As data generation keeps increasing daily, data integration tools have become essential for corporate success. Data integration tools help merge disparate data sources into a cohesive context, normally in a data warehouse. This allows you to create a single source of truth to enhance decision-making capabilities.
In this article, we will list the top data integration platforms of 2024 that you can use for unifying your data in a centralized storage system.
Top 10 Data Integration Platforms
There are several data integration platforms available, but they differ slightly in terms of their unique functions. Here are some of the top data integration tools of 2024:
Airbyte
Airbyte is one of the most widely used ELT (Extract, Load, Transform) solutions for data integration. The platform replicates data from databases, APIs, and files to data warehouses and analytical platforms. With a library of more than 300 pre-built connectors, it is designed to streamline the process of moving and syncing data from various sources to destinations. One of the unique advantages of Airbyte is that it supports collecting both structured and unstructured data. This allows you to curate data not only for descriptive analysis but also for machine learning use cases.
Key Features of Airbyte
- With Airbyte, you can plan full-refresh, incremental, and log-based CDC replications to all the destinations.
- Airbyte allows you to set up notifications to alert you about pipeline failures.
- Since Airbyte also has an open-source version, you can easily tweak pre-built connections to meet your specific requirements.
- Airbyte allows you to build custom connectors within 30 minutes with its easy-to-use CDK. This allows you to integrate data sources and destinations that are not available as a pre-built connector.
Aside from the open-source version, which is free, Airbyte offers two plans: Cloud and Enterprise. While each credit costs $2.50 in the Cloud Plan, the Enterprise plan has configurable pricing.
Oracle Data Integrator
Oracle Data Integrator (ODI) is one of the leading data integration platforms provided by Oracle Corporation. It offers the ability to handle all types of data integration needs, including event-driven, high-volume, and high-performance batch loads. However, it is popular mostly for connecting with Oracle products.
Key Features of Oracle Data Integrator
- ODI has an extensive library of connectors to connect and interact with various data sources, such as databases, flat files, applications, cloud services, and more.
- It supports the ELT approach in which data transformations are performed inside the target destination based on the requirements.
- ODI supports various file technologies like XML and ERPs and all RDBMSs, including Oracle, Teradata, Exadata, Netezza, IBM DB2, and Sybase IQ.
License fees for Oracle Data Integrator Enterprise Edition are $900 for a Named User Plus License, $198 for Software Update Registration & Support (Named User Plus), $30,000 for a Processor License, and $6,600 for Software Update License & Support (Processor).
SAP Data Services
SAP Data Services is a data integration tool specializing in improving data quality throughout the organization. It allows you to develop and execute workflows for extracting data from data sources, transform and refine the data, and then load it to the destination. SAP also supports change data capture (CDC), an important capability to provide input data for stream-processing systems and data warehousing.
Key Features of SAP
- This data integration platform includes adapters for Apache Hive, MongoDB, JDBC, HTTP, JMS, and OData.
- SAP Data Services has a built-in ETL and ELT process.
- SAP enables near-real-time data transportation, parallel processing, and grid computing.
- By using SAP data services, you can obtain information from unstructured documents and extract meaning from unstructured text data.
SAP data integration solution offers a custom Premium plan and the Standard plan, which costs $4,347 per user per month.
Talend
Acquired by Qlik, Talend is an ELT and ETL system offering over 1000 connectors for moving data. With Talend, you can not only pull data from cloud applications and databases but also connect with on-premise storage systems.
Talend is one of the few data integration tools that have been addressing the issue of managing data end-to-end with its range of solutions. Some of the popular solutions include Stitch for ELT, Big Data Platform for analytics and collaboration, and more.
In other words, it serves you from integration to delivery with end-to-end data management.
Key Features of Talend
- Talend can be installed on-site, in the cloud, across many clouds, or in a hybrid cloud environment.
- You can collaborate with your team members in real-time to prepare data.
- It complies with security and regulatory regulations.
Four plans are available from Talend: Data Management Platform, Big Data Platform, Data Fabric, and Stitch. Prices are available on request.
Informatica
Informatica is a comprehensive data integration platform made for integration, validation, and data transmission. Its Cloud Data Integrations platform allows you to efficiently move petabytes of data, transform it, and store data across multiple destinations. Informatica also allows you to create transformations — Mapplet — that you can reuse with different datasets. Such scalable features make Informatica a go-to platform for data integration.
Key Features of Informatica
- It offers a graphical user interface for creating and implementing complex data transformation rules, including data joining, sorting, filtering, and aggregation.
- The tool has rich features for data quality management, including data cleansing, profiling, and standardization. These features help locate and resolve problems with data quality and accuracy.
- Manage and monitor data pipelines to identify issues and fix them immediately.
For pricing, you need to talk to their sales team.
Hevo
Hevo is a modern cloud-native platform that markets itself as one of the few data integration tools that need no maintenance at all. It is a no-code data transfer platform that can be used by both technical and business users. Offering over 15 destinations (SaaS apps, data warehouses, databases, and more) and 150+ pre-built connectors, this platform allows you to streamline the process of connecting multiple sources to a destination. With its rich features and functionality for non-technical users, it also provides complex transformation abilities using Python code.
Key Features of Hevo
- To keep data sources and destinations in sync, Hevo provides various data replication options. You can choose to replicate whole databases, particular tables, or even individual columns to focus on only relevant data.
- With Hevo, you can automatically manage schema changes in the source database.
Hevo offers three different plans: Free, Starter, and Business. Small amounts of data can be moved from business tools if you have the Free plan. The Starter plan costs $239 monthly, and the Business plan is customizable.
SAS Data Integration Studio
SAS Data Integration Studio is one of the leading tools offered by SAS software. With visual representatives, it enables you to quickly implement and manage data integration. However, for complex workflow, you can still write scripts.
Key Features of SAS Data Integration Studio
- With the help of these tools and a user-friendly graphical interface, you can design data integration processes by simple drag and drop. This reduces the technical barriers and makes it more accessible.
- Tasks like profiling, cleaning, improving, and monitoring data can be done with integrated SAS tools for data quality to deliver reliable, consistent information.
- SAS data integration reduces the time and resources needed for development by expediting the establishment of data marts, data streams, and warehouses with built-in features.
The subscription plans of the tool are customizable. However, you can have a free trial to get started.
Fivetran
Fivetran is a cloud-based data integration tool. It is an ELT and ETL cloud service that assists in connecting and transporting data from many sources to destination, such as a database or data warehouse. With 400+ pre-built connectors that take only a few minutes to set up, it is one of the popular data integration tools.
The platform provides automated schema drift managing, normalization, deduplication, coordination, and administration of data transformation in addition to integrated automated administration and security features.
Key features of Fivetran
- The extensive library of pre-built connectors in Fivetran streamlines the ETL process from data sources to destinations. All the connectors in this platform are created and fully managed by the engineering team of Fivetran.
- Fivetran allows you to automatically synchronize the data with the destination while continuously checking the data source for updates. This reduces extra work of data synchronization and minimizes data latency.
- You can monitor data movements and transformations with visual data lineage graphs. This helps you diagnose and troubleshoot data pipelines effectively.
The paid version of Fivetran follows a pay-as-you-go subscription model.
Precisely Connect
Precisely Connect is a leading data integration tool specializing in ETL and Change Data Capture (CDC). The platform allows you to integrate data with seamless access and collection to several sources and destinations.
Key Features of Connect
- It supports JSON and XML data movement to cater to your semi-structured data requirements.
- Using its flexible modular architecture, the Data Integrity Suite of Connect can meet your needs no matter where you are in the process of obtaining data integrity.
- Connect uses over 80 integrated data processing algorithms to deliver exactly what you want.
Precisely plans are customizable, and the pricing will vary with your usage.
IBM DataStage
IBM DataStage is an enterprise-level data integration tool that makes planning, developing, and carrying out data transfer and transformation tasks easier. DataStage supports two basic ways of data integration: ELT and ETL. For optimal performance, it also supports parallel processing and load balancing.
Key Features of IBM
- DataStage allows you to integrate structured, semi-structured, and unstructured data.
- The platform offers many data quality capabilities, such as data profiling, uniformity, matching, enhancement, and active data-quality monitoring.
- You can transform vast amounts of raw data—regardless of format, complexity, or volume—into high-quality, usable information. This ensures you have consistent and readily assimilated data to perform data integration efficiently.
While IBM offers a free trial for its products, you can obtain a licensed and full version by contacting an IBM salesperson to see which plan option is best for you.
Conclusion
Selecting the best data integration solution might take time and effort. A burgeoning software market exists with the same goal as the ten data integration technologies you’ve seen in this article. These tools save organizations countless hours of work by automating data replications with minimal to no coding.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
What is ETL?
ETL (Extract, Transform, Load) is a process used to extract data from one or more data sources, transform the data to fit a desired format or structure, and then load the transformed data into a target database or data warehouse. ETL is typically used for batch processing and is most commonly associated with traditional data warehouses.
What is ELT?
More recently, ETL has been replaced by ELT (Extract, Load, Transform). ELT Tool is a variation of ETL one that automatically pulls data from even more heterogeneous data sources, loads that data into the target data repository - databases, data warehouses or data lakes - and then performs data transformations at the destination level. ELT provides significant benefits over ETL, such as:
- Faster processing times and loading speed
- Better scalability at a lower cost
- Support of more data sources (including Cloud apps), and of unstructured data
- Ability to have no-code data pipelines
- More flexibility and autonomy for data analysts with lower maintenance
- Better data integrity and reliability, easier identification of data inconsistencies
- Support of many more automations, including automatic schema change migration
Here is our recommendation for the criteria to consider:
- Connector need coverage: does the ETL tool extract data from all the multiple systems you need, should it be any cloud app or Rest API, relational databases or noSQL databases, csv files, etc.? Does it support the destinations you need to export data to - data warehouses, databases, or data lakes?
- Connector extensibility: for all those connectors, are you able to edit them easily in order to add a potentially missing endpoint, or to fix an issue on it if needed?
- Ability to build new connectors: all data integration solutions support a limited number of data sources.
- Support of change data capture: this is especially important for your databases.
- Data integration features and automations: including schema change migration, re-syncing of historical data when needed, scheduling feature
- Efficiency: how easy is the user interface (including graphical interface, API, and CLI if you need them)?
- Integration with the stack: do they integrate well with the other tools you might need - dbt, Airflow, Dagster, Prefect, etc. - ?
- Data transformation: Do they enable to easily transform data, and even support complex data transformations? Possibly through an integration with dbt
- Level of support and high availability: how responsive and helpful the support is, what are the average % successful syncs for the connectors you need. The whole point of using ETL solutions is to give back time to your data team.
- Data reliability and scalability: do they have recognizable brands using them? It also shows how scalable and reliable they might be for high-volume data replication.
- Security and trust: there is nothing worse than a data leak for your company, the fine can be astronomical, but the trust broken with your customers can even have more impact. So checking the level of certification (SOC2, ISO) of the tools is paramount. You might want to expand to Europe, so you would need them to be GDPR-compliant too.
1. Airbyte
Airbyte is the leading open-source ELT platform, created in July 2020. Airbyte offers the largest catalog of data connectors—350 and growing—and has 40,000 data engineers using it to transfer data, syncing several PBs per month, as of June 2023. Major users include brands such as Siemens, Calendly, Angellist, and more. Airbyte integrates with dbt for its data transformation, and Airflow/Prefect/Dagster for orchestration. It is also known for its easy-to-use user interface, and has an API and Terraform Provider available.
What's unique about Airbyte?
Their ambition is to commoditize data integration by addressing the long tail of connectors through their growing contributor community. All Airbyte connectors are open-source which makes them very easy to edit. Airbyte also provides a Connector Development Kit to build new connectors from scratch in less than 30 minutes, and a no-code connector builder UI that lets you build one in less than 10 minutes without help from any technical person or any local development environment required..
Airbyte also provides stream-level control and visibility. If a sync fails because of a stream, you can relaunch that stream only. This gives you great visibility and control over your data.
Data professionals can either deploy and self-host Airbyte Open Source, or leverage the cloud-hosted solution Airbyte Cloud where the new pricing model distinguishes databases from APIs and files. Airbyte offers a 99% SLA on Generally Available data pipelines tools, and a 99.9% SLA on the platform.
2. Fivetran
Fivetran is a closed-source, managed ELT service that was created in 2012. Fivetran has about 300 data connectors and over 5,000 customers.
Fivetran offers some ability to edit current connectors and create new ones with Fivetran Functions, but doesn't offer as much flexibility as an open-source tool would.
What's unique about Fivetran?
Being the first ELT solution in the market, they are considered a proven and reliable choice. However, Fivetran charges on monthly active rows (in other words, the number of rows that have been edited or added in a given month), and are often considered very expensive.
Here are more critical insights on the key differentiations between Airbyte and Fivetran
3. Stitch Data
Stitch is a cloud-based platform for ETL that was initially built on top of the open-source ETL tool Singer.io. More than 3,000 companies use it.
Stitch was acquired by Talend, which was acquired by the private equity firm Thoma Bravo, and then by Qlik. These successive acquisitions decreased market interest in the Singer.io open-source community, making most of their open-source data connectors obsolete. Only their top 30 connectors continue to be maintained by the open-source community.
What's unique about Stitch?
Given the lack of quality and reliability in their connectors, and poor support, Stitch has adopted a low-cost approach.
Here are more insights on the differentiations between Airbyte and Stitch, and between Fivetran and Stitch.
Other potential services
Matillion
Matillion is a self-hosted ELT solution, created in 2011. It supports about 100 connectors and provides all extract, load and transform features. Matillion is used by 500+ companies across 40 countries.
What's unique about Matillion?
Being self-hosted means that Matillion ensures your data doesn’t leave your infrastructure and stays on premise. However, you might have to pay for several Matillion instances if you’re multi-cloud. Also, Matillion has verticalized its offer from offering all ELT and more. So Matillion doesn't integrate with other tools such as dbt, Airflow, and more.
Here are more insights on the differentiations between Airbyte and Matillion.
Airflow
Apache Airflow is an open-source workflow management tool. Airflow is not an ETL solution but you can use Airflow operators for data integration jobs. Airflow started in 2014 at Airbnb as a solution to manage the company's workflows. Airflow allows you to author, schedule and monitor workflows as DAG (directed acyclic graphs) written in Python.
What's unique about Airflow?
Airflow requires you to build data pipelines on top of its orchestration tool. You can leverage Airbyte for the data pipelines and orchestrate them with Airflow, significantly lowering the burden on your data engineering team.
Here are more insights on the differentiations between Airbyte and Airflow.
Talend
Talend is a data integration platform that offers a comprehensive solution for data integration, data management, data quality, and data governance.
What’s unique with Talend?
What sets Talend apart is its open-source architecture with Talend Open Studio, which allows for easy customization and integration with other systems and platforms. However, Talend is not an easy solution to implement and requires a lot of hand-holding, as it is an Enterprise product. Talend doesn't offer any self-serve option.
Pentaho
Pentaho is an ETL and business analytics software that offers a comprehensive platform for data integration, data mining, and business intelligence. It offers ETL, and not ELT and its benefits.
What is unique about Pentaho?
What sets Pentaho data integration apart is its original open-source architecture, which allows for easy customization and integration with other systems and platforms. Additionally, Pentaho provides advanced data analytics and reporting tools, including machine learning and predictive analytics capabilities, to help businesses gain insights and make data-driven decisions.
However, Pentaho is also an Enterprise product, so hard to implement without any self-serve option.
Informatica PowerCenter
Informatica PowerCenter is an ETL tool that supported data profiling, in addition to data cleansing and data transformation processes. It was also implemented in their customers' infrastructure, and is also an Enterprise product, so hard to implement without any self-serve option.
Microsoft SQL Server Integration Services (SSIS)
MS SQL Server Integration Services is the Microsoft alternative from within their Microsoft infrastructure. It offers ETL, and not ELT and its benefits.
Singer
Singer is also worth mentioning as the first open-source JSON-based ETL framework. It was introduced in 2017 by Stitch (which was acquired by Talend in 2018) as a way to offer extendibility to the connectors they had pre-built. Talend has unfortunately stopped investing in Singer’s community and providing maintenance for the Singer’s taps and targets, which are increasingly outdated, as mentioned above.
Rivery
Rivery is another cloud-based ELT solution. Founded in 2018, it presents a verticalized solution by providing built-in data transformation, orchestration and activation capabilities. Rivery offers 150+ connectors, so a lot less than Airbyte. Its pricing approach is usage-based with Rivery pricing unit that are a proxy for platform usage. The pricing unit depends on the connectors you sync from, which makes it hard to estimate.
HevoData
HevoData is another cloud-based ELT solution. Even if it was founded in 2017, it only supports 150 integrations, so a lot less than Airbyte. HevoData provides built-in data transformation capabilities, allowing users to apply transformations, mappings, and enrichments to the data before it reaches the destination. Hevo also provides data activation capabilities by syncing data back to the APIs.
Meltano
Meltano is an open-source orchestrator dedicated to data integration, spined off from Gitlab on top of Singer’s taps and targets. Since 2019, they have been iterating on several approaches. Meltano distinguishes itself with its focus on DataOps and the CLI interface. They offer a SDK to build connectors, but it requires engineering skills and more time to build than Airbyte’s CDK. Meltano doesn’t invest in maintaining the connectors and leave it to the Singer community, and thus doesn’t provide support package with any SLA.
Once you've set up both the source and destination, you need to configure the connection. This includes selecting the data you want to extract - streams and columns, all are selected by default -, the sync frequency, where in the destination you want that data to be loaded, among other options.
And that's it! It is the same process between Airbyte Open Source that you can deploy within 5 minutes, or Airbyte Cloud which you can try here, free for 14-days.
What should you do next?
Hope you enjoyed the reading. Here are the 3 ways we can help you in your data journey:
Frequently Asked Questions
What is ETL?
ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.
This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps: set it up as a source, choose a destination among 50 available off the shelf, and define which data you want to transfer and how frequently.
The most prominent ETL tools to extract data include: Airbyte, Fivetran, StitchData, Matillion, and Talend Data Integration. These ETL and ELT tools help in extracting data from various sources (APIs, databases, and more), transforming it efficiently, and loading it into a database, data warehouse or data lake, enhancing data management capabilities.
What is ELT?
ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.
Difference between ETL and ELT?
ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.