6 Best Data Integration Tools of 2023

How to choose the right data integration tool for your organization

Not all data integration tools will answer your business needs. To ease your tool hunting process, consider the following criteria for picking the best data integration tool for your organization:

1. Target user: Who is the data integration tool made for? If it is made for data engineers, you’d expect the integration services to be based on code and include monitoring, versioning, logging, and the entire developer toolbox.

If the data integration platform is geared towards every citizen of your organization, it should come with an intuitive self-service user interface and no code or low code solutions for common tasks. 

2. Ease of use: Is the data integration tool easy to learn or is there a steep learning curve? The vendor might have proprietary languages and architectures that you need to learn before you can start using the tool. This delays the time to the data value. 

3. Price: Look at the total cost of ownership: from licensing and usage fees, but also to time saved via automation, and the time lost spent learning, debugging, and maintaining the tool (e.g. hosting for open source solutions).

The vendor needs to be transparent about the cost of its “worse case scenario”.

4. Scalability: Can the tool handle your current *and* future data needs? Look at volume, speed of processing (ingestion and integration), parallelization, multithreading, and all the other features that help you scale your data operations as your company grows.

5. Number and type of connectors offered: Meticulously check the tool offers connectors for all your needs. It would delay your time to value if the integration solution offered some connectors (e.g. for databases and warehouses), but not for all your use cases (e.g. left out the ability to collect Facebook Ads data).

If a pre-built connector does not exist, check if the tool offers a Generic Extractor or Loader, like Keboola does, that covers universal use cases. 

6. Support: When (not if) things go wrong, you want to have on your side a tool that comes with support. This can be direct customer support by the vendor, but it also includes documentation, the ability to view the code yourself (open source beats private vendors in this case), and educational materials that can onboard new team members without your involvement.

7. Data services beyond data integration: Most data integration platforms go beyond “just” data integration. They offer data services for security, big data, governance, … you name it! Check the full package that goes beyond integration to understand the value a tool can offer to your organization.

Pro tips for data engineers: The best data integration software will offer data services covering:

• Flexible data transformations. Your use cases might be covered by simple data transformations (sum, filter, aggregate). But flexibility (covered by an expressive coding language) is preferred.
• Data lineage. The ability to trace data and metadata through its lifecycle will be invaluable for regulatory compliance and root cause analysis techniques when debugging. 
• Data security. Check your SLAs and your own regulations before acquiring a tool. It’s your responsibility to keep data safe.
• Data virtualization. Sharing data or creating virtual sandboxes that access data without altering the underlying engineering architecture is invaluable for POCs and experimentation.
• DataOps. Versioning, CI/CD, developmental branches (development, staging, production, …), and other indirectly related features will help you scale your data operations beyond integration.

With these criteria in mind, let’s look at some of the best data integration tools on the market.

1. Keboola

Keboola is a data platform as a service that covers data integration with over 250 pre-built connectors. The connectors help you automate data pipelines integrating  anything from SaaS apps (Facebook Ads, Google Ads, Mailchimp, …) to data storages (PostgreSQL, Snowflake, AWS S3, data lakes …), APIs, webhooks, and anything in between (see the full integration list to get inspired).

With its native ELT capabilities, you can send enriched data back into tools that make it actionable.

Keboola’s powerful and flexible data transformation layer in SQL, Python, R, or dbt empowers you to fully customize integration pipelines. 

Keboola strikes a perfect balance between fast integrations via automated connectors and the flexibility of customized transformations.

PROs:

• Covers all the important integration use cases with an extensive list of connectors. If a connector is not pre-build, you can use the Generic Connectors to cover edge cases.
• Extensive end-to-end automation of common integration workflows (check Templates).
• Self-service tools for every citizen (the no-code drag-and-drop ETL flow builder).
• Best-in-class support: from direct support that gets standing ovations from customers (read the reviews) to self-serve documentation and certifications, Keboola will be there for you when things go wrong. 
• Extensive data services beyond data integration: enterprise data security for every business size, data governance and master data management, DataOps (development branches and versioning, CDC to speed up operations, CLI), Data Catalog for data sharing, machine learning toolbox, …
• Transparent pricing model based on usage, with an always free tier to get you started without breaking your piggy bank. 

CONs:

• Keboola offers near real-time data integration, with the orchestrators that can keep data fresh up to the 1 min granularity. It cannot handle full real-time data integrations (data streaming).
• To use the transformation layer users need to know a bit of coding (low-code design). 

Best For: The swiss data knife is the best data integration platform overall for both novice and expert data engineers as well as teams of savvy data users across multiple data tasks.

2. Snaplogic

Snaplogic is an Integration Platform as a Service (IPaas) geared towards enterprises. With 600 pre-built connectors (called “Snaps”), it integrates anything from Hadoop to NoSQL databases, from Twilio to Google Analytics.

PROs:

• High throughput optimization for big data (e.g. Spark and Hadoop processing).
• An AI assistant can help you integrate two or more platforms.
• The ETL job progression can be visualized via multiple graphs and charts.
• No-code: anyone with no technical knowledge can integrate a data source into a destination.
• Automatic data quality checks run in the background of their ETL tool.
• Great data integration platform for event-based and real-time processing.

CONs:

• Their pre-built connectors are focused on enterprise SaaS apps, so Snaplogic misses a lot of important connectors used by small and medium organizations (such as Facebook Ads, Google Ads, Linkedin Ads, Mailchimp, …).
• The target audience is non-technical people, so the platform does not offer a lot of customization or code introspections.

Best for: Non-technical enterprise citizen who wants to build simple data pipelines by themselves without assistance from the IT or data departments.

3. Dell Boomi

Boomi is Dell’s cloud-based data integration tool that helps you integrate data across apps, databases, and SaaS apps. Its mission control is cloud-based, but Boomi can easily be used for on-premise, cloud, and hybrid architectures.

PROs:

• The low-code and no-code connectors hit a sweet spot between automation and customizability for code-savvy users.
• Some connectors are open-sourced so product transparency is high.
• Devoted event-triggered connectors to build event analytics and near real-time data integrations.

CONs:

• Some obvious connectors are missing from their product offerings, such as Excel and CSV files, advertising SaaS platforms, etc.
• For a full real-time or data streaming service, the solution lacks parallelization, messaging features, and other streaming features. 
• Doesn’t scale well for big data use cases (high throughput, high speed, high volume).

Best for: Non-technical citizens at a small or medium-sized company whose integration problem can be solved by Boomi’s suite of connectors.

4. Talend Open Studio

Talend Open Studio is an open-source data integration platform that enables you to execute ETL tasks and cloud or on-premise jobs using Spark, Hadoop, and NoSQL databases. 

Talend Open Studio is a product of Talend which also offers paid data integration software, such as Talend Data Fabric as a managed data service for developers or Stitch as a no-code data ingestion tool geared towards analysts, but we’ll focus on its popular open-source offering.

PROs:

• Can build scalable ETL and ELT data pipelines.
• Simple GUI interface that helps you visualize data pipelines.
• Over 1000 connectors help you integrate business and data endpoints.
• Capable of simple and complex transformations.

CONs:

• The open source solution does not include tooling for CDC, versioning, continuous integration, any data catalog, or data security (check the full list of missing services). All of those features are available with the paid solution, which tends to be on the pricy end of the market. 
• Documentation for features is often lacking.
• Many components are reserved for the paid tear (check which ones are available as open source here).
• RAM hungry - not optimized for transformations and certain components tend to cause bottlenecks.
• A lot of big data features are locked behind the paywalls of its paid services.
• Writing transformations is labor-intensive.

Best for: The savvy data engineer who likes to tinker with code (solo data member or weekend hobbyist). The ideal user is willing to trade more coding time for a less polished (more high maintenance) solution but the solution saves money on licensing and usage costs.

5. Pentaho Data Integration

Pentaho Data Integration (PDI) is an open-source data integration tool that focuses on Extract, Transform, and Load (ETL) capabilities to facilitate data engineering work. 

PROs:

• Strong DBA offerings: database replication, data migration, supports slowly changing dimensions in data warehousing, etc.
• Canned transformations - Pentaho comes with samples that show you how to transform data. The canned transformations are customizable, and PDI offers strong support for complex transformation jobs.
• The visual representation of the ETL workflow helps you understand complex processes.
• Can parallelize data processing and use multithreading for removing data pipeline bottlenecks.
• Low-code - the tool is geared toward a technical audience but implemented as a clickable (not extensively codable) solution.

CONs:

• Click-intense: it takes a lot of steps to build a simple pipeline. 
• Missing a lot of business connectors, such as any SaaS apps. It is mostly geared for database-to-database data pipelines. So the “extract” part is only partially implemented. To integrate data across all company data sources, companies usually buy an additional tool next to Pentaho to take care of the extraction part.

Best for: Cost-sensitive database administrators who want to streamline their jobs with an open source tool.

6. Informatica

Informatica offers two similar tools: 

1. Informatica PowerCenter - an ETL platform for large enterprises.
2. Informatica Cloud Data Integration (ICDI) - a more affordable Integration Platform as a Service (IPaas).

The main difference between the two is that ICDI is comparatively cheaper, while Informatica Power Center offers more data governance and better ETL tooling.

Both are geared towards the largest enterprises (think Fortune 1000 companies) and are market leaders for high-performance data integration at scale.

PROs:

• Highly polished and resilient product.
• Scales seamlessly with big data needs.

CONs:

• There is an obvious lack of viable data destinations for Informatica. The only cloud data warehouse destination it supports is Amazon Redshift. The only data lake destination it supports is Microsoft Azure SQL Data Lake.
• Informatica is trustworthy, but it comes at the cost of vendor lock-in. To use the tool, you need to adapt your data architecture to the solution’s design, as well as learn their proprietary modeling language. 
• Non-transparent pricing and no self-service business model. You’ll have to talk to sales and go through contracting just to demo the product. The basic Data Integration Cloud service starts at $2000 per month.
• The tool is powerful but complex. Prepare your engineers (plural) to withstand a steep learning curve before unlocking the platform’s full potential.

Best for: A team of data engineers at a large enterprise who will invest and specialize in Informatica’s custom platform to reap the fruits of a powerful machine.

Which data integration tool should you choose?

We presented the best data integration tools on the market. But which one should you choose? 

Make a shortlist of your favorite candidates and go through the process of elimination:

1. Connectors. If the tool doesn’t offer the connectors you need, drop it.
2. Who will use it? Some tools are best for non-technical people and some tools are geared toward engineers.
3. Take the tool for a ride. Use the free trials to get a sense of how they work.
4. Price. Is the tool’s pricing a good trade-off between what you save with automation and how much you pay for the tool?
5. Support. Is the tool easy-to-use or will you become dependent on the vendor?
6. Extras: from machine learning to bespoke security. How can the tool help you make your lives easier?

Get Keboola for easy and automated data integration

Even with data integration tools, integrating different data sources can be time consuming or costly.

Keboola takes both worries away.

With its suite of ETL and integration tools you can set up, run, and automate end-to-end data pipelines in minutes. 

Alongside you get a toolbox that also covers security, data governance, machine learning, developer tools, and other features that streamline your data operations.

The best part? It costs 0 to get started.

Take Keboola for spin - risk-free - and pay only as you grow. We won’t even ask for your credit card details.