# Best Big Data Analytics Software

## How Many Big Data Analytics Software Products Does G2 Track?

**Total Products under this Category:** 110

### Category Stats (Aug 2026)

- **Average Rating:** 4.46/5 The average rating of products in this category, based on all submitted ratings
- **Top Trending Product:** Datacoves (+0.27%) - Among all products in this category, Datacoves recorded the largest rating increase compared to last month

_Last updated: August 04, 2026_

## How Does G2 Rank Big Data Analytics Software Products?

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

- 30 Analysts and Data Experts
- 8,400+ Authentic Reviews
- 110+ 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 Analytics Software
 ![G2 Grid® for Big Data Analytics Software plotting products by satisfaction and market presence](https://www.g2.com/categories/big-data-analytics/grids.png?focus%5B%5D=10470&focus%5B%5D=6073&focus%5B%5D=10938&focus%5B%5D=1308796&focus%5B%5D=989&focus%5B%5D=67962&focus%5B%5D=27024&focus%5B%5D=52199)

Highlighted products: Databricks, Google Cloud BigQuery, Snowflake, IBM watsonx.data, Alteryx, Azure Databricks, Kyvos Semantic Layer, and Azure Synapse Analytics.

Underlying data: [Grid® JSON](https://www.g2.com/categories/big-data-analytics/grids.json?focus%5B%5D=databricks&focus%5B%5D=google-cloud-bigquery&focus%5B%5D=snowflake&focus%5B%5D=ibm-watsonx-data&focus%5B%5D=alteryx&focus%5B%5D=azure-databricks&focus%5B%5D=kyvos-semantic-layer&focus%5B%5D=azure-synapse-analytics)

**Sponsored**

### Kpow for Apache Kafka®

Kpow is a sophisticated enterprise Kafka management tool designed to enhance the experience of engineering teams by providing a comprehensive solution for managing, monitoring, exploring, and securing Kafka environments. This JVM-based web application serves as an all-in-one console, empowering Kafka engineers with the capabilities they need to streamline their operations and improve productivity. Targeted primarily at engineering teams working with Kafka, Kpow addresses the complexities of managing multiple Kafka clusters, schema registries, and connection installations. With Kpow, users can efficiently monitor and control their Kafka resources from a single interface, simplifying the management process and reducing the time spent on routine tasks. The tool is particularly beneficial for organizations that rely heavily on Kafka for data streaming and processing, as it provides essential functionalities that enhance observability and operational efficiency. One of the standout features of Kpow is its real-time monitoring and visualization capabilities. Users can quickly identify unbalanced brokers and gain insights into how data is distributed across their Kafka Streams topologies. This level of visibility is crucial for diagnosing production issues and optimizing performance. Kpow's advanced search functionalities, including Data Inspect, Streaming Search, and kREPL, enable users to search through vast amounts of messages at remarkable speeds, allowing for rapid troubleshooting and data analysis. Kpow also prioritizes security and access control, making it suitable for enterprise environments. It integrates seamlessly with standard authentication providers and offers role-based access controls, ensuring that user actions can be finely tuned to meet organizational security requirements. Additional security features, such as data masking and audit logs, further enhance the tool's capability to operate in sensitive environments, including air-gapped installations. Installation of Kpow is straightforward, requiring only a single Docker container or JAR file, which operates efficiently with minimal resource requirements of 1GB memory and 1 CPU for production use. This ease of deployment, combined with its powerful features, positions Kpow as a valuable asset for organizations looking to maximize their Kafka infrastructure while maintaining robust security and operational control.

[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=1041&secure%5Bchosen_at%5D=2026-08-05T09%3A42%3A31Z&secure%5Bdisplayable_resource_id%5D=1041&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=1041&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=133071&secure%5Bresource_id%5D=1041&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fbig-data-analytics&secure%5Btoken%5D=636eace5911f0a62725e35b7c7d36afc09fbd9106045fd51f6c52ddbe9cefdf9&secure%5Burl%5D=http%3A%2F%2Ffactorhouse.io%2F&secure%5Burl_type%5D=custom_url)

### [Databricks](https://www.g2.com/es/products/databricks/reviews)

Databricks es la empresa de Datos e IA. Más de 20,000 organizaciones en todo el mundo, incluidas adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever y el 70% de las empresas Fortune 500, confían en la Plataforma de Datos + IA de Databricks para construir y escalar aplicaciones de datos e IA, análisis y agentes. Con sede en San Francisco y más de 30 oficinas en todo el mundo, Databricks ofrece una plataforma unificada que incluye Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse y Unity Catalog. Fundada en 2013 por los creadores originales de Apache Spark™, Delta Lake, MLflow y Unity Catalog, Databricks se basa en una arquitectura de lakehouse abierta que reúne datos, análisis e IA. La plataforma es utilizada por ingenieros de datos, científicos de datos, analistas, desarrolladores, equipos de aprendizaje automático, equipos de IA y usuarios empresariales para colaborar a lo largo de todo el ciclo de vida de los datos y la IA. Las capacidades clave de Databricks incluyen: - Ingeniería de datos: Construir, automatizar y gestionar flujos de datos por lotes, en streaming y en tiempo real de manera confiable. - Análisis e inteligencia empresarial: Ejecutar análisis SQL, crear paneles y permitir que los equipos empresariales exploren datos. - Gobernanza de datos: Descubrir, asegurar y gestionar activos de datos e IA a través de equipos, nubes y cargas de trabajo. - Aprendizaje automático e IA: Desarrollar modelos, construir aplicaciones de IA generativa y crear agentes de IA de calidad de producción. - Aplicaciones de datos: Construir y desplegar aplicaciones impulsadas por datos utilizando datos empresariales gobernados. Disponible en AWS, Azure y Google Cloud, Databricks ayuda a las organizaciones a trabajar a través de nubes, reducir silos de datos y simplificar la colaboración entre equipos y herramientas. Los clientes utilizan Databricks para casos de uso como personalización del cliente, detección de fraudes, mantenimiento predictivo, análisis en tiempo real, ciberseguridad, investigación en salud, gestión de riesgos financieros, optimización de la cadena de suministro y toma de decisiones impulsada por IA. Databricks se utiliza en industrias como servicios financieros, salud y ciencias de la vida, comercio minorista, manufactura, energía y el sector público. Las organizaciones utilizan la plataforma para modernizar la infraestructura de datos, acelerar la adopción de IA y convertir los datos empresariales en valor de negocio.

**Average Rating:** 4.6/5.0

**Total Reviews:** 1,329

#### How Do G2 Users Rate Databricks?

- **¿Ha sido the product un buen socio para hacer negocios?:** 8.9/10 (Category avg: 8.9/10)
- **Análisis de múltiples fuentes:** 9.0/10 (Category avg: 8.5/10)
- **Análisis en tiempo real:** 8.9/10 (Category avg: 8.5/10)
- **Flujo de trabajo de datos:** 8.9/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Databricks?

- **Vendedor:** [Databricks Inc.](https://www.g2.com/es/sellers/databricks-inc)
- **Sitio web de la empresa:** databricks.com
- **Año de fundación:** 2013
- **Ubicación de la sede:** San Francisco, CA
- **Twitter:** @databricks  
92,269 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=bddca64732f61b923d96364e8c8eb35711aab4f98797cb00ab071ff24fbdd392&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F3477522%2F&secure%5Burl_type%5D=linkedin_company_website)  
15,627 empleados en LinkedIn®

#### Who Uses This Product?

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

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

_AI-generated summary from verified user reviews_

##### Pros

- Los usuarios elogian la **facilidad de uso** y las **características completas** de Databricks para aplicaciones de almacenamiento de datos y ML.
- Los usuarios elogian la **facilidad de uso** de Databricks, mejorando su experiencia con interfaces intuitivas y servicios confiables.
- Los usuarios aprecian las **integraciones fluidas** de Databricks con AWS y otras herramientas, mejorando las operaciones diarias y la eficiencia.
- Los usuarios valoran la **colaboración sin fisuras** que ofrece Databricks, mejorando el trabajo en equipo en proyectos de datos con información en tiempo real.
- Los usuarios elogian las **funciones analíticas integradas** de Databricks, mejorando el procesamiento colaborativo de datos y la visualización de información.

##### Cons

- Los usuarios notan una **curva de aprendizaje pronunciada** inicialmente, con permisos confusos y modos de cómputo que afectan la usabilidad.
- Los usuarios señalan que los **costos pueden ser bastante altos** para utilizar Databricks de manera efectiva, especialmente para proyectos de datos grandes.
- Los usuarios encuentran una **curva de aprendizaje pronunciada** con Databricks, especialmente desafiante para los recién llegados a las herramientas de big data.
- Los usuarios encuentran la **complejidad** de Databricks desafiante, especialmente para equipos más pequeños y procesos de configuración inicial.
- Los usuarios enfrentan **desafíos complejos de configuración** inicialmente, aunque el soporte ayuda a simplificar la experiencia con el tiempo.

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

**["Plataforma confiable para construir canalizaciones de datos escalables"](https://www.g2.com/es/survey_responses/databricks-review-13198355)**

**Rating:** 5.0/5.0 stars

_— aravind k._

[Read full review](https://www.g2.com/es/survey_responses/databricks-review-13198355)

**["Databricks simplifica ETL y análisis con cuadernos escalables"](https://www.g2.com/es/survey_responses/databricks-review-13181721)**

**Rating:** 5.0/5.0 stars

_— Diana C._

[Read full review](https://www.g2.com/es/survey_responses/databricks-review-13181721)

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

- [What does Databricks software do?](https://www.g2.com/es/discussions/what-does-databricks-software-do) - 3 comments, 1 upvote
- [¿Qué es la plataforma de análisis unificada de Databricks?](https://www.g2.com/es/discussions/what-is-databricks-unified-analytics-platform) - 3 comments
- [¿Qué es Lakehouse en Databricks?](https://www.g2.com/es/discussions/what-is-lakehouse-in-databricks) - 4 comments, 2 upvotes
- [¿Cuáles son las características de Databricks?](https://www.g2.com/es/discussions/what-are-the-features-of-databricks) - 4 comments, 2 upvotes

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

BigQuery es un almacén de datos a escala de petabytes, rentable y listo para IA que te permite ejecutar análisis sobre grandes cantidades de datos casi en tiempo real. Almacena 10 GiB de datos y ejecuta hasta 1 TiB de consultas gratis por mes.

**Average Rating:** 4.5/5.0

**Total Reviews:** 1,144

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

- **¿Ha sido the product un buen socio para hacer negocios?:** 8.6/10 (Category avg: 8.9/10)
- **Análisis de múltiples fuentes:** 8.7/10 (Category avg: 8.5/10)
- **Análisis en tiempo real:** 8.8/10 (Category avg: 8.5/10)
- **Flujo de trabajo de datos:** 8.6/10 (Category avg: 8.5/10)

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

- **Vendedor:** [Google](https://www.g2.com/es/sellers/google)
- **Año de fundación:** 1998
- **Ubicación de la sede:** Mountain View, CA
- **Twitter:** @google  
31,899,995 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=fe4a5936665c9702418dd53c477fef5a7baea08078bb117ed67e966fc581b9ec&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1441%2F&secure%5Burl_type%5D=linkedin_company_website)  
341,888 empleados en LinkedIn®
- **Propiedad:** NASDAQ:GOOG

#### Who Uses This Product?

- **Who Uses This:** Ingeniero de Datos, Analista de Datos
- **Top Industries:** Tecnología de la información y servicios, Software de Computadora
- **Company Size:** 38% Large, 35% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Los usuarios aprecian la **facilidad de uso** de Google Cloud BigQuery, permitiendo un análisis rápido de conjuntos de datos masivos sin complicaciones.
- Los usuarios aprecian la **velocidad excepcional** de BigQuery, lo que permite un procesamiento rápido de grandes conjuntos de datos sin problemas.
- A los usuarios les encanta la **fácil integración** con los servicios de Google Cloud, lo que permite un análisis y gestión de datos sin problemas.
- Los usuarios aprecian las **capacidades de consulta rápida** de Google Cloud BigQuery, lo que permite un análisis sin esfuerzo de conjuntos de datos masivos.
- Los usuarios aprecian la **eficiencia de consulta** de BigQuery, procesando sin esfuerzo consultas complejas en conjuntos de datos masivos con rapidez.

##### Cons

- Los usuarios encuentran que los **costos pueden escalar rápidamente** con Google Cloud BigQuery, requiriendo una optimización cuidadosa de las consultas para gestionar los gastos.
- Los usuarios tienen dificultades con **problemas de consultas** en BigQuery, enfrentando costos crecientes y desafíos en la optimización y resolución de problemas de consultas.
- Los usuarios encuentran **la gestión de costos desafiante** con Google Cloud BigQuery debido a precios impredecibles e incidentes de cargos inesperados.
- Los usuarios experimentan **problemas de costos** con Google Cloud BigQuery, luchando con facturas inesperadamente altas y visibilidad limitada de precios.
- Los usuarios encuentran la **empinada curva de aprendizaje** de Google Cloud BigQuery desafiante, especialmente para las características avanzadas y las técnicas de optimización.

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

**["Herramienta en la nube fácil de usar con consultas guardadas y compartibles"](https://www.g2.com/es/survey_responses/google-cloud-bigquery-review-12958418)**

**Rating:** 4.0/5.0 stars

_— Reetika P._

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

**["BigQuery escalable y seguro que se conecta sin problemas a través de servicios"](https://www.g2.com/es/survey_responses/google-cloud-bigquery-review-12638747)**

**Rating:** 5.0/5.0 stars

_— Aayush M._

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

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

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

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

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

**Average Rating:** 4.5/5.0

**Total Reviews:** 712

#### How Do G2 Users Rate Snowflake?

- **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 8.9/10)
- **Multi-Source Analysis:** 9.1/10 (Category avg: 8.5/10)
- **Real-Time Analytics:** 9.2/10 (Category avg: 8.5/10)
- **Data Workflow:** 9.2/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Snowflake?

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

#### Who Uses This Product?

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

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

_AI-generated summary from verified user reviews_

##### Pros

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

##### Cons

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

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

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

**Rating:** 4.5/5.0 stars

_— Ravindra N._

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

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

**Rating:** 4.0/5.0 stars

_— Harshil A._

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

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

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

### [IBM watsonx.data](https://www.g2.com/products/ibm-watsonx-data/reviews)

IBM® watsonx.data® helps you access, integrate and understand all your data —structured and unstructured—across any environment. It optimizes workloads for price and performance while enforcing consistent governance across sources, formats and teams. Watch the demo to learn how watsonx.data empowers you to build gen AI apps and powerful AI agents. Free Trial available: https://ibm.biz/Watsonx-data\_Trial

**Average Rating:** 4.4/5.0

**Total Reviews:** 166

#### How Do G2 Users Rate IBM watsonx.data?

- **Has the product been a good partner in doing business?:** 8.7/10 (Category avg: 8.9/10)
- **Multi-Source Analysis:** 8.5/10 (Category avg: 8.5/10)
- **Real-Time Analytics:** 7.3/10 (Category avg: 8.5/10)
- **Data Workflow:** 8.1/10 (Category avg: 8.5/10)

#### Who Is the Company Behind IBM watsonx.data?

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Company Website:** www.ibm.com
- **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®

#### Who Uses This Product?

- **Who Uses This:** Software Engineer, CEO
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 34% Small, 32% Large

#### What Do G2 Reviewers Say About IBM watsonx.data?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** of IBM watsonx.data, finding it reliable and efficient for data management tasks.
- Users value the **organized data integration** and intuitive interface of IBM watsonx.data, enhancing efficiency and analytics.
- Users value the **organized and efficient data management** of IBM watsonx.data, enhancing analytics and reporting tasks seamlessly.
- Users value the **seamless data source integration** in IBM watsonx.data, enhancing flexibility and efficiency for diverse projects.
- Users value the **ability to unify data across hybrid environments** , enhancing flexibility and driving informed decision-making.

##### Cons

- Users find the **learning curve steep** , making initial setup and navigation challenging for newcomers to IBM watsonx.data.
- Users find the **complexity** of setting up IBM watsonx.data a barrier, especially for newcomers to IBM technologies.
- Users find the **pricing steep** for IBM watsonx.data, making it less accessible for smaller businesses and projects.
- Users find the **difficult setup** of IBM watsonx.data time-consuming, with a steep learning curve and complex configurations.
- Users find IBM watsonx.data **difficult to navigate** , especially for beginners and those unfamiliar with AI and data analytics.

#### What Are Recent G2 Reviews of IBM watsonx.data?

**["Clean, Smooth UI with Excellent Onboarding and Infrastructure Visuals"](https://www.g2.com/survey_responses/ibm-watsonx-data-review-13204444)**

**Rating:** 4.0/5.0 stars

_— Aliasgar B._

[Read full review](https://www.g2.com/survey_responses/ibm-watsonx-data-review-13204444)

**["Powerful Query Performance and Governance, But a Steep Onboarding Learning Curve"](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12836202)**

**Rating:** 4.0/5.0 stars

_— Arkajit D._

[Read full review](https://www.g2.com/survey_responses/ibm-watsonx-data-review-12836202)

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

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

**Average Rating:** 4.6/5.0

**Total Reviews:** 856

#### How Do G2 Users Rate Alteryx?

- **Has the product been a good partner in doing business?:** 8.8/10 (Category avg: 8.9/10)
- **Multi-Source Analysis:** 9.0/10 (Category avg: 8.5/10)
- **Real-Time Analytics:** 8.4/10 (Category avg: 8.5/10)
- **Data Workflow:** 9.2/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Alteryx?

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

#### Who Uses This Product?

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

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

_AI-generated summary from verified user reviews_

##### Pros

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

##### Cons

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

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

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

**Rating:** 4.5/5.0 stars

_— Ihor B._

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

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

**Rating:** 4.5/5.0 stars

_— Anushka S._

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

### [Azure Databricks](https://www.g2.com/es/products/azure-databricks/reviews)

Azure Databricks es una plataforma de análisis unificada y abierta desarrollada en colaboración por Microsoft y Databricks. Construida sobre la arquitectura de lakehouse, integra sin problemas la ingeniería de datos, la ciencia de datos y el aprendizaje automático dentro del ecosistema de Azure. Esta plataforma simplifica el desarrollo y la implementación de aplicaciones basadas en datos al proporcionar un espacio de trabajo colaborativo que admite múltiples lenguajes de programación, incluidos SQL, Python, R y Scala. Al aprovechar Azure Databricks, las organizaciones pueden procesar datos a gran escala de manera eficiente, realizar análisis avanzados y construir soluciones de IA, todo mientras se benefician de la escalabilidad y seguridad de Azure. Características y Funcionalidades Clave: - Arquitectura Lakehouse: Combina los mejores elementos de los lagos de datos y los almacenes de datos, permitiendo un almacenamiento y análisis de datos unificados. - Cuadernos Colaborativos: Espacios de trabajo interactivos que admiten múltiples lenguajes, facilitando el trabajo en equipo entre ingenieros de datos, científicos de datos y analistas. - Motor Optimizado de Apache Spark: Mejora el rendimiento para tareas de procesamiento de grandes volúmenes de datos, asegurando análisis más rápidos y confiables. - Integración con Delta Lake: Proporciona transacciones ACID y manejo escalable de metadatos, mejorando la fiabilidad y consistencia de los datos. - Integración Perfecta con Azure: Ofrece conectividad nativa a servicios de Azure como Power BI, Azure Data Lake Storage y Azure Synapse Analytics, agilizando los flujos de trabajo de datos. - Soporte Avanzado para Aprendizaje Automático: Incluye entornos preconfigurados para el desarrollo de aprendizaje automático e IA, con soporte para marcos y bibliotecas populares. Valor Principal y Soluciones Proporcionadas: Azure Databricks aborda los desafíos de gestionar y analizar grandes cantidades de datos al ofrecer una plataforma escalable y colaborativa que unifica la ingeniería de datos, la ciencia de datos y el aprendizaje automático. Simplifica los flujos de trabajo de datos complejos, acelera el tiempo para obtener información y permite el desarrollo de soluciones impulsadas por IA. Al integrarse sin problemas con los servicios de Azure, asegura un procesamiento de datos seguro y eficiente, ayudando a las organizaciones a tomar decisiones basadas en datos e innovar rápidamente.

**Average Rating:** 4.5/5.0

**Total Reviews:** 212

#### How Do G2 Users Rate Azure Databricks?

- **¿Ha sido the product un buen socio para hacer negocios?:** 8.8/10 (Category avg: 8.9/10)
- **Análisis de múltiples fuentes:** 9.0/10 (Category avg: 8.5/10)
- **Análisis en tiempo real:** 8.9/10 (Category avg: 8.5/10)
- **Flujo de trabajo de datos:** 8.7/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Azure Databricks?

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

#### Who Uses This Product?

- **Who Uses This:** Ingeniero de Datos, Ingeniero de software
- **Top Industries:** Tecnología de la información y servicios, Software de Computadora
- **Company Size:** 47% Large, 27% Medium

#### What Do G2 Reviewers Say About Azure Databricks?

_AI-generated summary from verified user reviews_

##### Pros

- Los usuarios encuentran que Azure Databricks es **muy fácil de usar e implementar** , mejorando significativamente su experiencia de desarrollo.
- Los usuarios valoran el **rico conjunto de características** de Azure Databricks, destacando su excelente integración y soporte para múltiples idiomas.
- Los usuarios valoran las **integraciones fluidas** de Azure Databricks con los servicios de Azure, mejorando la eficiencia y simplificando los flujos de trabajo.
- Los usuarios se benefician de la **impresionante velocidad** de Azure Databricks para el procesamiento de datos a gran escala e implementación.
- Los usuarios valoran la **eficiencia de los análisis** en Azure Databricks, lo que permite un procesamiento de datos optimizado y la generación de insights.

##### Cons

- Los usuarios encuentran la **complejidad** de Azure Databricks desafiante, especialmente durante la configuración inicial y la gestión de clústeres.
- Los usuarios encuentran la **configuración difícil** de Azure Databricks desafiante, lo que hace que la configuración inicial sea un proceso complejo.
- Los usuarios experimentan una **curva de aprendizaje pronunciada** con Azure Databricks, encontrándolo desafiante mientras se adaptan a su complejidad.
- Los usuarios experimentan **rendimiento lento** con Azure Databricks, particularmente durante el inicio del clúster y las tareas de procesamiento paralelo.
- Los usuarios a menudo encuentran **precios poco claros** para Azure Databricks, ya que los costos pueden escalar rápidamente sin un monitoreo adecuado.

#### What Are Recent G2 Reviews of Azure Databricks?

**["Una potencia para escalar flujos de trabajo de ML, pero mantén un ojo atento en tu facturación."](https://www.g2.com/es/survey_responses/azure-databricks-review-12976834)**

**Rating:** 5.0/5.0 stars

_— Lokesh S._

[Read full review](https://www.g2.com/es/survey_responses/azure-databricks-review-12976834)

**["Azure Databricks es eficiente para grandes volúmenes de datos, un poco tosco en los bordes."](https://www.g2.com/es/survey_responses/azure-databricks-review-12684122)**

**Rating:** 4.5/5.0 stars

_— Wealth A._

[Read full review](https://www.g2.com/es/survey_responses/azure-databricks-review-12684122)

#### What Are G2 Users Discussing About Azure Databricks?

- [¿Para qué se utiliza Azure Databricks?](https://www.g2.com/es/discussions/azure-databricks-what-is-azure-databricks-used-for) - 2 comments
- [Is Azure Databricks PaaS or SAAS?](https://www.g2.com/es/discussions/is-azure-databricks-paas-or-saas) - 2 comments
- [¿Microsoft posee Databricks?](https://www.g2.com/es/discussions/does-microsoft-own-databricks) - 2 comments
- [¿Qué es Databricks Azure?](https://www.g2.com/es/discussions/what-is-databricks-azure) - 1 comment
- [What is azure Databricks used for?](https://www.g2.com/es/discussions/what-is-azure-databricks-used-for)

### [Kyvos Semantic Layer](https://www.g2.com/es/products/kyvos-semantic-layer/reviews)

Kyvos es una capa semántica para IA y BI. Ofrece a las organizaciones una vista única, coherente y amigable para los negocios de todo su patrimonio de datos. Al estandarizar cómo se define y entiende la información, Kyvos elimina la deriva de métricas entre las herramientas de BI y asegura que los LLMs y agentes de IA trabajen con semánticas de negocio gobernadas en lugar de tablas en bruto. Kyvos también ofrece análisis ultrarrápidos a gran escala y con alta concurrencia, incluyendo análisis multidimensional granular en la nube, sin los tiempos de consulta lentos y los crecientes costos de la nube que típicamente lo acompañan. Por qué las Organizaciones Usan Kyvos Fundación Semántica Unificada para IA y BI La capa semántica de Kyvos estandariza cómo se modelan las métricas, KPIs, dimensiones, jerarquías, relaciones, cálculos y reglas de negocio en toda la empresa, de modo que los paneles, herramientas de análisis, cuadernos y sistemas de IA operen con el mismo entendimiento del negocio. Kyvos permite: - Semántica compartida: un lenguaje de datos común en cada herramienta, equipo y sistema - Acceso gobernado: exploración de datos dentro de límites definidos de seguridad, roles y permisos - Interoperabilidad de plataforma: contexto semántico consistente en diversas plataformas y entornos - Preparación para IA: LLMs y agentes trabajan con semánticas de negocio gobernadas en lugar de tablas en bruto o esquemas ambiguos IA Basada en Contexto de Negocio Kyvos fundamenta los sistemas de IA en el modelo semántico gobernado, asegurando que operen en un contexto de negocio establecido en lugar de esquemas en bruto, mejorando la precisión, trazabilidad y fiabilidad de los insights generados por IA. Métricas Consistentes en Herramientas de BI Kyvos centraliza las definiciones de métricas y KPIs en la capa semántica y las aplica consistentemente en cada interfaz de análisis, eliminando la deriva de métricas y mejorando la confianza en los análisis. Análisis de Alto Rendimiento a Escala Kyvos ofrece análisis de alto rendimiento que escalan con la demanda, permitiendo: - Rendimiento de consulta en subsegundos en conjuntos de datos masivos - Alta concurrencia entre miles de usuarios y cargas de trabajo - Tiempos de respuesta consistentes independientemente del volumen de datos o concurrencia - Sin degradación del rendimiento a medida que crece la adopción - Análisis Multidimensional en la Nube Kyvos permite análisis multidimensional profundos, apoyando: - Análisis granular en miles de millones de filas - Miles de medidas y dimensiones en un solo modelo - Rápido desglose en jerarquías complejas - Profundidad analítica completa sin sacrificar la velocidad de consulta Eficiencia de Costos en la Nube Kyvos ofrece análisis a través de su capa semántica en lugar de enrutar cada consulta al almacén, reduciendo el consumo de cómputo en cargas de trabajo de análisis e IA. A medida que crece la adopción, las organizaciones pueden escalar usuarios, cargas de trabajo y complejidad analítica sin un aumento correspondiente en los costos de cómputo del almacén.

**Average Rating:** 4.8/5.0

**Total Reviews:** 267

#### How Do G2 Users Rate Kyvos Semantic Layer?

- **¿Ha sido the product un buen socio para hacer negocios?:** 9.6/10 (Category avg: 8.9/10)
- **Análisis de múltiples fuentes:** 9.2/10 (Category avg: 8.5/10)
- **Análisis en tiempo real:** 10.0/10 (Category avg: 8.5/10)
- **Flujo de trabajo de datos:** 9.6/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Kyvos Semantic Layer?

- **Vendedor:** [Kyvos Insights](https://www.g2.com/es/sellers/kyvos-insights)
- **Sitio web de la empresa:** www.kyvosinsights.com
- **Año de fundación:** 2014
- **Ubicación de la sede:** Los Gatos, CA
- **Twitter:** @KyvosInsights  
689 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=900350c47a6a807c4765a28f52dcdbf3c06a5325a45d905c54e56a79e545cf9d&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fkyvos-insights-inc-%2F&secure%5Burl_type%5D=linkedin_company_website)  
152 empleados en LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Ingeniero de Software Senior, Ingeniero de software
- **Top Industries:** Tecnología de la información y servicios, Software de Computadora
- **Company Size:** 57% Medium, 38% Large

#### What Do G2 Reviewers Say About Kyvos Semantic Layer?

_AI-generated summary from verified user reviews_

##### Pros

- Los usuarios aprecian la **facilidad de uso** de Kyvos, lo que permite obtener rápidamente información y una experiencia amigable para datos complejos.
- A los usuarios les encanta la **velocidad** de Kyvos para obtener información en tiempo real, lo que permite consultas rápidas y una toma de decisiones más rápida en todos los métricas de datos.
- Los usuarios valoran el **rendimiento excepcional** de Kyvos para analizar rápidamente grandes conjuntos de datos y ofrecer información oportuna.
- Los usuarios admiran los **análisis ultrarrápidos** de Kyvos Semantic Layer, mejorando el rendimiento y la visualización de grandes conjuntos de datos.
- Los usuarios valoran las **capacidades de consulta rápida** de Kyvos Semantic Layer, lo que permite un análisis rápido de grandes conjuntos de datos de transacciones.

##### Cons

- Los usuarios encuentran la **curva de aprendizaje empinada** para las funciones avanzadas y las consultas MDX, lo que puede ralentizar los esfuerzos de uso.
- Los usuarios encuentran el **configuración difícil** de Kyvos Semantic Layer desafiante, aunque el soporte ayuda a facilitar el proceso.
- Los usuarios encuentran el **configuración inicial y la complejidad de MDX** desafiantes, aunque el soporte ayuda a facilitar el proceso de implementación.
- Los usuarios notan las **limitaciones de las funciones** de Kyvos, especialmente la falta de análisis avanzados y la integración para una exploración de datos sin problemas.
- Los usuarios experimentan **problemas de conectividad** , ya que la integración inicial con los sistemas existentes puede llevar mucho tiempo.

#### What Are Recent G2 Reviews of Kyvos Semantic Layer?

**["Exploración de Datos Rápida y Consistente a Través de Dimensiones con la Capa Semántica de Kyvos"](https://www.g2.com/es/survey_responses/kyvos-semantic-layer-review-12911098)**

**Rating:** 5.0/5.0 stars

_— ashish r._

[Read full review](https://www.g2.com/es/survey_responses/kyvos-semantic-layer-review-12911098)

**["La capa semántica de Kyvos mejora la precisión de la IA con datos listos para el negocio."](https://www.g2.com/es/survey_responses/kyvos-semantic-layer-review-13142366)**

**Rating:** 5.0/5.0 stars

_— Nikhil K._

[Read full review](https://www.g2.com/es/survey_responses/kyvos-semantic-layer-review-13142366)

### [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)
- **Multi-Source Analysis:** 8.9/10 (Category avg: 8.5/10)
- **Real-Time Analytics:** 8.9/10 (Category avg: 8.5/10)
- **Data Workflow:** 8.6/10 (Category avg: 8.5/10)

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

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

#### Who Uses This Product?

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

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

_AI-generated summary from verified user reviews_

##### Pros

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

##### Cons

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

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

**["Unified 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)

**["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)

#### 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)

### [Dataiku](https://www.g2.com/es/products/dataiku/reviews)

Dataiku es la plataforma para el éxito de la IA: la capa de orquestación de IA donde las empresas construyen, implementan y gobiernan análisis, modelos y agentes a escala. Se sitúa sobre las plataformas de datos, nubes y servicios de IA que ya utilizas, trabajando a través de todos ellos sin encerrarte en ninguno. Dataiku amplía quién puede construir IA de producción, poniendo las herramientas adecuadas en manos de científicos de datos y expertos en el dominio por igual, desde analistas de fraude hasta planificadores de demanda. Orquesta el aprendizaje automático, reglas, LLMs y agentes como un sistema gobernado, construido sobre más de una década de ejecución de IA de producción. La gobernanza es parte de la construcción en lugar de algo añadido después, por lo que los equipos envían más rápido mientras mantienen el rendimiento, el costo y el riesgo bajo control. El resultado: IA que pasa de la experimentación a una ejecución confiable y medible ahora, no en 18 meses.

**Average Rating:** 4.4/5.0

**Total Reviews:** 213

#### How Do G2 Users Rate Dataiku?

- **¿Ha sido the product un buen socio para hacer negocios?:** 8.6/10 (Category avg: 8.9/10)
- **Análisis de múltiples fuentes:** 8.8/10 (Category avg: 8.5/10)
- **Análisis en tiempo real:** 8.6/10 (Category avg: 8.5/10)
- **Flujo de trabajo de datos:** 9.1/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Dataiku?

- **Vendedor:** [Dataiku](https://www.g2.com/es/sellers/dataiku)
- **Sitio web de la empresa:** Dataiku.com
- **Año de fundación:** 2013
- **Ubicación de la sede:** New York, NY
- **Twitter:** @dataiku  
22,917 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=e59ec8fccc02ecc4f883419e54da56d3f6fc8b1e556153f0cc01cd05e3b77faa&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fdataiku%2F&secure%5Burl_type%5D=linkedin_company_website)  
1,619 empleados en LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Científico de Datos, Analista de Datos
- **Top Industries:** Servicios Financieros, Farmacéuticos
- **Company Size:** 60% Large, 22% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Los usuarios aprecian cómo Dataiku facilita el **desarrollo de ML fácil** , permitiendo centrarse en construir modelos sin la complejidad.
- A los usuarios les encanta la **facilidad de uso** en Dataiku, simplificando tareas complejas y mejorando su experiencia de análisis de datos.
- Los usuarios aprecian la **facilidad de uso** en Dataiku, lo que permite la colaboración tanto para usuarios técnicos como no técnicos.
- Los usuarios aprecian las **fáciles integraciones** de Dataiku, facilitando una colaboración y un despliegue fluidos a través de varias herramientas analíticas.
- Los usuarios se benefician de la **mejora de la productividad** de Dataiku, lo que permite un desarrollo de proyectos más rápido y un crecimiento profesional mejorado.

##### Cons

- Los usuarios encuentran la **empinada curva de aprendizaje** de Dataiku desafiante, lo que hace que sea difícil para los principiantes dominar la plataforma.
- Los usuarios encuentran la **curva de aprendizaje pronunciada** desafiante para los principiantes, lo que afecta su capacidad para usar Dataiku de manera efectiva.
- Los usuarios encuentran la **difícil curva de aprendizaje** desafiante, especialmente para los principiantes que navegan por funciones avanzadas.
- Los usuarios experimentan **rendimiento lento** con Dataiku al manejar grandes conjuntos de datos, afectando la eficiencia y la productividad.
- Los usuarios encuentran Dataiku **caro** , especialmente para organizaciones y proyectos más pequeños, lo que afecta la accesibilidad y la asequibilidad.

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

**["Plataforma unificada de bajo código que impulsa la productividad de datos y IA de extremo a extremo"](https://www.g2.com/es/survey_responses/dataiku-review-13125252)**

**Rating:** 4.5/5.0 stars

_— Ravindra N._

[Read full review](https://www.g2.com/es/survey_responses/dataiku-review-13125252)

**["Construye flujos de trabajo más rápidos con datos conectados de muchos proveedores o fuentes de datos distintas."](https://www.g2.com/es/survey_responses/dataiku-review-13120436)**

**Rating:** 4.5/5.0 stars

_— Adalberto G._

[Read full review](https://www.g2.com/es/survey_responses/dataiku-review-13120436)

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

- [Is Dataiku an ETL tool?](https://www.g2.com/es/discussions/is-dataiku-an-etl-tool)
- [Is Dataiku web based?](https://www.g2.com/es/discussions/is-dataiku-web-based)
- [What is DSS Dataiku?](https://www.g2.com/es/discussions/what-is-dss-dataiku)
- [What is Dataiku DSS used for?](https://www.g2.com/es/discussions/what-is-dataiku-dss-used-for)

### [Splunk Enterprise](https://www.g2.com/es/products/splunk-enterprise/reviews)

Descubre lo que está sucediendo en tu negocio y toma medidas significativas rápidamente con Splunk Enterprise. Automatiza la recopilación, indexación y alerta de datos de máquinas que son críticos para tus operaciones. Descubre las ideas accionables de todos tus datos, sin importar la fuente o el formato. Aprovecha la inteligencia artificial y el aprendizaje automático para decisiones empresariales predictivas y proactivas.

**Average Rating:** 4.3/5.0

**Total Reviews:** 415

#### How Do G2 Users Rate Splunk Enterprise?

- **¿Ha sido the product un buen socio para hacer negocios?:** 8.7/10 (Category avg: 8.9/10)
- **Análisis de múltiples fuentes:** 8.4/10 (Category avg: 8.5/10)
- **Análisis en tiempo real:** 8.7/10 (Category avg: 8.5/10)
- **Flujo de trabajo de datos:** 9.1/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Splunk Enterprise?

- **Vendedor:** [Cisco](https://www.g2.com/es/sellers/cisco)
- **Año de fundación:** 1984
- **Ubicación de la sede:** San Jose, CA
- **Twitter:** @Cisco  
720,366 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=476aeabc5a712d049453edd5c54ea0318890d9e60d93782e37fe028224df1cbd&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fcisco%2F&secure%5Burl_type%5D=linkedin_company_website)  
95,545 empleados en LinkedIn®
- **Propiedad:** NASDAQ:CSCO

#### Who Uses This Product?

- **Who Uses This:** Ingeniero de software, Ingeniero de Software Senior
- **Top Industries:** Tecnología de la información y servicios, Software de Computadora
- **Company Size:** 64% Large, 27% Medium

#### What Do G2 Reviewers Say About Splunk Enterprise?

_AI-generated summary from verified user reviews_

##### Pros

- Los usuarios valoran la **innovación** de Splunk Enterprise, apreciando sus características fáciles de usar y sus potentes capacidades analíticas.
- Los usuarios valoran las **funciones de personalización** de Splunk Enterprise, lo que permite obtener información personalizada y paneles dinámicos para un monitoreo efectivo.
- Los usuarios elogian la **facilidad de uso** de Splunk Enterprise, mejorando el monitoreo efectivo y la rápida resolución de problemas.
- Los usuarios valoran las capacidades de **gestión eficiente de registros** de Splunk Enterprise para un análisis e información precisos.
- Los usuarios valoran las **potentes funciones de informes** de Splunk Enterprise, mejorando significativamente las capacidades de análisis y visualización de datos.

##### Cons

- Los usuarios encuentran que Splunk Enterprise es **caro** , especialmente a medida que los volúmenes de datos crecen, afectando las operaciones de los equipos más pequeños.
- Los usuarios enfrentan una **curva de aprendizaje pronunciada** con Splunk Enterprise, lo que puede obstaculizar el rápido dominio de sus características.
- Los usuarios destacan el **costoso licenciamiento** de Splunk Enterprise, lo que dificulta su adopción por parte de algunas empresas.
- Los usuarios enfrentan **problemas de integración** con Splunk Enterprise, requiriendo mejores complementos y una arquitectura más simple para facilitar la implementación.
- Los usuarios sienten que Splunk Enterprise tiene **características faltantes** , carece de complementos importantes y opciones más flexibles para la incorporación de datos.

#### What Are Recent G2 Reviews of Splunk Enterprise?

**["Las búsquedas y paneles de control de SPL son realmente útiles."](https://www.g2.com/es/survey_responses/splunk-enterprise-review-12547655)**

**Rating:** 4.0/5.0 stars

_— Nishith J._

[Read full review](https://www.g2.com/es/survey_responses/splunk-enterprise-review-12547655)

**["Excelente solución de observabilidad empresarial y gestión de registros para infraestructura de nube híbrida"](https://www.g2.com/es/survey_responses/splunk-enterprise-review-12045230)**

**Rating:** 4.5/5.0 stars

_— RaviShankar S._

[Read full review](https://www.g2.com/es/survey_responses/splunk-enterprise-review-12045230)

#### What Are G2 Users Discussing About Splunk Enterprise?

- [What is Splunk Enterprise used for?](https://www.g2.com/es/discussions/what-is-splunk-enterprise-used-for) - 1 comment
- [¿Cuál es la diferencia entre Splunk Enterprise y Splunk Enterprise Security?](https://www.g2.com/es/discussions/splunk-enterprise-what-is-the-difference-between-splunk-enterprise-and-splunk-enterprise-security) - 1 comment
- [¿Cuáles son los componentes de Splunk Enterprise?](https://www.g2.com/es/discussions/what-are-splunk-enterprise-components) - 1 comment
- [¿Qué aplicaciones se incluyen con Splunk Enterprise?](https://www.g2.com/es/discussions/which-apps-ship-with-splunk-enterprise) - 1 comment
- [¿Qué hace Splunk Enterprise?](https://www.g2.com/es/discussions/what-does-splunk-enterprise-do) - 1 comment

### [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)
- **Multi-Source Analysis:** 8.5/10 (Category avg: 8.5/10)
- **Real-Time Analytics:** 8.5/10 (Category avg: 8.5/10)
- **Data Workflow:** 9.3/10 (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 love the **ease of use** of dbt, thanks to its clear structure, intuitive documentation, and seamless integration.
- Users value dbt for its **integration of software engineering best practices** , enhancing maintainability and collaboration in SQL transformations.
- Users value the **automation** features of dbt, significantly enhancing SQL code maintainability and transforming data workflows.
- Users value the **transformative power** of dbt, efficiently organizing and modeling data for actionable insights.
- Users value dbt for its **high data quality** , ensuring integrity and enhancing analytics workflows through modularization and documentation.

##### Cons

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

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

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

**Rating:** 5.0/5.0 stars

_— Anish G._

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

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

**Rating:** 5.0/5.0 stars

_— Hithesh P._

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

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

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

### [Teradata Autonomous Knowledge Platform](https://www.g2.com/es/products/teradata-autonomous-knowledge-platform/reviews)

La Plataforma de Conocimiento Autónomo de Teradata activa la inteligencia empresarial unificando datos, conocimiento y contexto empresarial para lograr resultados tangibles. Con Teradata, las organizaciones pueden proporcionar a los agentes el contexto completo para tener impacto cuando importa. Nuestra solución permite a las empresas conectarse y escalar en las instalaciones, en la nube o a través de un enfoque híbrido. Teradata ofrece un valor comercial real con IA. Aprende más en Teradata.com.

**Average Rating:** 4.3/5.0

**Total Reviews:** 356

#### How Do G2 Users Rate Teradata Autonomous Knowledge Platform?

- **¿Ha sido the product un buen socio para hacer negocios?:** 8.2/10 (Category avg: 8.9/10)
- **Análisis de múltiples fuentes:** 7.9/10 (Category avg: 8.5/10)
- **Análisis en tiempo real:** 8.2/10 (Category avg: 8.5/10)
- **Flujo de trabajo de datos:** 7.8/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Teradata Autonomous Knowledge Platform?

- **Vendedor:** [Teradata Autonomous Knowledge Platform](https://www.g2.com/es/sellers/teradata-autonomous-knowledge-platform)
- **Año de fundación:** 1979
- **Ubicación de la sede:** San Diego, CA
- **Twitter:** @Teradata  
93,113 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=06895b9a8db4fa478ba7da480ccd214a14ef698abd028e4642e62f189e82b650&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1466%2F&secure%5Burl_type%5D=linkedin_company_website)  
9,901 empleados en LinkedIn®
- **Propiedad:** NYSE:TDC

#### Who Uses This Product?

- **Who Uses This:** Ingeniero de Datos, Ingeniero de software
- **Top Industries:** Tecnología de la información y servicios, Servicios Financieros
- **Company Size:** 69% Large, 22% Medium

#### What Do G2 Reviewers Say About Teradata Autonomous Knowledge Platform?

_AI-generated summary from verified user reviews_

##### Pros

- Los usuarios destacan el **rendimiento extremo** de la Plataforma de Conocimiento Autónomo de Teradata, especialmente para procesar grandes volúmenes de datos de manera eficiente.
- Los usuarios valoran la **alta ejecución de consultas de rendimiento** en Teradata, mejorando significativamente sus capacidades de análisis empresarial.
- Los usuarios valoran la **escalabilidad** de la Plataforma de Conocimiento Autónomo de Teradata, mejorando significativamente la integración de datos y la eficiencia operativa.
- Los usuarios elogian el **alto rendimiento y velocidad** de Teradata, procesando eficientemente grandes conjuntos de datos sin problemas.
- Los usuarios valoran el **rápido procesamiento de grandes conjuntos de datos** con Teradata, elogiando su rendimiento y estabilidad durante las operaciones.

##### Cons

- Los usuarios encuentran la **empinada curva de aprendizaje** de la Plataforma de Conocimiento Autónomo de Teradata desafiante, lo que afecta temporalmente la adopción y la productividad.
- Los usuarios encuentran la **empinada curva de aprendizaje** de la Plataforma de Conocimiento Autónomo de Teradata desafiante, especialmente para aquellos que carecen de experiencia técnica.
- Los usuarios encuentran la **complejidad** de la plataforma de Teradata desafiante, especialmente para los usuarios no técnicos y los nuevos adoptantes.
- Los usuarios expresan preocupaciones sobre los **requisitos de gestión de costos** necesarios para evitar un posible mal uso y problemas de rendimiento.
- Los usuarios sienten que el **alto costo** de la Plataforma de Conocimiento Autónomo de Teradata es un inconveniente significativo que afecta la accesibilidad.

#### What Are Recent G2 Reviews of Teradata Autonomous Knowledge Platform?

**["Rendimiento de Consulta Rápida de Teradata Vantage y Análisis Potente para Big Data"](https://www.g2.com/es/survey_responses/teradata-autonomous-knowledge-platform-review-12821668)**

**Rating:** 5.0/5.0 stars

_— Muzammil M._

[Read full review](https://www.g2.com/es/survey_responses/teradata-autonomous-knowledge-platform-review-12821668)

**["Teradata Vantage se destaca en el procesamiento de grandes datos y análisis avanzados."](https://www.g2.com/es/survey_responses/teradata-autonomous-knowledge-platform-review-12739181)**

**Rating:** 4.5/5.0 stars

_— Nijat I._

[Read full review](https://www.g2.com/es/survey_responses/teradata-autonomous-knowledge-platform-review-12739181)

#### What Are G2 Users Discussing About Teradata Autonomous Knowledge Platform?

- [What does Teradata Data Lab do?](https://www.g2.com/es/discussions/what-does-teradata-data-lab-do)
- [Is Teradata a premiership?](https://www.g2.com/es/discussions/is-teradata-a-premiership)
- [What is Teradata Vantage?](https://www.g2.com/es/discussions/what-is-teradata-vantage)
- [How much does Teradata cost?](https://www.g2.com/es/discussions/how-much-does-teradata-cost)
- [What is Sandbox in Teradata?](https://www.g2.com/es/discussions/what-is-sandbox-in-teradata)

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

Cloud-native service for data in motion built by the original creators of Apache Kafka® Today’s consumers have the world at their fingertips and hold an unforgiving expectation for end-to-end real-time brand experiences. Data in motion is the underlying, fundamental ingredient to any truly connected customer experience. It provides a continuous supply of real- time event streams coupled with real-time stream processing to power the data-driven backend operations and rich front-end experiences necessary for any business to succeed within today’s competitive, consumer-driven markets. Set your data in motion while avoiding the headaches of infrastructure management and focus on what matters most: your business. Built by the original creators of Apache Kafka, Confluent Cloud is a fully managed, cloud-native service for connecting and processing all of your real-time data, everywhere it’s needed.

**Average Rating:** 4.4/5.0

**Total Reviews:** 111

#### How Do G2 Users Rate Confluent?

- **Has the product been a good partner in doing business?:** 8.5/10 (Category avg: 8.9/10)
- **Multi-Source Analysis:** 8.3/10 (Category avg: 8.5/10)
- **Real-Time Analytics:** 8.9/10 (Category avg: 8.5/10)
- **Data Workflow:** 7.9/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Confluent?

- **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?

- **Who Uses This:** Senior Software Engineer, Software Engineer
- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 36% Large, 33% Small

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

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **simplicity and scalability** of Confluent's cloud services, enhancing their experience with Kafka and Flink.
- Users appreciate the **effortless real-time data integration** through Confluent's managed cloud services, enhancing their workflow significantly.
- Users appreciate the **wide range of connectors** in Confluent, simplifying real-time data integration and enhancing productivity.
- Users appreciate the **simplified real-time data integration** with Confluent, benefiting from its managed cloud services and wide connectors.
- Users appreciate the **ease of use** of Confluent, making data integration and stream processing effortless and efficient.

##### Cons

- Users note that the **cost estimation can be high** as data volume increases, requiring time to learn the system.
- Users find Confluent to be **expensive** as costs rise with data volume and features are limited in lower editions.
- Users face a **steep learning curve** with Confluent, alongside rising costs as data volume increases.
- Users find a **lack of features** in Confluent, especially with essential tools restricted to the Enterprise edition.
- Users find the **steep learning curve** challenging, requiring significant time to grasp Confluent's workflow and features.

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

**["Effortless Kafka Management with Confluent"](https://www.g2.com/survey_responses/confluent-review-12744384)**

**Rating:** 4.5/5.0 stars

_— Abhishek g._

[Read full review](https://www.g2.com/survey_responses/confluent-review-12744384)

**["seamless experience"](https://www.g2.com/survey_responses/confluent-review-8457785)**

**Rating:** 5.0/5.0 stars

_— Anup M._

[Read full review](https://www.g2.com/survey_responses/confluent-review-8457785)

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

- [What is your primary use case for Confluent, and how does it enhance your real-time data streaming?](https://www.g2.com/discussions/what-is-your-primary-use-case-for-confluent-and-how-does-it-enhance-your-real-time-data-streaming) - 1 upvote
- [What is Confluent product?](https://www.g2.com/discussions/what-is-confluent-product)
- [What does Confluent software do?](https://www.g2.com/discussions/what-does-confluent-software-do)
- [What is the difference between Confluent and Kafka?](https://www.g2.com/discussions/what-is-the-difference-between-confluent-and-kafka)
- [Is Confluent SaaS or PaaS?](https://www.g2.com/discussions/is-confluent-saas-or-paas)

### [Starburst](https://www.g2.com/es/products/starburst/reviews)

Starburst es la plataforma de datos para análisis, aplicaciones e inteligencia artificial, unificando datos a través de nubes y en las instalaciones para acelerar la innovación en IA. Organizaciones, desde startups hasta empresas Fortune 500 en más de 60 países, confían en Starburst para un acceso rápido a los datos, colaboración sin problemas y gobernanza de nivel empresarial en un data lakehouse híbrido abierto. Dondequiera que vivan los datos, Starburst desbloquea su máximo potencial, impulsando datos e IA desde el desarrollo hasta la implementación. Al preparar la arquitectura de datos para el futuro, Starburst ayuda a las empresas a impulsar la innovación con IA. Aprende más en starburst.ai

**Average Rating:** 4.4/5.0

**Total Reviews:** 100

#### How Do G2 Users Rate Starburst?

- **¿Ha sido the product un buen socio para hacer negocios?:** 9.0/10 (Category avg: 8.9/10)
- **Análisis de múltiples fuentes:** 8.9/10 (Category avg: 8.5/10)
- **Análisis en tiempo real:** 8.0/10 (Category avg: 8.5/10)
- **Flujo de trabajo de datos:** 7.9/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Starburst?

- **Vendedor:** [Starburst](https://www.g2.com/es/sellers/starburst)
- **Sitio web de la empresa:** www.starburst.io
- **Año de fundación:** 2017
- **Ubicación de la sede:** Boston, MA
- **Twitter:** @starburstdata  
3,454 seguidores en Twitter
- **Página de LinkedIn®:** [www.linkedin.com](https://www.g2.com/es/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=6e8e7115417f25433cd515a6e146b579fb0353f1130e9be509f37e24c1adb7f6&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fstarburstdata%2F&secure%5Burl_type%5D=linkedin_company_website)  
539 empleados en LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Tecnología de la información y servicios, Servicios Financieros
- **Company Size:** 46% Large, 33% Small

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

_AI-generated summary from verified user reviews_

##### Pros

- Los usuarios destacan las capacidades de **consulta rápida** de Starburst, permitiendo un acceso y análisis de datos rápido a través de diversas fuentes.
- Los usuarios aprecian la **eficiencia de consulta** de Starburst, permitiendo un acceso rápido a diversas fuentes de datos sin esfuerzo.
- Los usuarios valoran la **integración perfecta** con diversas fuentes de datos, mejorando significativamente la eficiencia del acceso y análisis de datos.
- Los usuarios aprecian la **facilidad de uso** de Starburst, lo que permite un acceso eficiente a los datos y un análisis en tiempo real desde diversas fuentes.
- Los usuarios elogian a Starburst por su **rendimiento superior** , recuperando datos precisos rápidamente y mejorando la productividad general.

##### Cons

- Los usuarios a menudo experimentan **problemas de consulta** con Starburst, encontrándolo menos eficiente para consultas complejas en comparación con los editores de SQL tradicionales.
- Los usuarios encuentran la **complejidad de la configuración inicial** de Starburst desafiante, especialmente con la configuración y la optimización de consultas.
- Los usuarios notan una **curva de aprendizaje pronunciada** al configurar Starburst, lo que hace que la incorporación y la optimización sean desafiantes para los nuevos usuarios.
- Los usuarios a menudo experimentan **rendimiento lento** en Starburst, especialmente durante el uso máximo y con consultas complejas.
- Los usuarios informan de **problemas de rendimiento** con Starburst, especialmente al ejecutar consultas complejas, lo que provoca ralentizaciones significativas.

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

**["Revisión de Starburst Enterprise"](https://www.g2.com/es/survey_responses/starburst-review-10604384)**

**Rating:** 4.0/5.0 stars

_— Rajiv B._

[Read full review](https://www.g2.com/es/survey_responses/starburst-review-10604384)

**["Transformación de la Analítica Empresarial y Habilitación de la IA a través de la Federación de Datos"](https://www.g2.com/es/survey_responses/starburst-review-11962416)**

**Rating:** 4.0/5.0 stars

_— Usuario verificado en Servicios Financieros_

[Read full review](https://www.g2.com/es/survey_responses/starburst-review-11962416)

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

- [What does Starburst do?](https://www.g2.com/es/discussions/what-does-starburst-do)
- [What is Starburst Presto?](https://www.g2.com/es/discussions/what-is-starburst-presto)
- [What is Starburst tech?](https://www.g2.com/es/discussions/what-is-starburst-tech)
- [What does Starburst data do?](https://www.g2.com/es/discussions/what-does-starburst-data-do)

### [Azure Data Lake Analytics](https://www.g2.com/es/products/azure-data-lake-analytics/reviews)

Azure Data Lake Analytics es una arquitectura de procesamiento de datos distribuida y basada en la nube ofrecida por Microsoft en la nube de Azure. Se basa en YARN, al igual que la plataforma de código abierto Hadoop.

**Average Rating:** 4.2/5.0

**Total Reviews:** 28

#### How Do G2 Users Rate Azure Data Lake Analytics?

- **¿Ha sido the product un buen socio para hacer negocios?:** 8.6/10 (Category avg: 8.9/10)
- **Análisis de múltiples fuentes:** 7.9/10 (Category avg: 8.5/10)
- **Análisis en tiempo real:** 8.1/10 (Category avg: 8.5/10)
- **Flujo de trabajo de datos:** 8.5/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Azure Data Lake Analytics?

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

#### Who Uses This Product?

- **Top Industries:** Tecnología de la información y servicios, Software de Computadora
- **Company Size:** 54% Large, 27% Medium

#### What Are Recent G2 Reviews of Azure Data Lake Analytics?

**["La central de gestión de datos"](https://www.g2.com/es/survey_responses/azure-data-lake-analytics-review-7674920)**

**Rating:** 5.0/5.0 stars

_— Sam L._

[Read full review](https://www.g2.com/es/survey_responses/azure-data-lake-analytics-review-7674920)

**["Gran servicio para gestionar sus necesidades de big data."](https://www.g2.com/es/survey_responses/azure-data-lake-analytics-review-5253914)**

**Rating:** 4.5/5.0 stars

_— Abhishek C._

[Read full review](https://www.g2.com/es/survey_responses/azure-data-lake-analytics-review-5253914)

#### What Are G2 Users Discussing About Azure Data Lake Analytics?

- [¿Para qué se utiliza Azure Data Lake Analytics?](https://www.g2.com/es/discussions/what-is-azure-data-lake-analytics-used-for)
- [How do I make Azure Data Lake Analytics?](https://www.g2.com/es/discussions/how-do-i-make-azure-data-lake-analytics)
- [What is Azure Data lake and stream analytics tools?](https://www.g2.com/es/discussions/what-is-azure-data-lake-and-stream-analytics-tools)
- [What are the key capabilities of Microsoft Azure Data Lake Analytics?](https://www.g2.com/es/discussions/what-are-the-key-capabilities-of-microsoft-azure-data-lake-analytics)
- [What is Azure Data Lake Analytics?](https://www.g2.com/es/discussions/what-is-azure-data-lake-analytics)

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 ![Bijou Barry](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Bijou Barry")
BB

Researched and written by [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)

Updated April 9, 2026

Big data analytics software provides insights into large, complex data sets collected from big data clusters, helping business users understand data trends, patterns, and anomalies through visualizations, reports, and dashboards, often requiring query languages to extract data from unstructured file systems.

### Core Capabilities of Big Data Analytics Software

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

- Consume data, query file systems, and connect directly to big data clusters
- Allow users to prepare complex big data sets into helpful and understandable data visualizations
- Create business-applicable reports, visualizations, and dashboards based on discoveries inside the data sets

### Common Use Cases for Big Data Analytics Software

Data engineers, analysts, and business intelligence teams use big data analytics software to extract value from large-scale, unstructured data environments. Common use cases include:

- Querying and analyzing large Hadoop or distributed data clusters to surface business insights
- Detecting patterns and anomalies in high-volume data sets for operational or strategic decision-making
- Building self-service charts and dashboards for non-technical stakeholders from big data sources

### How Big Data Analytics Software Differs from Other Tools

Big data analytics software is solely focused on manipulating complex, large-scale data clusters into understandable visualizations, differentiating it from [analytics platforms](https://www.g2.com/categories/analytics-platforms), which support a wide range of data sources and connectors beyond big data. The two categories are mutually exclusive. Big data analytics tools are commonly used at companies running Hadoop in conjunction with [big data processing and distribution software](https://www.g2.com/categories/big-data-processing-and-distribution) and integrate with [data warehouse software](https://www.g2.com/categories/data-warehouse) as the central hub for integrated data. Some solutions also leverage [machine learning](https://www.g2.com/categories/machine-learning) and [natural language processing](https://www.g2.com/categories/natural-language-processing-nlp) to enable natural language querying.

### Insights from G2 on Big Data Analytics Software

Based on category trends on G2, query flexibility and scalability for large data sets stand out as standout capabilities. Faster insight generation from complex data environments stand out as the primary benefit 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_a6c205d533dba77b318af96d91beb2ac/databricks.jpeg "Product Avatar Image")](https://www.g2.com/products/databricks/reviews)[Databricks](https://www.g2.com/products/databricks/reviews)[4.6/5(1,357)](https://www.g2.com/products/databricks/reviews) | Unified lakehouse ETL, analytics, and ML pipelines | "Reliable Platform for Building Scalable Data Pipelines" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_96b275379465d759df5bffd0099d849a/google-cloud-bigquery.png "Product Avatar Image")](https://www.g2.com/products/google-cloud-bigquery/reviews)[BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews)[4.5/5(1,223)](https://www.g2.com/products/google-cloud-bigquery/reviews) | Serverless SQL analytics on petabyte-scale datasets | "Easy-to-Use Cloud Tool with Shareable, Saved Queries" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_2b00e05c107c3273cea5264090c3c1d0/snowflake.jpg "Product Avatar Image")](https://www.g2.com/products/snowflake/reviews)[Snowflake](https://www.g2.com/products/snowflake/reviews)[4.5/5(761)](https://www.g2.com/products/snowflake/reviews) | Elastic multi-workload analytics with zero-infrastructure overhead | "Elastic Scaling and Fast Analytics with Snowflake" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_24bb2b0b5af8e7d875ea09d767bcb097/ibm-watsonx-data.jpg "Product Avatar Image")](https://www.g2.com/products/ibm-watsonx-data/reviews)[IBM watsonx.data](https://www.g2.com/products/ibm-watsonx-data/reviews)[4.4/5(171)](https://www.g2.com/products/ibm-watsonx-data/reviews) | Federated lakehouse querying across hybrid data environments | "Powerful Query Performance and Governance, But a Steep Onboarding Learning Curve" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_2c6c77d8284b2f6609c2f9dc0ba6b5a9/alteryx.png "Product Avatar Image")](https://www.g2.com/products/alteryx/reviews)[Alteryx](https://www.g2.com/products/alteryx/reviews)[4.6/5(889)](https://www.g2.com/products/alteryx/reviews) | No-code ETL and multi-source data blending | "Scales Operations and Saves Time with Automated Data Workflows" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_13aba37ae908d9cf29a2452925257ccc/azure-databricks.jpeg "Product Avatar Image")](https://www.g2.com/products/azure-databricks/reviews)[Azure Databricks](https://www.g2.com/products/azure-databricks/reviews)[4.5/5(238)](https://www.g2.com/products/azure-databricks/reviews) | Unified Spark-native lakehouse ETL and ML | "A powerhouse for scaling ML workflows, but keep a close eye on your billing." |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_370c360364dc3d4f1f97e00cc7534fb5/kyvos-semantic-layer.png "Product Avatar Image")](https://www.g2.com/products/kyvos-semantic-layer/reviews)[Kyvos Semantic Layer](https://www.g2.com/products/kyvos-semantic-layer/reviews)[4.8/5(288)](https://www.g2.com/products/kyvos-semantic-layer/reviews) | Sub-second OLAP querying on cloud-scale datasets | "Kyvos Semantic Layer Boosts AI Accuracy with Business-Ready Data" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_756e21e5ff45db664431b3ea10f16115/azure-synapse-analytics.jpg "Product Avatar Image")](https://www.g2.com/products/azure-synapse-analytics/reviews)[Azure Synapse Analytics](https://www.g2.com/products/azure-synapse-analytics/reviews)[4.4/5(38)](https://www.g2.com/products/azure-synapse-analytics/reviews) | Unified ETL and big data warehousing on Azure | "Unified Data Warehousing and Big Data in One Powerful Platform" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_791b528c516cc1b08151fa6da3988161/dataiku.png "Product Avatar Image")](https://www.g2.com/products/dataiku/reviews)[Dataiku](https://www.g2.com/products/dataiku/reviews)[4.4/5(224)](https://www.g2.com/products/dataiku/reviews) | End-to-end ML pipelines with low-code collaboration | "Unified, Low-Code Platform That Boosts End-to-End Data & AI Productivity" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_91bcb2c063fcfb0a82dfedcf1a6463d1/splunk-enterprise.jpg "Product Avatar Image")](https://www.g2.com/products/splunk-enterprise/reviews)[Splunk Enterprise](https://www.g2.com/products/splunk-enterprise/reviews)[4.3/5(434)](https://www.g2.com/products/splunk-enterprise/reviews) | Cross-source log correlation and security analytics | "Excellent Enterprise Observability and Log Management Solution for Hybrid Cloud Infrastructure" |

* * *

Show More

### Big Data Analytics Topics

- [What is Big Data Analytics Software?](#what-is-big-data-analytics-software)
- [What are the Common Features of Big Data Analytics Software?](#what-are-the-common-features-of-big-data-analytics-software)
- [What are the Benefits of Big Data Analytics Software?](#what-are-the-benefits-of-big-data-analytics-software)
- [Who Uses Big Data Analytics Software?](#who-uses-big-data-analytics-software)
- [What are the Alternatives to Big Data Analytics Software?](#what-are-the-alternatives-to-big-data-analytics-software)
- [Challenges with Big Data Analytics Software](#challenges-with-big-data-analytics-software)
- [Which Companies Should Buy Big Data Analytics Software?](#which-companies-should-buy-big-data-analytics-software)
- [How to Buy Big Data Analytics Software](#how-to-buy-big-data-analytics-software)
- [What Does Big Data Analytics Software Cost?](#what-does-big-data-analytics-software-cost)
- [Implementation of Big Data Analytics Software](#implementation-of-big-data-analytics-software)
- [Big Data Analytics Software Trends](#big-data-analytics-software-trends)

[
### Big Data Analytics Topics
Expand/Collapse ](#)
- [What is Big Data Analytics Software?](#what-is-big-data-analytics-software)
- [What are the Common Features of Big Data Analytics Software?](#what-are-the-common-features-of-big-data-analytics-software)
- [What are the Benefits of Big Data Analytics Software?](#what-are-the-benefits-of-big-data-analytics-software)
- [Who Uses Big Data Analytics Software?](#who-uses-big-data-analytics-software)
- [What are the Alternatives to Big Data Analytics Software?](#what-are-the-alternatives-to-big-data-analytics-software)
- [Challenges with Big Data Analytics Software](#challenges-with-big-data-analytics-software)
- [Which Companies Should Buy Big Data Analytics Software?](#which-companies-should-buy-big-data-analytics-software)
- [How to Buy Big Data Analytics Software](#how-to-buy-big-data-analytics-software)
- [What Does Big Data Analytics Software Cost?](#what-does-big-data-analytics-software-cost)
- [Implementation of Big Data Analytics Software](#implementation-of-big-data-analytics-software)
- [Big Data Analytics Software Trends](#big-data-analytics-software-trends)

## Learn More About Big Data Analytics Software

### What is Big Data Analytics Software?

The huge amount of data that is accessible to businesses today has made it a near necessity for them to implement some type of analytics software to better understand and act on that data. Implementing big data analytics software has been a major initiative for companies undergoing digital transformation, as these tools offer deeper visibility into an organization's data. Companies adopt these solutions to make sense of large data sets collected from big data clusters.

With the ability to visualize and understand business data, employees can make informed decisions. For example, retailers can use these tools to better understand inventory distribution across their channels and make data-driven decisions based on this data. Some big data analytics solutions may offer artificial intelligence or machine learning features, such as natural language processing, as an interface capability to further aid nontechnical users.

#### What Types of Big Data Analytics Software Exist?

Many types of big data analytics solutions share overlapping functionality, while simultaneously catering to different user personas such as data analysts and financial analysts or providing unique services.

Because of the unstructured nature of big data clusters, these analytics solutions require a query language to pull the data out of the file system. Most commercial table databases allow SQL queries; however, big data analytics tools do not necessarily offer such SQL language capabilities and may require a more intricate knowledge of querying from a data scientist. As an alternative, some solutions may offer self-service features so that the average employee can assemble their own charts and graphs from big data sets.

**Self-service big data analytics tools**

Self-service big data analytics tools do not require coding knowledge, so end users with limited to no coding knowledge can take advantage of them for data needs. This enables business users like sales representatives, human resource managers, marketers, and other nondata team members to make decisions based on relevant business data. Self-service solutions often provide drag-and-drop functionality for building dashboards, prebuilt templates for querying data, and, occasionally, natural language querying for data discovery. Similar to [analytics platforms](https://www.g2.com/categories/analytics-platforms), organizations use these tools to build interactive dashboards for discovering actionable insights. 

**Embedded analytics solutions**

Embedded analytics solutions offer the ability to integrate proprietary analytics functionality within other business applications. Commonly, businesses embed analytics solutions in software such as CRMs, ERP, and portals (e.g., intranets or extranets). Businesses may choose an embedded product to promote user adoption; by placing the analytics inside regularly used software, companies enable employees to take advantage of available data. These solutions provide self-service functionality so average business end users can take advantage of data for improved decision making. ** **

### What are the Common Features of Big Data Analytics Software?

Big data analytics software helps companies get a better understanding of their data. The following are some core features of this software: 

**Data connectivity:** If businesses cannot connect the requisite data, then there is no use for big data analytics software. The methods for connecting data include Hadoop and [Spark integration](https://www.g2.com/categories/big-data-analytics/f/spark-integration) which allows for processing and distribution workflows on top of Apache Hadoop and Apache Spark, respectively. In addition, this software should allow for analyzing data that is stored in [data lakes](https://www.g2.com/categories/big-data-analytics/f/data-lake), data warehouses, and data lake houses.

**Data transformation:** For data to be analyzed, it needs to be properly cleaned and transformed into a usable format. Big data analytics software provides features such as real-time analytics and data querying. With these features, businesses can gain a high-level view of their data in real time, allowing one to query it and better understand it. Through query languages like SQL, users can query their data and dig deeper into particular data sets and data points.

**Data operations:** Once the data is connected (or integrated) and transformed, it can be analyzed. Firstly, it is important to establish data workflows, which can help in stringing together specific functions and data sets to automate analytics iterations. In addition, big data analytics software provides the ability to visualize data through dashboards, as well as [notebooks](https://www.g2.com/categories/big-data-analytics/f/notebooks) which can be used to create visualization with predefined or scheduled queries. 

It is not always the case that one will access analytics via a standalone analytics platform. Therefore, some products provide [embedded analytics capabilities](https://www.g2.com/categories/big-data-analytics/f/embedded-analytics). This allows users to access analytics inside business applications, which allows for more streamlined work since the users need not switch between applications. 

Other Features of Big Data Analytics Software: [Governed Discovery](https://www.g2.com/categories/big-data-analytics/f/governed-discovery),

### What are the Benefits of Big Data Analytics Software?

Data is both common and invaluable and within that data lies insights that could impact an organization's processes and performance. There are seemingly infinite insights a business can pull from their data and numerous reasons to utilize big data analytics software. 

Big data analytics software helps people make decisions easier by allowing teams to gain deeper insight into their data. With increased data literacy, teams across a business, from sales to marketing to finance can become more efficient and better understand how they can improve through data-driven initiatives. 

With big data analytics software, businesses can ingest, integrate, and prepare big data sources. Subsequently, they can connect all company data sources into a single platform to make cross-department connections, visualize and understand company data, encourage data-driven decision making for business optimization, and discover new insights that can enhance the bottom line.

**Enable data-driven decision making:** Businesses can use big data analytics software to fuel digital transformation by leveraging data to drive business decisions. Companies can leverage analytics and business intelligence (BI) tools to understand all aspects of the business, including hiring forecasts, which marketing campaign should be used to target certain demographics, which sales prospects to target first, supply chain optimization, and many others.

**Measure and understand company performance:** Organizations often leverage data visualization tools to track company key performance indicators (KPIs) in real time. From there, big data analytics software can be used to determine why the business is either exceeding or falling short of those important company metrics. When stakeholders develop a keen understanding of why the business is performing the way it is, they can make adjustments and pivots; if a team is falling short of a goal, they can examine and adjust processes as needed. It is one thing to simply know the performance of sales or web traffic numbers, but it is another to dig into the reasons behind it and adapt based on what is successful and what is not.

**Discover new actionable insights:** Analytics tools combine data from a variety of sources, including [accounting software](https://www.g2.com/categories/accounting), [enterprise resource planning (ERP) software](https://www.g2.com/categories/erp), [CRM software](https://www.g2.com/categories/crm),[marketing automation software](https://www.g2.com/categories/marketing-automation), and others. Data analysts can leverage this integrated data to find correlations between different departments, and their processes and actions, to discover previously hidden insights. For example, it is possible that certain sales tactics have varying impacts on the numbers for one specific product versus another. 

Analysts can discover this impact by comparing the list of closed accounts from their company CRM with products shipped in their ERP system. Teams are generally siloed and use disparate software, so these insights that were traditionally more difficult to discover, are now made easier. 

### Who Uses Big Data Analytics Software?

**Data analysts:** Depending on the complexity of the software, it is likely that analysts will be required. They can help set up the requisite queries, dashboards, and notebooks for other employees and teams. They can create complex queries inside the platforms to gather a deeper understanding of business-critical data.

**Operations and supply chain teams:** A company’s supply chain frequently has many touchpoints, and as a result, many data points. Therefore, employees working in operations and supply chain teams are able to use big data analytics software to gain a better understanding of their departments and the data that is generated, such as from an ERP system. These applications track everything from accounting to supply chain and distribution; by inputting supply chain data into this software, supply chain managers can optimize a number of processes to save time and resources.

**Finance teams:** Finance teams leverage big data analytics software to gain insight and understanding into the factors that impact an organization's bottom line. Through integrations with financial systems such as [accounting software](https://www.g2.com/categories/accounting), employees such as chief financial officers (CFOs) can see how well the business is performing. As mentioned above, these employees will likely be accessing the software via self-service dashboards that were set up by data analysts. By integrating financial data with sales, marketing, and other operations data, accounting and finance teams pull actionable insights that might not have been uncovered through the use of traditional tools.

**Sales and marketing teams:** Sales teams also seek to improve financial metrics and can benefit tremendously from being more data-driven. Through the use of both self-service analytics tools and embedded analytics solutions, they can obtain insights into prospective accounts, sales performance, and pipeline forecasting, among many other use cases. Using analytics tools in a sales team can help businesses optimize their sales processes and influence revenue.

For marketing teams, tracking the performance of campaigns is key. Since they run different types of campaigns, including email marketing, digital advertising, or even traditional advertising campaigns, analytics tools allow marketing teams to track the performance of those campaigns in one central location.

**Consultants:** Businesses do not always have the luxury to build, develop, and optimize their own analytics solutions. Some businesses opt to employ external consultants, such as [business intelligence (BI) consulting providers](https://www.g2.com/categories/business-intelligence-bi-consulting). These providers seek to understand a business and its goals, interpret data, and offer advice to ensure goals are met. BI consultants frequently have industry-specific knowledge alongside their technical backgrounds, with experience in healthcare, business, and other fields. 

### What are the Alternatives to Big Data Analytics Software?

Alternatives to big data analytics software can replace this type of software, either partially or completely:

[Analytics platforms](https://www.g2.com/categories/analytics-platforms) **:** Analytics platforms might include big data integrations, but are broader-focused tools that facilitate the following five elements: data preparation, data modeling, data blending, data visualization, and insights delivery.

[Log analysis software](https://www.g2.com/categories/log-analysis): Businesses that are focused on log data might benefit from deploying log analysis software, which is used to analyze log data from applications and systems. It should be kept in mind that this software is much more limited in terms of data types and data sources to which it can be connected to. However, since log analysis software focuses on logs, it frequently provides more granular details around log-related data.

[Stream analytics software](https://www.g2.com/categories/stream-analytics) **:** When one is looking for tools specifically geared toward analyzing data in real time, stream analytics software is a go-to solution. These tools help users analyze data in transfer through APIs, between applications, and more. This software can be helpful with internet of things (IoT) data, which one frequently wants to analyze in real time.

[Predictive analytics software](https://www.g2.com/categories/predictive-analytics): Broad-purpose big data analytics software allows businesses to conduct various forms of analysis, such as prescriptive, descriptive, and predictive. Businesses that are focused on looking at their past and present data to predict future outcomes can use predictive analytics software for a more finetuned solution. 

[Text analysis software](https://www.g2.com/categories/text-analysis): Big data analytics software is focused on structured or numerical data, allowing users to drill down and dig into numbers to inform business decisions. If the user is looking to focus on unstructured or text data, text analysis solutions are the best bet. These tools help users quickly understand and pull sentiment analysis, key phrases, themes, and other insights from unstructured text data.

#### Software Related to Big Data Analytics Software

Related solutions that can be used together with big data analytics software include:

[Data warehouse software](https://www.g2.com/categories/data-warehouse) **:** Most companies have a large number of disparate data sources, so to best integrate all their data, they implement a data warehouse. Data warehouses can house data from multiple databases and business applications, which allows BI and analytics tools to pull all company data from a single repository. This organization is critical to the quality of the data that is ingested by analytics software.

[Data preparation software](https://www.g2.com/categories/data-preparation) **:** A key solution necessary for easy data analysis is a data preparation tool and other related data management tools. These solutions allow users to discover, combine, clean, and enrich data for simple analysis. Data preparation tools are often used by IT teams or data analysts tasked with using BI tools. Some BI platforms offer data preparation features, but businesses with a wide range of data sources often opt for a dedicated preparation tool.

### Challenges with Big Data Analytics Software

Software solutions can come with their own set of challenges. 

**Need for skilled employees:** Big data analytics software is not necessarily simple. Often, these tools require a dedicated administrator to help implement the solution and assist others with adoption. However, there is a shortage of skilled data scientists and analysts that are equipped to set up such solutions. Additionally, those same data scientists will be tasked with deriving actionable insights from within the data. 

Without people skilled in these areas, businesses cannot effectively leverage the tools or their data. Even the self-service tools, which are to be used by the average business user, require someone to help deploy them. Companies can turn to vendor support teams or third-party consultants to assist if they are unable to bring someone in house.

**Data organization:** To get the most of analytics solutions, that data needs to be organized. This means that databases should be set up correctly and integrated properly. This may require building a data warehouse, which can store data from a variety of applications and databases in a central location. 

Businesses may need to purchase a dedicated [data preparation software](https://www.g2.com/categories/data-preparation) as well to ensure that data is joined and is clean for the analytics solution to consume in the right way. In the context of big data, a company might want to specifically consider big data processing and distribution software. This often requires a skilled data analyst, IT employee, or an outside consultant to help ensure data quality is at its finest for easy analysis.

**User adoption:** It is not always easy to transform a business into a data-driven company. Particularly at more established companies that have done things the same way for years, it is not simple to force analytics tools upon employees, especially if there are ways for them to avoid it. If there are other options, such as spreadsheets or existing tools that employees can use instead of analytics software, they will most likely go that route. However, if managers and leaders ensure that analytics tools are a necessity in an employee’s day to day, then adoption rates will increase.

### Which Companies Should Buy Big Data Analytics Software?

As has often been said, data is the fuel that drives modern businesses. Although it is cliche, it no doubt has truth to it. Therefore, businesses across the globe and across industries should consider some sort of analytics solution, such as big data analytics in order to make sense of that data and begin to make data-driven decisions. 

**Financial services:** Within financial institutions, such as insurance brokerages, banks, and credit unions, it is common for a host of different systems to be used. These companies have data ranging from customer records, to transactions, to market data, and more. With the proliferation of systems comes more data. With a robust analytics solution in place, they can get a better understanding of the data that is being produced from the various systems across the business. As an industry that is heavily regulated, users can benefit from governed access capabilities which can be particularly beneficial, since it can assist in auditing company processes.

**Healthcare:** Within the space of healthcare, bad data practices might have dire or even deadly consequences. Big data analytics software can help these organizations with having an overarching view of their data, such as patient records, insurance claims, finances, and more. Through the implementation of analytics, healthcare companies can lower risk and costs, and make their billing and collections smarter.

**Retail** : Retail organizations, whether they be B2C, B2B, D2C, or others, rely on data to make informed decisions. For example, a seller of printers, in order to run a successful business, must keep track of many things such as their inventory, sales, their sales team, and returns. If all of this data is kept siloed within different systems, there is no single source of truth and departments cannot have a conversation around the actual state of the business’ data. With big data analytics software set up and connected to all of the relevant data sources, any retail business can see benefits and make meaningful data-driven decisions.

### How to Buy Big Data Analytics Software

#### Requirements Gathering (RFI/RFP) for Big Data Analytics Software

If a company is just starting out on their analytics journey, g2.com can help in selecting the best software for the particular company and use case. Since the particular solution might vary based on company size and industry, G2 is a great place to sort and filter reviews based on these criteria, along with many more.

As mentioned above, the variety, volume, and velocity of data are vast. Therefore, users should think about how the particular solution fits their particular needs, as well as their future needs as they accumulate more data. 

To find the right solution, buyers should determine 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 this software, 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 a request for information (RFI), a one-page list with a few bullet points describing what is needed from a big data analytics software.

#### Compare Big Data Analytics Software 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 solutions.

**Conduct demos**

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

#### Selection of Big Data Analytics Software

**Choose a selection team**

As big data analytics software is all about the data, the user must make sure that the selection process is data driven as well. The selection team should compare notes and facts and figures which they noted during the process, such as time to insight, number of visualizations, and availability of advanced analytics capabilities.

**Negotiation**

Just because something is written on a company’s pricing page, does not mean it is not negotiable (although some companies will not budge). 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.

**Final decision**

After this stage, and before going all in, it is recommended to roll out a test run or pilot program to test adoption with a small sample size of users. If the tool is well used and well received, the buyer can be confident that the selection was correct. If not, it might be time to go back to the drawing board.

### What Does Big Data Analytics Software Cost?

Businesses decide to deploy big data analytics software with the goal of deriving some degree of a return on investment (ROI).

#### Return on Investment (ROI)

As they are looking to recoup their losses that they spent on the software, it is critical to understand the costs associated with it. As mentioned above, this software is typically billed per user, which is sometimes tiered depending on the company size. More users will typically translate into more licenses, which means more money.

Users must consider how much is spent and compare that to what is gained, both in terms of efficiency as well as revenue. Therefore, businesses can compare processes between pre- and post-deployment of the software to better understand how processes have been improved and how much time has been saved. They can even produce a case study (either for internal or external purposes) to demonstrate the gains they have seen from their use of the big data analytics tool.

### Implementation of Big Data Analytics Software

**How is Big Data Analytics Software Implemented?**

Implementation differs drastically depending on the complexity and scale of the data. In organizations with vast amounts of data in disparate sources (e.g., applications, databases, etc.), it is often wise to utilize an external party, whether that be an implementation specialist from the vendor or a third-party consultancy. With vast experience, they can help businesses understand how to connect and consolidate their data sources and how to use the software efficiently and effectively.

**Who is Responsible for Big Data Analytics Software Implementation?**

It may require a lot of people, or many teams, to properly deploy an analytics platform. This is because data can cut across teams and functions. As a result, it is rare that one person or even one team has a full understanding of all of a company’s data assets. With a cross-functional team in place, a business can piece together their data and begin the journey of analytics, starting with proper data preparation and management.

### Big Data Analytics Software Trends

**Data literacy**

Business data is no longer locked up in silos. With big data analytics solutions, more users across a business can find, access, and analyze this data. In addition, [artificial intelligence (AI) software](https://www.g2.com/categories/artificial-intelligence) such as [natural language processing (NLP) software](https://www.g2.com/categories/natural-language-processing-nlp) help make searching through and for data easier and more powerful, providing more accurate results.

Implementing analytics software has been a major initiative for companies undergoing digital transformation as these tools offer deeper visibility into an organization's data. Companies adopt these solutions to make sense of large data sets collected from all their various sources.

**Shift to the cloud**

The move from on-premises data analytics to the cloud has been underway for a number of years, with more and more businesses moving their data and data insights into the cloud. This is taking place for various reasons, such as time to insights. The move away from on-premises infrastructure has helped many companies enable data work anywhere one has access to the cloud—anywhere with internet access. 

**Conversational AI**

Historically, to query data within an analytics solution, users needed to master a query language like SQL. With the rise of conversational interfaces, users uncover the data and insights they are looking for using intuitive language. Intuitive methods of querying data mean enabling a larger user base to access and make sense of company data.

**Machine learning**

AI is quickly becoming a promising feature of analytics solutions throughout the whole data journey, from ingestion to insights. From AI-powered data preparation to smart insights, in which the platform suggests visualizations to the end user, big data analytics solutions are quickly becoming more powerful. Machine learning is helping end users discover hidden insights, allowing them to make sense of data and helping them to understand what they are seeing.