# Best Big Data Integration Platforms

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

**Total Products under this Category:** 134

### 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:** Palantir Foundry (+1.5%) - Among all products in this category, Palantir Foundry recorded the largest rating increase compared to last month

_Last updated: August 19, 2026_

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

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

- 30 Analysts and Data Experts
- 10,600+ Authentic Reviews
- 134+ 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=87449)

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

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=aws-lake-formation)

**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=2026-08-20T21%3A14%3A31Z&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=3bda2a088045d9b366c4ec9fa1c163c472e6be43ea6bfe0b2443ea40fc422135&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:** 1,146

#### 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: 8.8/10)
- **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 value the **ease of use** of Google Cloud BigQuery, enabling fast analysis without needing to manage infrastructure.
- Users appreciate the **incredible speed** of BigQuery, making data processing effortless and efficient for large datasets.
- Users value the **seamless integrations** of Google Cloud BigQuery, enhancing analytics and supporting various data types effortlessly.
- Users appreciate the **fast querying capabilities** of Google Cloud BigQuery, enabling quick analysis of massive datasets effortlessly.
- Users value the **query efficiency** of BigQuery, enabling fast analysis of massive datasets with minimal effort.

##### Cons

- Users find the **cost structure expensive** , especially with complex queries leading to rapidly escalating charges.
- Users often face **query issues** with BigQuery, as inefficient queries can rapidly increase costs and complicate budgeting.
- Users find the **cost management challenging** , facing unpredictable pricing and needing strict governance to maintain budgets.
- Users face **cost issues** with Google Cloud BigQuery, often leading to unexpectedly high bills and budget management challenges.
- Users find the **steep learning curve** for advanced features challenging, requiring significant time and effort to master.

#### 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)

**["BigQuery Delivers Fast, Intuitive Analytics with Seamless Integrations"](https://www.g2.com/survey_responses/google-cloud-bigquery-review-12575892)**

**Rating:** 5.0/5.0 stars

_— Rakshith N._

[Read full review](https://www.g2.com/survey_responses/google-cloud-bigquery-review-12575892)

#### 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:** 862

#### 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: 8.8/10)
- **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** in Alteryx, finding it simple to automate tasks with drag and drop functionality.
- Users value the **automation capabilities** of Alteryx, streamlining data processes and enhancing analytical efficiency.
- Users find Alteryx to be **very intuitive** , making it easy for non-technical users to learn and utilize.
- Users find that Alteryx's interface makes **learning technology easy** for everyone, even those without a tech background.
- Users value Alteryx for its **efficiency** in managing data, streamlining workflows, and enhancing overall productivity.

##### Cons

- Users highlight the **expensive pricing** of Alteryx, making it difficult for small teams or startups to afford licenses.
- Users face a **steep learning curve** with Alteryx, requiring time to master its complex features.
- Users find that Alteryx suffers from **missing features** , such as lack of direct database access and limited reporting tools.
- Users find the **learning difficulty** of Alteryx steep, especially for those unfamiliar with RegEx and SQL.
- Users experience **slow performance** with Alteryx, particularly when handling large workflows and during data wrangling tasks.

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

**["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)

**["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)

### [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:** 713

#### 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: 8.8/10)
- **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, which simplifies data sharing and enhances productivity across teams.
- Users value the **reliable features and user-friendly interface** of Snowflake, enhancing data management and analytics efficiency.
- Users appreciate the **ease of use and efficient data integration** in Snowflake for their warehousing projects.
- Users value the **seamless scalability** of Snowflake, enabling efficient handling of large datasets and workload changes without performance loss.
- Users value the **fast and efficient data processing** capabilities of Snowflake, enhancing their analysis experience significantly.

##### Cons

- Users highlight the **high costs** of Snowflake, making it less accessible for smaller businesses with limited budgets.
- Users find **feature limitations** in Snowflake, such as lack of code blocks and restricted permissions, frustrating.
- Users find the **learning curve steep** , requiring training due to its complexity and overwhelming interface for beginners.
- Users often struggle with **high costs** due to unoptimized queries and inadequate cost control measures in Snowflake.
- Users find the **cost structure challenging** , requiring time to optimize for efficient use of Snowflake.

#### 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:** 809

#### 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: 8.8/10)
- **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 integration and maintenance** with Fivetran, enhancing their ROI and workflow efficiency.
- Users value the **easy setup** of Fivetran, appreciating its seamless integration with existing applications.
- Users value the **easy integration** of Fivetran, seamlessly connecting with existing applications and enhancing data access.
- 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 efficient and accessible.

##### Cons

- Users report experiencing **sync issues** that disrupt functionality, causing unpredictable failures and complications in workflow management.
- Users find Fivetran's **pricing to be quite expensive** , which limits accessibility and flexibility for wider usage.
- Users face **integration issues** with Fivetran, particularly regarding schema ownership and modifying connections amidst platform changes.
- Users find the **learning curve steep** , requiring technical knowledge to navigate Fivetran effectively at first.
- Users find Fivetran's **pricing issues** hinder accessibility and flexibility, making it less feasible for broader use.

#### 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/products/workato/reviews)

Workato is the #1-rated iPaaS and the leader in Enterprise MCP — the platform enterprises trust to unify integration, automation, and AI in one secure, cloud-native runtime. Trusted by over 12,000 customers including half the Fortune 500, Workato connects every system, process, and data source with 14,000+ pre-built connectors. What sets Workato apart: Enterprise MCP turns proven business processes into governed, agent-ready skills that any AI agent — Claude, ChatGPT, Cursor, or custom-built — can execute safely and predictably. No rip-and-replace required. Whether modernizing legacy integrations or deploying agentic AI at scale, Workato delivers the orchestration, governance, and trust needed in the enterprise.

**Average Rating:** 4.7/5.0

**Total Reviews:** 748

#### How Do G2 Users Rate Workato?

- **Has the product been a good partner in doing business?:** 9.4/10 (Category avg: 8.9/10)
- **Quality of Support:** 9.2/10 (Category avg: 8.9/10)
- **Ease of Use:** 9.0/10 (Category avg: 8.8/10)
- **Ease of Admin:** 9.0/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Workato?

- **Seller:** [Workato](https://www.g2.com/sellers/workato)
- **Company Website:** www.workato.com
- **Year Founded:** 2013
- **HQ Location:** Mountain View, California
- **Twitter:** @Workato  
3,641 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/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 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Software Engineer, Senior Software Engineer
- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 43% Medium, 33% Large

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

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** of Workato, enabling quick automation without needing technical expertise.
- Users love the **easy integrations** with Workato, allowing quick automation of tools like Salesforce and Slack.
- Users love how Workato's **easy integrations** simplify automation, saving them hours and making processes seamless.
- Users value the **user-friendly and low-code interface** of Workato, making business process automation seamless and efficient.
- Users appreciate the **ease of automation** with Workato, enabling seamless integrations and saving significant time on repetitive tasks.

##### Cons

- Users often find the **complexity of Workato** overwhelming, particularly regarding pricing and onboarding, leading to confusion.
- Users find the **learning curve steep** , with complexity and confusion hindering smooth onboarding and workflow management.
- Users are frustrated by **data limitations** in Workato, impacting email sends, job reporting, and large file transfers.
- Users find the **missing features** in Workato's connector library limit their integration capabilities with lesser-known applications.
- Users find the **steep learning curve** of Workato daunting, especially during onboarding and initial usage.

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

**["The Platform That Grew With Us"](https://www.g2.com/survey_responses/workato-review-12941177)**

**Rating:** 5.0/5.0 stars

_— Anshu b._

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

**["Workato helps us building complex integrations at lightning speed."](https://www.g2.com/survey_responses/workato-review-10305521)**

**Rating:** 5.0/5.0 stars

_— Sreenath B._

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

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

- [What does Workato do?](https://www.g2.com/discussions/what-does-workato-do)
- [How much does Workato cost?](https://www.g2.com/discussions/how-much-does-workato-cost) - 1 comment
- [What is a Workato recipe?](https://www.g2.com/discussions/what-is-a-workato-recipe) - 3 comments
- [What is Workato used for?](https://www.g2.com/discussions/what-is-workato-used-for)

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

Azure Data Factory (ADF) è un servizio di integrazione dati completamente gestito e senza server progettato per semplificare il processo di acquisizione, preparazione e trasformazione dei dati da fonti diverse. Consente alle organizzazioni di costruire e orchestrare flussi di lavoro di Estrazione, Trasformazione, Caricamento (ETL) e Estrazione, Caricamento, Trasformazione (ELT) in un ambiente senza codice, facilitando il movimento e la trasformazione dei dati tra sistemi locali e basati su cloud. Caratteristiche e Funzionalità Chiave: - Connettività Estesa: ADF offre oltre 90 connettori integrati, consentendo l'integrazione con una vasta gamma di fonti di dati, inclusi database relazionali, sistemi NoSQL, applicazioni SaaS, API e servizi di archiviazione cloud. - Trasformazione Dati Senza Codice: Utilizzando flussi di dati di mapping alimentati da Apache Spark™, ADF consente agli utenti di eseguire trasformazioni dati complesse senza scrivere codice, semplificando il processo di preparazione dei dati. - Rehosting di Pacchetti SSIS: Le organizzazioni possono facilmente migrare ed estendere i loro pacchetti SQL Server Integration Services (SSIS) esistenti al cloud, ottenendo significativi risparmi sui costi e una scalabilità migliorata. - Scalabile ed Economico: Come servizio senza server, ADF si scala automaticamente per soddisfare le esigenze di integrazione dei dati, offrendo un modello di prezzo pay-as-you-go che elimina la necessità di investimenti infrastrutturali anticipati. - Monitoraggio e Gestione Completi: ADF fornisce strumenti di monitoraggio robusti, consentendo agli utenti di tracciare le prestazioni delle pipeline, impostare avvisi e garantire un funzionamento efficiente dei flussi di lavoro dei dati. Valore Primario e Soluzioni per gli Utenti: Azure Data Factory affronta le complessità dell'integrazione dati moderna fornendo una piattaforma unificata che connette fonti di dati disparate, automatizza i flussi di lavoro dei dati e facilita trasformazioni dati avanzate. Questo consente alle organizzazioni di derivare intuizioni azionabili dai loro dati, migliorare i processi decisionali e accelerare le iniziative di trasformazione digitale. Offrendo un ambiente scalabile, economico e senza codice, ADF riduce il carico operativo sui team IT e consente agli ingegneri dei dati e agli analisti aziendali di concentrarsi sulla fornitura di valore attraverso strategie basate sui dati.

**Average Rating:** 4.6/5.0

**Total Reviews:** 96

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

- **Ritiene che the product sia stato un valido partner commerciale?:** 9.1/10 (Category avg: 8.9/10)
- **Qualità del supporto:** 8.8/10 (Category avg: 8.9/10)
- **Facilità d'uso:** 8.9/10 (Category avg: 8.8/10)
- **Facilità di amministrazione:** 8.7/10 (Category avg: 8.5/10)

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

- **Venditore:** [Microsoft](https://www.g2.com/it/sellers/microsoft)
- **Anno di Fondazione:** 1975
- **Sede centrale:** Redmond, Washington
- **Twitter:** @microsoft  
13,091,739 follower su Twitter
- **Pagina LinkedIn®:** [www.linkedin.com](https://www.g2.com/it/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 dipendenti su LinkedIn®
- **Proprietà:** MSFT

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Software Engineer
- **Top Industries:** Tecnologia dell'informazione e servizi, Software per computer
- **Company Size:** 59% Large, 31% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Gli utenti apprezzano le capacità di **integrazione dei dati senza soluzione di continuità** di Azure Data Factory, semplificando i flussi di lavoro complessi tra più fonti.
- Gli utenti apprezzano la **facilità d'uso** di Azure Data Factory, semplificando l'integrazione dei dati con un'interfaccia visiva a basso codice.
- Gli utenti evidenziano la **facilità di collegare varie fonti di dati** in Azure Data Factory, semplificando notevolmente l'integrazione dei dati.
- Gli utenti apprezzano le **capacità di integrazione senza soluzione di continuità** di Azure Data Factory per un movimento e un'automazione dei dati efficienti.
- Gli utenti apprezzano la **scalabilità** di Azure Data Factory, che consente un'integrazione e una gestione senza soluzione di continuità di grandi flussi di lavoro dati senza sforzo.

##### Cons

- Gli utenti trovano **difficile il debugging** in Azure Data Factory, citando limitazioni e un'esperienza complessa per la risoluzione dei problemi delle pipeline.
- Gli utenti trovano **difficile il debug** in Azure Data Factory frustrante, in particolare con pipeline complesse e strumenti di risoluzione dei problemi limitati.
- Gli utenti trovano Azure Data Factory **costoso** a causa dei potenziali alti costi derivanti da grandi volumi di dati e dall'uso frequente delle pipeline.
- Gli utenti trovano **limitazioni delle funzionalità** in Azure Data Factory, in particolare nel logging, nel monitoraggio e nelle trasformazioni complesse.
- Gli utenti esprimono frustrazione per la **complessità** di Azure Data Factory, citando sfide nel debug e nella gestione di flussi di lavoro complessi.

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

**["Integrazione Intuitiva e Scalabile dei Dati con Azure Data Factory"](https://www.g2.com/it/survey_responses/azure-data-factory-review-12454264)**

**Rating:** 4.5/5.0 stars

_— Alan R._

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

**["Trascinamento e rilascio a basso codice che rende lo sviluppo facile per sviluppatori e utenti aziendali"](https://www.g2.com/it/survey_responses/azure-data-factory-review-12746463)**

**Rating:** 4.5/5.0 stars

_— Shyam s._

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

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

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

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

La piattaforma SnapLogic è una soluzione di integrazione e automazione agentica che aiuta i team aziendali a connettere applicazioni, fonti di dati e API, e orchestrare flussi di lavoro potenziati dall'IA in ambienti cloud e on-premises. SnapLogic ha sede a San Mateo, California. Fondata nel 2006, l'azienda serve clienti in diversi settori, tra cui servizi finanziari, farmaceutica, manifatturiero, software e istruzione superiore, con uffici in Nord America, Europa e Asia Pacifico. La piattaforma è progettata per team IT, ingegneri dei dati e specialisti dell'integrazione che necessitano di spostare dati tra sistemi, automatizzare processi aziendali e governare l'attività degli agenti IA su larga scala. Supporta casi d'uso tra cui integrazione di applicazioni, gestione di pipeline di dati, gestione del ciclo di vita delle API, modernizzazione di sistemi legacy e orchestrazione di IA aziendale. \*\*Caratteristiche e capacità chiave della piattaforma SnapLogic includono:\*\* - Costruttore di pipeline visivo e low-code: Un designer drag-and-drop che consente ai team di costruire, testare e distribuire integrazioni senza scrivere codice personalizzato, riducendo la dipendenza dalle risorse degli sviluppatori. - Libreria di connettori Snaps pre-costruiti: Più di 1.000 connettori riutilizzabili per applicazioni aziendali, database, servizi cloud e piattaforme di dati, configurabili per modelli di integrazione sia semplici che complessi. - SnapGPT: Un co-pilota IA integrato nella piattaforma che genera pipeline di integrazione, suggerisce mappature dei dati e assiste nella risoluzione dei problemi delle pipeline utilizzando input in linguaggio naturale. - Gestione delle API: Strumenti per creare, pubblicare, proteggere e monitorare le API, consentendo alle organizzazioni di esporre e consumare servizi dati attraverso sistemi interni ed esterni. - Automazione dei flussi di lavoro agentici: Capacità di progettare e orchestrare agenti IA che eseguono processi aziendali multi-step, con supporto nativo per il Model Context Protocol (MCP) per gestire le interazioni degli agenti attraverso modelli e strumenti. - Integrazione e trasformazione dei dati: Supporto per pipeline di dati batch, in tempo reale e in streaming con capacità di trasformazione, mappatura e arricchimento integrate per dati strutturati e non strutturati. - Monitoraggio e governance centralizzati: Un dashboard unificato per tracciare le prestazioni delle pipeline, gestire i controlli di accesso e mantenere l'auditabilità di tutte le attività di integrazione e automazione. La piattaforma SnapLogic affronta le sfide comuni che le organizzazioni incontrano quando scalano le loro operazioni tecnologiche: dati frammentati attraverso sistemi disconnessi, alti costi di sviluppo dell'integrazione e la complessità di distribuire l'IA in ambienti regolamentati o critici per la missione. Fornendo una piattaforma unificata sia per l'integrazione tradizionale che per l'automazione agentica, riduce la dipendenza da connettori codificati su misura e consente ai team di costruire e gestire integrazioni senza richiedere una profonda competenza in ingegneria del software.

**Average Rating:** 4.4/5.0

**Total Reviews:** 379

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

- **Ritiene che the product sia stato un valido partner commerciale?:** 8.8/10 (Category avg: 8.9/10)
- **Qualità del supporto:** 8.3/10 (Category avg: 8.9/10)
- **Facilità d'uso:** 8.9/10 (Category avg: 8.8/10)
- **Facilità di amministrazione:** 8.6/10 (Category avg: 8.5/10)

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

- **Venditore:** [SnapLogic](https://www.g2.com/it/sellers/snaplogic)
- **Sito web dell'azienda:** www.snaplogic.com
- **Anno di Fondazione:** 2006
- **Sede centrale:** San Mateo, CA
- **Twitter:** @SnapLogic  
7,348 follower su Twitter
- **Pagina LinkedIn®:** [www.linkedin.com](https://www.g2.com/it/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)  
305 dipendenti su LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Consultant
- **Top Industries:** Tecnologia dell'informazione e servizi, Software per computer
- **Company Size:** 46% Large, 36% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Gli utenti apprezzano la **facilità d'uso** di SnapLogic IIP, che consente connessioni rapide e flussi di lavoro semplificati senza la necessità di una programmazione estesa.
- Gli utenti apprezzano molto le **facili integrazioni** offerte da SnapLogic, rendendo semplici ed efficienti i complessi compiti di gestione dei dati.
- Gli utenti apprezzano la **facilità di integrazione** di SnapLogic, che consente connessioni senza soluzione di continuità tra vari strumenti e sistemi.
- Gli utenti apprezzano l' **interfaccia utente intuitiva** di SnapLogic IIP, che consente integrazioni complesse facili ed efficienti.
- Gli utenti apprezzano le **capacità di automazione** di SnapLogic IIP, semplificando integrazioni complesse e migliorando l'efficienza del flusso di lavoro.

##### Cons

- Gli utenti affrontano **problemi di prestazioni** con caricamenti lenti e frequenti problemi di memoria quando gestiscono grandi set di dati in SnapLogic.
- Gli utenti riscontrano **difficoltà tecniche** con SnapLogic, inclusi trasformazioni complesse, sfide di debug e problemi di prestazioni.
- Gli utenti sperimentano **scarse prestazioni** quando elaborano carichi di lavoro pesanti, portando a frustrazione e inefficienza durante i compiti di integrazione.
- Gli utenti trovano la **complessità del debugging** impegnativa, portando a confusione e influenzando la loro esperienza complessiva con SnapLogic IIP.
- Gli utenti trovano che **la segnalazione degli errori manchi di dettagli** , rendendo il processo di risoluzione dei problemi lungo e confuso.

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

**["Effective data integration tool"](https://www.g2.com/it/survey_responses/snaplogic-intelligent-integration-platform-iip-review-13334121)**

**Rating:** 4.5/5.0 stars

_— Vinay S._

[Read full review](https://www.g2.com/it/survey_responses/snaplogic-intelligent-integration-platform-iip-review-13334121)

**["Integrazioni veloci e scalabili con il designer intuitivo drag-and-drop di SnapLogic"](https://www.g2.com/it/survey_responses/snaplogic-intelligent-integration-platform-iip-review-13212820)**

**Rating:** 5.0/5.0 stars

_— Ramakrishna k._

[Read full review](https://www.g2.com/it/survey_responses/snaplogic-intelligent-integration-platform-iip-review-13212820)

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

- [A cosa serve la SnapLogic Intelligent Integration Platform (IIP)?](https://www.g2.com/it/discussions/what-is-snaplogic-intelligent-integration-platform-iip-used-for) - 1 comment

### [AWS Lake Formation](https://www.g2.com/it/products/aws-lake-formation/reviews)

AWS Lake Formation è un servizio completamente gestito per costruire, gestire, proteggere e condividere dati nei data lake in pochi giorni. Puoi centralizzare la sicurezza e la governance e abilitare la condivisione dei dati in tutta l'organizzazione.

**Average Rating:** 4.4/5.0

**Total Reviews:** 33

#### How Do G2 Users Rate AWS Lake Formation?

- **Ritiene che the product sia stato un valido partner commerciale?:** 9.0/10 (Category avg: 8.9/10)
- **Qualità del supporto:** 8.3/10 (Category avg: 8.9/10)
- **Facilità d'uso:** 8.7/10 (Category avg: 8.8/10)
- **Facilità di amministrazione:** 8.0/10 (Category avg: 8.5/10)

#### Who Is the Company Behind AWS Lake Formation?

- **Venditore:** [Amazon Web Services (AWS)](https://www.g2.com/it/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Anno di Fondazione:** 2006
- **Sede centrale:** Seattle, WA
- **Twitter:** @awscloud  
2,232,483 follower su Twitter
- **Pagina LinkedIn®:** [www.linkedin.com](https://www.g2.com/it/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 dipendenti su LinkedIn®
- **Proprietà:** NASDAQ: AMZN

#### Who Uses This Product?

- **Top Industries:** Tecnologia dell'informazione e servizi
- **Company Size:** 47% Small, 37% Large

#### What Are Recent G2 Reviews of AWS Lake Formation?

**["Il miglior servizio di Data lake gestito nel cloud"](https://www.g2.com/it/survey_responses/aws-lake-formation-review-7819323)**

**Rating:** 5.0/5.0 stars

_— Ravi B._

[Read full review](https://www.g2.com/it/survey_responses/aws-lake-formation-review-7819323)

**["Semplifica la governance, richiede esperienza per l'installazione"](https://www.g2.com/it/survey_responses/aws-lake-formation-review-12863344)**

**Rating:** 4.0/5.0 stars

_— Atharva P._

[Read full review](https://www.g2.com/it/survey_responses/aws-lake-formation-review-12863344)

#### What Are G2 Users Discussing About AWS Lake Formation?

- [Is AWS Lake Formation free?](https://www.g2.com/it/discussions/is-aws-lake-formation-free)
- [How do I create AWS data lake?](https://www.g2.com/it/discussions/how-do-i-create-aws-data-lake)
- [How does Lake formation work?](https://www.g2.com/it/discussions/how-does-lake-formation-work)
- [What does AWS Lake formation do?](https://www.g2.com/it/discussions/what-does-aws-lake-formation-do)

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

Maia è una piattaforma di automazione dei dati AI alimentata da agenti AI autonomi che costruiscono, mantengono ed evolvono prodotti di dati, eliminando così il lavoro manuale sui dati. Maia consente ai CDAOs e ai team di dati aziendali di fornire prodotti di dati su scala macchina mantenendo la governance. La sua piattaforma integrata combina agenti specializzati in Maia Team, basati sull'intelligenza dei dati organizzativi del Maia Context Engine ed eseguiti attraverso gli strumenti di dati governati di Maia Foundation. Organizzazioni come EDF, St. James’ Place e Nature’s Touch utilizzano Maia per automatizzare il lavoro sui dati su larga scala, modernizzare le piattaforme e accelerare le roadmap AI senza aumentare il personale. Guarda Maia di persona.

**Average Rating:** 4.5/5.0

**Total Reviews:** 120

#### How Do G2 Users Rate Maia?

- **Ritiene che the product sia stato un valido partner commerciale?:** 8.4/10 (Category avg: 8.9/10)
- **Qualità del supporto:** 8.6/10 (Category avg: 8.9/10)
- **Facilità d'uso:** 9.0/10 (Category avg: 8.8/10)
- **Facilità di amministrazione:** 8.3/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Maia?

- **Venditore:** [Matillion](https://www.g2.com/it/sellers/matillion)
- **Sito web dell'azienda:** www.matillion.com
- **Anno di Fondazione:** 2011
- **Sede centrale:** Salford, GB
- **Twitter:** @matillion  
7,362 follower su Twitter
- **Pagina LinkedIn®:** [www.linkedin.com](https://www.g2.com/it/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 dipendenti su LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Engineer
- **Top Industries:** Software per computer, Tecnologia dell'informazione e servizi
- **Company Size:** 47% Medium, 31% Large

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

_AI-generated summary from verified user reviews_

##### Pros

- Gli utenti apprezzano la **facilità d'uso** di Maia, rendendo la configurazione e la comprensione delle funzionalità accessibili a tutti gli utenti.
- Gli utenti apprezzano l' **automazione senza soluzione di continuità** di Matillion, migliorando l'efficienza e semplificando il processo ETL senza sforzo.
- Gli utenti apprezzano l' **interfaccia semplice e intuitiva** di Matillion, rendendo i processi ETL senza sforzo per tutti.
- Gli utenti apprezzano l' **interfaccia intuitiva** di Matillion che semplifica compiti complessi e migliora il comfort dell'utente per tutti i livelli di esperienza.
- Gli utenti evidenziano l' **efficienza ETL** di Maia, apprezzando la sua integrazione intuitiva e scalabile con le principali piattaforme cloud.

##### Cons

- Gli utenti affrontano **limitazioni delle funzionalità** in Maia, influenzando le prestazioni lavorative e complicando gli sforzi di riutilizzo dei modelli.
- Gli utenti trovano Maia **costosa** , soprattutto quando il volume dei dati aumenta e i costi possono accumularsi rapidamente.
- Gli utenti affrontano **problemi di prestazioni** con Jython e limitazioni del singolo thread, mentre le funzionalità cloud sono carenti rispetto alla versione server.
- Gli utenti esprimono preoccupazione per la **dipendenza dal cloud** , affrontando sfide con personalizzazione limitata e costi aggiuntivi su larga scala.
- Gli utenti segnalano **limitazioni dell'API** in Maia, in particolare la mancanza di funzionalità di accesso amministrativo e opzioni di esportazione dei dati grezzi.

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

**["Maia rende l'onboarding veloce con un'interfaccia utente intuitiva e pipeline a basso codice"](https://www.g2.com/it/survey_responses/maia-review-12942268)**

**Rating:** 4.5/5.0 stars

_— Anthony S._

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

**["Maia ha scalato oltre 800 migrazioni di pipeline senza ulteriore sovraccarico"](https://www.g2.com/it/survey_responses/maia-review-12920298)**

**Rating:** 5.0/5.0 stars

_— Keith G._

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

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

- [A cosa serve Matillion ETL?](https://www.g2.com/it/discussions/what-is-matillion-etl-used-for) - 1 comment
- [A cosa serve Matillion Data Loader?](https://www.g2.com/it/discussions/what-is-matillion-data-loader-used-for)
- [What are ETL tools used for?](https://www.g2.com/it/discussions/what-are-etl-tools-used-for)
- [Is Matillion open source?](https://www.g2.com/it/discussions/is-matillion-open-source)
- [What is ETL in software?](https://www.g2.com/it/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: 8.8/10)
- **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 appreciate the **fast querying** capabilities of Amazon Redshift, enjoying efficient and smooth data access for large datasets.
- Users appreciate the **seamless integrations** of Amazon Redshift, enhancing functionality and efficiency across their data solutions.
- Users appreciate the **ease of use** of Amazon Redshift, finding it simple to connect and manage data effectively.
- Users appreciate the **easy integrations** with other software and AWS services, enhancing their data management experience.
- Users appreciate the **impressive speed and scalability** of Amazon Redshift, enhancing their data warehousing experience significantly.

##### Cons

- Users note notable **feature limitations** in Amazon Redshift, particularly in advanced analytics and cross-language coding.
- Users find **software limitations** in Redshift, experiencing issues with performance, concurrency, and data type support.
- Users face significant **complexity in optimizations** with Redshift, requiring extensive management and specialized knowledge for effective use.
- Users face **query issues** , requiring significant time for optimization, tuning, and managing complexity and concurrency challenges.
- Users face a significant **query optimization challenge** with Redshift, requiring considerable effort and specialized knowledge.

#### 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/it/products/5x/reviews)

5X è una piattaforma end-to-end per dati e AI. La piattaforma organizza i tuoi dati indipendentemente dalla fonte o dal formato. Che tu abbia o meno un team dedicato ai dati, la nostra piattaforma trasforma dati frammentati in approfondimenti e applicazioni azionabili. Il feedback che riceviamo più spesso dai clienti è: "È autoesplicativo" e "È super facile da usare". Ed è esattamente questo il nostro obiettivo: creare una piattaforma potente e tutto-in-uno che sia incredibilmente facile da usare. Lo stack moderno dei dati si è evoluto. Non si tratta più di mettere insieme fornitori. La prossima generazione dello stack moderno dei dati è una piattaforma tutto-in-uno che offre velocità, semplicità e riduzione dei costi di proprietà. Ed è esattamente ciò che abbiamo creato in 5X. Le aziende utilizzano 5X per molteplici ragioni: 1) Velocità e produttività. Le piattaforme dati tutto-in-uno sono incredibilmente efficienti. Abbiamo visto aziende costruire casi d'uso già dal primo giorno. Contattaci per vedere se ti qualifichi per un avvio rapido gratuito di 48 ore! 🚀 2) Riduci il tuo costo totale di proprietà del 30% rispetto alla costruzione della tua piattaforma. Questo non tiene conto delle ore di lavoro necessarie per supportare la costruzione di una piattaforma 🤯 3) Usa la nostra consulenza completa sui dati per supporto su ingegneria dei dati e analisi 👨‍💻 5X è stata fondata nel 2020 con presenza negli USA, Singapore, Regno Unito e India. Il nostro team globale è composto da oltre 70 persone ed è in rapida crescita. Abbiamo recentemente raccolto il nostro round seed da Flybridge Capital e siamo supportati dai migliori fondatori di aziende come Datadog, Preset, Astronomer, Mode, Rudderstack e altri importanti investitori angel. Per maggiori informazioni, visita 5X.co Non parliamo solo di velocità e semplicità; lo dimostriamo con i fatti. Parla con noi del nostro avvio rapido di 48 ore in cui possiamo costruire un caso d'uso end-to-end per te in 48 ore gratuitamente.

**Average Rating:** 4.9/5.0

**Total Reviews:** 81

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

- **Ritiene che the product sia stato un valido partner commerciale?:** 9.8/10 (Category avg: 8.9/10)
- **Qualità del supporto:** 9.8/10 (Category avg: 8.9/10)
- **Facilità d'uso:** 9.5/10 (Category avg: 8.8/10)
- **Facilità di amministrazione:** 9.6/10 (Category avg: 8.5/10)

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

- **Venditore:** [5X](https://www.g2.com/it/sellers/5x)
- **Anno di Fondazione:** 2020
- **Sede centrale:** San Francisco
- **Twitter:** @DataWith5x  
49 follower su Twitter
- **Pagina LinkedIn®:** [www.linkedin.com](https://www.g2.com/it/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 dipendenti su LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Software per computer, Servizi finanziari
- **Company Size:** 56% Medium, 40% Small

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

_AI-generated summary from verified user reviews_

##### Pros

- Gli utenti apprezzano la **facilità d'uso** di 5X, grazie alla sua interfaccia intuitiva e all'integrazione senza soluzione di continuità con gli strumenti esistenti.
- Gli utenti apprezzano il **supporto clienti reattivo** di 5X, migliorando la loro esperienza complessiva con aggiornamenti rapidi delle funzionalità.
- Gli utenti apprezzano le **capacità di integrazione senza soluzione di continuità** di 5X, migliorando l'efficienza nella gestione di diverse fonti di dati senza sforzo.
- Gli utenti apprezzano le **facili integrazioni** di 5X, che migliorano i loro processi di acquisizione dati e l'efficienza operativa complessiva.
- Gli utenti apprezzano l' **integrazione senza soluzione di continuità e il design intuitivo** di 5X, che consente una gestione efficiente dei dati e l'automazione.

##### Cons

- I nuovi utenti affrontano una **ripida curva di apprendimento** , ma la formazione aiuta a facilitare la transizione alle funzionalità della piattaforma.
- Gli utenti trovano il **processo di configurazione complesso** , portando a ritardi e sfide nell'implementazione di funzionalità avanzate e flussi di lavoro.
- Gli utenti trovano una **curva di apprendimento ripida** inizialmente, ma diventano competenti con l'addestramento e il supporto nel tempo.
- Gli utenti trovano l' **installazione difficile** di 5X impegnativa a causa della complessità e di una curva di apprendimento ripida.
- Gli utenti notano le **limitazioni delle funzionalità** di 5X, poiché alcune opzioni avanzate sono ancora in fase di sviluppo e potrebbero richiedere pazienza.

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

**["Un partner di dati affidabile e scalabile"](https://www.g2.com/it/survey_responses/5x-review-11889175)**

**Rating:** 5.0/5.0 stars

_— Varinderjit K._

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

**["Supporto eccezionale e piattaforma intuitiva che guidano la nostra trasformazione dei dati"](https://www.g2.com/it/survey_responses/5x-review-11903408)**

**Rating:** 4.0/5.0 stars

_— Shuming F._

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

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

Per i team di dati che cercano di aumentare la disponibilità di dati affidabili, Astronomer fornisce Astro, la moderna piattaforma di orchestrazione dei dati, alimentata da Airflow. Astro consente agli ingegneri dei dati, agli scienziati dei dati e agli analisti dei dati di costruire, eseguire e osservare pipeline-as-code. Astronomer è la forza trainante dietro Apache Airflow™, lo standard de facto per esprimere i flussi di dati come codice. Airflow viene scaricato più di 31 milioni di volte ogni mese ed è utilizzato da centinaia di migliaia di team in tutto il mondo.

**Average Rating:** 4.5/5.0

**Total Reviews:** 135

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

- **Ritiene che the product sia stato un valido partner commerciale?:** 9.0/10 (Category avg: 8.9/10)
- **Qualità del supporto:** 8.9/10 (Category avg: 8.9/10)
- **Facilità d'uso:** 9.0/10 (Category avg: 8.8/10)
- **Facilità di amministrazione:** 8.8/10 (Category avg: 8.5/10)

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

- **Venditore:** [Astronomer](https://www.g2.com/it/sellers/astronomer)
- **Sito web dell'azienda:** www.astronomer.io
- **Anno di Fondazione:** 2018
- **Sede centrale:** New York, US
- **Twitter:** @astronomerio  
19,697 follower su Twitter
- **Pagina LinkedIn®:** [www.linkedin.com](https://www.g2.com/it/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)  
4,562 dipendenti su LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Senior Data Engineer
- **Top Industries:** Tecnologia dell'informazione e servizi, Servizi finanziari
- **Company Size:** 47% Medium, 38% Large

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

_AI-generated summary from verified user reviews_

##### Pros

- Gli utenti apprezzano la **facilità d'uso** di Astro, trovandolo intuitivo ed efficace per gestire flussi di lavoro complessi.
- Gli utenti apprezzano i **miglioramenti dell'efficienza** di Astro, semplificando i flussi di lavoro e riducendo la necessità di risorse DevOps dedicate.
- Gli utenti apprezzano l' **interfaccia utente intuitiva** di Astro, che facilita il monitoraggio e la gestione dei flussi di lavoro complessi.
- Gli utenti apprezzano le **capacità di automazione** di Astro by Astronomer, che semplificano i flussi di lavoro e migliorano l'orchestrazione dei dati.
- Gli utenti apprezzano la **facilità di distribuzione** di Astro by Astronomer, migliorando la produttività con configurazioni semplici, affidabili ed efficienti.

##### Cons

- Gli utenti evidenziano che Astro by Astronomer può essere piuttosto **costoso** , ponendo sfide per i team più piccoli e i vincoli di budget.
- Gli utenti notano una **ripida curva di apprendimento** con Astro, che richiede un tempo significativo per l'adattamento e la formazione.
- Gli utenti trovano la **ripida curva di apprendimento** di Astro impegnativa, richiedendo tempo extra per i nuovi membri del team per adattarsi.
- Gli utenti notano una **ripida curva di apprendimento** con Astro, rendendo l'adattamento impegnativo per i nuovi membri del team.
- Gli utenti notano le **limitazioni delle funzionalità** di Astro, citando alti costi e mancanza di flessibilità rispetto all'auto-hosting.

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

**["Eccellente esperienza per sviluppatori e clienti"](https://www.g2.com/it/survey_responses/astro-by-astronomer-review-8428848)**

**Rating:** 5.0/5.0 stars

_— Juan Roberto H._

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

**["Asro letteralmente assiste nel lavoro di ingegneria dei dati, rendendolo più facile e produttivo."](https://www.g2.com/it/survey_responses/astro-by-astronomer-review-8519803)**

**Rating:** 5.0/5.0 stars

_— Lakshminarayanan K._

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

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

- [Qual è la tua esperienza con Astro di Astronomer per l'orchestrazione dei dati e quali sfide hai affrontato?](https://www.g2.com/it/discussions/what-is-your-experience-with-astro-by-astronomer-for-data-orchestration-and-what-challenges-have-you-faced)
- [A cosa serve Astro di Astronomer?](https://www.g2.com/it/discussions/what-is-astro-by-astronomer-used-for)

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

Simplify the complexity of how you B2B with IBM webMethods B2B. The B2B integration allows you to share documents—purchase orders, invoices, shipping notices, contracts and more—in the cloud and keep everything in sync with APIs.

**Average Rating:** 4.5/5.0

**Total Reviews:** 56

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

- **Has the product been a good partner in doing business?:** 8.4/10 (Category avg: 8.9/10)
- **Quality of Support:** 8.7/10 (Category avg: 8.9/10)
- **Ease of Use:** 8.9/10 (Category avg: 8.8/10)
- **Ease of Admin:** 8.2/10 (Category avg: 8.5/10)

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

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Year Founded:** 1911
- **HQ Location:** Armonk, New York, United States
- **Twitter:** @IBMSecurity  
74,660 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/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 employees on LinkedIn®
- **Ownership:** SWX:IBM

#### Who Uses This Product?

- **Top Industries:** Staffing and Recruiting, Computer Software
- **Company Size:** 42% Medium, 35% Large

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

_AI-generated summary from verified user reviews_

##### Pros

- Users praise the **ease of use** of IBM webMethods B2B, appreciating its simplicity and user-friendly design.
- Users appreciate the **scalable and user-friendly features** of IBM webMethods B2B for seamless data exchange and automation.
- Users praise the **strong security features** of IBM webMethods B2B, ensuring data protection and secure transactions.
- Users value the **end-to-end automation** provided by IBM webMethods B2B, enhancing efficiency in data exchange with partners.
- Users commend the **integration capabilities** of IBM webMethods B2B, enabling seamless communication and data sharing across enterprises.

##### Cons

- Users find the **complexity** of IBM webMethods B2B challenging, especially for those without prior experience or expertise.
- Users highlight that the product can be **expensive** , leading to concerns about budget management and overall value.
- Users find **difficult learning** curves due to a lack of familiarity with B2B integration concepts on IBM webMethods.
- Users express concerns about **pricing transparency** , with a desire for clearer cost breakdowns for better budgeting.
- Users experience a **learning curve** with webMethods B2B, requiring time to adapt and understand its features.

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

**["Strongly recommend to use"](https://www.g2.com/survey_responses/ibm-webmethods-b2b-review-10173432)**

**Rating:** 5.0/5.0 stars

_— Shilpa J._

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

**["Efficient tool for business document processing over cloud infrastructure"](https://www.g2.com/survey_responses/ibm-webmethods-b2b-review-9536771)**

**Rating:** 4.0/5.0 stars

_— Mahesh B._

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

### [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: 8.8/10)
- **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 appreciate the **unified analytics experience** of Azure Synapse Analytics, streamlining data processes and enhancing efficiency.
- Users value the **seamless integration and automation** of Azure Synapse Analytics, enhancing efficiency in data analytics solutions.
- Users value the **seamless cloud integration** of Azure Synapse Analytics, enhancing workflows and streamlining data analytics solutions.
- Users value the **cost-effective** benefits of Azure Synapse Analytics, maximizing efficiency with flexible, on-demand resources.
- Users value the **seamless data integration** capabilities of Azure Synapse Analytics, enhancing efficiency and reducing complexity in analytics solutions.

##### Cons

- Users find **cost estimation complex** , especially with serverless queries and various resource management requirements.
- Users find **cost management complex** , particularly when optimizing across serverless queries and Spark jobs without proper governance.
- Users often face **debugging issues** with complex pipeline failures, resulting in increased troubleshooting time and frustration.
- Users find **difficult debugging** issues due to lack of error transparency, complicating troubleshooting and performance tuning.
- Users find Azure Synapse Analytics to be **expensive** , with costs complicating monitoring and optimization efforts.

#### 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: 8.8/10)
- **Ease of Admin:** 8.5/10 (Category avg: 8.5/10)

#### Who Is the Company Behind dbt?

- **Seller:** [Fivetran](https://www.g2.com/sellers/fivetran)
- **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, 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 find dbt's **ease of use** exceptional, with straightforward setup and intuitive features enhancing their data transformation processes.
- Users appreciate the **maintainability and clarity** of dbt's SQL code base, enhancing collaboration and data transformations.
- Users value the **automation of data workflows** with dbt, enhancing maintainability and collaboration in SQL transformations.
- Users love the **ease of transforming data with dbt** , allowing for organized and efficient analytics workflows.
- Users value the **data quality** of dbt, praising its effectiveness in ensuring data integrity and operational efficiency.

##### Cons

- Users find that dbt has **limited functionality** due to rigidness and complex debugging, hindering project progress.
- Users face **dependency issues** in dbt, as model errors and upstream changes complicate troubleshooting and disrupt workflows.
- Users find the **steep learning curve** of dbt daunting, needing mastery of concepts like Jinja and Git.
- Users encounter **poor error handling** with unclear messages, making troubleshooting frustrating and complicating the user experience.
- Users often face **confusing error reporting** and unclear messages, making troubleshooting and identifying issues challenging.

#### 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

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 ![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(1,224)](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(895)](https://www.g2.com/products/alteryx/reviews) | No-code ETL and multi-source data blending | "Makes Data Prep Faster with an Intuitive Drag-and-Drop Workflow" |
| [![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(763)](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(777)](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(101)](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(407)](https://www.g2.com/products/snaplogic-intelligent-integration-platform-iip/reviews) | Low-code ETL pipeline building across hybrid environments | "Effective data integration tool" |
| [![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 | "Powerful Analytics Tool with Some Flexibility Limitations" |

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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.