# Best Big Data Integration Platforms

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

**Total Products under this Category:** 132

### Category Stats (Aug 2026)

- **Average Rating:** 4.51/5 The average rating of products in this category, based on all submitted ratings
- **Top Trending Product:** Palantir Foundry (+1.33%) - Among all products in this category, Palantir Foundry recorded the largest rating increase compared to last month

_Last updated: August 21, 2026_

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

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

- 30 Analysts and Data Experts
- 10,600+ Authentic Reviews
- 132+ Products
- Unbiased Rankings

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

## G2 Grid® for Big Data Integration Platforms
 ![G2 Grid® for Big Data Integration Platforms plotting products by satisfaction and market presence](https://www.g2.com/categories/big-data-integration-platforms/grids.png?focus%5B%5D=6073&focus%5B%5D=989&focus%5B%5D=10938&focus%5B%5D=23920&focus%5B%5D=15884&focus%5B%5D=52204&focus%5B%5D=2975&focus%5B%5D=87449)

Highlighted products: Google Cloud BigQuery, Alteryx, Snowflake, Fivetran, Workato, Azure Data Factory, SnapLogic Agentic Integration and Applied AI Platform, and AWS Lake Formation.

Underlying data: [Grid® JSON](https://www.g2.com/categories/big-data-integration-platforms/grids.json?focus%5B%5D=google-cloud-bigquery&focus%5B%5D=alteryx&focus%5B%5D=snowflake&focus%5B%5D=fivetran&focus%5B%5D=workato&focus%5B%5D=azure-data-factory&focus%5B%5D=snaplogic-agentic-integration-and-applied-ai-platform&focus%5B%5D=aws-lake-formation)

**Sponsored**

### SAP Integration Suite

SAP Integration Suite (formerly SAP Cloud Platform Integration Suite) is an integration platform as a service (iPaaS) that allows the user to integrate on-premise and cloud-based applications and processes with tools and prebuilt content managed by SAP. With SAP Integration Suite, you can become future ready and scale up your integration capabilities to connect and contextualize experiences for customers, partners, and employees across the enterprise and extended ecosystems.

[Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list_llm&secure%5Bcategory_id%5D=1186&secure%5Bchosen_at%5D=2026-08-30T09%3A29%3A37Z&secure%5Bdisplayable_resource_id%5D=1186&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=1186&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=6306&secure%5Bresource_id%5D=1186&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fbig-data-integration-platforms&secure%5Btoken%5D=58d105f07e4aa6e40a9139034de928b15475992a7cbbd2572d52a086180d015a&secure%5Burl%5D=https%3A%2F%2Fwww.sap.com%2Fcmp%2Fdg%2Fcommon-integration-challenges%2Findex.html%3Fcampaigncode%3DCRM-YJ25-BTP-408007%26source%3DINTEGRATION-click-campaign-G2&secure%5Burl_type%5D=custom_url)

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

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

**Average Rating:** 4.5/5.0

**Total Reviews:** 1,145

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

- **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 8.9/10)
- **Quality of Support:** 8.3/10 (Category avg: 8.9/10)
- **Ease of Use:** 8.7/10 (Category avg: 8.8/10)
- **Ease of Admin:** 8.5/10 (Category avg: 8.5/10)

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

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

#### Who Uses This Product?

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

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

_AI-generated summary from verified user reviews_

##### Pros

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

##### Cons

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

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

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

**Rating:** 5.0/5.0 stars

_— Rakshith N._

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

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

**Rating:** 4.0/5.0 stars

_— Reetika P._

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

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

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

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

Alteryx, através da sua plataforma Alteryx One, ajuda as empresas a transformar dados complexos e desconectados em um estado limpo e pronto para IA. Seja criando previsões financeiras, analisando o desempenho de fornecedores, segmentando dados de clientes, analisando a retenção de funcionários ou construindo aplicações de IA competitivas a partir dos seus dados proprietários, o Alteryx One facilita a limpeza, combinação e análise de dados para desbloquear os insights únicos que impulsionam decisões impactantes. Análises Guiadas por IA O Alteryx automatiza e simplifica cada etapa da preparação e análise de dados, desde a validação e enriquecimento até análises preditivas e insights automatizados. Incorpore IA generativa diretamente em seus fluxos de trabalho para agilizar tarefas complexas de dados e gerar insights mais rapidamente. Flexibilidade incomparável, seja você preferir fluxos de trabalho sem código, comandos em linguagem natural ou opções de baixo código, o Alteryx se adapta às suas necessidades. Confiável. Seguro. Pronto para Empresas. O Alteryx é confiado por mais da metade das empresas do Global 2000 e 19 dos 20 maiores bancos globais. Com automação, governança e segurança integradas, seus fluxos de trabalho podem escalar e manter a conformidade enquanto entregam resultados consistentes. E não importa se seus sistemas estão no local, híbridos ou na nuvem; o Alteryx se encaixa perfeitamente na sua infraestrutura. Fácil de Usar. Profundamente Conectado. O que realmente diferencia o Alteryx é nosso foco na eficiência e facilidade de uso para analistas e nossa comunidade ativa de 700.000 usuários do Alteryx para apoiá-lo em cada etapa da sua jornada. Com integração perfeita a dados em todos os lugares, incluindo plataformas como Databricks, Snowflake, AWS, Google, SAP e Salesforce, nossa plataforma ajuda a unificar dados isolados e acelerar a obtenção de insights. Visite Alteryx.com para mais informações e para começar seu teste gratuito.

**Average Rating:** 4.6/5.0

**Total Reviews:** 864

#### How Do G2 Users Rate Alteryx?

- **the product tem sido um bom parceiro comercial?:** 8.8/10 (Category avg: 8.9/10)
- **Qualidade do Suporte:** 8.5/10 (Category avg: 8.9/10)
- **Facilidade de Uso:** 8.7/10 (Category avg: 8.8/10)
- **Facilidade de administração:** 8.3/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Alteryx?

- **Vendedor:** [Alteryx](https://www.g2.com/pt/sellers/alteryx)
- **Website da Empresa:** www.alteryx.com
- **Ano de Fundação:** 1997
- **Localização da Sede:** Irvine, CA
- **Twitter:** @alteryx  
26,149 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/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 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Analista de Dados, Analista
- **Top Industries:** Serviços Financeiros, Contabilidade
- **Company Size:** 63% Large, 21% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários apreciam a **facilidade de uso** no Alteryx, achando simples automatizar tarefas com a funcionalidade de arrastar e soltar.
- Os usuários valorizam as **capacidades de automação** do Alteryx, simplificando processos de dados e aprimorando a eficiência analítica.
- Os usuários acham o Alteryx **muito intuitivo** , tornando-o fácil para usuários não técnicos aprenderem e utilizarem.
- Os usuários acham que a interface do Alteryx torna **o aprendizado de tecnologia fácil** para todos, mesmo para aqueles sem formação em tecnologia.
- Os usuários valorizam o Alteryx por sua **eficiência** em gerenciar dados, simplificar fluxos de trabalho e aumentar a produtividade geral.

##### Cons

- Os usuários destacam o **preço elevado** do Alteryx, tornando difícil para pequenas equipes ou startups arcar com as licenças.
- Os usuários enfrentam uma **curva de aprendizado acentuada** com o Alteryx, exigindo tempo para dominar seus recursos complexos.
- Os usuários acham que o Alteryx sofre de **falta de recursos** , como a ausência de acesso direto ao banco de dados e ferramentas de relatório limitadas.
- Os usuários acham a **dificuldade de aprendizado** do Alteryx acentuada, especialmente para aqueles que não estão familiarizados com RegEx e SQL.
- Os usuários experimentam **desempenho lento** com o Alteryx, particularmente ao lidar com grandes fluxos de trabalho e durante tarefas de manipulação de dados.

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

**["Escala Operações e Economiza Tempo com Fluxos de Trabalho de Dados Automatizados"](https://www.g2.com/pt/survey_responses/alteryx-review-13047637)**

**Rating:** 4.5/5.0 stars

_— Ihor B._

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

**["Torna a Preparação de Dados Mais Rápida com um Fluxo de Trabalho Intuitivo de Arrastar e Soltar"](https://www.g2.com/pt/survey_responses/alteryx-review-13190742)**

**Rating:** 4.5/5.0 stars

_— Anushka S._

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

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

A Snowflake permite que todas as organizações mobilizem seus dados com o AI Data Cloud da Snowflake. Os clientes usam o AI Data Cloud para unir dados isolados, descobrir e compartilhar dados com segurança, alimentar aplicativos de dados e executar diversas cargas de trabalho de IA/ML e analíticas. Onde quer que os dados ou usuários estejam, a Snowflake oferece uma experiência de dados única que abrange várias nuvens e geografias. Milhares de clientes em muitos setores, incluindo 691 dos 2000 maiores do mundo segundo a Forbes em 2023 (G2K) até 31 de janeiro, usam o AI Data Cloud da Snowflake para impulsionar seus negócios.

**Average Rating:** 4.5/5.0

**Total Reviews:** 716

#### How Do G2 Users Rate Snowflake?

- **the product tem sido um bom parceiro comercial?:** 9.0/10 (Category avg: 8.9/10)
- **Qualidade do Suporte:** 8.7/10 (Category avg: 8.9/10)
- **Facilidade de Uso:** 9.0/10 (Category avg: 8.8/10)
- **Facilidade de administração:** 8.7/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Snowflake?

- **Vendedor:** [Snowflake, Inc.](https://www.g2.com/pt/sellers/snowflake-inc)
- **Website da Empresa:** www.snowflake.com
- **Ano de Fundação:** 2012
- **Localização da Sede:** 135 Constitution Drive, Menlo Park CA
- **Twitter:** @SnowflakeDB  
278 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/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 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Engenheiro de Dados, Analista de Dados
- **Top Industries:** Tecnologia da Informação e Serviços, Software de Computador
- **Company Size:** 45% Medium, 43% Large

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

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários apreciam a **facilidade de uso** do Snowflake, que simplifica o compartilhamento de dados e aumenta a produtividade entre as equipes.
- Os usuários valorizam os **recursos confiáveis e a interface amigável** do Snowflake, melhorando a eficiência do gerenciamento de dados e análises.
- Os usuários apreciam a **facilidade de uso e a integração eficiente de dados** no Snowflake para seus projetos de armazenamento.
- Os usuários valorizam a **escalabilidade contínua** do Snowflake, permitindo o manuseio eficiente de grandes conjuntos de dados e mudanças de carga de trabalho sem perda de desempenho.
- Os usuários valorizam as **capacidades de processamento de dados rápidas e eficientes** do Snowflake, melhorando significativamente sua experiência de análise.

##### Cons

- Os usuários destacam os **altos custos** do Snowflake, tornando-o menos acessível para pequenas empresas com orçamentos limitados.
- Os usuários acham **as limitações de recursos** no Snowflake, como a falta de blocos de código e permissões restritas, frustrantes.
- Os usuários acham a **curva de aprendizado íngreme** , exigindo treinamento devido à sua complexidade e interface avassaladora para iniciantes.
- Os usuários muitas vezes enfrentam dificuldades com **altos custos** devido a consultas não otimizadas e medidas inadequadas de controle de custos no Snowflake.
- Os usuários acham a **estrutura de custos desafiadora** , exigindo tempo para otimizar o uso eficiente do Snowflake.

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

**["Snowflake Simplifica o Gerenciamento de Dados em Escala"](https://www.g2.com/pt/survey_responses/snowflake-review-12898129)**

**Rating:** 4.0/5.0 stars

_— Harshil A._

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

**["Escalonamento Elástico e Análises Rápidas com Snowflake"](https://www.g2.com/pt/survey_responses/snowflake-review-13129003)**

**Rating:** 4.5/5.0 stars

_— Ravindra N._

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

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

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

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

Fivetran é a base de dados para IA. Uma plataforma move, gerencia e transforma dados de cada aplicação, banco de dados, fluxo de eventos e arquivo que sua empresa utiliza em uma base governada que análises, operações e IA podem atuar. Conectores são implantados em minutos, funcionam automaticamente e se ajustam automaticamente quando uma fonte muda, para que sua equipe de dados gaste seu tempo construindo, não mantendo pipelines.

**Average Rating:** 4.3/5.0

**Total Reviews:** 811

#### How Do G2 Users Rate Fivetran?

- **the product tem sido um bom parceiro comercial?:** 8.6/10 (Category avg: 8.9/10)
- **Qualidade do Suporte:** 8.5/10 (Category avg: 8.9/10)
- **Facilidade de Uso:** 9.0/10 (Category avg: 8.8/10)
- **Facilidade de administração:** 8.8/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Fivetran?

- **Vendedor:** [Fivetran](https://www.g2.com/pt/sellers/fivetran)
- **Website da Empresa:** www.fivetran.com
- **Ano de Fundação:** 2012
- **Localização da Sede:** Oakland, CA
- **Twitter:** @fivetran  
5,767 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/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 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Engenheiro de Dados, Analista de Dados
- **Top Industries:** Software de Computador, Tecnologia da Informação e Serviços
- **Company Size:** 59% Medium, 27% Small

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

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários apreciam a **facilidade de integração e manutenção** com o Fivetran, melhorando seu ROI e a eficiência do fluxo de trabalho.
- Os usuários valorizam a **configuração fácil** do Fivetran, apreciando sua integração perfeita com aplicativos existentes.
- Os usuários valorizam a **fácil integração** do Fivetran, conectando-se perfeitamente com aplicativos existentes e melhorando o acesso aos dados.
- Os usuários valorizam o **suporte ao cliente responsivo** da Fivetran, melhorando sua experiência e satisfação geral.
- Os usuários apreciam o **layout intuitivo e simples** do Fivetran, tornando o gerenciamento de dados eficiente e acessível.

##### Cons

- Os usuários relatam experimentar **problemas de sincronização** que interrompem a funcionalidade, causando falhas imprevisíveis e complicações na gestão do fluxo de trabalho.
- Os usuários acham que a **precificação da Fivetran é bastante cara** , o que limita a acessibilidade e flexibilidade para um uso mais amplo.
- Os usuários enfrentam **problemas de integração** com o Fivetran, particularmente em relação à propriedade do esquema e à modificação de conexões em meio a mudanças na plataforma.
- Os usuários acham a **curva de aprendizado íngreme** , exigindo conhecimento técnico para navegar no Fivetran de forma eficaz no início.
- Os usuários acham que os **problemas de preços** do Fivetran dificultam a acessibilidade e a flexibilidade, tornando-o menos viável para um uso mais amplo.

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

**["Pipelines sem código e auto-monitoramento com conectores que simplesmente funcionam"](https://www.g2.com/pt/survey_responses/fivetran-review-13137995)**

**Rating:** 5.0/5.0 stars

_— Umesh ._

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

**["Integrações de Dados Sem Código Rápidas e Confiáveis com Grande Retorno sobre o Investimento"](https://www.g2.com/pt/survey_responses/fivetran-review-13138238)**

**Rating:** 4.5/5.0 stars

_— Jose Maria P._

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

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

- [What is Fivetran used for?](https://www.g2.com/pt/discussions/fivetran-what-is-fivetran-used-for) - 1 comment
- [Para que é usado o Censo?](https://www.g2.com/pt/discussions/what-is-census-used-for)
- [Who owns Fivetran?](https://www.g2.com/pt/discussions/who-owns-fivetran)
- [Quanto custa o Fivetran?](https://www.g2.com/pt/discussions/how-much-does-fivetran-cost) - 1 comment
- [O Fivetran é uma ferramenta ETL?](https://www.g2.com/pt/discussions/is-fivetran-an-etl-tool) - 2 comments

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

Workato é a iPaaS classificada como número 1 e a líder em MCP Empresarial — a plataforma em que as empresas confiam para unificar integração, automação e IA em um ambiente seguro e nativo da nuvem. Confiada por mais de 12.000 clientes, incluindo metade das empresas da Fortune 500, a Workato conecta todos os sistemas, processos e fontes de dados com mais de 14.000 conectores pré-construídos. O que diferencia a Workato: o MCP Empresarial transforma processos de negócios comprovados em habilidades governadas e prontas para agentes que qualquer agente de IA — Claude, ChatGPT, Cursor ou personalizado — pode executar de forma segura e previsível. Não é necessário substituir e descartar. Seja modernizando integrações legadas ou implantando IA agentiva em escala, a Workato oferece a orquestração, governança e confiança necessárias na empresa.

**Average Rating:** 4.7/5.0

**Total Reviews:** 748

#### How Do G2 Users Rate Workato?

- **the product tem sido um bom parceiro comercial?:** 9.4/10 (Category avg: 8.9/10)
- **Qualidade do Suporte:** 9.2/10 (Category avg: 8.9/10)
- **Facilidade de Uso:** 9.0/10 (Category avg: 8.8/10)
- **Facilidade de administração:** 9.0/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Workato?

- **Vendedor:** [Workato](https://www.g2.com/pt/sellers/workato)
- **Website da Empresa:** www.workato.com
- **Ano de Fundação:** 2013
- **Localização da Sede:** Mountain View, California
- **Twitter:** @Workato  
3,641 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=229b7e77382a8d2c3a0aeebe68dfc2316ea2caa6dd5d95ed0ee0d08884e6fc88&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F3675685&secure%5Burl_type%5D=linkedin_company_website)  
1,401 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Engenheiro de Software, Engenheiro de Software Sênior
- **Top Industries:** Software de Computador, Tecnologia da Informação e Serviços
- **Company Size:** 43% Medium, 33% Large

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

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários apreciam a **facilidade de uso** do Workato, permitindo automação rápida sem necessidade de conhecimento técnico.
- Os usuários adoram as **integrações fáceis** com o Workato, permitindo a rápida automação de ferramentas como Salesforce e Slack.
- Os usuários adoram como as **integrações fáceis** do Workato simplificam a automação, economizando horas e tornando os processos contínuos.
- Os usuários valorizam a **interface amigável e de baixo código** do Workato, tornando a automação de processos de negócios contínua e eficiente.
- Os usuários apreciam a **facilidade de automação** com o Workato, permitindo integrações perfeitas e economizando tempo significativo em tarefas repetitivas.

##### Cons

- Os usuários muitas vezes acham a **complexidade do Workato** esmagadora, particularmente em relação ao preço e à integração, levando à confusão.
- Os usuários acham a **curva de aprendizado íngreme** , com a complexidade e a confusão dificultando a integração suave e o gerenciamento de fluxo de trabalho.
- Os usuários estão frustrados com as **limitações de dados** no Workato, impactando o envio de e-mails, relatórios de trabalho e transferências de arquivos grandes.
- Os usuários acham que os **recursos ausentes** na biblioteca de conectores do Workato limitam suas capacidades de integração com aplicativos menos conhecidos.
- Os usuários acham a **curva de aprendizado íngreme** do Workato assustadora, especialmente durante a integração e o uso inicial.

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

**["A Plataforma Que Cresceu Conosco"](https://www.g2.com/pt/survey_responses/workato-review-12941177)**

**Rating:** 5.0/5.0 stars

_— Anshu b._

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

**["Workato nos ajuda a construir integrações complexas em uma velocidade impressionante."](https://www.g2.com/pt/survey_responses/workato-review-10305521)**

**Rating:** 5.0/5.0 stars

_— Sreenath B._

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

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

- [What does Workato do?](https://www.g2.com/pt/discussions/what-does-workato-do)
- [Quanto custa o Workato?](https://www.g2.com/pt/discussions/how-much-does-workato-cost) - 1 comment
- [O que é uma receita Workato?](https://www.g2.com/pt/discussions/what-is-a-workato-recipe) - 3 comments
- [What is Workato used for?](https://www.g2.com/pt/discussions/what-is-workato-used-for)

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

Azure Data Factory (ADF) é um serviço de integração de dados totalmente gerenciado e sem servidor, projetado para simplificar o processo de ingestão, preparação e transformação de dados de fontes diversas. Ele permite que as organizações construam e orquestrem fluxos de trabalho de Extração, Transformação, Carga (ETL) e Extração, Carga, Transformação (ELT) em um ambiente sem código, facilitando o movimento e a transformação de dados entre sistemas locais e baseados em nuvem. Principais Recursos e Funcionalidades: - Conectividade Extensa: ADF oferece mais de 90 conectores integrados, permitindo a integração com uma ampla gama de fontes de dados, incluindo bancos de dados relacionais, sistemas NoSQL, aplicativos SaaS, APIs e serviços de armazenamento em nuvem. - Transformação de Dados Sem Código: Utilizando fluxos de dados de mapeamento alimentados pelo Apache Spark™, o ADF permite que os usuários realizem transformações de dados complexas sem escrever código, simplificando o processo de preparação de dados. - Rehospedagem de Pacotes SSIS: As organizações podem facilmente migrar e estender seus pacotes existentes do SQL Server Integration Services (SSIS) para a nuvem, alcançando economias significativas de custos e escalabilidade aprimorada. - Escalável e Econômico: Como um serviço sem servidor, o ADF escala automaticamente para atender às demandas de integração de dados, oferecendo um modelo de preços pay-as-you-go que elimina a necessidade de investimentos iniciais em infraestrutura. - Monitoramento e Gerenciamento Abrangentes: O ADF fornece ferramentas robustas de monitoramento, permitindo que os usuários acompanhem o desempenho dos pipelines, configurem alertas e garantam a operação eficiente dos fluxos de trabalho de dados. Valor Principal e Soluções para Usuários: O Azure Data Factory aborda as complexidades da integração de dados moderna, fornecendo uma plataforma unificada que conecta fontes de dados díspares, automatiza fluxos de trabalho de dados e facilita transformações de dados avançadas. Isso capacita as organizações a derivar insights acionáveis de seus dados, aprimorar os processos de tomada de decisão e acelerar iniciativas de transformação digital. Ao oferecer um ambiente escalável, econômico e sem código, o ADF reduz a carga operacional nas equipes de TI e permite que engenheiros de dados e analistas de negócios se concentrem em entregar valor por meio de estratégias orientadas por dados.

**Average Rating:** 4.6/5.0

**Total Reviews:** 96

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

- **the product tem sido um bom parceiro comercial?:** 9.1/10 (Category avg: 8.9/10)
- **Qualidade do Suporte:** 8.8/10 (Category avg: 8.9/10)
- **Facilidade de Uso:** 8.9/10 (Category avg: 8.8/10)
- **Facilidade de administração:** 8.7/10 (Category avg: 8.5/10)

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

- **Vendedor:** [Microsoft](https://www.g2.com/pt/sellers/microsoft)
- **Ano de Fundação:** 1975
- **Localização da Sede:** Redmond, Washington
- **Twitter:** @microsoft  
13,091,739 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/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 funcionários no LinkedIn®
- **Propriedade:** MSFT

#### Who Uses This Product?

- **Who Uses This:** Engenheiro de Dados, Engenheiro de Software
- **Top Industries:** Tecnologia da Informação e Serviços, Software de Computador
- **Company Size:** 59% Large, 31% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários valorizam as **capacidades de integração de dados sem interrupções** do Azure Data Factory, simplificando fluxos de trabalho complexos em várias fontes.
- Os usuários valorizam a **facilidade de uso** do Azure Data Factory, simplificando a integração de dados com uma interface visual de baixo código.
- Os usuários destacam a **facilidade de conectar várias fontes de dados** no Azure Data Factory, simplificando significativamente a integração de dados.
- Os usuários valorizam as **capacidades de integração perfeita** do Azure Data Factory para movimentação e automação de dados eficientes.
- Os usuários valorizam a **escalabilidade** do Azure Data Factory, permitindo a integração e o gerenciamento contínuos de grandes fluxos de trabalho de dados sem esforço.

##### Cons

- Os usuários acham **difícil depurar** no Azure Data Factory, citando limitações e uma experiência complexa para solucionar problemas em pipelines.
- Os usuários acham **difícil depurar** no Azure Data Factory frustrante, especialmente com pipelines complexos e ferramentas de solução de problemas limitadas.
- Os usuários acham o Azure Data Factory **caro** devido aos potenciais altos custos de grandes volumes de dados e uso frequente de pipelines.
- Os usuários encontram **limitações de recursos** no Azure Data Factory, particularmente em registro, monitoramento e transformações complexas.
- Os usuários expressam frustração com a **complexidade** do Azure Data Factory, citando desafios na depuração e no gerenciamento de fluxos de trabalho complexos.

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

**["Arrastar e Soltar de Baixo Código que Facilita o Desenvolvimento para Desenvolvedores e Usuários de Negócios"](https://www.g2.com/pt/survey_responses/azure-data-factory-review-12746463)**

**Rating:** 4.5/5.0 stars

_— Shyam s._

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

**["Integração de Dados Intuitiva e Escalável com o Azure Data Factory"](https://www.g2.com/pt/survey_responses/azure-data-factory-review-12454264)**

**Rating:** 4.5/5.0 stars

_— Alan R._

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

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

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

### [SnapLogic Agentic Integration and Applied AI Platform](https://www.g2.com/products/snaplogic-agentic-integration-and-applied-ai-platform/reviews)

The SnapLogic Platform is an agentic integration and automation solution designed to assist enterprise teams in connecting applications, data sources, and APIs while orchestrating AI-powered workflows across both cloud and on-premises environments. Established in 2006 and headquartered in San Mateo, California, SnapLogic serves a diverse range of industries, including financial services, pharmaceuticals, manufacturing, software, and higher education, with a global presence across North America, Europe, and Asia Pacific. Targeted primarily at IT teams, data engineers, and integration specialists, the SnapLogic Platform addresses the critical need for seamless data movement between systems, automation of business processes, and governance of AI agent activities at scale. The platform supports various use cases, including application integration, data pipeline management, API lifecycle management, legacy system modernization, and enterprise AI orchestration. This versatility makes it a valuable tool for organizations looking to enhance their operational efficiency and data accessibility. Key features of the SnapLogic Platform include a visual, low-code pipeline builder that allows teams to create, test, and deploy integrations without the need for extensive coding knowledge. This drag-and-drop designer significantly reduces reliance on developer resources, enabling quicker project turnaround. Additionally, the platform boasts a pre-built Snaps connector library, offering over 1,000 reusable connectors for various enterprise applications, databases, cloud services, and data platforms. This extensive library supports both simple and complex integration patterns, streamlining the integration process. Another notable feature is SnapGPT, an AI co-pilot integrated into the platform that assists users in generating integration pipelines, suggesting data mappings, and troubleshooting issues using natural language inputs. This innovative tool enhances user experience and efficiency, making it easier for teams to navigate the complexities of integration. The platform also includes robust API management tools for creating, publishing, securing, and monitoring APIs, facilitating the exposure and consumption of data services across internal and external systems. Moreover, SnapLogic enables agentic workflow automation, allowing users to design and orchestrate AI agents that execute multi-step business processes. With built-in support for the Model Context Protocol (MCP), organizations can effectively manage agent interactions across various models and tools. The platform also supports data integration and transformation for batch, real-time, and streaming data pipelines, equipped with capabilities for mapping, enrichment, and transformation of both structured and unstructured data. Centralized monitoring and governance features provide a unified dashboard for tracking pipeline performance, managing access controls, and ensuring auditability across all integration and automation activities. The SnapLogic Platform addresses common challenges organizations face when scaling their technology operations: fragmented data across disconnected systems, high integration development costs, and the complexity of deploying AI in regulated or mission-critical environments. By providing a unified platform for both traditional integration and agentic automation, it reduces reliance on custom-coded connectors and enables teams to build and manage integrations without requiring deep software engineering expertise.

**Average Rating:** 4.4/5.0

**Total Reviews:** 380

#### How Do G2 Users Rate SnapLogic Agentic Integration and Applied AI Platform?

- **Has the product been a good partner in doing business?:** 8.8/10 (Category avg: 8.9/10)
- **Quality of Support:** 8.3/10 (Category avg: 8.9/10)
- **Ease of Use:** 8.9/10 (Category avg: 8.8/10)
- **Ease of Admin:** 8.6/10 (Category avg: 8.5/10)

#### Who Is the Company Behind SnapLogic Agentic Integration and Applied AI Platform?

- **Seller:** [SnapLogic](https://www.g2.com/sellers/snaplogic)
- **Company Website:** www.snaplogic.com
- **Year Founded:** 2006
- **HQ Location:** San Mateo, CA
- **Twitter:** @SnapLogic  
7,348 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=5261a75f1a744444604a852bca79d92e4b9b8336bca6cd31b6688370561a9346&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F210766%2F&secure%5Burl_type%5D=linkedin_company_website)  
305 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Consultant
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 46% Large, 36% Medium

#### What Do G2 Reviewers Say About SnapLogic Agentic Integration and Applied AI Platform?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** of SnapLogic IIP, enabling quick connections and streamlined workflows without extensive coding.
- Users highly value the **easy integrations** offered by SnapLogic, making complex data management tasks simple and efficient.
- Users appreciate the **easy integration capability** of SnapLogic, enabling seamless connections between various tools and systems.
- Users appreciate the **intuitive user interface** of SnapLogic IIP, enabling easy and efficient complex integrations.
- Users value the **automation capabilities** of SnapLogic IIP, streamlining complex integrations and enhancing workflow efficiency.

##### Cons

- Users face **performance issues** with slow loading and frequent memory problems when handling large datasets in SnapLogic.
- Users find **technical difficulties** with SnapLogic, including complex transformations, debugging challenges, and performance issues.
- Users experience **poor performance** when processing large workloads, leading to frustration and inefficiency during integration tasks.
- Users find the **debugging complexity** challenging, leading to confusion and impacting their overall experience with SnapLogic IIP.
- Users find that **error reporting lacks detail** , making troubleshooting a time-consuming and confusing process.

#### What Are Recent G2 Reviews of SnapLogic Agentic Integration and Applied AI Platform?

**["Effective data integration tool"](https://www.g2.com/survey_responses/snaplogic-agentic-integration-and-applied-ai-platform-review-13334121)**

**Rating:** 4.5/5.0 stars

_— Vinay S._

[Read full review](https://www.g2.com/survey_responses/snaplogic-agentic-integration-and-applied-ai-platform-review-13334121)

**["Fast, Scalable Integrations with SnapLogic’s Intuitive Drag-and-Drop Designer"](https://www.g2.com/survey_responses/snaplogic-agentic-integration-and-applied-ai-platform-review-13212820)**

**Rating:** 5.0/5.0 stars

_— Ramakrishna k._

[Read full review](https://www.g2.com/survey_responses/snaplogic-agentic-integration-and-applied-ai-platform-review-13212820)

#### What Are G2 Users Discussing About SnapLogic Agentic Integration and Applied AI Platform?

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

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

AWS Lake Formation é um serviço totalmente gerenciado para construir, gerenciar, proteger e compartilhar dados em data lakes em poucos dias. Você pode centralizar a segurança e a governança, e permitir o compartilhamento de dados em toda a organização.

**Average Rating:** 4.4/5.0

**Total Reviews:** 33

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

- **the product tem sido um bom parceiro comercial?:** 9.0/10 (Category avg: 8.9/10)
- **Qualidade do Suporte:** 8.3/10 (Category avg: 8.9/10)
- **Facilidade de Uso:** 8.7/10 (Category avg: 8.8/10)
- **Facilidade de administração:** 8.0/10 (Category avg: 8.5/10)

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

- **Vendedor:** [Amazon Web Services (AWS)](https://www.g2.com/pt/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Ano de Fundação:** 2006
- **Localização da Sede:** Seattle, WA
- **Twitter:** @awscloud  
2,232,483 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=072881eee28a2afe24f8d1bda9f20e3e146b9fb4b214f216411ce2ed6898b31e&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Famazon-web-services%2F&secure%5Burl_type%5D=linkedin_company_website)  
147,094 funcionários no LinkedIn®
- **Propriedade:** NASDAQ: AMZN

#### Who Uses This Product?

- **Top Industries:** Tecnologia da Informação e Serviços
- **Company Size:** 47% Small, 37% Large

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

**["Simplifica a Governança, Requer Experiência para Configuração"](https://www.g2.com/pt/survey_responses/aws-lake-formation-review-12863344)**

**Rating:** 4.0/5.0 stars

_— Atharva P._

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

**["Melhor serviço de Data Lake gerenciado na nuvem"](https://www.g2.com/pt/survey_responses/aws-lake-formation-review-7819323)**

**Rating:** 5.0/5.0 stars

_— Ravi B._

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

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

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

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

Maia é uma plataforma de automação de dados impulsionada por agentes autônomos de IA que constroem, mantêm e evoluem produtos de dados, eliminando assim o trabalho manual com dados. Maia capacita CDAOs e equipes de dados empresariais a entregar produtos de dados em escala de máquina enquanto mantém a governança. Sua plataforma integrada combina agentes especializados no Maia Team, fundamentados na inteligência de dados organizacional do Maia Context Engine e executados através das ferramentas de dados governadas do Maia Foundation. Organizações como EDF, St. James’ Place e Nature’s Touch usam Maia para automatizar o trabalho com dados em escala, modernizar plataformas e acelerar roteiros de IA sem aumentar o número de funcionários. Veja Maia por si mesmo.

**Average Rating:** 4.5/5.0

**Total Reviews:** 120

#### How Do G2 Users Rate Maia?

- **the product tem sido um bom parceiro comercial?:** 8.4/10 (Category avg: 8.9/10)
- **Qualidade do Suporte:** 8.6/10 (Category avg: 8.9/10)
- **Facilidade de Uso:** 9.0/10 (Category avg: 8.8/10)
- **Facilidade de administração:** 8.3/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Maia?

- **Vendedor:** [Matillion](https://www.g2.com/pt/sellers/matillion)
- **Website da Empresa:** www.matillion.com
- **Ano de Fundação:** 2011
- **Localização da Sede:** Salford, GB
- **Twitter:** @matillion  
7,362 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=9b553fae1e51bb2b58491b1c1d561a15b7dd370d485b1ff7affe8e7cbd75e1cf&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F2360297%2F&secure%5Burl_type%5D=linkedin_company_website)  
463 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Engenheiro de Dados
- **Top Industries:** Software de Computador, Tecnologia da Informação e Serviços
- **Company Size:** 47% Medium, 31% Large

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

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários apreciam a **facilidade de uso** do Maia, tornando a configuração e o entendimento das funcionalidades acessíveis para todos os usuários.
- Os usuários valorizam a **automação perfeita** do Matillion, aumentando a eficiência e simplificando o processo ETL sem esforço.
- Os usuários apreciam a **interface simples e amigável** do Matillion, tornando os processos de ETL sem esforço para todos.
- Os usuários apreciam a **interface intuitiva** do Matillion que simplifica tarefas complexas e melhora o conforto do usuário para todos os níveis de experiência.
- Os usuários destacam a **eficiência do ETL** do Maia, apreciando sua integração amigável e escalável com as principais plataformas de nuvem.

##### Cons

- Os usuários enfrentam **limitações de recursos** no Maia, impactando o desempenho no trabalho e complicando os esforços de reutilização de modelos.
- Os usuários acham Maia **cara** , especialmente à medida que o volume de dados aumenta e os custos podem se acumular rapidamente.
- Os usuários enfrentam **problemas de desempenho** com Jython e limitações de thread único, enquanto os recursos de nuvem são insuficientes em comparação com a versão do servidor.
- Os usuários expressam preocupação com a **dependência da nuvem** , enfrentando desafios com personalização limitada e custos adicionais em escala.
- Os usuários relatam **limitações da API** no Maia, particularmente a falta de recursos de acesso administrativo e opções de exportação de dados brutos.

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

**["Maia escalou mais de 800 migrações de pipeline sem sobrecarga adicional"](https://www.g2.com/pt/survey_responses/maia-review-12920298)**

**Rating:** 5.0/5.0 stars

_— Keith G._

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

**["Maia torna a integração rápida com uma interface intuitiva e pipelines de baixo código"](https://www.g2.com/pt/survey_responses/maia-review-12942268)**

**Rating:** 4.5/5.0 stars

_— Anthony S._

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

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

- [Para que é usado o Matillion ETL?](https://www.g2.com/pt/discussions/what-is-matillion-etl-used-for) - 1 comment
- [Para que é usado o Matillion Data Loader?](https://www.g2.com/pt/discussions/what-is-matillion-data-loader-used-for)
- [What are ETL tools used for?](https://www.g2.com/pt/discussions/what-are-etl-tools-used-for)
- [Is Matillion open source?](https://www.g2.com/pt/discussions/is-matillion-open-source)
- [What is ETL in software?](https://www.g2.com/pt/discussions/what-is-etl-in-software)

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

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

**Average Rating:** 4.3/5.0

**Total Reviews:** 371

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

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

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

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

#### Who Uses This Product?

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

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

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **fast querying** capabilities of Amazon Redshift, enjoying efficient and smooth data access for large datasets.
- Users appreciate the **seamless integrations** of Amazon Redshift, enhancing functionality and efficiency across their data solutions.
- Users appreciate the **ease of use** of Amazon Redshift, finding it simple to connect and manage data effectively.
- Users appreciate the **easy integrations** with other software and AWS services, enhancing their data management experience.
- Users appreciate the **impressive speed and scalability** of Amazon Redshift, enhancing their data warehousing experience significantly.

##### Cons

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

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

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

**Rating:** 4.5/5.0 stars

_— Swaroop W._

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

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

**Rating:** 4.0/5.0 stars

_— Atharva P._

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

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

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

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

5X é uma plataforma de dados e IA de ponta a ponta. A plataforma organiza seus dados independentemente da fonte ou formato. Quer você tenha uma equipe de dados dedicada ou não, nossa plataforma transforma dados fragmentados em insights e aplicativos acionáveis. O feedback que mais recebemos dos clientes é: "É autoexplicativo" e "É super fácil de usar". E esse era exatamente o nosso objetivo—criar uma plataforma poderosa, tudo-em-um, que seja incrivelmente fácil de usar. A pilha de dados moderna evoluiu. Não se trata mais de juntar fornecedores. A próxima geração da pilha de dados moderna é uma plataforma tudo-em-um que oferece velocidade, simplicidade e custo de propriedade reduzido. Isso é exatamente o que criamos na 5X. As empresas usam a 5X por vários motivos: 1) Velocidade e produtividade. Plataformas de dados tudo-em-um são incrivelmente eficientes. Vimos empresas construírem casos de uso no primeiro dia. Entre em contato conosco para ver se você se qualifica para um jumpstart gratuito de 48 horas! 🚀 2) Reduza seu custo total de propriedade em 30% em comparação com a construção de sua própria plataforma. Isso não contabiliza as horas de trabalho necessárias para dar suporte à construção de uma plataforma 🤯 3) Use nossa consultoria de dados completa para suporte em engenharia de dados e análises 👨‍💻 A 5X foi fundada em 2020 com presença nos EUA, Singapura, Reino Unido e Índia. Nossa equipe global é composta por mais de 70 pessoas e está crescendo rapidamente. Recentemente, levantamos nossa rodada seed da Flybridge Capital e somos apoiados por fundadores de destaque de empresas como Datadog, Preset, Astronomer, Mode, Rudderstack e outros investidores-anjo proeminentes. Para mais informações, visite 5X.co Não falamos apenas sobre velocidade e simplicidade; nós comprovamos isso com provas. Fale conosco sobre nosso jumpstart de 48 horas, onde podemos construir um caso de uso de ponta a ponta para você em 48 horas gratuitamente.

**Average Rating:** 4.9/5.0

**Total Reviews:** 81

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

- **the product tem sido um bom parceiro comercial?:** 9.8/10 (Category avg: 8.9/10)
- **Qualidade do Suporte:** 9.8/10 (Category avg: 8.9/10)
- **Facilidade de Uso:** 9.5/10 (Category avg: 8.8/10)
- **Facilidade de administração:** 9.6/10 (Category avg: 8.5/10)

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

- **Vendedor:** [5X](https://www.g2.com/pt/sellers/5x)
- **Ano de Fundação:** 2020
- **Localização da Sede:** San Francisco
- **Twitter:** @DataWith5x  
49 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=123d4698fdf0b6036b49dc7dd7e99f12fd6c91a432ee93cdc153ec13a4d274d7&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fdatawith5x%2F&secure%5Burl_type%5D=linkedin_company_website)  
111 funcionários no LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Software de Computador, Serviços Financeiros
- **Company Size:** 56% Medium, 40% Small

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

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários apreciam a **facilidade de uso** do 5X, graças à sua interface intuitiva e integração perfeita com ferramentas existentes.
- Os usuários apreciam o **suporte ao cliente responsivo** da 5X, melhorando sua experiência geral com rápidas atualizações de recursos.
- Os usuários valorizam as **capacidades de integração perfeita** do 5X, aumentando a eficiência na gestão de diversas fontes de dados sem esforço.
- Os usuários valorizam as **integrações fáceis** do 5X, que melhoram seus processos de ingestão de dados e a eficiência operacional geral.
- Os usuários valorizam a **integração perfeita e o design intuitivo** do 5X, permitindo uma gestão de dados e automação eficientes.

##### Cons

- Novos usuários enfrentam uma **curva de aprendizado íngreme** , mas o treinamento ajuda a facilitar a transição para os recursos da plataforma.
- Os usuários acham o **processo de configuração complexo** , levando a atrasos e desafios na implementação de recursos e fluxos de trabalho avançados.
- Os usuários encontram uma **curva de aprendizado acentuada** inicialmente, mas tornam-se proficientes com treinamento e suporte ao longo do tempo.
- Os usuários acham a **configuração difícil** do 5X desafiadora devido à complexidade e a uma curva de aprendizado acentuada.
- Os usuários observam as **limitações de recursos** do 5X, já que algumas opções avançadas ainda estão em desenvolvimento e podem exigir paciência.

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

**["Um parceiro de dados confiável e escalável"](https://www.g2.com/pt/survey_responses/5x-review-11889175)**

**Rating:** 5.0/5.0 stars

_— Varinderjit K._

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

**["Suporte Excepcional e Plataforma Amigável Impulsionando Nossa Transformação de Dados"](https://www.g2.com/pt/survey_responses/5x-review-11903408)**

**Rating:** 4.0/5.0 stars

_— Shuming F._

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

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

Para equipes de dados que buscam aumentar a disponibilidade de dados confiáveis, a Astronomer oferece o Astro, a moderna plataforma de orquestração de dados, alimentada pelo Airflow. O Astro permite que engenheiros de dados, cientistas de dados e analistas de dados construam, executem e observem pipelines como código. A Astronomer é a força motriz por trás do Apache Airflow™, o padrão de fato para expressar fluxos de dados como código. O Airflow é baixado mais de 31 milhões de vezes a cada mês e é usado por centenas de milhares de equipes ao redor do mundo.

**Average Rating:** 4.5/5.0

**Total Reviews:** 135

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

- **the product tem sido um bom parceiro comercial?:** 9.0/10 (Category avg: 8.9/10)
- **Qualidade do Suporte:** 8.9/10 (Category avg: 8.9/10)
- **Facilidade de Uso:** 9.0/10 (Category avg: 8.8/10)
- **Facilidade de administração:** 8.8/10 (Category avg: 8.5/10)

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

- **Vendedor:** [Astronomer](https://www.g2.com/pt/sellers/astronomer)
- **Website da Empresa:** www.astronomer.io
- **Ano de Fundação:** 2018
- **Localização da Sede:** New York, US
- **Twitter:** @astronomerio  
19,697 seguidores no Twitter
- **Página do LinkedIn®:** [www.linkedin.com](https://www.g2.com/pt/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=14de7865093cbe981bc53aa7236a5a16a9a7e76fb4d0a5b96f23ea594dfac03b&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F10019299&secure%5Burl_type%5D=linkedin_company_website)  
4,562 funcionários no LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Engenheiro de Dados, Engenheiro de Dados Sênior
- **Top Industries:** Tecnologia da Informação e Serviços, Serviços Financeiros
- **Company Size:** 47% Medium, 38% Large

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

_AI-generated summary from verified user reviews_

##### Pros

- Os usuários apreciam a **facilidade de uso** do Astro, achando-o intuitivo e eficaz para gerenciar fluxos de trabalho complexos.
- Os usuários apreciam as **melhorias de eficiência** do Astro, simplificando fluxos de trabalho e reduzindo a necessidade de recursos dedicados de DevOps.
- Os usuários apreciam a **interface de usuário intuitiva** do Astro, facilitando o monitoramento e gerenciamento fácil de fluxos de trabalho complexos.
- Os usuários apreciam as **capacidades de automação** do Astro by Astronomer, simplificando fluxos de trabalho e aprimorando a orquestração de dados.
- Os usuários valorizam a **facilidade de implantação** do Astro by Astronomer, aumentando a produtividade com configurações simples, confiáveis e eficientes.

##### Cons

- Os usuários destacam que o Astro by Astronomer pode ser bastante **caro** , representando desafios para equipes menores e restrições orçamentárias.
- Os usuários observam uma **curva de aprendizado acentuada** com o Astro, exigindo tempo significativo para adaptação e treinamento.
- Os usuários acham a **curva de aprendizado acentuada** do Astro desafiadora, exigindo tempo extra para que novos membros da equipe se adaptem.
- Os usuários observam uma **curva de aprendizado acentuada** com o Astro, tornando a adaptação desafiadora para novos membros da equipe.
- Os usuários observam as **limitações de recursos** do Astro, citando altos custos e falta de flexibilidade em comparação com a hospedagem própria.

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

**["Asro literalmente auxilia no trabalho de engenharia de dados, tornando-o mais fácil e produtivo."](https://www.g2.com/pt/survey_responses/astro-by-astronomer-review-8519803)**

**Rating:** 5.0/5.0 stars

_— Lakshminarayanan K._

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

**["Excelente experiência para desenvolvedores e clientes"](https://www.g2.com/pt/survey_responses/astro-by-astronomer-review-8428848)**

**Rating:** 5.0/5.0 stars

_— Juan Roberto H._

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

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

- [What is your experience with Astro by Astronomer for data orchestration, and what challenges have you faced?](https://www.g2.com/pt/discussions/what-is-your-experience-with-astro-by-astronomer-for-data-orchestration-and-what-challenges-have-you-faced)
- [Para que serve o Astro da Astronomer?](https://www.g2.com/pt/discussions/what-is-astro-by-astronomer-used-for)

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

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

**Average Rating:** 4.4/5.0

**Total Reviews:** 37

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

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

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

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

#### Who Uses This Product?

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

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

_AI-generated summary from verified user reviews_

##### Pros

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

##### Cons

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

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

**["Unified 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)

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

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

**Average Rating:** 4.5/5.0

**Total Reviews:** 56

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

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

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

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Year Founded:** 1911
- **HQ Location:** Armonk, New York, United States
- **Twitter:** @IBMSecurity  
74,660 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=14b544adaece4fdbc987f1d7f7028048c22259946811200cc751263825586af9&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F1009%2F&secure%5Burl_type%5D=linkedin_company_website)  
328,202 employees on LinkedIn®
- **Ownership:** SWX:IBM

#### Who Uses This Product?

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

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

_AI-generated summary from verified user reviews_

##### Pros

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

##### Cons

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

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

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

**Rating:** 5.0/5.0 stars

_— Shilpa J._

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

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

**Rating:** 4.0/5.0 stars

_— Mahesh B._

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

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

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

**Average Rating:** 4.7/5.0

**Total Reviews:** 208

#### How Do G2 Users Rate dbt?

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

#### Who Is the Company Behind dbt?

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

#### Who Uses This Product?

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

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

_AI-generated summary from verified user reviews_

##### Pros

- Users find dbt's **ease of use** exceptional, with straightforward setup and intuitive features enhancing their data transformation processes.
- Users appreciate the **maintainability and clarity** of dbt's SQL code base, enhancing collaboration and data transformations.
- Users value the **automation of data workflows** with dbt, enhancing maintainability and collaboration in SQL transformations.
- Users love the **ease of transforming data with dbt** , allowing for organized and efficient analytics workflows.
- Users value the **data quality** of dbt, praising its effectiveness in ensuring data integrity and operational efficiency.

##### Cons

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

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

**["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)

**["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)

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

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

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Similar Categories

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

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

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

Updated April 9, 2026

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

### Core Capabilities of Big Data Integration Platforms

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

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

### Common Use Cases for Big Data Integration Platforms

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

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

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

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

### Insights from G2 on Big Data Integration Platforms

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

Top Tools at a Glance

| Product | Best for | User Review |
| --- | --- | --- |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_96b275379465d759df5bffd0099d849a/google-cloud-bigquery.png "Product Avatar Image")](https://www.g2.com/products/google-cloud-bigquery/reviews)[BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews)[4.5/5(1,223)](https://www.g2.com/products/google-cloud-bigquery/reviews) | Serverless SQL analytics across Google-native data pipelines | "Easy-to-Use Cloud Tool with Shareable, Saved Queries" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_2c6c77d8284b2f6609c2f9dc0ba6b5a9/alteryx.png "Product Avatar Image")](https://www.g2.com/products/alteryx/reviews)[Alteryx](https://www.g2.com/products/alteryx/reviews)[4.6/5(895)](https://www.g2.com/products/alteryx/reviews) | No-code ETL and multi-source data blending | "Scales Operations and Saves Time with Automated Data Workflows" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_2b00e05c107c3273cea5264090c3c1d0/snowflake.jpg "Product Avatar Image")](https://www.g2.com/products/snowflake/reviews)[Snowflake](https://www.g2.com/products/snowflake/reviews)[4.5/5(764)](https://www.g2.com/products/snowflake/reviews) | Multi-workload analytics with compute-storage separation | "Snowflake Simplifies Data Management at Scale" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_50b25c72253e48a2b66e69f996196956/workato.png "Product Avatar Image")](https://www.g2.com/products/workato/reviews)[Workato](https://www.g2.com/products/workato/reviews)[4.7/5(777)](https://www.g2.com/products/workato/reviews) | Cross-application data orchestration with low-code recipes | "The Platform That Grew With Us" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_f176b4154a751d10150daa67a57b7dc5/azure-data-factory.jpg "Product Avatar Image")](https://www.g2.com/products/azure-data-factory/reviews)[Azure Data Factory](https://www.g2.com/products/azure-data-factory/reviews)[4.6/5(101)](https://www.g2.com/products/azure-data-factory/reviews) | Azure-native ETL orchestration across hybrid data sources | "Low-Code Drag-and-Drop That Makes Development Easy for Developers and Business Users" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_c476c9375398c3b68bbf5ff649015bba/snaplogic-agentic-integration-and-applied-ai-platform.png "Product Avatar Image")](https://www.g2.com/products/snaplogic-agentic-integration-and-applied-ai-platform/reviews)[SnapLogic Agentic Integration and Applied AI Platform](https://www.g2.com/products/snaplogic-agentic-integration-and-applied-ai-platform/reviews)[4.4/5(407)](https://www.g2.com/products/snaplogic-agentic-integration-and-applied-ai-platform/reviews) | Low-code ETL pipeline building across hybrid environments | "Effective data integration tool" |
| [![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_b3390b4cc3d92e87d570895f7358c003/amazon-redshift.jpg "Product Avatar Image")](https://www.g2.com/products/amazon-redshift/reviews)[Amazon Redshift](https://www.g2.com/products/amazon-redshift/reviews)[4.3/5(404)](https://www.g2.com/products/amazon-redshift/reviews) | AWS-native analytical data warehousing at petabyte scale | "Powerful Analytics Tool with Some Flexibility Limitations" |

* * *

Show More

### Big Data Integration Platforms Topics

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

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

## Learn More About Big Data Integration Platforms

### What are Big Data Integration Platforms?
 

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

 

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

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

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

 

**Middleware data integration**

 

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

 

**Data consolidation**

 

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

 

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

 

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

 

**Enterprise data integration**

 

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

 

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

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

 

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

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

 

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

 

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

 

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

 

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

 

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

 

### Who Uses Big Data Integration Platforms?

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

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

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

#### Software Related to Big Data Integration Platforms

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

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

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

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

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

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

### Challenges with Big Data Integration Platforms

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

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

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

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

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

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

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

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

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

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

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

### How to Buy Big Data Integration Platforms?

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

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

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

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

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

#### Compare Big Data Integration Platforms Products

**Create a long list**

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

**Create a short list**

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

**Conduct demos**

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

#### Selection of Big Data Integration Platforms

**Choose a selection team**

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

**Negotiation**

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

**Final decision**

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

### What Do Big Data Integration Platforms Cost?

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

#### Return on Investment (ROI)

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

### Implementation of Big Data Integration Platforms

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

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

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

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

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

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

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

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

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

### Big Data Integration Platforms Trends

**Hybrid integration platforms**

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

**Integration using artificial intelligence and machine learning**

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

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

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

**Blockchain for data and analytics**

Blockchain technology can help in more than one way: 

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

### Big Data Integration Platforms FAQs

### Most Popular FAQs

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

### Small Business FAQs

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

### Enterprise FAQs

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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