TL;DR Short answer: Kafka is a log, not a database, so a Kafka ETL tool has to handle offsets, schema evolution and the fact that topics are append-only streams rather than tables you can requery. That is what separates the thirteen below.
Airbyte Fivetran Stitch Matillion Airflow Talend Pentaho Informatica PowerCenter Microsoft SSIS Singer Rivery Hevo Data Meltano The other nine are Airflow, Talend, Pentaho, Informatica PowerCenter, Microsoft SSIS, Singer, Rivery, Hevo Data and Meltano, covered in full below.
What is Kafka, and how does it tie in with ETL? Apache Kafka is an open-source distributed event streaming platform, used to move high volumes of events between systems in real time. Producers write events to topics, consumers read from them, and the log retains events for a configured period rather than storing current state. Kafka Connect, introduced in version 0.9 and standard ever since, is the built-in framework for moving data between Kafka and other systems. The reason teams reach for an ETL tool instead is that Connect solves movement but not the rest: you still need somewhere to land the data, a way to handle schema changes, and a mechanism for replaying history when a downstream table needs rebuilding.
For simplicity, this guide uses "Kafka ETL" to refer to all data integration tools, ETL and ELT alike, that can read from Kafka.
Why move Kafka data into a warehouse? Business intelligence: Kafka data may need to be loaded into a data warehouse for analysis, reporting, and business intelligence purposes.Data Consolidation: Companies may need to consolidate data with other systems or applications to gain a more comprehensive view of their business operationsCompliance: Certain industries may have specific data retention or compliance requirements, which may necessitate extracting data for archiving purposes.How should you choose a Kafka ETL tool? Which Kafka ETL tools should you consider?
Tool Type Reading from Kafka Pricing
Airbyte Open-source ELT Kafka source consumes topics and tracks offsets, so a restarted sync resumes Free self-hosted; Cloud capacity-based Fivetran Managed ELT Managed Kafka connector, no offset handling to maintain Monthly active rows Stitch Extract and load Limited streaming support; batch-oriented Rows per month Matillion ELT Loads events, then transforms in the warehouse Credits Apache Airflow Orchestrator You write the consumer; Airflow only schedules it Free, you run it Talend Integration platform Native Kafka components in the studio Quoted, no free tier Pentaho ETL and analytics Kafka consumer and producer steps built in Open core, paid enterprise Informatica PowerCenter Enterprise ETL Kafka connector, heavier setup Quoted; 10.5 out of standard support Microsoft SSIS ETL Needs custom script components Included with SQL Server Singer Tap and target spec Batch-oriented; a poor fit for streaming Free Rivery Cloud ELT Kafka source available; now Boomi Data Integration Credits Hevo Data Managed ELT Kafka source with near real-time loading Events per month Meltano CLI-first ELT Singer taps managed as a Git project; batch-oriented Free, you run it
Here are the top Kafka ETL tools based on their popularity and the criteria listed above:
1. Airbyte Airbyte is the leading open-source ELT platform, created in July 2020. It offers 700+ connectors and a community of more than 25,000 members. Its Kafka source consumes topics and tracks offsets, so a restarted sync resumes where it stopped rather than replaying everything.
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 750+ sources 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 is a cloud extract-and-load platform with 140+ connectors, originally built on the open-source Singer specification. It has no user-defined transformations and no log-based change capture.
What's unique about Stitch? Since Qlik acquired Talend, and Stitch with it, in 2023, Stitch has become one product line inside a much larger portfolio, and Qlik now publishes a formal migration path from Stitch to Qlik Talend Cloud. It is still quick to set up, but that direction of travel is worth weighing first.
Here are more insights on the differentiations between Airbyte and Stitch .
What else should you consider? 4. Matillion Matillion is an ELT platform created in 2011, built around pushdown transformation that runs inside your cloud warehouse. It supports 100+ connectors and covers extract, load and transform. It also integrates with dbt, which has shipped with the product since version 1.70.
What's unique about Matillion? Running in your own cloud account means Kafka data stays inside your infrastructure, though a multi-cloud setup may need more than one instance. Transformation is pushed down to the warehouse, so reshaping event data uses compute you already pay for.
Here are more insights on the differentiations between Airbyte and Matillion .
5. 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 .
6. 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? Talend pairs integration with data quality and governance. Two things to check before shortlisting it: Qlik acquired Talend in 2023 and now sells it as Qlik Talend Cloud, and Talend Open Studio, the free open-source edition, was retired on 31 January 2024, so there is no free tier or self-serve route in.
7. 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.
8. 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.
9. 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.
10. 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.
11. 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.
12. 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.
13. 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.
All those ETL tools are not specific to Kafka, you might also find some other specific data loader for Kafka data. But you will most likely not want to be loading data from only Kafka in your data stores.
Kafka's API gives access to various types of data, including:
1. Event data: Kafka is primarily used for streaming event data, such as user actions, sensor readings, and log data.
2. Metadata: Kafka provides metadata about the topics, partitions, and brokers in a cluster.
3. Consumer offsets: Kafka tracks the offset of each message consumed by a consumer, allowing for reliable message delivery.
4. Producer metrics: Kafka provides metrics on the performance of producers, such as message send rate and error rate.
5. Consumer metrics: Kafka provides metrics on the performance of consumers, such as message consumption rate and lag.
6. Log data: Kafka stores log data for a configurable amount of time, allowing for historical analysis and debugging.
7. Administrative data: Kafka provides APIs for managing topics, partitions, and consumer groups.
Overall, Kafka's API gives access to a wide range of data related to event streaming, metadata, performance metrics, and administrative tasks.
How do you start pulling data from Kafka? If you decide to test Airbyte, you can start analyzing your Kafka data within minutes in three easy steps:
Step 1: Set up Kafka as a source connector 1. First, you need to have a Kafka source connector that you want to connect to Airbyte. You can download the connector from the Apache Kafka website or any other reliable source.
2. Once you have the Kafka source connector, you need to configure it with the necessary settings such as the Kafka broker URL, topic name, and other relevant parameters.
3. Next, you need to create a new connection in Airbyte by clicking on the ""New Connection"" button on the dashboard.
4. Select the Kafka source connector from the list of available connectors and provide the necessary details such as the connector name, version, and configuration settings.
5. After providing the required details, click on the ""Test Connection"" button to ensure that the connection is established successfully.
6. If the connection is successful, you can proceed to create a new pipeline by clicking on the ""New Pipeline"" button on the dashboard.
7. Select the Kafka source connector as the source and choose the destination connector where you want to send the data.
8. Configure the pipeline settings such as the data mapping, transformation, and other relevant parameters.
9. Once you have configured the pipeline, click on the ""Run"" button to start the data transfer process.
10. Monitor the pipeline progress and ensure that the data is transferred successfully from the Kafka source connector to the destination connector.
Step 2: Set up a destination for your extracted Kafka data Choose the destination where you want your Kafka data to land. This can be a cloud data warehouse, data lake, database, cloud storage, or any other supported Airbyte destination.
Step 3: Configure the Kafka data pipeline in Airbyte 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.
Which Kafka ETL tool should you choose? This article outlined the criteria that you should consider when choosing a data integration solution for Kafka ETL/ELT. Based on your requirements, you can select from any of the top 10 ETL/ELT tools listed above. We hope this article helped you understand why you should consider doing Kafka ETL and how to best do it.
💡Suggested Reads Teradata ETL Tools
Snowflake Data Cloud ETL Tools
Babelforce ETL Tools
Datadog ETL Tools
ETL Tools
Data Migration Tools
Data Ingestion Tools
Open Source ETL Tools
Data Integration Tools
Data Synchronization Tools
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