6 Data Integration Tools: Key Features, Benefits & Top Picks
Data flows nonstop in every business. Some of it comes from: customer sign-ups, sales platforms, and day-to-day operations. When each system keeps its own version, the odds of numbers not matching are higher.
Data integration tools fix that and link sources into a single view. You get faster reports, fewer manual edits, and cleaner inputs that improve data quality. Other platforms even push updates the moment they happen, so you get real-time data integration for dashboards and alerts.
In this article, you'll learn about the top data integration tools and which options fit different needs.
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What Is Data Integration?
Data integration brings information together so you can actually use it. In practice, it's the process for combining data from several disparate sources into a single setup that supports precise reporting and planning.
The stages follow a typical way:
- Pull records from source systems.
- Run data preparation to fix errors and line up formats.
- Do data transformation to shape values into something consistent.
- Transfer data into a data warehouse or data lake.
- Keep it up to date with either batches or nonstop data ingestion.
Different methods cover different needs. ETL handles cleaning before loading, while ELT relies on cloud systems to process raw records. APIs link apps for direct sync, and streaming allows instant updates.
All of it matters not only for reporting but also for future work, since machine learning depends on large, reliable datasets.
Benefits of Using Data Integration Tools
When data stays locked in data silos, your reports turn messy. Connecting everything through robust data integration gives you a single, trusted view. You don't waste hours second-guessing numbers because the information already lines up.
Day-to-day work gets easier, too. Automated flows cut down on mistakes that come from manual data entry, and you can spend more time on real data operations. Integration also raises the quality of data management since records move in consistent formats and errors get flagged early.
You can pull in unstructured data too, such as emails or logs, and combine it with structured records for deeper insights. Alongside that, data migration tools move data between computers, storage systems, or application formats, so old systems can shift into modern platforms without losing history.
With everything in place, you cut costs, meet compliance needs, and keep your data ready for whatever comes next.
Types of Data Integration Software
Integration tools vary by method and setup, and each fits different needs. Common categories include:
- ETL tools handle data extraction, clean records, and finish by loading data into a warehouse.
- ELT tools move raw records directly into cloud-based data warehouses, using the warehouse itself for heavy processing.
- Replication tools manage constant data movement between systems, which helps with backups and disaster recovery.
- Virtualization tools create a single view without moving the information physically.
- Streaming tools process events as they arrive, which is useful for IoT or real-time analytics.
- Change data capture tools track inserts, updates, and deletes to keep systems aligned.
Some setups extend further. Enterprise data fabric combines multiple methods to manage complex projects. Deployment also varies, from on-premises systems that provide control to open-source frameworks that give direct control over data structures to cloud services that scale quickly.
6 Best Data Integration Tools in 2025
Selecting the right data integration tool means looking beyond features: robust customer support, metadata management, and a data catalog for the organization. The tools below lead in 2025 in making integration faster.
1. Activepieces
Activepieces works as an open-source automation tool that removes the need for manual data entry. It connects apps through "flows," which link triggers and actions across different services.
A new form submission, for example, can start a flow that updates a spreadsheet, alerts a team on Slack, and adds the lead to a CRM.
Inside Activepieces, there are more than 400 pieces that you can connect. That includes CRMs, finance tools, project managers, and AI services. Marketing data is easy to route through flows, so your reports stay current and campaigns stay consistent.
The intuitive user interface lets anyone build automations without technical expertise or extensive coding knowledge. Developers still have the freedom to create custom "pieces" through its TypeScript-based framework.
For enterprises, the option to self-host Activepieces means tighter control over data security. Teams in regulated industries prefer that setup. On the other side, the cloud edition offers predictable flat-rate pricing for unlimited tasks, which helps you scale without cost surprises.
Features
Activepieces comes with a mix of no-code and developer-level features.
- No-code builder - Drag-and-drop interface to create flows without writing code.
- Pre-built pieces - Hundreds of connectors for apps like Google Sheets, HubSpot, Stripe, and Slack.
- Custom pieces - Developers can extend the platform with TypeScript to build new integrations.
- AI integration - Native support for AI agents that can analyze, summarize, or generate content in workflows.
- Self-hosting - Full control over data through on-premise deployment.
- Open ecosystem - Community-driven library of pieces on npm with ongoing contributions.
- AI Copilot - Assists users inside the builder in designing flows faster.
- Security controls - Role-based access, audit logs, and the ability to run in isolated environments.
Use Cases
Companies use Activepieces to automate repetitive work across departments:
- New leads from forms flow straight into a CRM, while alerts notify the sales team instantly.
- Campaign data updates across Google Sheets, email platforms, and ad tools with no manual work.
- Customer tickets from web forms can sync with Slack and desk platforms, cutting response times.
- Payment data from Stripe can move into spreadsheets or accounting systems in real time.
- Build flows where AI generates replies, summarizes documents, or drafts content directly inside the workflow.
Integrations
Activepieces offers a growing library of integrations, referred to as "pieces." As of now, the number sits at 425 pre-built connectors, but the count continues to rise since the community contributes new ones regularly.
Some of the pieces you could use:
- OpenAI
- ElevenLabs
- HubSpot
- Google Sheets
- Microsoft Excel 365
- Slack
- Discord
- WooCommerce
- ClickUp
- ActiveCampaign
- Microsoft Dynamics CRM
- Zoho Invoice
The open-source framework means developers can also create custom pieces, so the library expands beyond what's officially listed.
Pricing
The free Community Edition is open-source and self-hosted, with no limits on tasks, but requires managing your own server.
Cloud plans start with a Free plan that covers 1,000 tasks per month, then the Plus plan at $25 monthly for small teams with unlimited tasks. The Business plan is $150 monthly for users needing more flows and AI credits.
The Enterprise plan provides custom features, resources, and dedicated support.
Talk to sales and find out how Activepieces fits your team's automation needs!
2. Matillion
Matillion runs as a cloud-based integration platform that relies on ELT. Data from multiple sources goes straight into warehouses like Snowflake, BigQuery, Redshift, or Databricks, and the process happens inside those systems.
Analysts just drag and drop steps when designing flows inside Matillion, while engineers write SQL or Python when they need deeper control. For organizations with complex data workflows, it adds scheduling, automation, and monitoring.
It further shapes records into accurate data for reporting and AI projects. Acting as a data quality tool, it checks formats, maps values, and resolves errors before you run analysis.
Compliance features also help with data governance, offering role-based access, audit logs, and version control. Together, these functions let you keep quality and security in focus.
Features
Matillion combines automation with controls for advanced teams. Main features include:
- Data connectivity - Links to hundreds of databases, SaaS tools, APIs, and files. Custom connectors extend coverage further.
- Change data capture - Tracks inserts, updates, and deletes in real time to keep downstream systems aligned.
- Data transformation - Offers a visual ELT builder for simple use and scripting for engineers.
- AI and machine learning - Includes Maia for natural language pipeline design and support for unstructured inputs.
- Pipeline orchestration - Handles scheduling, automation, and monitoring with lineage tracking.
- Security and governance - Provides audit logs, version control, and access control for enterprise compliance.
Pros
- Visual builder helps non-technical users.
- Connects with cloud warehouses like Snowflake and BigQuery.
- AI assistant for building and optimizing pipelines.
- Central hub for orchestration and monitoring.
Cons
- Less flexible for code-heavy projects.
- Features differ across supported warehouses.
- Platform instability reported by some teams.
- Support response is sometimes slow.
Pricing
Matillion uses a credit system for billing. The Developer plan costs $2.50 per credit and includes core pipeline features with limited users.
Subscription plans add more functions and scale up across Basic, Advanced, and Enterprise levels. Pricing for those tiers, however, isn't listed publicly.
3. Airbyte
Airbyte is an open-source platform that moves data from one place to another without locking you into a single vendor.
It follows the ELT model, so records come out of data sources like SaaS apps, APIs, or databases and land directly in a data warehouse or data lakes. Processing then happens inside the destination system, which saves time and scales better than older methods.
You have the option to host it yourself for full control. Either way, you can pick a managed cloud version or set up a hybrid that mixes both.
Since it's open-source, developers can build new connectors when needed. That makes Airbyte a fit for teams that need to integrate data from many systems while still keeping costs under control.
The connector library is large and active, with community members adding new options often. For anyone looking to consolidate their data in their data warehouses, data lakes, and databases, Airbyte covers a wide range of use cases while leaving room for customization.
Features
Airbyte covers the basics of moving and preparing information using these features:
- Connector library - Over 600 pre-built connectors for APIs, databases, files, and apps.
- Custom connectors - Toolkits let developers design their own when pre-built options fall short.
- Flexible deployment - Can be self-hosted, cloud-managed, or set up as a hybrid.
- Change data capture - Tracks inserts, updates, and deletes for efficient sync.
- ELT and ETL support - ELT is the default, but ETL is possible when transformations need to happen first.
- Integration with modern stacks - Works with dbt for transformations and with Airflow or Dagster for orchestration.
- Enterprise security - Paid versions include audit logs, access controls, and single sign-on.
Pros
- Large and growing connector library.
- Open-source with customization options.
- Near real-time sync with change data capture.
- AI-ready for structured and unstructured inputs.
Cons
- Self-hosted setups need technical skill.
- Resource-heavy to keep running smoothly.
- The user interface is less polished than some tools.
- Open-source support depends on community help.
Pricing
Airbyte Core is free for self-hosted use. The Managed Cloud Version starts at $10 per month on the Standard plan, which runs on credits. Then, the Pro plan is priced case by case and tied to data worker capacity.
4. Oracle Data Integrator
Oracle Data Integrator, or ODI, moves information using an ELT approach. Data comes from source systems and lands in the target database. Once there, the database itself runs the data processes and handles the transformations without an external server.
The platform is often used in complex data environments where you need both speed and precision. Developers can build mappings that describe the flow of information from source to target.
Reusable components, called "knowledge modules," supply the code patterns needed for loading, transforming, and checking records. These pieces make large projects more consistent and easier to maintain.
ODI connects with a wide set of technologies as well, so you can bring all the data together for reporting or analytics. It supports robust integration to traditional relational databases, modern cloud platforms, and big data tools like Hadoop and Spark.
Features
ODI offers a collection of features, such as:
- ELT architecture - Pushes transformation logic into the target database for faster and more efficient execution.
- Knowledge modules - Provide pre-built, reusable templates for common integration tasks while allowing customization.
- Broad connectivity - Links to databases, cloud services, SaaS applications, and big data platforms.
- Change data capture - Captures inserts, updates, and deletes in real time to keep systems aligned.
- Declarative design - Developers describe the integration flow, and ODI generates the required code automatically.
- Orchestration and monitoring - Includes scheduling, job management, and error tracking within the platform.
- Metadata-driven governance - Tracks lineage, supports compliance, and offers clear reporting for audits.
Pros
- Handles large datasets efficiently with ELT.
- Reduces hardware costs by using the target database's resources.
- Connects with a wide range of systems.
- Real-time updates with change data capture.
Cons
- Steep learning curve for new users.
- Relies on the target database's performance.
- Interface feels dated compared to modern tools.
- Initial load can put pressure on resources.
Pricing
ODI pricing follows Oracle Cloud's usage model. Workspace Usage costs $0.16 per hour. Data Processed is charged at $0.04 per gigabyte per hour.
Pipeline Operator Execution is billed at $0.30 per hour. Larger enterprises often negotiate custom rates based on workload and contract size.
5. Fivetran
Fivetran is a managed platform that moves information from many applications and databases into a central location without much manual work.
It automates the steps needed to build a pipeline, so you don't spend hours writing code. The system connects to hundreds of sources, pulls records out, and keeps them up to date with little effort from engineers.
Other than that, it automates data pipeline setup and takes care of changes that often break pipelines, like new columns or renamed fields. And through an automated hands-off approach, it does data mapping to reduce errors and help maintain data accuracy over time.
That makes it possible for your analysts and engineers to spend less time fixing sync problems and more time working with the information itself. Centralizing records into cloud data warehouses and lakes supports analytics, reporting, and AI use cases too.
Features
Fivetran covers the extract and load steps while leaving the transformation to other tools. Key features include:
- Pre-built connectors - Over 700 fully managed connectors for databases, SaaS apps, and event streams.
- Automated sync - Continuously pulls data from sources and updates destinations on a schedule.
- Change data capture - Detects inserts, updates, and deletes for efficient incremental updates.
- Schema management - Adjusts automatically when fields are added or renamed.
- Historical syncs - Loads complete history on first setup and supports free re-syncs when needed.
- dbt integration - Works with dbt for transformations once the data is in the warehouse.
- Security and compliance - Encryption at rest and in transit, SOC 2, GDPR, and HIPAA compliance.
- Monitoring and logs - Provides metadata and sync activity logs for auditing.
Pros
- Very easy to set up, even for non-technical users.
- Automates updates and schema changes.
- Large set of stable connectors.
- Reliable incremental updates keep data fresh.
Cons
- Costs rise quickly with large or fast-changing datasets.
- Limited flexibility compared to custom pipelines.
- Troubleshooting requires reliance on support.
- Lacks built-in dashboards or reporting features.
Pricing
Fivetran charges are based on monthly active rows (MAR), which count how many rows are added, updated, or deleted in a destination each month. The Free plan covers up to 500,000 rows and 5,000 transformation runs.
Paid tiers include Standard, Enterprise, and Business Critical, but prices are not posted publicly.
6. SAP Data Services
SAP Data Services, often called SAP BODS, is for ETL projects that need to combine and standardize records from many systems. SAP Business Technology hosts SAP BODS, which functions effectively for companies using other SAP software. Although it still requires connections to external databases or cloud applications.
As it handles both structured and unstructured content, it'll be useful for analytics, reporting, and regulatory needs. It also includes data connectors that link to SAP applications as well as third-party systems.
You can build data flows that cover extracting data, transforming it into consistent data formats, and then loading it into SAP HANA or another target system.
Beyond basic ETL, SAP BODS supports intelligent data integration through profiling, cleansing, and validation steps. These features maintain consistent standards while ensuring data integrity across different systems.
You can further do batch processing for scheduled jobs and real-time handling for situations where timing matters. For many enterprises, it doubles as both an integration layer and a full data quality management solution.
Features
SAP Data Services comes with a wide set of functions that go beyond simple ETL work:
- Universal connectivity - Connects to SAP and third-party systems, including databases, big data platforms, and flat files.
- ETL and ELT support - Handles classic extract, transform, and load jobs, or pushes logic into the database for better performance.
- Batch and real-time options - Runs large scheduled jobs or reacts instantly to new events.
- Data profiling - Scans records to flag structural or content issues before use.
- Data cleansing - Fixes, standardizes, and matches records to improve quality.
- Governance tools - Central repository for rules, lineage, and metadata tracking.
- Unstructured content handling - Pulls insights from text files, emails, or social data.
- Performance scaling - Supports parallel work and grid setups for high-volume loads.
- Integration with SAP ecosystem - Links with SAP BW, HANA, and MDG for master data and governance work.
Pros
- Native connectivity with SAP ERP, BW, and HANA.
- Built-in profiling, cleansing, and validation for quality.
- Can process unstructured data for deeper insight.
- Centralized governance with lineage and metadata.
Cons
- Steep learning curve and need for technical expertise.
- Limited real-time capability compared to newer tools.
- Requires heavy hardware for massive workloads.
- The on-premise model feels outdated compared to modern cloud platforms.
Pricing
SAP doesn't publish exact prices for Data Services.
Elevate Every Integration Project With Activepieces
Activepieces handles work at every scale. The interface is simple for a beginner, yet flexible enough for enterprise teams.
Anyone can drag and drop pieces to build flows, while developers get full freedom with TypeScript to create custom logic. That mix means businesses don't need separate tools for different skill levels.
The open ecosystem keeps growing fast. Every piece is open source and published on npm, with more than half contributed by the community.
You can drop in an AI agent to write, summarize, or analyze inside a workflow without leaving the builder as well. Copilot guides you while you build, so automations take minutes instead of hours. For companies that demand full control, the self-hosted option provides network isolation and strong security by design.
With its balance of no-code simplicity, developer-level customization, and enterprise readiness, Activepieces is a top pick for integration projects today.
Try Activepieces free and put your data and AI workflows on autopilot!
FAQs About Data Integration Tools
What is a data integration platform?
A data integration platform is software that connects multiple systems, moves raw data between them, applies data encryption for security, and handles tasks like transforming data, data loading, and even ways to sync data across environments. It supports organizations dealing with growing data volume and makes it easier to import data from different sources into warehouses or lakes for analysis.
Is ETL a data integration tool?
ETL is not a platform by itself but a type of data integration tool that extracts, transforms, and then loads data into a target system.
What are the top five data integration patterns?
The top five data integration patterns are ETL (Extract, Transform, Load), ELT (Extract, Load, Transform), data replication, data virtualization, and change data capture.
Is SQL a data integration tool?
SQL on its own is not a data integration tool, but it is used inside integration platforms to query, manipulate, and join data as part of the process.