TL;DR Short answer: Kafka ETL is usually solved by the Kafka ecosystem and the destination, not by a general ETL tool. Kafka Connect sinks, Snowflake's own connector, Confluent Tableflow, Databricks and Redshift streaming ingestion all consume topics continuously. A batch ETL tool earns its place when Kafka is one of many sources feeding the same warehouse on a schedule. Ten options:
Airbyte Fivetran Kafka Connect sink connectors Snowflake Connector for Kafka Confluent Tableflow Databricks Structured Streaming Amazon Redshift streaming ingestion Talend Meltano Apache Airflow One Kafka constraint: every tool can only read messages still inside the topic's retention period. If a pipeline is down longer than retention, or you add a pipeline after the fact, the older events are gone from Kafka. Land raw events durably first, and treat the warehouse, not Kafka, as your history.What makes Kafka different from other ETL sources? Apache Kafka is a distributed event streaming platform. Producers append events to topics, consumers read them in order, and a topic keeps events for a configured retention period rather than holding current state. That makes Kafka unlike a database or SaaS API as a source in three ways. There is no table to re-query: a consumer tracks offsets per partition and moves forward. Messages are serialised, often as Avro or Protobuf with schemas held in a schema registry, so a reader has to decode them. And the data is a stream of changes, not a snapshot, so the warehouse model usually needs to rebuild current state from events.
How do the Kafka ETL tools compare? Tool Latency Destinations Formats Hosting Airbyte Scheduled batches Any JSON, Avro Cloud or self-hosted Fivetran Scheduled batches Any JSON, Avro, Protobuf Cloud Kafka Connect sinks Continuous S3, BigQuery, Postgres and many more Any, via converters Self-managed or Confluent Cloud Snowflake Connector for Kafka Seconds to minutes Snowflake only JSON, Avro, Protobuf Runs on Kafka Connect Confluent Tableflow Continuous Iceberg and Delta tables Schema registry formats Confluent Cloud Databricks Structured Streaming Continuous or micro-batch Delta tables Any, decoded in code Databricks Redshift streaming ingestion Seconds Redshift only JSON and raw bytes AWS Talend Batch or streaming jobs Broad Common formats Cloud or on-premises Meltano Scheduled batches Any Singer target Depends on the tap Self-hosted Apache Airflow Scheduled tasks Whatever you write Whatever you decode Self-hosted or managed
Which Kafka ETL tools should you consider? The list mixes batch ETL platforms, which read Kafka on a schedule alongside other sources, with streaming routes built into Kafka, Confluent and the warehouses themselves. Which side you need depends mostly on latency.
1. Airbyte Airbyte is an open-source data integration platform with 700+ connectors and a community of 25,000+ data engineers. Its Kafka source subscribes to topics by name or pattern, deserialises JSON or Avro messages, using a schema registry for Avro, and supports SASL and SSL connections. It syncs in batches with full refresh or incremental append, which suits landing Kafka events next to database and SaaS data in one warehouse. The connector is at marketplace support level, so test it against your topics and volumes.
What's unique about Airbyte? Kafka rarely feeds a warehouse alone, and Airbyte brings the databases and applications behind your events through the same pipeline and schedule. Connectors are open source and editable, and the Connector Development Kit covers anything else. It runs as self-hosted open source, on Airbyte Cloud, or as Enterprise Flex.
2. Fivetran Fivetran is a closed-source, managed ELT service with 700+ connectors, including Apache Kafka. It creates one table per topic, decodes JSON, Avro and Protobuf messages using your schema registry, and can unpack top-level fields into columns or keep the message packed. It bills on monthly active rows, which for high-volume topics adds up quickly.
Here is a closer look at Airbyte vs Fivetran .
3. Kafka Connect sink connectors Kafka Connect is Kafka's own framework for moving data in and out of topics, and sink connectors stream topics into a destination continuously, tracking offsets and decoding messages with converters that understand your schema registry. There are widely used sinks for S3 and other object storage, BigQuery, JDBC databases such as Postgres, Elasticsearch and more. You run Connect yourself, on Amazon MSK Connect, or as fully managed connectors in Confluent Cloud. It is the default answer for continuous Kafka-to-destination delivery, at the cost of operating Connect workers and individual connector configurations.
4. Snowflake Connector for Kafka Snowflake's own Kafka Connect sink loads topics into Snowflake tables, using Snowpipe Streaming for low-latency, row-level ingestion or Snowpipe for file-based loads. It handles JSON, Avro and Protobuf and is the most direct route for Snowflake users who already run Kafka Connect or Confluent. Messages land as semi-structured columns that you model further in Snowflake.
5. Confluent Tableflow Tableflow, part of Confluent Cloud, materialises Kafka topics as Apache Iceberg or Delta Lake tables, using the topic's schema, without you running a separate pipeline. Warehouses and query engines that read open table formats can then query the data directly. It is the least-effort route for Confluent Cloud customers building a lakehouse, and is only available on Confluent's platform.
6. Databricks Structured Streaming Databricks reads Kafka natively through Structured Streaming, writing to Delta tables with checkpointed offsets, either continuously or in scheduled micro-batches. Declarative pipelines on Databricks can wrap the same source with data quality expectations. It is the natural choice when the lakehouse is on Databricks, and you write the decoding and table logic in SQL or Python.
7. Amazon Redshift streaming ingestion Redshift streaming ingestion reads topics from Amazon MSK straight into a materialized view, refreshed every few seconds, with no connector or staging in S3. You define an external schema pointing at the cluster and a materialized view over the stream, then parse the JSON payload in SQL. It suits AWS teams who want low-latency Kafka data in Redshift without extra infrastructure; check AWS's documentation for support of Kafka platforms other than MSK.
8. Talend Talend has native Kafka input and output components and pairs integration with data quality and governance, which matters when event data feeds regulated reporting. 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 edition, was retired on 31 January 2024, so there is no free tier or self-serve route in.
9. Meltano Meltano is an open-source, CLI-first platform spun out of GitLab that manages taps and targets as a version-controlled project, with a Singer Kafka tap for batch extraction. It suits engineers who want pipelines in code and occasional batch loads of Kafka topics. For continuous delivery, a Connect sink is the better fit, and connector maintenance falls to the community.
10. Apache Airflow Apache Airflow is an open-source orchestrator, not an ETL tool. Its Kafka provider includes operators to consume from and produce to topics within a scheduled task, which suits small or periodic jobs. For sustained, high-volume consumption, Airflow is better used to orchestrate the tools above than to read Kafka itself.
Here is a closer look at Airbyte vs Airflow .
How do you move Kafka data into Snowflake, Redshift, BigQuery or S3? Each destination has a route built for it, and that is usually the place to start:
Snowflake: the Snowflake Connector for Kafka with Snowpipe Streaming.Redshift: streaming ingestion from Amazon MSK into materialized views.BigQuery: a BigQuery sink connector on Kafka Connect or Confluent Cloud, or a Dataflow job that reads Kafka and writes to BigQuery.S3: an S3 sink connector writing Parquet or JSON files, on MSK Connect, self-managed Connect or Confluent Cloud, or Confluent Tableflow if you want Iceberg tables.Postgres: a JDBC sink connector, with upsert mode if topics carry keyed updates.A batch ETL tool such as Airbyte or Fivetran makes more sense than these when minute-level latency is not needed and you would rather run one pipeline for Kafka and your other sources than operate Connect alongside an ETL platform.
How do you start syncing Kafka data with Airbyte? Step 1: Set up Kafka as a source In Airbyte, open Sources, choose New source and select Kafka. Enter your bootstrap servers, the topics to subscribe to by name or pattern, and a consumer group ID. Choose the message format, JSON or Avro, and for Avro the schema registry URL, credentials and subject name strategy. Set the security protocol, such as SASL_SSL with your credentials, and run the connection test.
Step 2: Set up a destination Choose where the data lands: a cloud warehouse such as Snowflake, BigQuery or Redshift, a data lake, a database or cloud storage.
Step 3: Configure the connection Select the topics, use incremental append so each sync picks up from the last committed offset, and schedule syncs well inside the topics' retention period.
The process is the same in Airbyte Open Source, which you can deploy yourself , and in Airbyte Cloud, which you can try free for 14 days .
Which Kafka ETL tool should you choose? Start with latency. If you need events within seconds or minutes, use the streaming route for your destination: Snowflake's connector, Redshift streaming ingestion, Databricks, Tableflow on Confluent Cloud, or a Kafka Connect sink for everything else. If hourly or daily is fine and Kafka is one source among many, a batch ETL platform keeps everything in one place: Airbyte, self-hosted or in Cloud, or Fivetran fully managed. Talend fits governed enterprise pipelines, and Meltano and Airflow suit small, code-defined jobs.
For tools that cover more than Kafka, see our guide to the top data integration tools .
Frequently Asked Questions (FAQs) 1. Do you need an ETL tool if you already run Kafka Connect? Often not for Kafka itself, since Connect sinks already stream topics into most destinations. An ETL tool helps when you want Kafka and your other sources on one platform and schedule, or would rather not operate Connect workers.
2. How do you get Kafka data into Snowflake? Use the Snowflake Connector for Kafka, a Kafka Connect sink that loads topics with Snowpipe Streaming or Snowpipe. For batch loading alongside other sources, an ETL tool with a Kafka source and Snowflake destination also works.
3. What happens to Kafka data after the retention period? It is deleted from the topic, and no consumer can read it again. Any pipeline paused longer than retention, or started later, can only load the messages that remain, so treat the warehouse or lake as the long-term history.
4. What is Confluent Tableflow? A Confluent Cloud feature that represents Kafka topics as Apache Iceberg or Delta Lake tables, so engines that read open table formats can query streaming data without a separate pipeline.
Suggested reads Change Data Capture Tools
Data Pipeline Tools
Redshift ETL Tools
Data Ingestion Tools
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