-premises systems with cloud infrastructure, rather than assuming everything already lives in one place.

- [Azure Data Factory](https://www.g2.com/products/azure-data-factory/reviews): Connects on-premises databases with cloud storage and analytics through built-in connectors and integration runtime options, orchestrating pipelines centrally across both environments.
- [IBM StreamSets](https://www.g2.com/products/ibm-streamsets/reviews): Built for DataOps in hybrid environments specifically, with pipeline monitoring and governance for streaming workloads that need to move continuously across systems.
- [AWS Glue](https://www.g2.com/products/aws-glue/reviews): Cloud-native at its core, but supports hybrid setups by connecting on-premises sources to AWS services as teams gradually shift workloads to the cloud.

#### Which big data integration platform offers the fastest processing?

Speed here usually comes down to architecture — whether compute and storage scale independently, or whether a pipeline gets bottlenecked waiting on infrastructure.

- [Skyvia](https://www.g2.com/products/skyvia/reviews): A pipeline gets running quickly, cutting out the wait that used to come with pulling data from SaaS tools like Salesforce.
- [ILUM](https://www.g2.com/products/ilum-ilum/reviews): A newer name here, but its handling of Spark workloads on Kubernetes shows up repeatedly in reviews as fast and repeatable rather than a one-off engineering project.
- [Snowflake](https://www.g2.com/products/snowflake/reviews): Its separation of storage and compute is built specifically to scale workloads without one team's queries slowing down another's.

#### What are the best data integration platforms?

The right answer depends on the specific job, but a few platforms come up across a wide range of data integration use cases.

- [Maia](https://www.g2.com/products/matillion-maia/reviews): A rich library of components and low-code pipelines make it easy to pick up even for teams new to data integration.
- [SnapLogic Intelligent Integration Platform (IIP)](https://www.g2.com/products/snaplogic-intelligent-integration-platform-iip/reviews): Its library of pre-built "snaps" connects to a wide range of data sources and destinations without writing custom code for each one.
- [Adverity](https://www.g2.com/products/adverity/reviews): A large number of pre-existing sources and destinations connect the same set of tools cleanly, even across different companies using it.

#### Which big data integration platforms support secure data sharing without copying datasets?

The distinction that matters here is physical vs. logical access: platforms built for secure sharing let another team or an outside partner query a dataset directly, rather than requiring a copy to be exported, moved, and kept in sync.

- [Snowflake](https://www.g2.com/products/snowflake/reviews): Its Secure Data Sharing feature is repeatedly cited by reviewers as letting teams and external partners access datasets directly, with one reviewer specifically noting they can "share datasets across teams without copying anything."
- [Denodo](https://www.g2.com/products/denodo/reviews): Its own product listing describes "unified, real-time-updated data views without expensive replication or copying of data," which is the core promise of the data virtualization approach this guide's Features section already covers.
- [Cloudera](https://www.g2.com/products/cloudera/reviews): Its Unified Data Fabric is built to connect disparate data sources into one governed, secure view rather than requiring each team to maintain its own copy.

#### Which big data integration platforms are most popular and most relied on for structured and unstructured data?

The platforms senior data engineers reach for here aren't the ones that only handle clean, structured tables, they're the ones built to hold structured, semi-structured, and unstructured data side by side without forcing a separate system for each type.

- [Snowflake](https://www.g2.com/products/snowflake/reviews): Reviewers specifically cite support for both structured and semi-structured data, including JSON and Parquet formats, within the same warehouse rather than a bolt-on for non-tabular data.
- [Cloudera](https://www.g2.com/products/cloudera/reviews): Built around asserting control over data "in all forms," with an open data lakehouse designed to unify structured and unstructured sources rather than keeping them in separate silos.
- [Alteryx](https://www.g2.com/products/alteryx/reviews): Reviewers describe blending genuinely different source types on a single canvas, an Excel file, a SQL database, and a cloud API, without writing custom code for each format.

### Small Business FAQs

#### What is the most affordable big data integration platform for SMBs?

Within the[](https://www.g2.com/categories/big-data-integration-platforms/small-business)[small business segment of Big Data Integration Platforms](https://www.g2.com/categories/big-data-integration-platforms/small-business), the platforms that come up most often are the ones built for lean teams without a large data engineering budget.

- [Skyvia](https://www.g2.com/products/skyvia/reviews): A team of five running data for a 40-person SaaS company got this running quickly with a single analytics engineer, without needing a bigger budget or team.
- [EazyDI](https://www.g2.com/products/eazydi/reviews): A newer name in this data set, though its low-code approach let operations and analytics teams collaborate without building custom integrations from scratch.
- [5X](https://www.g2.com/products/5x/reviews): A smaller footprint so far, but its team is described as extremely supportive in transforming raw data into a structured model without a large upfront investment.

#### What is the best big data integration platform for startups?

Startups evaluating[](https://www.g2.com/categories/big-data-integration-platforms/small-business)[small business big data integration](https://www.g2.com/categories/big-data-integration-platforms/small-business) tools tend to prioritize a platform that a lean team can run without dedicated data engineering headcount.

- [Workato](https://www.g2.com/products/workato/reviews): Builds flows that stay accessible even for non-technical people, which matters when a startup doesn't have a dedicated integration engineer yet.
- [Astro by Astronomer](https://www.g2.com/products/astro-by-astronomer/reviews): Handles the infrastructure, monitoring, and upgrades behind Airflow, freeing up a small engineering team to focus on actual data work instead of managing orchestration.
- [Alteryx](https://www.g2.com/products/alteryx/reviews): Its visual, no code data preparation workflow, reviewers describe it as bridging the gap between non-technical business users and data scientists, makes onboarding fast, backed by a large support community when questions come up.

#### Which big data integration platform is the most user-friendly for startups?

Ease of use matters most at this stage, since the person building pipelines is often the same person analyzing the results.

- [Maia](https://www.g2.com/products/matillion-maia/reviews): New users pick it up quickly thanks to a simple layout and low-code pipelines that don't require deep integration experience up front.
- [dbt](https://www.g2.com/products/dbt/reviews): All a team really needs is SQL, and its lineage feature makes it easy to see exactly how data flows through each transformation.
- [Workato](https://www.g2.com/products/workato/reviews): Building a flow stays simple even for non-technical team members, thanks to a clean, accessible interface.

#### Which big data integration tool is easiest to set up for small teams?

Setup speed is one of the more differentiated ratings in this category, and small teams generally do best with a platform that skips a long implementation project.

- [Peliqan](https://www.g2.com/products/peliqan/reviews): Replaced several separate tool integrations, each with its own auth and config, with one ready-to-use data foundation set up in a fraction of the time.
- [Coefficient](https://www.g2.com/products/coefficient/reviews): Pulls data straight into Google Sheets and keeps it synced automatically, with no separate infrastructure for a small team to stand up.
- [Skyvia](https://www.g2.com/products/skyvia/reviews): Gets a first pipeline running quickly, which matters most for a team without a dedicated engineer to spend weeks on setup.

#### Which big data integration tool works best for lean teams without dedicated data engineers?

Teams without a dedicated data engineering function tend to do best with platforms built around simplicity and active support rather than deep customization.

- [Keboola](https://www.g2.com/products/keboola/reviews): Ease of use and simplicity stand out, with connectors that are particularly valuable for ingestion and customer support that stays genuinely active.
- [Skyvia](https://www.g2.com/products/skyvia/reviews): Built for exactly this situation, a small team with one analytics engineer handling data for the whole company.
- [Peliqan](https://www.g2.com/products/peliqan/reviews): Gives a lean team a ready-to-use data foundation with built-in connectors, rather than requiring separate setups for every tool.

### Enterprise FAQs

#### What is best-rated big data integration software for large enterprises?

Within the[](https://www.g2.com/categories/big-data-integration-platforms/enterprise)[Enterprise segment of Big Data Integration Platforms](https://www.g2.com/categories/big-data-integration-platforms/enterprise), a smaller set of platforms have the review volume from large organizations to back up a strong rating.

- [Workato](https://www.g2.com/products/workato/reviews): Holds up well at the enterprise level, with the same clean, accessible interface that smaller teams rely on scaling across much larger organizations.
- [Alteryx](https://www.g2.com/products/alteryx/reviews): Carries a large enterprise review base, with its visual workflow and broad integrations continuing to hold up as data volume grows.
- [Snowflake](https://www.g2.com/products/snowflake/reviews): The same storage-compute separation that helps smaller teams carries over here, letting many teams query independently without stepping on each other.

#### What is the most reliable big data integration tool for enterprises?

Reliability at this scale tends to come down to support responsiveness and administrative control, since large organizations need both to keep pipelines running across many teams.

- [Workato](https://www.g2.com/products/workato/reviews): Enterprise accounts report support and administrative scores among the strongest in the category.
- [Snowflake](https://www.g2.com/products/snowflake/reviews): Its architecture keeps performance consistent even as more teams and workloads get added, which large organizations lean on for reliability.
- [Astro by Astronomer](https://www.g2.com/products/astro-by-astronomer/reviews): Manages Airflow infrastructure, monitoring, and upgrades directly, cutting down on the operational surprises that hit reliability at scale.

#### What is best-reviewed big data integration software for enterprise hybrid cloud environments?

Enterprise hybrid rollouts depend on a platform that treats on-premises and cloud systems as one connected environment rather than two separate projects.

- [Azure Data Factory](https://www.g2.com/products/azure-data-factory/reviews): Purpose-built to orchestrate pipelines centrally across on-premises databases and cloud storage, which is exactly the coordination large hybrid rollouts need.
- [IBM StreamSets](https://www.g2.com/products/ibm-streamsets/reviews): Its governance and pipeline monitoring are designed specifically for DataOps in hybrid environments at enterprise scale.
- [AWS Glue](https://www.g2.com/products/aws-glue/reviews): Supports hybrid setups directly, letting large organizations connect on-premises sources into AWS as they migrate workloads gradually rather than all at once.

#### Which big data integration platform offers the strongest data lineage and governance for enterprise teams?

Governance needs at this scale go beyond basic access control — enterprise teams need to trace exactly where data came from and how it changed along the way.

- [dbt](https://www.g2.com/products/dbt/reviews): Its lineage feature makes it possible to trace exactly how data flows through every transformation, which large teams rely on for audit and troubleshooting.
- [ILUM](https://www.g2.com/products/ilum-ilum/reviews): A smaller footprint at enterprise scale so far, but it bakes RBAC, secrets, cost tags, and lineage into every workspace the same way, rather than leaving governance as an afterthought.
- [EazyDI](https://www.g2.com/products/eazydi/reviews): Balances automation with governance and analytics readiness, with data profiling that helps large teams catch issues before they spread downstream.

#### Which big data integration platform is best for orchestrating large-scale enterprise workflows?

At enterprise scale, orchestration means coordinating many interdependent pipelines across teams rather than scheduling a handful of standalone jobs.

- [Control-M](https://www.g2.com/products/control-m/reviews): A single, unified view lets teams track workflows across multiple platforms at once, cutting down manual monitoring and catching operational bottlenecks early.
- [Astro by Astronomer](https://www.g2.com/products/astro-by-astronomer/reviews): Makes managing Airflow at scale effortless by handling infrastructure, monitoring, and upgrades that would otherwise fall on an internal platform team.
- [Azure Data Factory](https://www.g2.com/products/azure-data-factory/reviews): Centralizes orchestration across data-driven workflows, giving large organizations one place to schedule and monitor pipelines spanning multiple systems.