How to load data from Kafka to Databricks Lakehouse
Learn how to use Airbyte to synchronize your Kafka data into Databricks Lakehouse within minutes.
Building your pipeline or Using Airbyte
Airbyte is the only open source solution empowering data teams to meet all their growing custom business demands in the new AI era.
Building in-house pipelines
- Inconsistent and inaccurate data
- Laborious and expensive
- Brittle and inflexible
After Airbyte
- Reliable and accurate
- Extensible and scalable for all your needs
- Deployed and governed your way
Start syncing with Airbyte in 3 easy steps within 10 minutes
Take a virtual tour
Demo video of Airbyte Cloud
Demo video of AI Connector Builder
Setup Complexities simplified!
Simple & Easy to use Interface
Airbyte is built to get out of your way. Our clean, modern interface walks you through setup, so you can go from zero to sync in minutes—without deep technical expertise.
Guided Tour: Assisting you in building connections
Whether you’re setting up your first connection or managing complex syncs, Airbyte’s UI and documentation help you move with confidence. No guesswork. Just clarity.
Airbyte AI Assistant that will act as your sidekick in building your data pipelines in Minutes
Airbyte’s built-in assistant helps you choose sources, set destinations, and configure syncs quickly. It’s like having a data engineer on call—without the overhead.
What sets Airbyte Apart
Modern GenAI Workflows
Streamline AI workflows with Airbyte: load unstructured data into vector stores like Pinecone, Weaviate, and Milvus. Supports RAG transformations with LangChain chunking and embeddings from OpenAI, Cohere, etc., all in one operation.
Move Large Volumes, Fast
Quickly get up and running with a 5-minute setup that enables both incremental and full refreshes for databases of any size, seamlessly scaling to handle large data volumes. Our optimized architecture overcomes performance bottlenecks, ensuring efficient data synchronization even as your datasets grow from gigabytes to petabytes.
An Extensible Open-Source Standard
More than 1,000 developers contribute to Airbyte’s connectors, different interfaces (UI, API, Terraform Provider, Python Library), and integrations with the rest of the stack. Airbyte’s AI Connector Builder lets you edit or add new connectors in minutes.
Full Control & Security
Airbyte secures your data with cloud-hosted, self-hosted or hybrid deployment options. Single Sign-On (SSO) and Role-Based Access Control (RBAC) ensure only authorized users have access with the right permissions. Airbyte acts as a HIPAA conduit and supports compliance with CCPA, GDPR, and SOC2.
Fully Featured & Integrated
Airbyte automates schema evolution for seamless data flow, and utilizes efficient Change Data Capture (CDC) for real-time updates. Select only the columns you need, and leverage our dbt integration for powerful data transformations.
Enterprise Support with SLAs
Airbyte Self-Managed Enterprise comes with dedicated support and guaranteed service level agreements (SLAs), ensuring that your data movement infrastructure remains reliable and performant, and expert assistance is available when needed.
What our users say
Raman Singh
Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend
Chase Zieman
“Airbyte helped us accelerate our progress by years, compared to our competitors. We don’t need to worry about connectors and focus on creating value for our users instead of building infrastructure. That’s priceless. The time and energy saved allows us to disrupt and grow faster.”
Rupak Patel
"With Airbyte, we could just push a few buttons, allow API access, and bring all the data into Google BigQuery. By blending all the different marketing data sources, we can gain valuable insights."
How to Sync to Manually
Step 1: Set Up Your Kafka Environment
Begin by ensuring that your Kafka environment is up and running. This includes having your Kafka broker and Zookeeper properly configured. Verify that your Kafka topic is set up to receive the data you intend to stream. You can use the Kafka command-line tools to create and manage topics and to check the status of your Kafka cluster.
Step 2: Prepare Your Databricks Environment
Access your Databricks account and create a new cluster if you haven't already. Ensure that your cluster has the necessary resources and configurations to handle streaming data. You will also need to have a workspace set up in Databricks where you can write and execute your code.
Step 3: Install Required Libraries on Databricks
In your Databricks environment, install the Apache Spark Kafka integration library. This is typically included in Databricks by default, but you should verify its availability. If not present, you can install it via the Databricks library interface using Maven coordinates, for example: `org.apache.spark:spark-sql-kafka-0-10_2.12:`.
Step 4: Configure Kafka as a Source in Databricks Notebook
Open a new notebook in Databricks. Use the Spark Kafka integration to configure Kafka as a source. You can create a streaming DataFrame by specifying the Kafka server, the topic you want to subscribe to, and any other necessary Kafka configurations such as starting offsets and security protocol if your Kafka setup requires authentication.
```python
kafka_data_df = (spark
.readStream
.format("kafka")
.option("kafka.bootstrap.servers", ":")
.option("subscribe", "")
.load())
```
Step 5: Process the Streaming Data in Databricks
Once you have the streaming DataFrame, process it as needed. This might involve parsing the data from its serialized form, applying transformations, or filtering. Use Spark SQL or DataFrame operations to handle this processing. For example, if your data is in JSON format, you can use the `from_json` function to parse it.
```python
from pyspark.sql.functions import from_json, col
from pyspark.sql.types import StructType, StringType
schema = StructType().add("field1", StringType()).add("field2", StringType())
processed_df = kafka_data_df.selectExpr("CAST(value AS STRING)") \
.select(from_json(col("value"), schema).alias("data")) \
.select("data.")
```
Step 6: Write the Stream to Databricks Lakehouse
Configure the output sink to write the processed data to Databricks Lakehouse. This is typically done by writing the stream to a Delta table. Specify the output mode (e.g., append), the location or Delta table name, and any checkpointing details needed for fault tolerance.
```python
query = (processed_df
.writeStream
.format("delta")
.outputMode("append")
.option("checkpointLocation", "/path/to/checkpoint/dir")
.start("/mnt/delta/your-delta-table"))
```
Step 7: Monitor and Manage the Streaming Job
Once your streaming job is running, monitor it using the Databricks job interface. Check for any errors or performance issues and adjust resource allocation or code optimizations as necessary. Use the streaming metrics and logs available in Databricks to debug and enhance your data pipeline.
By following these steps, you can efficiently move data from Kafka to your Databricks Lakehouse using only the built-in capabilities of Kafka and Databricks.