# 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** ### CData Connect AI CData Connect AI is the first managed MCP platform built for enterprise AI. It delivers live, governed access to hundreds of data sources—databases, SaaS apps, ERPs, and APIs—through a standardized relational interface. It closes three critical gaps: Connectivity with real-time, in-place data access and full read/write capabilities; Context through semantic intelligence so AI understands your business terms and data relationships; and Control via passthrough authentication, audit trails, and policy enforcement. The result is AI that can act on enterprise data—securely, accurately, and at scale. [Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=paid_promo&secure%5Bad_slot%5D=category_product_list&secure%5Bcategory_id%5D=1186&secure%5Bchosen_at%5D=2026-07-28T21%3A45%3A31Z&secure%5Bmedium%5D=sponsored&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=151045&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%3Fopen_modal_url%3D%252Fproducts%252Fascend-io-ascend-io%252Fwishlists%253Fhost_path%253D%25252Fcategories%25252Fbig-data-integration-platforms%2526source%253Dcategory&secure%5Btoken%5D=5aa8332a0e5c4d2cf0cc13d43dd45ed300b3aa630bdec2914561eb640f1f70e7&secure%5Burl%5D=https%3A%2F%2Fwww.cdata.com%2Fai%2F&secure%5Burl_type%5D=paid_promos) [ Google Cloud BigQuery ](https://www.g2.com/products/google-cloud-bigquery/reviews) By [Google](https://www.g2.com/sellers/google) [ 4.5/5(1,223) ](https://www.g2.com/products/google-cloud-bigquery/reviews) What do users say? Users consistently praise the fast performance and serverless architecture of Google Cloud BigQuery, which allows for efficient handling of large datasets without the need for infrastructure managemen Pros and Cons [ Ease of Use (129) ](https://www.g2.com/products/google-cloud-bigquery/reviews?qs=pros-and-cons)[ Expensive (112) ](https://www.g2.com/products/google-cloud-bigquery/reviews?qs=pros-and-cons) [ Alteryx ](https://www.g2.com/products/alteryx/reviews) By [Alteryx](https://www.g2.com/sellers/alteryx) [ 4.6/5(886) ](https://www.g2.com/products/alteryx/reviews) What do users say? Users consistently praise the intuitive drag-and-drop interface of Alteryx, which simplifies complex data preparation and automation tasks, allowing for significant time savings. The platform's abilit Pros and Cons [ Ease of Use (333) ](https://www.g2.com/products/alteryx/reviews?qs=pros-and-cons)[ Expensive (88) ](https://www.g2.com/products/alteryx/reviews?qs=pros-and-cons) [ Snowflake ](https://www.g2.com/products/snowflake/reviews) By [Snowflake, Inc.](https://www.g2.com/sellers/snowflake-inc) [ 4.5/5(760) ](https://www.g2.com/products/snowflake/reviews) What do users say? Users consistently praise Snowflake for its ease of use and scalability, allowing teams to manage large datasets without heavy infrastructure. The platform's separation of compute and storage enhances Pros and Cons [ Ease of Use (183) ](https://www.g2.com/products/snowflake/reviews?qs=pros-and-cons)[ Expensive (91) ](https://www.g2.com/products/snowflake/reviews?qs=pros-and-cons) [ Workato ](https://www.g2.com/products/workato/reviews) By [Workato](https://www.g2.com/sellers/workato) [ 4.7/5(777) ](https://www.g2.com/products/workato/reviews) What do users say? Users consistently praise the ease of use and intuitive interface of Workato, which allows both technical and non-technical users to create complex automations effortlessly. The platform's extensive l Pros and Cons [ Ease of Use (239) ](https://www.g2.com/products/workato/reviews?qs=pros-and-cons)[ Complexity (69) ](https://www.g2.com/products/workato/reviews?qs=pros-and-cons) [ Azure Data Factory ](https://www.g2.com/products/azure-data-factory/reviews) By [Microsoft](https://www.g2.com/sellers/microsoft) [ 4.6/5(100) ](https://www.g2.com/products/azure-data-factory/reviews) What do users say? Users consistently praise the ease of use and visual interface of Azure Data Factory, which simplifies the creation and management of complex data pipelines. The low-code environment allows for effici Pros and Cons [ Data Integration (7) ](https://www.g2.com/products/azure-data-factory/reviews?qs=pros-and-cons)[ Debugging Difficulty (5) ](https://www.g2.com/products/azure-data-factory/reviews?qs=pros-and-cons) [ SnapLogic ](https://www.g2.com/products/snaplogic-intelligent-integration-platform-iip/reviews) By [SnapLogic](https://www.g2.com/sellers/snaplogic) [ 4.4/5(400) ](https://www.g2.com/products/snaplogic-intelligent-integration-platform-iip/reviews) What do users say? Users consistently praise SnapLogic for its ease of use and quick implementation, allowing both technical and non-technical users to build integrations efficiently. The platform's drag-and-drop interf Pros and Cons [ Ease of Use (85) ](https://www.g2.com/products/snaplogic-intelligent-integration-platform-iip/reviews?qs=pros-and-cons)[ Performance Issues (31) ](https://www.g2.com/products/snaplogic-intelligent-integration-platform-iip/reviews?qs=pros-and-cons) [ Maia ](https://www.g2.com/products/matillion-maia/reviews) By [Matillion](https://www.g2.com/sellers/matillion) [ 4.5/5(125) ](https://www.g2.com/products/matillion-maia/reviews) What do users say? Users consistently praise Maia for its ease of use and ability to streamline workflows, making it a valuable tool for automating data processes. The intuitive interface and quick setup allow teams to Pros and Cons [ Ease of Use (10) ](https://www.g2.com/products/matillion-maia/reviews?qs=pros-and-cons)[ Feature Limitations (9) ](https://www.g2.com/products/matillion-maia/reviews?qs=pros-and-cons) [ Amazon Redshift ](https://www.g2.com/products/amazon-redshift/reviews) By [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd) [ 4.3/5(404) ](https://www.g2.com/products/amazon-redshift/reviews) What do users say? Users consistently praise the scalability and performance of Amazon Redshift, noting its ability to handle large datasets efficiently and integrate seamlessly with other AWS services. Many appreciate Pros and Cons [ Fast Querying (5) ](https://www.g2.com/products/amazon-redshift/reviews?qs=pros-and-cons)[ Feature Limitations (4) ](https://www.g2.com/products/amazon-redshift/reviews?qs=pros-and-cons) [ 5X ](https://www.g2.com/products/5x/reviews) By [5X](https://www.g2.com/sellers/5x) [ 4.9/5(81) ](https://www.g2.com/products/5x/reviews) What do users say? Users consistently praise the platform for its ease of use and exceptional support, which streamline data management and integration processes. Many appreciate how it consolidates various data tools i Pros and Cons [ Ease of Use (20) ](https://www.g2.com/products/5x/reviews?qs=pros-and-cons)[ Steep Learning Curve (5) ](https://www.g2.com/products/5x/reviews?qs=pros-and-cons) [ Astro by Astronomer ](https://www.g2.com/products/astro-by-astronomer/reviews) By [Astronomer](https://www.g2.com/sellers/astronomer) [ 4.5/5(136) ](https://www.g2.com/products/astro-by-astronomer/reviews) What do users say? Users consistently praise the product for its ease of use and excellent support, highlighting how it simplifies Airflow management and allows teams to focus on building data pipelines rather than infr Pros and Cons [ Ease of Use (25) ](https://www.g2.com/products/astro-by-astronomer/reviews?qs=pros-and-cons)[ Expensive (8) ](https://www.g2.com/products/astro-by-astronomer/reviews?qs=pros-and-cons) [ IBM webMethods B2B ](https://www.g2.com/products/ibm-webmethods-b2b/reviews) By [IBM](https://www.g2.com/sellers/ibm) [ 4.5/5(57) ](https://www.g2.com/products/ibm-webmethods-b2b/reviews) What do users say? Users consistently praise the product for its ease of use and strong security features, which facilitate seamless integration and data exchange between business partners. The platform's ability to aut Pros and Cons [ Ease of Use (16) ](https://www.g2.com/products/ibm-webmethods-b2b/reviews?qs=pros-and-cons)[ Complexity (10) ](https://www.g2.com/products/ibm-webmethods-b2b/reviews?qs=pros-and-cons) [ Azure Synapse Analytics ](https://www.g2.com/products/azure-synapse-analytics/reviews) By [Microsoft](https://www.g2.com/sellers/microsoft) [ 4.4/5(38) ](https://www.g2.com/products/azure-synapse-analytics/reviews) What do users say? Users consistently praise the ease of use and scalability of Azure Synapse Analytics, highlighting its ability to integrate various data services seamlessly. The platform's comprehensive features allo Pros and Cons [ Analytics (1) ](https://www.g2.com/products/azure-synapse-analytics/reviews?qs=pros-and-cons)[ Cost Estimation (1) ](https://www.g2.com/products/azure-synapse-analytics/reviews?qs=pros-and-cons) [ dbt ](https://www.g2.com/products/dbt/reviews) By [dbt Labs](https://www.g2.com/sellers/dbt-labs) [ 4.7/5(209) ](https://www.g2.com/products/dbt/reviews) What do users say? Users consistently praise ease of use and seamless integration with various data platforms, highlighting how dbt simplifies data transformation processes and enhances collaboration among teams. The to Pros and Cons [ Ease of Use (34) ](https://www.g2.com/products/dbt/reviews?qs=pros-and-cons)[ Limited Functionality (13) ](https://www.g2.com/products/dbt/reviews?qs=pros-and-cons) [ IBM StreamSets ](https://www.g2.com/products/ibm-streamsets/reviews) By [IBM](https://www.g2.com/sellers/ibm) [ 4/5(116) ](https://www.g2.com/products/ibm-streamsets/reviews) What do users say? Users consistently praise the user-friendly interface and drag-and-drop functionality of IBM StreamSets, which simplifies the creation and management of data pipelines. The platform's ability to handl Pros and Cons [ Ease of Use (30) ](https://www.g2.com/products/ibm-streamsets/reviews?qs=pros-and-cons)[ Learning Curve (13) ](https://www.g2.com/products/ibm-streamsets/reviews?qs=pros-and-cons) [ Skyvia ](https://www.g2.com/products/skyvia/reviews) By [Devart](https://www.g2.com/sellers/devart) [ 4.8/5(324) ](https://www.g2.com/products/skyvia/reviews) What do users say? Users consistently praise the ease of use and reliable automation of Skyvia, highlighting how quickly they can set up data integrations without needing extensive technical knowledge. The no-code inter Pros and Cons [ Ease of Use (46) ](https://www.g2.com/products/skyvia/reviews?qs=pros-and-cons)[ Information Deficiency (8) ](https://www.g2.com/products/skyvia/reviews?qs=pros-and-cons) - ‹ Prev‹ Prev - 1 - [2](/categories/big-data-integration-platforms?open_modal_url=%2Fproducts%2Fascend-io-ascend-io%2Fwishlists%3Fhost_path%3D%252Fcategories%252Fbig-data-integration-platforms%26source%3Dcategory&order=g2_score&page=2#product-list) - [3](/categories/big-data-integration-platforms?open_modal_url=%2Fproducts%2Fascend-io-ascend-io%2Fwishlists%3Fhost_path%3D%252Fcategories%252Fbig-data-integration-platforms%26source%3Dcategory&order=g2_score&page=3#product-list) - [4](/categories/big-data-integration-platforms?open_modal_url=%2Fproducts%2Fascend-io-ascend-io%2Fwishlists%3Fhost_path%3D%252Fcategories%252Fbig-data-integration-platforms%26source%3Dcategory&order=g2_score&page=4#product-list) - [5](/categories/big-data-integration-platforms?open_modal_url=%2Fproducts%2Fascend-io-ascend-io%2Fwishlists%3Fhost_path%3D%252Fcategories%252Fbig-data-integration-platforms%26source%3Dcategory&order=g2_score&page=5#product-list) - … - [8](/categories/big-data-integration-platforms?open_modal_url=%2Fproducts%2Fascend-io-ascend-io%2Fwishlists%3Fhost_path%3D%252Fcategories%252Fbig-data-integration-platforms%26source%3Dcategory&order=g2_score&page=8#product-list) - [9](/categories/big-data-integration-platforms?open_modal_url=%2Fproducts%2Fascend-io-ascend-io%2Fwishlists%3Fhost_path%3D%252Fcategories%252Fbig-data-integration-platforms%26source%3Dcategory&order=g2_score&page=9#product-list) - [Next ›Next ›](/categories/big-data-integration-platforms?open_modal_url=%2Fproducts%2Fascend-io-ascend-io%2Fwishlists%3Fhost_path%3D%252Fcategories%252Fbig-data-integration-platforms%26source%3Dcategory&order=g2_score&page=2#product-list) Spotlight Categories [Retail POS Systems](https://www.g2.com/categories/retail-pos) [Video Conferencing Software](https://www.g2.com/categories/video-conferencing) [Contact Center Workforce Software](https://www.g2.com/categories/contact-center-workforce) [Time Tracking Software](https://www.g2.com/categories/time-tracking-software) [Product Analytics Software](https://www.g2.com/categories/product-analytics) Similar Categories - [Cloud Migration](/categories/cloud-migration) - [Embedded Integration Platforms](/categories/embedded-integration-platforms) - [Enterprise Service Bus (ESB)](/categories/enterprise-service-bus-esb) - [ETL Tools](/categories/etl-tools) - [iPaaS](/categories/ipaas) - [Reverse ETL](/categories/reverse-etl) - [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.