# Best Big Data Integration Platforms

  *By [Shalaka Joshi](https://research.g2.com/insights/author/shalaka-joshi)*

   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.





## Category Overview

**Total Products under this Category:** 130


## Trust & Credibility Stats

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

- 30 Analysts and Data Experts
- 9,100+ Authentic Reviews
- 130+ Products
- Unbiased Rankings

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


## Best Big Data Integration Platforms At A Glance

- **Leader:** [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews)
- **Highest Performer:** [5X](https://www.g2.com/products/5x/reviews)
- **Easiest to Use:** [5X](https://www.g2.com/products/5x/reviews)
- **Top Trending:** [Astro by Astronomer](https://www.g2.com/products/astro-by-astronomer/reviews)
- **Best Free Software:** [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews)


---

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---

## Top-Rated Products (Ranked by G2 Score)
### 1. [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews)
  BigQuery is a fully managed, AI-ready data analytics platform that helps you maximize value from your data and is designed to be multi-engine, multi-format, and multi-cloud. 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:** 1,157

**User Satisfaction Scores:**

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


**Seller Details:**

- **Seller:** [Google](https://www.g2.com/sellers/google)
- **Year Founded:** 1998
- **HQ Location:** Mountain View, CA
- **Twitter:** @google (31,910,461 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1441/ (336,169 employees on LinkedIn®)
- **Ownership:** NASDAQ:GOOG

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer, Data Analyst
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 37% Enterprise, 35% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (156 reviews)
- Speed (143 reviews)
- Fast Querying (120 reviews)
- Integrations (118 reviews)
- Query Efficiency (114 reviews)

**Cons:**

- Expensive (127 reviews)
- Query Issues (78 reviews)
- Cost Issues (63 reviews)
- Cost Management (60 reviews)
- Learning Curve (54 reviews)

### 2. [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:** 4.6/5.0
  **Total Reviews:** 675

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 8.9/10)
- **Quality of Support:** 8.7/10 (Category avg: 8.9/10)
- **Ease of Use:** 9.0/10 (Category avg: 8.9/10)
- **Ease of Admin:** 8.6/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [Snowflake, Inc.](https://www.g2.com/sellers/snowflake-inc)
- **Company Website:** https://www.snowflake.com
- **Year Founded:** 2012
- **HQ Location:** San Mateo, CA
- **Twitter:** @SnowflakeDB (246 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/snowflake-computing/ (10,857 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer, Data Analyst
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 44% Mid-Market, 43% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (89 reviews)
- Scalability (68 reviews)
- Data Management (67 reviews)
- Features (66 reviews)
- Integrations (61 reviews)

**Cons:**

- Expensive (53 reviews)
- Cost (36 reviews)
- Cost Management (32 reviews)
- Learning Curve (25 reviews)
- Feature Limitations (21 reviews)

### 3. [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:** 652

**User Satisfaction Scores:**

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


**Seller Details:**

- **Seller:** [Alteryx](https://www.g2.com/sellers/alteryx)
- **Company Website:** https://www.alteryx.com
- **Year Founded:** 1997
- **HQ Location:** Irvine, CA
- **Twitter:** @alteryx (26,218 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/903031/ (2,268 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Analyst, Analyst
  - **Top Industries:** Financial Services, Accounting
  - **Company Size:** 62% Enterprise, 22% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (333 reviews)
- Automation (148 reviews)
- Intuitive (132 reviews)
- Easy Learning (102 reviews)
- Efficiency (102 reviews)

**Cons:**

- Expensive (88 reviews)
- Learning Curve (80 reviews)
- Missing Features (62 reviews)
- Learning Difficulty (55 reviews)
- Slow Performance (41 reviews)

### 4. [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:** 725

**User Satisfaction Scores:**

- **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:** 8.9/10 (Category avg: 8.9/10)
- **Ease of Admin:** 9.0/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [Workato](https://www.g2.com/sellers/workato)
- **Company Website:** https://www.workato.com
- **Year Founded:** 2013
- **HQ Location:** Mountain View, California
- **Twitter:** @Workato (3,604 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/3675685 (1,348 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer, Senior Software Engineer
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 43% Mid-Market, 33% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (366 reviews)
- Integrations (245 reviews)
- Easy Integrations (232 reviews)
- Automation (198 reviews)
- Features (195 reviews)

**Cons:**

- Complexity (83 reviews)
- Learning Curve (77 reviews)
- Missing Features (77 reviews)
- Data Limitations (76 reviews)
- Expensive (71 reviews)

### 5. [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:** 93

**User Satisfaction Scores:**

- **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: 8.9/10)
- **Ease of Admin:** 8.7/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft)
- **Year Founded:** 1975
- **HQ Location:** Redmond, Washington
- **Twitter:** @microsoft (13,114,353 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (227,697 employees on LinkedIn®)
- **Ownership:** MSFT

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer, Software Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 60% Enterprise, 30% Mid-Market


#### Pros & Cons

**Pros:**

- Data Integration (7 reviews)
- Ease of Use (7 reviews)
- Connectors (6 reviews)
- Integrations (6 reviews)
- Scalability (5 reviews)

**Cons:**

- Debugging Difficulty (5 reviews)
- Difficult Debugging (4 reviews)
- Expensive (4 reviews)
- Feature Limitations (4 reviews)
- Complexity (3 reviews)

### 6. [SnapLogic Intelligent Integration Platform (IIP)](https://www.g2.com/products/snaplogic-intelligent-integration-platform-iip/reviews)
  SnapLogic is the leader in generative integration. As a pioneer in AI-led integration, the SnapLogic Platform accelerates digital transformation across the enterprise and empowers everyone to integrate faster and easier. Whether you are automating business processes, democratizing data, or delivering digital products and services, SnapLogic enables you to simplify your technology stack and take your enterprise further. Thousands of enterprises around the globe rely on SnapLogic to integrate, automate and orchestrate the flow of data across their business. Join the generative integration movement at snaplogic.com.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 370

**User Satisfaction Scores:**

- **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: 8.9/10)
- **Ease of Admin:** 8.6/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [SnapLogic](https://www.g2.com/sellers/snaplogic)
- **Company Website:** https://www.snaplogic.com
- **Year Founded:** 2006
- **HQ Location:** San Mateo, CA
- **Twitter:** @SnapLogic (7,354 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/210766/ (327 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer, Consultant
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 46% Enterprise, 36% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (85 reviews)
- Easy Integrations (70 reviews)
- Integrations (54 reviews)
- User Interface (50 reviews)
- Automation (43 reviews)

**Cons:**

- Performance Issues (31 reviews)
- Poor Performance (25 reviews)
- Technical Difficulties (25 reviews)
- Complexity (22 reviews)
- Error Reporting (22 reviews)

### 7. [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:** 367

**User Satisfaction Scores:**

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


**Seller Details:**

- **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 (2,225,864 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®)
- **Ownership:** NASDAQ: AMZN

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer, Senior Data Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 40% Enterprise, 39% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (7 reviews)
- Integrations (7 reviews)
- Easy Integrations (5 reviews)
- Fast Querying (5 reviews)
- Scalability (5 reviews)

**Cons:**

- Complexity (5 reviews)
- Feature Limitations (5 reviews)
- Software Limitations (5 reviews)
- Query Issues (4 reviews)
- Query Optimization (4 reviews)

### 8. [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

**User Satisfaction Scores:**

- **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: 8.9/10)
- **Ease of Admin:** 9.6/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [5X](https://www.g2.com/sellers/5x)
- **Year Founded:** 2020
- **HQ Location:** San Francisco
- **Twitter:** @DataWith5x (49 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/datawith5x/ (128 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Financial Services
  - **Company Size:** 56% Mid-Market, 40% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (28 reviews)
- Customer Support (18 reviews)
- Features (14 reviews)
- Integrations (13 reviews)
- Data Integration (10 reviews)

**Cons:**

- Steep Learning Curve (5 reviews)
- Complex Setup (4 reviews)
- Feature Limitations (4 reviews)
- Learning Curve (4 reviews)
- Difficult Setup (3 reviews)

### 9. [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

**User Satisfaction Scores:**

- **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: 8.9/10)
- **Ease of Admin:** 7.8/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Year Founded:** 1911
- **HQ Location:** Armonk, NY
- **Twitter:** @IBM (709,390 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®)
- **Ownership:** SWX:IBM

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer, Software Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 42% Enterprise, 33% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (30 reviews)
- User Interface (16 reviews)
- Data Management (15 reviews)
- Data Pipelining (15 reviews)
- Integrations (14 reviews)

**Cons:**

- Learning Curve (13 reviews)
- Expensive (10 reviews)
- Learning Difficulty (8 reviews)
- Slow Performance (8 reviews)
- Steep Learning Curve (8 reviews)

### 10. [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

**User Satisfaction Scores:**

- **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: 8.9/10)
- **Ease of Admin:** 8.2/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [IBM](https://www.g2.com/sellers/ibm)
- **Year Founded:** 1911
- **HQ Location:** Armonk, NY
- **Twitter:** @IBM (709,390 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1009/ (324,553 employees on LinkedIn®)
- **Ownership:** SWX:IBM

**Reviewer Demographics:**
  - **Top Industries:** Staffing and Recruiting, Computer Software
  - **Company Size:** 42% Mid-Market, 35% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (16 reviews)
- Features (9 reviews)
- Security (7 reviews)
- Automation (5 reviews)
- Integration Capabilities (5 reviews)

**Cons:**

- Complexity (10 reviews)
- Expensive (8 reviews)
- Difficult Learning (5 reviews)
- Pricing Issues (5 reviews)
- Learning Curve (4 reviews)

### 11. [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

**User Satisfaction Scores:**

- **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: 8.9/10)
- **Ease of Admin:** 8.8/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [Astronomer](https://www.g2.com/sellers/astronomer)
- **Company Website:** https://www.astronomer.io/
- **Year Founded:** 2018
- **HQ Location:** New York, US
- **Twitter:** @astronomerio (19,780 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/10019299 (4,630 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer, Senior Data Engineer
  - **Top Industries:** Information Technology and Services, Financial Services
  - **Company Size:** 47% Mid-Market, 38% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (25 reviews)
- Efficiency Improvement (14 reviews)
- User Interface (13 reviews)
- Automation (11 reviews)
- Deployment Ease (10 reviews)

**Cons:**

- Expensive (8 reviews)
- Learning Difficulty (8 reviews)
- Learning Curve (6 reviews)
- Difficult Learning (5 reviews)
- Feature Limitations (5 reviews)

### 12. [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

**User Satisfaction Scores:**

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


**Seller Details:**

- **Seller:** [Microsoft](https://www.g2.com/sellers/microsoft)
- **Year Founded:** 1975
- **HQ Location:** Redmond, Washington
- **Twitter:** @microsoft (13,114,353 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/microsoft/ (227,697 employees on LinkedIn®)
- **Ownership:** MSFT

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 45% Mid-Market, 32% Enterprise


#### Pros & Cons

**Pros:**

- Analytics (1 reviews)
- Automation (1 reviews)
- Cloud Integration (1 reviews)
- Cost-Effective (1 reviews)
- Data Integration (1 reviews)

**Cons:**

- Cost Estimation (1 reviews)
- Cost Management (1 reviews)
- Debugging Issues (1 reviews)
- Difficult Debugging (1 reviews)
- Expensive (1 reviews)

### 13. [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:** 201

**User Satisfaction Scores:**

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


**Seller Details:**

- **Seller:** [Fivetran](https://www.g2.com/sellers/fivetran)
- **Year Founded:** 2012
- **HQ Location:** Oakland, CA
- **Twitter:** @fivetran (5,737 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/fivetran/ (1,738 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer, Analytics Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 57% Mid-Market, 27% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (38 reviews)
- Features (22 reviews)
- Automation (19 reviews)
- Transformation (17 reviews)
- Integrations (15 reviews)

**Cons:**

- Limited Functionality (14 reviews)
- Dependency Issues (12 reviews)
- Steep Learning Curve (10 reviews)
- Error Handling (9 reviews)
- Error Reporting (9 reviews)

### 14. [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:** 316

**User Satisfaction Scores:**

- **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: 8.9/10)
- **Ease of Admin:** 9.4/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [Devart](https://www.g2.com/sellers/devart)
- **Year Founded:** 1997
- **HQ Location:** Wilmington, Delaware, USA
- **Twitter:** @DevartSoftware (1,739 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/800325/ (254 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** CEO, CTO
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 51% Small-Business, 40% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (50 reviews)
- Easy Integrations (34 reviews)
- Easy Setup (33 reviews)
- Setup Ease (31 reviews)
- Data Management (27 reviews)

**Cons:**

- Information Deficiency (8 reviews)
- Difficult Setup (7 reviews)
- Feature Limitations (7 reviews)
- Learning Curve (7 reviews)
- Poor Documentation (7 reviews)

### 15. [Control-M](https://www.g2.com/products/control-m/reviews)
  Control-M from BMC Software is a digital operations orchestration platform designed to help organizations connect applications, data pipelines, and infrastructure processes within a unified ecosystem. This solution is specifically tailored to manage complex hybrid environments, providing a robust framework for designing, automating, and governing workflows that span both on-premises and cloud technologies. By simplifying the management of operational dependencies, Control-M enables IT and business teams to maintain resilience, compliance, and efficiency at scale. The platform is particularly beneficial for organizations that require continuous operations, as it fosters collaboration among development, data, and operations teams through a shared environment. This collaborative approach enhances transparency and significantly reduces manual effort, allowing teams to focus on strategic initiatives rather than routine tasks. Control-M's orchestration capabilities facilitate the coordination of workloads across traditional systems, modern cloud applications, and emerging data technologies, ensuring that all components work seamlessly together. Centralized visibility and control empower teams to identify potential disruptions early, thereby ensuring smooth end-to-end process execution. Control-M incorporates predictive analytics and event-driven automation, which are essential for anticipating performance issues and adapting to changing business or system conditions. This proactive stance allows operations teams to maintain service levels and accelerate incident resolution without the burden of constant manual oversight. Furthermore, the platform's integration with DevOps and DataOps workflows ensures that automation efforts align with organizational goals, thereby supporting both innovation and governance. Industries such as finance, healthcare, manufacturing, and telecommunications widely utilize Control-M, where reliability, compliance, and operational continuity are paramount. By connecting people, systems, and data, Control-M transforms fragmented operational environments into cohesive, data-driven systems of execution. With BMC’s extensive expertise in intelligent automation, Control-M empowers enterprises to reduce complexity, enhance agility, and continuously deliver business value in an ever-evolving digital landscape. The platform stands out by providing a comprehensive solution that not only addresses current operational challenges but also prepares organizations for future demands.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 151

**User Satisfaction Scores:**

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


**Seller Details:**

- **Seller:** [BMC Software](https://www.g2.com/sellers/bmc-software)
- **Company Website:** https://www.bmc.com
- **Year Founded:** 1980
- **HQ Location:** Houston, TX
- **Twitter:** @BMCSoftware (48,041 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/1597/ (9,008 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Banking
  - **Company Size:** 52% Enterprise, 22% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (50 reviews)
- Automation (33 reviews)
- Features (32 reviews)
- Time-saving (31 reviews)
- Task Automation (27 reviews)

**Cons:**

- Complexity (35 reviews)
- Learning Curve (24 reviews)
- Complex UI (19 reviews)
- Difficult Learning (19 reviews)
- Expensive (19 reviews)

### 16. [ILUM](https://www.g2.com/products/ilum-ilum/reviews)
  Ilum: A Data Platform Built by Data Engineers, for Data Engineers Ilum is a Data Lakehouse platform that unifies data management, distributed processing, analytics, and AI workflows for AI engineers, data engineers, data scientists, and analysts. It belongs to the Data Platform, Data Lakehouse, and Data Engineering software categories and supports flexible deployment across cloud, on-premise, and hybrid environments. Ilum enables technical teams to build, operate, and scale modern data infrastructure using open standards. It integrates tools for batch processing, stream processing, notebook-based exploration, workflow orchestration, and business intelligence, All In a Single Platform. Ilum supports modern open table formats like Delta Lake, Apache Iceberg, Apache Hudi, and Apache Paimon. It also offers native integration with Apache Spark and Trino for compute, with Apache Flink support currently in development. Key features include: - SQL Editor: Query Delta, Iceberg, Hudi, or Spark SQL with autocomplete, result previews, and metadata inspection. - Data Lineage & Catalog: Visualize data flow using OpenLineage and explore datasets through a searchable Data Catalog. - Notebook Integration: Use built-in Jupyter notebooks pre-wired to Spark, metadata, and your data environment for exploration or modeling. - Spark Job Management: Submit, monitor, and debug Spark jobs with integrated logs, metrics, scheduling, and a built-in Spark History Server. - Trino Support: Run federated queries across multiple data sources using Trino directly from within Ilum. - Declarative Pipelines: Define repeatable ETL and analytics pipelines, with dependency tracking and recovery logic. - Automatic ERD Diagrams: Instantly generate ER diagrams from schemas to aid in data understanding and onboarding. - ML Experimentation & Tracking: Includes MLflow for managing experiments, tracking parameters, metrics, and artifacts, fully integrated with notebooks and data pipelines to streamline model development workflows. - AI Integration & Deployment: Supports both classical ML and modern AI use cases, including GenAI workflows, vector search, and embedding-based applications. Models can be registered, versioned, and deployed for inference within declarative pipelines. - Built-in AI Agent Interface: Ilum integrates, providing a GPT-style interface to interact with your data, trigger pipelines, generate SQL, or explore metadata using natural language, bringing GenAI capabilities directly into your data platform. - BI Dashboards: Native support for Apache Superset, with JDBC integration for Tableau, Power BI, and other BI tools. Additional highlights: - Multi-Cluster Management: Connect multiple Spark or Kubernetes clusters to scale and isolate workloads. - Fine-Grained Access Control: LDAP, OAuth2, and Hydra integration for secure, role-based access. - Hybrid Ready: Designed to replace Databricks or Cloudera in environments where cloud adoption is partial, regulated, or not possible.


  **Average Rating:** 4.9/5.0
  **Total Reviews:** 23

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.7/10 (Category avg: 8.9/10)
- **Quality of Support:** 9.5/10 (Category avg: 8.9/10)
- **Ease of Use:** 9.3/10 (Category avg: 8.9/10)
- **Ease of Admin:** 9.2/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [Ilum](https://www.g2.com/sellers/ilum)
- **Company Website:** https://ilum.cloud/
- **Year Founded:** 2019
- **HQ Location:** Santa Fe, US
- **Twitter:** @IlumCloud (19 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/ilum-cloud/ (4 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Telecommunications
  - **Company Size:** 52% Enterprise, 35% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (17 reviews)
- Features (17 reviews)
- Integrations (17 reviews)
- Setup Ease (16 reviews)
- Easy Integrations (15 reviews)

**Cons:**

- Complex Setup (9 reviews)
- Difficult Setup (9 reviews)
- Learning Curve (9 reviews)
- UX Improvement (8 reviews)
- Complexity (7 reviews)

### 17. [Elastic Stack](https://www.g2.com/products/elastic-stack/reviews)
  The Elastic Stack, commonly known as the ELK Stack, is a comprehensive suite of open-source tools designed for ingesting, storing, analyzing, and visualizing data in real-time. It comprises Elasticsearch, Kibana, Beats, and Logstash, enabling users to handle data from any source and in any format efficiently. Key Features and Functionality: - Elasticsearch: A distributed, JSON-based search and analytics engine that allows for rapid storage, search, and analysis of large volumes of data. - Kibana: An extensible user interface that provides powerful visualizations, dashboards, and management tools to interpret and present data effectively. - Beats and Logstash: Data ingestion tools that collect and process data from various sources, transforming and forwarding it to Elasticsearch for indexing. - Integrations: A multitude of pre-built integrations that facilitate seamless data collection and connection with the Elastic Stack, enabling quick insights. Primary Value and User Solutions: The Elastic Stack empowers organizations to harness the full potential of their data by providing a scalable and resilient platform for real-time search and analytics. It addresses challenges such as managing large datasets, ensuring high availability, and delivering relevant search results swiftly. By offering a unified solution for data ingestion, storage, analysis, and visualization, the Elastic Stack enables users to gain actionable insights, enhance operational efficiency, and make informed decisions based on their data.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 97

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 8.4/10 (Category avg: 8.9/10)
- **Quality of Support:** 8.1/10 (Category avg: 8.9/10)
- **Ease of Use:** 8.0/10 (Category avg: 8.9/10)
- **Ease of Admin:** 7.5/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [Elastic](https://www.g2.com/sellers/elastic)
- **Year Founded:** 2012
- **HQ Location:** San Francisco, CA
- **Twitter:** @elastic (64,544 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/814025/ (4,986 employees on LinkedIn®)
- **Ownership:** NYSE: ESTC

**Reviewer Demographics:**
  - **Who Uses This:** Software Engineer, Senior Software Engineer
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 45% Mid-Market, 34% Enterprise


#### Pros & Cons

**Pros:**

- Ease of Use (3 reviews)
- Flexibility (3 reviews)
- Log Management (3 reviews)
- Search Efficiency (3 reviews)
- Versatility (3 reviews)

**Cons:**

- Resource Management (3 reviews)
- Complexity Issues (2 reviews)
- Expensive (2 reviews)
- High Memory Usage (2 reviews)
- Learning Curve (2 reviews)

### 18. [Gathr.ai](https://www.g2.com/products/gathr-ai/reviews)
  Gathr.ai powers AI with complete data context for higher quality intelligence. With day-zero, high-fidelity data discourse, users can get data-backed answers to the ‘why’, ‘what-if’, and ‘how do I’ questions that drive business KPIs forward. This intelligence is delivered natively on top of the organization’s existing data estate — including data warehouses, databases, federated SQL engines, and operational systems. Leading businesses across industries also rely on Gathr.ai to build high-performance data pipelines, bespoke Data+AI solutions, and action-driven analytics experiences. Built for builders, Gathr.ai delivers agility, performance, and control. It snaps into the existing stack — integrating upstream and downstream systems with no extra plumbing. It gives developers starter-kit speed and full extension freedom.


  **Average Rating:** 4.8/5.0
  **Total Reviews:** 33

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 10.0/10 (Category avg: 8.9/10)
- **Quality of Support:** 9.8/10 (Category avg: 8.9/10)
- **Ease of Use:** 9.7/10 (Category avg: 8.9/10)
- **Ease of Admin:** 10.0/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [Gathr.ai](https://www.g2.com/sellers/gathr-ai)
- **Year Founded:** 2022
- **HQ Location:** Los Gatos, CA, US
- **LinkedIn® Page:** https://www.linkedin.com/company/gathr-one (73 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Associate Software Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 79% Mid-Market, 21% Enterprise


#### Pros & Cons

**Pros:**

- Integrations (9 reviews)
- Data Management (7 reviews)
- Drag (6 reviews)
- Ease of Use (6 reviews)
- Easy Integrations (6 reviews)

**Cons:**

- Access Issues (1 reviews)
- Connection Issues (1 reviews)
- Difficult Setup (1 reviews)
- Lack of Real-Time Data (1 reviews)
- Performance Optimization (1 reviews)

### 19. [Peliqan](https://www.g2.com/products/peliqan/reviews)
  Peliqan.io is an all-in-one AI-first data integration and automation platform designed for business teams, scale-ups and consultants. Unlike traditional data tools that demand heavy engineering effort, Peliqan enables both business users and technical teams to connect, manage, and activate their data in one collaborative environment - without requiring a dedicated data engineer. With 250+ built-in connectors, Peliqan connects to databases, SaaS business applications (ERP, CRM, Accounting, HRM/ATS etc.), cloud storage, files and APIs as well as on-prem data sources. New connectors are available on demand within 5 business days. Peliqan offers one-click ELT pipelines to the built-in data warehouse, or you can bring your own data warehouse. Peliqan supports all major data warehouses. Thanks to Peliqan’s Excel add-in, business users and consultants can work with real-time data in Excel. Analysts and power users can use Peliqan’s advanced SQL editor with the support of an AI assistant to transform data and prepare business-ready data sets, which can be used in any BI tool such as Microsoft Power BI, Metabase, Tableau, Qlik, Looker etc. Users can also set up Reverse ETL flows. Developers can go even further with Peliqan’s low-code environment, with a built-in virtual AI Data Engineer, where they can: - Build & Publish interactive data apps - Automate writebacks into source systems - Publish API endpoints for data sharing - Implement custom pipelines - Build out internal AI Agents By empowering business users, analysts, consultants and developers, Peliqan dramatically reduces reliance on IT support and speeds up decision-making. Peliqan is not just an ELT data pipeline tool, it’s a complete solution for data orchestration, automation, and activation. Peliqan also acts as the data foundation for Agentic AI, ensuring that AI agents work with trusted, up-to-date 360° views of customers, products, orders, and more - at the speed of a cloud data warehouse. Peliqan’s data warehouse provides an AI-ready data layer out-of-the-box including: - Automatic vectorizing of structured and non-structured data for RAG (Retrieval-Augmented Generation) - Text-to-SQL - MCP Gateway In today’s landscape, a data warehouse is no longer just for BI - it’s the foundation for both BI and AI. With Peliqan.io, organizations can integrate, analyze, and activate their data seamlessly, empowering both humans and AI agents to make smarter, faster decisions.


  **Average Rating:** 4.8/5.0
  **Total Reviews:** 71

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 8.9/10)
- **Quality of Support:** 9.1/10 (Category avg: 8.9/10)
- **Ease of Use:** 9.3/10 (Category avg: 8.9/10)
- **Ease of Admin:** 8.7/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [Peliqan](https://www.g2.com/sellers/peliqan)
- **Company Website:** https://peliqan.io
- **Year Founded:** 2022
- **HQ Location:** Gent
- **Twitter:** @Peliqan_io (8 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/peliqan-data (27 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 47% Mid-Market, 42% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (45 reviews)
- Integrations (43 reviews)
- Easy Integrations (37 reviews)
- Connectors (36 reviews)
- Data Management (36 reviews)

**Cons:**

- Learning Difficulty (18 reviews)
- Required Technical Skills (12 reviews)
- Feature Limitations (10 reviews)
- Learning Curve (10 reviews)
- Steep Learning Curve (9 reviews)

### 20. [AWS Glue](https://www.g2.com/products/aws-glue/reviews)
  AWS Glue is a serverless data integration service that makes it easier for analytics users to discover, prepare, move, and integrate data from multiple sources for analytics, machine learning, and application develop-ment. You can discover and connect to 70+ diverse data sources, manage your data in a centralized data catalog, and visually create, run, and monitor ETL pipelines to load data into your data lakes. You can im-mediately search and query catalogued data using Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum.


  **Average Rating:** 4.3/5.0
  **Total Reviews:** 191

**User Satisfaction Scores:**

- **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:** 8.4/10 (Category avg: 8.9/10)
- **Ease of Admin:** 8.3/10 (Category avg: 8.5/10)


**Seller Details:**

- **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 (2,225,864 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®)
- **Ownership:** NASDAQ: AMZN

**Reviewer Demographics:**
  - **Who Uses This:** Data Engineer, Software Engineer
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 48% Enterprise, 29% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (6 reviews)
- Data Integration (3 reviews)
- ETL Solutions (3 reviews)
- Features (3 reviews)
- Simple (3 reviews)

**Cons:**

- Slow Performance (3 reviews)
- Debugging Difficulty (2 reviews)
- Difficult Debugging (2 reviews)
- Performance Issues (2 reviews)
- Time-Consuming (2 reviews)

### 21. [Coefficient](https://www.g2.com/products/coefficient/reviews)
  Coefficient is a new way to work with your company data better, faster, and more accurately without ever leaving your spreadsheet, integrating with the tools you already use. Install the Coefficient Excel or Google Sheets extension and use it in a new or existing sheet in seconds. Once installed, Coefficient lives as a sidebar companion so your company data is only a couple of clicks away at any time. Any data source that you work with is available directly in your Coefficient sidebar – such as Salesforce, HubSpot, Snowflake, NetSuite, QuickBooks, MySQL, and Looker – with the ability to consolidate your data from multiple systems into one spreadsheet. Use Coefficient filters to easily customize your imports to only work with the data you need, keeping your spreadsheets performant. Quickly go back anytime to add more data in the same report. Never rebuild the same analysis twice by keeping your data up to date with scheduled updates. And, use Coefficient alerts to trigger Slack or email messages anytime your spreadsheet updates. Now, you can turn your spreadsheet into the most flexible, powerful monitoring system across all of your company data. Say “goodbye” to manual data workflows and “hello” to connected spreadsheets.


  **Average Rating:** 4.7/5.0
  **Total Reviews:** 177

**User Satisfaction Scores:**

- **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:** 9.2/10 (Category avg: 8.9/10)
- **Ease of Admin:** 9.2/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [Coefficient](https://www.g2.com/sellers/coefficient)
- **Company Website:** https://coefficient.io/
- **Year Founded:** 2020
- **HQ Location:** Palo Alto, CA
- **Twitter:** @coefficient_io (351 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/coefficientworks/ (70 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 49% Mid-Market, 36% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (72 reviews)
- Automation (42 reviews)
- Integrations (42 reviews)
- Time-saving (36 reviews)
- Easy Integrations (31 reviews)

**Cons:**

- Limited Features (18 reviews)
- Feature Limitations (17 reviews)
- Limitations (13 reviews)
- Missing Features (12 reviews)
- Integration Issues (11 reviews)

### 22. [Weld](https://www.g2.com/products/weld-weld/reviews)
  Weld delivers an ultra-fast, secure, and reliable way to move data from all your tools, applications, and databases into cloud data warehouses, such as Snowflake, BigQuery, and Databricks. Deploy data pipelines in minutes with connectors that adapt to schema changes, detect duplicates, self-heal on failure, and run without maintenance, so your data team can focus on insights, not infrastructure.


  **Average Rating:** 4.8/5.0
  **Total Reviews:** 101

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.7/10 (Category avg: 8.9/10)
- **Quality of Support:** 9.7/10 (Category avg: 8.9/10)
- **Ease of Use:** 8.8/10 (Category avg: 8.9/10)
- **Ease of Admin:** 9.1/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [Weld](https://www.g2.com/sellers/weld-733aad41-2e36-4f42-9349-7d847f41d873)
- **Year Founded:** 2021
- **HQ Location:** Copenhagen, DK
- **Twitter:** @WeldHQ (98 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/weldhq/ (97 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** CEO
  - **Top Industries:** Computer Software, Retail
  - **Company Size:** 58% Small-Business, 41% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (16 reviews)
- Customer Support (13 reviews)
- Features (12 reviews)
- Automation (11 reviews)
- Data Integration (9 reviews)

**Cons:**

- Limited Connectors (8 reviews)
- Feature Limitations (6 reviews)
- Missing Features (5 reviews)
- Limited Integrations (4 reviews)
- Connectivity Issues (3 reviews)

### 23. [Keboola](https://www.g2.com/products/keboola/reviews)
  Keboola is the unified AI & Data orchestration platform that empowers organizations to turn data into business value faster and more securely than ever. It acts as your agentic AI co-pilot for data workflows, automating everything from integration to insight. With Keboola, Engineering teams, digital natives, startup CTOs, and innovation leads alike can rapidly build and manage data products, applications, AI agents, and autonomous crews seamlessly—without sacrificing compliance or security. Built for Every Data Persona: Whether you’re a seasoned data engineer or a business analyst, Keboola is built to make you successful. Data engineers love the open extensibility – code in SQL, Python, R, or use our API/CLI to tailor any step. Analysts and non-coders love the self-service UI – point-and-click data pipeline assembly, drag-and-drop transformations with text to SQL on semantic layer, and one-click deployment of pre-built workflows. Collaboration is seamless, with shared workspaces and sandboxes that let teams build and share data products freely without affecting production. What sets us apart? With Keboola, you can build and manage data products, applications, AI agents, and autonomous crews seamlessly—without sacrificing compliance or security. 🔗 Unified Connectivity: Effortlessly connect to 700+ data sources (databases, SaaS apps, and APIs) .Real-time Streams, Change Data Capture or batch. 🤖 Agentic AI Orchestration: Keboola’s AI-driven engine orchestrates data pipelines and ML workflows automatically. It can trigger the next steps based on data events or quality checks, and dynamically allocate resources. Think of it as an autopilot for your data & AI, ensuring pipelines run optimally and recover on their own from hiccups. 🛡️ Built-in Governance & Security: Every dataset and process in Keboola is governed. Fine-grained access controls, lineage tracking, and audit logs are native to the platform. Compliance is simplified – SOC 2, GDPR, and industry standards are supported out-of-the-box. 🚀 Rapid Development & Prototyping: Innovate without constraints. Spin up isolated dev/test sandboxes in seconds to prototype new data products or AI models. 🌎 Multi-Cloud Scalability: Built on a cloud-native architecture, Keboola scales with your needs. Deploy on your preferred cloud (AWS, Azure, GCP) and let Keboola handle the heavy lifting – elastic compute, parallel processing, and workload optimization. Start small and scale to enterprise workloads globally, without re-architecting. 💡 End-to-End Insight Activation: Because Keboola unifies your data pipelines, analytics, and ML, you can go from raw data to AI-driven insights in record time. Why Keboola: Instead of cobbling together multiple tools for integration, ETL/ELT, data catalogs, automation, and AI, Keboola delivers a single platform that does it all – with unprecedented ease and intelligence. Our customers have replaced 5-10 disparate tools with Keboola’s unified solution, drastically accelerating delivery. Join 30,000+ companies and industry leaders who use Keboola to supercharge their data teams. Whether you need to deliver data to AI Agents, streamline a complex data estate, or build and share data products to business, Keboola’s AI orchestration platform adapts to your needs – freeing you to focus on innovation and business growth.


  **Average Rating:** 4.6/5.0
  **Total Reviews:** 134

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 8.9/10)
- **Quality of Support:** 9.5/10 (Category avg: 8.9/10)
- **Ease of Use:** 8.6/10 (Category avg: 8.9/10)
- **Ease of Admin:** 8.1/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [Keboola](https://www.g2.com/sellers/keboola)
- **Company Website:** https://www.keboola.com
- **Year Founded:** 2008
- **HQ Location:** Prague
- **Twitter:** @keboola (2,006 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/keboola/ (113 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Data Analyst, Data Engineer
  - **Top Industries:** Information Technology and Services, Marketing and Advertising
  - **Company Size:** 64% Mid-Market, 21% Small-Business


#### Pros & Cons

**Pros:**

- Ease of Use (35 reviews)
- Features (27 reviews)
- Data Management (26 reviews)
- Integrations (26 reviews)
- Customer Support (25 reviews)

**Cons:**

- Learning Curve (14 reviews)
- Complexity (13 reviews)
- Steep Learning Curve (11 reviews)
- Data Management (9 reviews)
- UX Improvement (9 reviews)

### 24. [AWS Lake Formation](https://www.g2.com/products/aws-lake-formation/reviews)
  AWS Lake Formation is a fully managed service to build, manage, secure, and share data in data lakes in days. You can centralize security and governance, and enable data sharing across the organization.


  **Average Rating:** 4.4/5.0
  **Total Reviews:** 31

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.0/10 (Category avg: 8.9/10)
- **Quality of Support:** 8.3/10 (Category avg: 8.9/10)
- **Ease of Use:** 8.7/10 (Category avg: 8.9/10)
- **Ease of Admin:** 8.0/10 (Category avg: 8.5/10)


**Seller Details:**

- **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 (2,225,864 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®)
- **Ownership:** NASDAQ: AMZN

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 50% Small-Business, 33% Enterprise


### 25. [CData Connectors](https://www.g2.com/products/cdata-connectors/reviews)
  CData Drivers & Connectors is a data connectivity platform that provides standards-based drivers and connectors for real-time access to over 300 SaaS applications, databases, APIs, and big data sources. The solution enables organizations to integrate live data from any source into their existing BI tools, analytics platforms, ETL processes, and custom applications using familiar interfaces like ODBC, JDBC, ADO.NET, and Python without requiring data replication or complex custom coding. This data integration software serves enterprises, mid-market companies, and development teams who need to connect disparate data sources for business intelligence, reporting, analytics, application development, and data warehousing initiatives. Users can access live data from popular platforms including Salesforce, SharePoint, QuickBooks, SAP, NetSuite, Snowflake, Amazon Redshift, and MongoDB through a unified SQL interface that eliminates the technical complexity of API integration. Key Features and Benefits: • Universal Data Connectivity: Access 300+ data sources through a single platform with support for major database systems, cloud applications, NoSQL databases, and web APIs, reducing integration complexity and development time • Standards-Based Integration: Native support for ODBC, JDBC, ADO.NET, Python, Excel, SSIS, and PowerShell enables seamless integration with existing tools and applications without requiring specialized technical expertise • Live Data Access: Real-time connectivity ensures users always work with current information without data movement, replication, or synchronization delays, maintaining data accuracy and reducing storage costs • High-Performance Architecture: Optimized drivers feature dynamic metadata discovery, intelligent caching, query pushdown optimization, and parallel processing capabilities that deliver enterprise-grade performance for large-scale data operations The platform processes over 2.7 billion queries monthly across 7,000+ enterprise customers and has been recognized in the 2024 Gartner Magic Quadrant for Data Integration Tools, demonstrating proven scalability and market validation for mission-critical data connectivity requirements.


  **Average Rating:** 4.2/5.0
  **Total Reviews:** 16

**User Satisfaction Scores:**

- **Has the product been a good partner in doing business?:** 9.2/10 (Category avg: 8.9/10)
- **Quality of Support:** 9.3/10 (Category avg: 8.9/10)
- **Ease of Use:** 8.3/10 (Category avg: 8.9/10)
- **Ease of Admin:** 7.8/10 (Category avg: 8.5/10)


**Seller Details:**

- **Seller:** [CData](https://www.g2.com/sellers/cdata)
- **Company Website:** https://cdata.com
- **Year Founded:** 2014
- **HQ Location:** Chapel Hill, NC
- **Twitter:** @cdatasoftware (2,005 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/cdatasoftware/ (496 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 31% Enterprise, 31% Mid-Market


#### Pros & Cons

**Pros:**

- Ease of Use (9 reviews)
- Connectors (6 reviews)
- Easy Setup (5 reviews)
- Implementation Ease (3 reviews)
- Integrations (3 reviews)

**Cons:**

- Expensive (5 reviews)
- Connection Issues (3 reviews)
- Poor Performance (3 reviews)
- Data Inaccuracy (2 reviews)
- Large Datasets Management (2 reviews)



## Parent Category

[Cloud Data Integration Software](https://www.g2.com/categories/cloud-data-integration)



## Related Categories

- [ETL Tools](https://www.g2.com/categories/etl-tools)
- [iPaaS Software](https://www.g2.com/categories/ipaas)
- [Data Extraction Tools](https://www.g2.com/categories/data-extraction-tools)



---

## Buyer Guide

### 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. 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?

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.