Learn More About Big Data Integration Platforms
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: 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: 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: 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.
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.
Big Data Integration Platforms FAQs
Most Popular FAQs
Which big data integration software has the best reviews?
Across the 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.
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ILUM: 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.
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Orchestra: A smaller sample so far, but its clean, modern interface and built-in agents come up repeatedly as reasons teams stick with it.
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Workato: 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.
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Alteryx: Carries the largest review volume in this category, with a fully visual workflow that lets you watch data move through each processing step.
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Snowflake: Separates storage from compute, letting multiple teams query the same data simultaneously without one workload slowing down another.
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Google Cloud BigQuery: 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.
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Azure Data Factory: Connects on-premises databases with cloud storage and analytics through built-in connectors and integration runtime options, orchestrating pipelines centrally across both environments.
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IBM StreamSets: Built for DataOps in hybrid environments specifically, with pipeline monitoring and governance for streaming workloads that need to move continuously across systems.
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AWS Glue: 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.
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Skyvia: A pipeline gets running quickly, cutting out the wait that used to come with pulling data from SaaS tools like Salesforce.
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ILUM: 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.
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Snowflake: 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.
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Maia: A rich library of components and low-code pipelines make it easy to pick up even for teams new to data integration.
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SnapLogic Intelligent Integration Platform (IIP): Its library of pre-built "snaps" connects to a wide range of data sources and destinations without writing custom code for each one.
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Adverity: 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.
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Snowflake: 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."
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Denodo: 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.
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Cloudera: 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.
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Snowflake: 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.
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Cloudera: 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.
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Alteryx: 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 small business segment of Big Data Integration Platforms, the platforms that come up most often are the ones built for lean teams without a large data engineering budget.
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Skyvia: 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.
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EazyDI: A newer name in this data set, though its low-code approach let operations and analytics teams collaborate without building custom integrations from scratch.
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5X: 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 small business big data integration tools tend to prioritize a platform that a lean team can run without dedicated data engineering headcount.
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Workato: Builds flows that stay accessible even for non-technical people, which matters when a startup doesn't have a dedicated integration engineer yet.
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Astro by Astronomer: Handles the infrastructure, monitoring, and upgrades behind Airflow, freeing up a small engineering team to focus on actual data work instead of managing orchestration.
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Alteryx: 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.
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Maia: New users pick it up quickly thanks to a simple layout and low-code pipelines that don't require deep integration experience up front.
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dbt: All a team really needs is SQL, and its lineage feature makes it easy to see exactly how data flows through each transformation.
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Workato: 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.
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Peliqan: 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.
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Coefficient: Pulls data straight into Google Sheets and keeps it synced automatically, with no separate infrastructure for a small team to stand up.
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Skyvia: 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.
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Keboola: Ease of use and simplicity stand out, with connectors that are particularly valuable for ingestion and customer support that stays genuinely active.
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Skyvia: Built for exactly this situation, a small team with one analytics engineer handling data for the whole company.
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Peliqan: 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 Enterprise segment of Big Data Integration Platforms, a smaller set of platforms have the review volume from large organizations to back up a strong rating.
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Workato: Holds up well at the enterprise level, with the same clean, accessible interface that smaller teams rely on scaling across much larger organizations.
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Alteryx: Carries a large enterprise review base, with its visual workflow and broad integrations continuing to hold up as data volume grows.
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Snowflake: 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.
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Workato: Enterprise accounts report support and administrative scores among the strongest in the category.
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Snowflake: Its architecture keeps performance consistent even as more teams and workloads get added, which large organizations lean on for reliability.
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Astro by Astronomer: 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.
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Azure Data Factory: Purpose-built to orchestrate pipelines centrally across on-premises databases and cloud storage, which is exactly the coordination large hybrid rollouts need.
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IBM StreamSets: Its governance and pipeline monitoring are designed specifically for DataOps in hybrid environments at enterprise scale.
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AWS Glue: 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.
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dbt: Its lineage feature makes it possible to trace exactly how data flows through every transformation, which large teams rely on for audit and troubleshooting.
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ILUM: 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.
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EazyDI: 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.
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Control-M: A single, unified view lets teams track workflows across multiple platforms at once, cutting down manual monitoring and catching operational bottlenecks early.
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Astro by Astronomer: Makes managing Airflow at scale effortless by handling infrastructure, monitoring, and upgrades that would otherwise fall on an internal platform team.
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Azure Data Factory: Centralizes orchestration across data-driven workflows, giving large organizations one place to schedule and monitor pipelines spanning multiple systems.
