# Best Big Data Integration Platforms

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

**Total Products under this Category:** 129

### Category Stats (Jul 2026)

- **Average Rating:** 4.52/5 The average rating of products in this category, based on all submitted ratings
- **Top Trending Product:** Control-M (+0.37%) - Among all products in this category, Control-M recorded the largest rating increase compared to last month

_Last updated: July 27, 2026_

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

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

- 30 Analysts and Data Experts
- 9,500+ Authentic Reviews
- 129+ 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=15884&focus%5B%5D=52204&focus%5B%5D=2975&focus%5B%5D=41374&focus%5B%5D=1243833)

Highlighted products: Google Cloud BigQuery, Alteryx, Snowflake, Workato, Azure Data Factory, SnapLogic Intelligent Integration Platform (IIP), Maia, and 5X.

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=workato&focus%5B%5D=azure-data-factory&focus%5B%5D=snaplogic-intelligent-integration-platform-iip&focus%5B%5D=matillion-maia&focus%5B%5D=5x)

**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&secure%5Bcategory_id%5D=1186&secure%5Bchosen_at%5D=2026-07-29T10%3A34%3A36Z&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=f375f188bce2091c7b9785cab17fe559240d98f9ee80ac4b9fe1ef8339b936e2&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:** 1,144

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

- **Has the product been a good partner in doing business?:** 8.6/10 (Category avg: 8.9/10)
- **Quality of Support:** 8.3/10 (Category avg: 8.9/10)
- **Ease of Use:** 8.7/10 (Category avg: 8.8/10)
- **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 appreciate the **ease of use** of Google Cloud BigQuery, enabling quick analysis of massive datasets without hassle.
- Users appreciate the **exceptional speed** of BigQuery, allowing for quick processing of large datasets seamlessly.
- Users love the **easy integration** with Google Cloud services, enabling smooth data analysis and management.
- Users appreciate the **fast querying capabilities** of Google Cloud BigQuery, allowing effortless analysis of massive datasets.
- Users appreciate the **query efficiency** of BigQuery, effortlessly processing complex queries on massive datasets with speed.

##### Cons

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

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

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

**Rating:** 4.0/5.0 stars

_— Reetika P._

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

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

**Rating:** 5.0/5.0 stars

_— Aayush M._

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

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

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

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

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

**Average Rating:** 4.6/5.0

**Total Reviews:** 852

#### How Do G2 Users Rate Alteryx?

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

#### Who Is the Company Behind Alteryx?

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

#### Who Uses This Product?

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

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

_AI-generated summary from verified user reviews_

##### Pros

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

##### Cons

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

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

**["Powerful, Time-Saving Data Prep But Pricey, Windows-Only, and Weak on Reporting"](https://www.g2.com/survey_responses/alteryx-review-12999843)**

**Rating:** 4.5/5.0 stars

_— jayesh l._

[Read full review](https://www.g2.com/survey_responses/alteryx-review-12999843)

**["Intuitive Drag-and-Drop Analytics That Speeds Up Data Prep and Insights"](https://www.g2.com/survey_responses/alteryx-review-12983224)**

**Rating:** 4.5/5.0 stars

_— Akhil S._

[Read full review](https://www.g2.com/survey_responses/alteryx-review-12983224)

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

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

**Average Rating:** 4.5/5.0

**Total Reviews:** 711

#### How Do G2 Users Rate Snowflake?

- **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 8.9/10)
- **Quality of Support:** 8.7/10 (Category avg: 8.9/10)
- **Ease of Use:** 9.0/10 (Category avg: 8.8/10)
- **Ease of Admin:** 8.7/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Snowflake?

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

#### Who Uses This Product?

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

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

_AI-generated summary from verified user reviews_

##### Pros

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

##### Cons

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

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

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

**Rating:** 4.5/5.0 stars

_— Ravindra N._

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

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

**Rating:** 4.0/5.0 stars

_— Harshil A._

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

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

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

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

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

**Average Rating:** 4.7/5.0

**Total Reviews:** 749

#### How Do G2 Users Rate Workato?

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

#### Who Is the Company Behind Workato?

- **Seller:** [Workato](https://www.g2.com/sellers/workato)
- **Company Website:** www.workato.com
- **Year Founded:** 2013
- **HQ Location:** Mountain View, California
- **Twitter:** @Workato  
3,641 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=229b7e77382a8d2c3a0aeebe68dfc2316ea2caa6dd5d95ed0ee0d08884e6fc88&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F3675685&secure%5Burl_type%5D=linkedin_company_website)  
1,401 employees on LinkedIn®

#### Who Uses This Product?

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

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

_AI-generated summary from verified user reviews_

##### Pros

- Users find Workato to be **user-friendly and efficient** , allowing easy automation without technical expertise.
- Users love the **easy integrations** offered by Workato, making automation between tools simple and efficient.
- Users value the **ease of integrations** with Workato, appreciating its user-friendly interface and extensive pre-built connectors.
- Users appreciate Workato's **user-friendly design and automation capabilities** , enhancing productivity and simplifying complex workflows.
- Users love the **ease of automation** in Workato, saving hours by effortlessly integrating various tools and systems.

##### Cons

- Users find the **complexity** of Workato daunting, especially regarding terminology and pricing structures that confuse newcomers.
- Users find the **learning curve steep** , with complex workflows and overwhelming onboarding complicating initial use.
- Users express frustration over **data limitations** in Workato, hindering email sends and file transfers for larger tasks.
- Users find the **limited application library** of Workato restrictive, requiring manual setup for less common integrations.
- Users face a **steep learning curve** with Workato, finding onboarding and initial setup quite overwhelming and complex.

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

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

**Rating:** 5.0/5.0 stars

_— Sreenath B._

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

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

**Rating:** 5.0/5.0 stars

_— Anshu b._

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

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

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

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

Azure Data Factory (ADF) is a fully managed, serverless data integration service designed to simplify the process of ingesting, preparing, and transforming data from diverse sources. It enables organizations to construct and orchestrate Extract, Transform, Load (ETL) and Extract, Load, Transform (ELT) workflows in a code-free environment, facilitating seamless data movement and transformation across on-premises and cloud-based systems. Key Features and Functionality: - Extensive Connectivity: ADF offers over 90 built-in connectors, allowing integration with a wide array of data sources, including relational databases, NoSQL systems, SaaS applications, APIs, and cloud storage services. - Code-Free Data Transformation: Utilizing mapping data flows powered by Apache Spark™, ADF enables users to perform complex data transformations without writing code, streamlining the data preparation process. - SSIS Package Rehosting: Organizations can easily migrate and extend their existing SQL Server Integration Services (SSIS) packages to the cloud, achieving significant cost savings and enhanced scalability. - Scalable and Cost-Effective: As a serverless service, ADF automatically scales to meet data integration demands, offering a pay-as-you-go pricing model that eliminates the need for upfront infrastructure investments. - Comprehensive Monitoring and Management: ADF provides robust monitoring tools, allowing users to track pipeline performance, set up alerts, and ensure efficient operation of data workflows. Primary Value and User Solutions: Azure Data Factory addresses the complexities of modern data integration by providing a unified platform that connects disparate data sources, automates data workflows, and facilitates advanced data transformations. This empowers organizations to derive actionable insights from their data, enhance decision-making processes, and accelerate digital transformation initiatives. By offering a scalable, cost-effective, and code-free environment, ADF reduces the operational burden on IT teams and enables data engineers and business analysts to focus on delivering value through data-driven strategies.

**Average Rating:** 4.6/5.0

**Total Reviews:** 95

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

- **Has the product been a good partner in doing business?:** 9.1/10 (Category avg: 8.9/10)
- **Quality of Support:** 8.8/10 (Category avg: 8.9/10)
- **Ease of Use:** 8.9/10 (Category avg: 8.8/10)
- **Ease of Admin:** 8.7/10 (Category avg: 8.5/10)

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

- **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?

- **Who Uses This:** Data Engineer, Software Engineer
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 60% Large, 30% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Users love the **seamless data integration** capabilities of Azure Data Factory, simplifying complex workflows and enhancing productivity.
- Users value the **ease of use** of Azure Data Factory, simplifying data integration with its low-code visual interface.
- Users appreciate the **seamless connectivity** of Azure Data Factory for integrating diverse data sources with minimal coding.
- Users value the **seamless integration capabilities** of Azure Data Factory, simplifying complex data workflows across various sources.
- Users value the **scalability** of Azure Data Factory, enabling them to effortlessly manage and integrate vast data sources.

##### Cons

- Users find the **debugging difficulty** in Azure Data Factory frustrating, particularly for complex pipelines and troubleshooting failures.
- Users find **difficult debugging** with Azure Data Factory, often facing challenges in troubleshooting complex pipelines effectively.
- Users find Azure Data Factory to be **expensive** due to unpredictable costs linked to pipeline executions and data movements.
- Users find the **feature limitations** of Azure Data Factory hinder their ability to efficiently monitor and integrate with Power BI.
- Users find Azure Data Factory's **complexity and limitations** challenging, particularly with debugging and managing intricate workflows.

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

**["Intuitive, Scalable Data Integration with Azure Data Factory"](https://www.g2.com/survey_responses/azure-data-factory-review-12454264)**

**Rating:** 4.5/5.0 stars

_— Alan R._

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

**["Low-Code Drag-and-Drop That Makes Development Easy for Developers and Business Users"](https://www.g2.com/survey_responses/azure-data-factory-review-12746463)**

**Rating:** 4.5/5.0 stars

_— Shyam s._

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

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

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

### [SnapLogic Intelligent Integration Platform (IIP)](https://www.g2.com/products/snaplogic-intelligent-integration-platform-iip/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:** 373

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

- **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:** 8.8/10 (Category avg: 8.8/10)
- **Ease of Admin:** 8.6/10 (Category avg: 8.5/10)

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

- **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)  
307 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Consultant
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 47% Large, 36% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** of SnapLogic, allowing quick integration and straightforward pipeline setup.
- Users appreciate the **easy integrations** of SnapLogic, benefiting from a user-friendly interface and various connectors.
- Users appreciate the **seamless integration** capabilities of SnapLogic IIP, enabling efficient connections across diverse systems.
- Users enjoy the **user-friendly interface** of SnapLogic, simplifying tasks and enhancing overall productivity.
- Users love the **simplicity of automation** in SnapLogic IIP, enabling faster development and efficient integration processes.

##### Cons

- Users report **performance issues** with SnapLogic, especially under heavy workloads and in load balancing functionality.
- Users experience **poor performance** under heavy workloads, leading to frustration with debugging and complex integrations.
- Users struggle with **technical difficulties** , including debugging challenges and performance drops during heavy workloads.
- Users find the **complexity of understanding Snaps** and tracing errors to be quite challenging in SnapLogic IIP.
- Users report **poor error reporting** and lack of clarity in debugging, complicating the troubleshooting process.

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

**["SnapLogic Snaps Make No-Code Data Integration Simple and Powerful"](https://www.g2.com/survey_responses/snaplogic-intelligent-integration-platform-iip-review-13058311)**

**Rating:** 5.0/5.0 stars

_— Verified User in Computer Software_

[Read full review](https://www.g2.com/survey_responses/snaplogic-intelligent-integration-platform-iip-review-13058311)

**["Effortless Integration, Minor Long-Run Hiccups"](https://www.g2.com/survey_responses/snaplogic-intelligent-integration-platform-iip-review-10786237)**

**Rating:** 4.0/5.0 stars

_— Sayeedul R._

[Read full review](https://www.g2.com/survey_responses/snaplogic-intelligent-integration-platform-iip-review-10786237)

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

- [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

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

Maia is an AI Data Automation platform powered by autonomous AI agents that build, maintain, and evolve data products, thus eliminating manual data work. Maia empowers CDAOs and enterprise data teams to deliver data products at machine scale while maintaining governance. Its integrated platform combines specialized agents in Maia Team, grounded in the organizational data intelligence of the Maia Context Engine and executed through the governed data tools of Maia Foundation. Organizations like EDF, St. James’ Place, and Nature’s Touch use Maia to automate data work at scale, modernize platforms, and accelerate AI roadmaps without expanding headcount. See Maia for yourself.

**Average Rating:** 4.5/5.0

**Total Reviews:** 120

#### How Do G2 Users Rate Maia?

- **Has the product been a good partner in doing business?:** 8.4/10 (Category avg: 8.9/10)
- **Quality of Support:** 8.6/10 (Category avg: 8.9/10)
- **Ease of Use:** 9.0/10 (Category avg: 8.8/10)
- **Ease of Admin:** 8.3/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Maia?

- **Seller:** [Matillion](https://www.g2.com/sellers/matillion)
- **Company Website:** www.matillion.com
- **Year Founded:** 2011
- **HQ Location:** Salford, GB
- **Twitter:** @matillion  
7,362 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/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 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Engineer
- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 47% Medium, 31% Large

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

_AI-generated summary from verified user reviews_

##### Pros

- Users find the **ease of use** in Maia's interface invaluable for seamless ETL configuration and understanding.
- Users appreciate the **seamless automation** of Matillion, making ETL processes effortless and efficient across platforms.
- Users appreciate the **simple UI** of Matillion, which makes configuration easy and user-friendly for everyone.
- Users love Matillion's **intuitive and simple UI** , making it easy for beginners to configure and navigate.
- Users commend Maia for its **ETL efficiency** , making complex workflows user-friendly and scalable for all levels of analysts.

##### Cons

- Users experience **job performance issues** due to the limitations of the Jython interpreter and single-thread workflows.
- Users find the **pricing model expensive** , especially as data volume increases, impacting overall affordability.
- Users report **job performance issues** with the Jython interpreter and single-thread workflow limitations affecting efficiency.
- Users express concern over **cloud dependency** , feeling locked into the environment with limited customization options.
- Users find **API limitations** in Maia frustrating, as administrative features lack sufficient access and usability options.

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

**["Maia Makes Onboarding Fast with an Intuitive UI and Low-Code Pipelines"](https://www.g2.com/survey_responses/maia-review-12942268)**

**Rating:** 4.5/5.0 stars

_— Anthony S._

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

**["Maia Scaled 800+ Pipeline Migrations Without Added Overhead"](https://www.g2.com/survey_responses/maia-review-12920298)**

**Rating:** 5.0/5.0 stars

_— Keith G._

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

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

- [What is Matillion ETL used for?](https://www.g2.com/discussions/what-is-matillion-etl-used-for) - 1 comment
- [What is Matillion Data Loader used for?](https://www.g2.com/discussions/what-is-matillion-data-loader-used-for)
- [What are ETL tools used for?](https://www.g2.com/discussions/what-are-etl-tools-used-for)
- [Is Matillion open source?](https://www.g2.com/discussions/is-matillion-open-source)
- [What is ETL in software?](https://www.g2.com/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 value the **fast querying capabilities** of Amazon Redshift, enabling efficient analysis of large datasets seamlessly.
- Users praise the **easy integrations** with other software, enhancing data solutions within the Amazon Redshift ecosystem.
- Users find Amazon Redshift's **ease of use** exceptional, facilitating quick access and efficient data management.
- Users value the **easy integrations** of Amazon Redshift, enhancing their ability to build seamless data solutions.
- Users praise the **impressive speed and scalability** of Amazon Redshift, optimizing data management and query performance.

##### Cons

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

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

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

**Rating:** 4.5/5.0 stars

_— Swaroop W._

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

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

**Rating:** 4.0/5.0 stars

_— Atharva P._

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

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

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

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

5X is an end-to-end data and AI platform. The platform organizes your data regardless of source or format. Whether you have a dedicated data team or not, our platform
transforms fragmented data into actionable insights and apps. The customer feedback we get most often is, "This is self-explanatory," and "It's super easy to use." And that is exactly what our goal was—to create a powerful, all-in-one platform that's incredibly easy to use.  The modern data stack has evolved. It's no longer about stitching together vendors. The next-generation modern data stack is an all-in-one platform that offers speed, simplicity, and decreased cost of ownership. That's exactly what we have created at 5X. Companies use 5X for multiple reasons: 1) Speed & productivity. All-in-one data platforms are incredibly efficient. We've seen companies build use cases on day 1.  Contact us to see if you qualify for a free 48 hour jumpstart! 🚀 2) Decrease your total cost of ownership by 30% compared to building your own platform. This doesn't account the people hours needed to support a platform build 🤯 3) Use our full stack data consultancy for support on data engineering & analytics 👨‍💻 5X was founded in 2020 with presence in the USA, Singapore, UK and India. Our global team is 70+ people strong and rapidly growing. We’ve recently raised our seed round from Flybridge Capital and backed by top founders from companies like Datadog, Preset, Astronomer, Mode, Rudderstack and other prominent angel investors. For more information, visit 5X.co We don't just talk about speed and simplicity; we back it up with proof. Speak to us about our 48-hour jumpstart where we can build an end-to-end use case for you in 48 hours for free.

**Average Rating:** 4.9/5.0

**Total Reviews:** 81

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

- **Has the product been a good partner in doing business?:** 9.8/10 (Category avg: 8.9/10)
- **Quality of Support:** 9.8/10 (Category avg: 8.9/10)
- **Ease of Use:** 9.5/10 (Category avg: 8.8/10)
- **Ease of Admin:** 9.6/10 (Category avg: 8.5/10)

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

- **Seller:** [5X](https://www.g2.com/sellers/5x)
- **Year Founded:** 2020
- **HQ Location:** San Francisco
- **Twitter:** @DataWith5x  
49 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/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 employees on LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Computer Software, Financial Services
- **Company Size:** 56% Medium, 40% Small

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

_AI-generated summary from verified user reviews_

##### Pros

- Users value the **ease of use** of 5X, appreciating its intuitive interface and seamless integration with existing tools.
- Users appreciate the **responsive customer support** of 5X, which quickly addresses inquiries and implements requested features.
- Users praise 5X for its **seamless integration capabilities** , enhancing workflow automation and simplifying data management across platforms.
- Users value the **easy integrations** of 5X, enhancing their data ingestion and overall operational efficiency effortlessly.
- Users praise 5X for its **intuitive design and integrated capabilities** , facilitating efficient data management and seamless workflows.

##### Cons

- Users find a **steep learning curve** with 5X initially, but training helps to ease the transition.
- Users find the **complex setup** of 5X challenging, often requiring additional support for successful deployment.
- Users find a **steep learning curve** initially, requiring enablement sessions to ease into the platform's complexity.
- Users find the **difficult setup** of 5X to be time-consuming, requiring extensive learning and support for complex integrations.
- Users note **feature limitations** as some advanced tools are still in development, affecting overall functionality and workflows.

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

**["A reliable and scalable data partner"](https://www.g2.com/survey_responses/5x-review-11889175)**

**Rating:** 5.0/5.0 stars

_— Varinderjit K._

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

**["Exceptional Support and User-Friendly Platform Driving Our Data Transformation"](https://www.g2.com/survey_responses/5x-review-11903408)**

**Rating:** 4.0/5.0 stars

_— Shuming F._

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

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

For data teams looking to increase the availability of trusted data, Astronomer provides Astro, the modern data orchestration platform, powered by Airflow. Astro enables data engineers, data scientists, and data analysts to build, run, and observe pipelines-as-code. Astronomer is the driving force behind Apache Airflow™, the de facto standard for expressing data flows as code. Airflow is downloaded more than 31 million times each month and is used by hundreds of thousands of teams around the world.

**Average Rating:** 4.5/5.0

**Total Reviews:** 135

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

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

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

- **Seller:** [Astronomer](https://www.g2.com/sellers/astronomer)
- **Company Website:** www.astronomer.io
- **Year Founded:** 2018
- **HQ Location:** New York, US
- **Twitter:** @astronomerio  
19,697 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=14de7865093cbe981bc53aa7236a5a16a9a7e76fb4d0a5b96f23ea594dfac03b&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F10019299&secure%5Burl_type%5D=linkedin_company_website)  
4,572 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Senior Data Engineer
- **Top Industries:** Information Technology and Services, Financial Services
- **Company Size:** 47% Medium, 38% Large

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

_AI-generated summary from verified user reviews_

##### Pros

- Users highlight the **ease of use** of Astro, with intuitive UI and seamless Slack integration for monitoring.
- Users appreciate the **efficiency improvement** of Astro, enhancing workflow management and saving valuable time.
- Users appreciate the **intuitive user interface** of Astro, simplifying complex workflows and enhancing team collaboration.
- Users love the **automation capabilities** of Astro by Astronomer, enhancing efficiency in data orchestration and monitoring.
- Users value the **deployment ease** of Astro by Astronomer, streamlining Airflow and ensuring reliable data pipeline management.

##### Cons

- Users express concerns over Astro by Astronomer's **high pricing** and lack of transparency, particularly for smaller teams.
- Users report a **steep learning curve** for new team members, requiring significant time for adaptation and training.
- Users struggle with a **steep learning curve** , making adaptation and training for Astro challenging for new team members.
- Users often find a **steep learning curve** with Astro, requiring extra time for adaptation and training for new users.
- Users find the **limited customization** of Astro restrictive, especially compared to self-hosted Airflow options.

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

**["Excellent developer and customer experience"](https://www.g2.com/survey_responses/astro-by-astronomer-review-8428848)**

**Rating:** 5.0/5.0 stars

_— Juan Roberto H._

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

**["Asro literally assists in data engineering work, making it easier and more productive."](https://www.g2.com/survey_responses/astro-by-astronomer-review-8519803)**

**Rating:** 5.0/5.0 stars

_— Lakshminarayanan K._

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

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

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

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

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

**Average Rating:** 4.5/5.0

**Total Reviews:** 56

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

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

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

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

#### Who Uses This Product?

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

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

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** in IBM webMethods B2B, simplifying communication and process management with trade partners.
- Users value the **seamless data exchange** and robust features that simplify B2B integration and automation.
- Users highly value the **strong security features** of IBM webMethods B2B, ensuring safe and reliable transactions.
- Users value the **end-to-end automation** of B2B processes, enhancing efficiency and simplifying data exchange with partners.
- Users value the **integration capabilities** of IBM webMethods B2B, facilitating seamless connections and document exchanges with partners.

##### Cons

- Users find the **complexity** of IBM webMethods B2B to be challenging, impacting user experience and customization options.
- Users find the pricing structure of IBM webMethods B2B to be **expensive** and challenging for smaller businesses.
- Users find the **difficult learning curve** of IBM webMethods B2B to be challenging, especially for newcomers to B2B integrations.
- Users express concerns over the **complex pricing structure** of webMethods B2B, finding it challenging to fit budgets.
- Users may experience a **learning curve** with webMethods.io B2B, especially those new to integration tools.

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

**["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)

**["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)

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

##### Cons

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

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

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

**Rating:** 4.0/5.0 stars

_— Ashish D._

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

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

**Rating:** 4.5/5.0 stars

_— Daniel H._

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

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

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

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

#### Who Uses This Product?

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

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

_AI-generated summary from verified user reviews_

##### Pros

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

##### Cons

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

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

**["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

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

IBM StreamSets is a robust streaming data integration tool for hybrid, multi-cloud environments that enables real-time decision making. It allows ingestion and in-flight transformation of structured, unstructured, and semi-structured data from streaming sources, and reliably delivers trusted data into diverse destinations. Flexible deployment options promote security, cost-effectiveness and performance. With several pre-built connectors, an intuitive no-code/low-code interface, and automatic adaptability to data drifts, StreamSets accelerates data pipeline operationalization. It integrates with IBM’s broader data integration capabilities, enabling reliable pipelines that unify multiple data integration patterns, underpinned by data observability capabilities for continuous data quality monitoring and remediation. That’s why the largest companies in the world trust StreamSets to power millions of data pipelines for modern analytics, data science, smart applications, and hybrid integration.

**Average Rating:** 4.0/5.0

**Total Reviews:** 115

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

- **Has the product been a good partner in doing business?:** 8.2/10 (Category avg: 8.9/10)
- **Quality of Support:** 8.0/10 (Category avg: 8.9/10)
- **Ease of Use:** 8.4/10 (Category avg: 8.8/10)
- **Ease of Admin:** 7.8/10 (Category avg: 8.5/10)

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

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

#### Who Uses This Product?

- **Who Uses This:** Data Engineer, Software Engineer
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 42% Large, 34% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Users value the **ease of use** in IBM StreamSets, highlighting its intuitive interface and beginner-friendly features.
- Users praise the **user-friendly drag-and-drop interface** of IBM StreamSets, enhancing visualization and debugging efficiency.
- Users value the **effective data management capabilities** of IBM StreamSets for seamless integration and automation across environments.
- Users appreciate the **simplification of data integration workflows** in IBM StreamSets, making pipeline creation intuitive and efficient.
- Users value the **wide range of integrations** offered by IBM StreamSets, enhancing cloud and on-premise data connectivity.

##### Cons

- Users experience a **steep learning curve** , particularly when using advanced features and managing complex pipelines effectively.
- Users find the **pricing to be excessive** , especially for smaller teams, impacting overall satisfaction with IBM StreamSets.
- Users find the **learning difficulty** of advanced features in StreamSets to be time-consuming and not well-supported.
- Users often experience **slow performance** with IBM StreamSets, particularly when processing large volumes of data or complex pipelines.
- Users find the **steep learning curve** of IBM StreamSets challenging, especially with advanced features and configurations.

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

**["Powerful Data Integration With IBM Stream sets."](https://www.g2.com/survey_responses/ibm-streamsets-review-11654909)**

**Rating:** 5.0/5.0 stars

_— Abhishek D._

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

**["Simplifies Real-Time Data Pipelines with Mixed Customization"](https://www.g2.com/survey_responses/ibm-streamsets-review-12240946)**

**Rating:** 4.0/5.0 stars

_— Sravya A._

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

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

- [What is StreamSets used for?](https://www.g2.com/discussions/what-is-streamsets-used-for)
- [What is StreamSets data collector?](https://www.g2.com/discussions/what-is-streamsets-data-collector)
- [What is StreamSets control hub?](https://www.g2.com/discussions/what-is-streamsets-control-hub)
- [Are StreamSets free?](https://www.g2.com/discussions/are-streamsets-free)
- [What is StreamSets tool?](https://www.g2.com/discussions/what-is-streamsets-tool) - 1 comment

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

Skyvia is a no-code cloud data integration and data pipeline platform that enables ETL, ELT, Reverse ETL, data migration, one-way and bi-directional data sync, workflow automation, real-time connectivity, and much more. Benefits of Using Skyvia: • Cost efficiency: With affordable, flexible pricing plans for each product, Skyvia suites for businesses of any size. • Ease of Use: Based on extensive customer feedback, ease of use is Skyvia's strongest quality. • Flexibility: Skyvia provides adaptable, no-code integration tools for both basic and advanced business scenarios. • Trust: Skyvia is trusted by thousands of data-driven organizations around the globe. With a vast library of 200+ connectors, Skyvia provides seamless integration among various cloud applications, databases, and data warehouses, including Salesforce, Dynamics CRM, QuickBooks Online, SQL Server, Amazon Redshift, Google BigQuery, and others.

**Average Rating:** 4.8/5.0

**Total Reviews:** 322

#### How Do G2 Users Rate Skyvia?

- **Has the product been a good partner in doing business?:** 9.3/10 (Category avg: 8.9/10)
- **Quality of Support:** 9.3/10 (Category avg: 8.9/10)
- **Ease of Use:** 9.3/10 (Category avg: 8.8/10)
- **Ease of Admin:** 9.4/10 (Category avg: 8.5/10)

#### Who Is the Company Behind Skyvia?

- **Seller:** [Devart](https://www.g2.com/sellers/devart)
- **Year Founded:** 1997
- **HQ Location:** Wilmington, Delaware, USA
- **Twitter:** @DevartSoftware  
1,736 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=79979469a3bee56c782598382543e7a4af65b7f1bcf71aea7d9fde07241e81e6&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F800325%2F&secure%5Burl_type%5D=linkedin_company_website)  
252 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** CEO, CTO
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 52% Small, 42% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Users find Skyvia's **ease of use** remarkable, highlighting its intuitive interface for seamless data integration.
- Users appreciate the **easy integrations** with a low-code interface, making complex data connections effortless and efficient.
- Users highlight the **easy setup** of Skyvia, finding it intuitive and hassle-free for seamless data integration.
- Users find the **initial setup easy** , enabling quick integrations without requiring coding skills or developer involvement.
- Users appreciate the **easy integrations** Skyvia offers, enabling quick connections across multiple systems effortlessly and intuitively.

##### Cons

- Users highlight a need for **expanded documentation and more detailed logs** to improve troubleshooting and job tracking.
- Users find the **difficult setup** of Skyvia challenging due to its outdated interface and complex configurations.
- Users find the **learning curve steep** due to complex configurations and a dense interface that requires time to master.
- Users find the **poor documentation** of Skyvia lacking real-world examples, making integrations more complex and less intuitive.
- Users find the **setup difficulty** with Skyvia can be complex, requiring time and effort for configuration and field mapping.

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

**["Easy, No-Code Data Sync Setup with Straightforward Pricing"](https://www.g2.com/survey_responses/skyvia-review-12828558)**

**Rating:** 5.0/5.0 stars

_— Emily C._

[Read full review](https://www.g2.com/survey_responses/skyvia-review-12828558)

**["Straightforward, Reliable Data Sync Between Cloud Apps and Databases"](https://www.g2.com/survey_responses/skyvia-review-12843850)**

**Rating:** 5.0/5.0 stars

_— Marcus W._

[Read full review](https://www.g2.com/survey_responses/skyvia-review-12843850)

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

- [What is Skyvia used for?](https://www.g2.com/discussions/what-is-skyvia-used-for) - 1 comment

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Similar Categories

- [Cloud Migration](/categories/cloud-migration)
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- [Enterprise Service Bus (ESB)](/categories/enterprise-service-bus-esb)

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- [Stream Analytics](/categories/stream-analytics)

[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

| 

 | 

Serverless SQL analytics across Google-native data pipelines

 | 

User Review

"Easy-to-Use Cloud Tool with Shareable, Saved Queries"

 |
| 

 | 

No-code ETL and multi-source data blending

 | 

User Review

"Intuitive Drag-and-Drop Analytics That Speeds Up Data Prep and Insights"

 |
| 

 | 

Multi-workload analytics with compute-storage separation

 | 

User Review

"Snowflake Simplifies Data Management at Scale"

 |
| 

 | 

Cross-application data orchestration with low-code recipes

 | 

User Review

"The Platform That Grew With Us"

 |
| 

 | 

Azure-native ETL orchestration across hybrid data sources

 | 

User Review

"Low-Code Drag-and-Drop That Makes Development Easy for Developers and Business Users"

 |
| 

 | 

Low-code ETL pipeline building across hybrid environments

 | 

User Review

"Effortless Integration, Minor Long-Run Hiccups"

 |
| 

 | 

AWS-native analytical data warehousing at petabyte scale

 | 

User Review

"Powerful Analytics Tool with Some Flexibility Limitations"

 |
| 

 | 

End-to-end data stack consolidation with managed dbt orchestration

 | 

User Review

"A reliable and scalable data partner"

 |
| 

 | 

Managed Airflow orchestration with infrastructure-free pipeline delivery

 | 

User Review

"Asro literally assists in data engineering work, making it easier and more productive."

 |

* * *

Show More

* * *

## How Do You Choose the Right Big Data Integration Platforms?

### What You Should Know 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.