Sync data from Databricks to Apache Kafka
Connect your data from Databricks to Apache Kafka with Hightouch. No APIs, no months-long implementations, and no CSV files. Just your data synced forever.
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Sync data from Databricks to Apache Kafka
- Publish messages into different topics whenever rows are added, changed, or removed in your data models.
- Compose your messages using SQL or our Liquid-based templating engine, which supports variable injection, control flow, and loops.
- Define custom ordering and partition keys.
- Authenticate with SASL (SCRAM, AWS IAM, etc.) and bring your own certificate authority.
- Hightouch supports all managed Kafka services (e.g., Amazon MSK and Confluent Cloud), as well as self-hosted instances.
What methods can I use to model my Databricks data?
dbt model selector
Sync directly with your dbt models saved in a git repository.
SQL editor
Create and Edit SQL from your browser. Hightouch supports SQL native to Databricks.
Sigma model
Hightouch converts your Sigma workbook element into a SQL query that runs directly on Databricks.
Table selector
Select available tables and sheets from Databricks and sync using existing views without having to write SQL.
Customer Studio
For less technical users, pass traits and audiences from Databricks using our visual segmentation builder.
FAQs
There are several options to sync data between sources. You can manually build and maintain a data pipeline, use a point-to-point solution such as Zapier, or you can manually upload CSVs.
With Hightouch, you get:
- Automation: You do not need to build and maintain custom data pipelines and you do not have to have your team do manual work
- Simplicity: You avoid a complex web of integrations caused by point-to-point solutions by syncing data from your source
- Speed: You can get set up in quickly - the average Hightouch customer starts syncing data in 23 minutes
- Control: companies of all sizes have access to enterprise-level controls including observability, dbt integrations, and version control
- Security: Hightouch never stores your data and is HIPAA, GDPR, CCPA, and SOC-2 compliant
90% of all Hightouch syncs complete in 30 seconds or less, and the platform enables non-technical users to self-serve.
With Hightouch, you can sync data as frequently as it changes within your Databricks. You can trigger data syncs manually or schedule them to run at an interval or custom recurrence as often as once per minute.
Hightouch offers a basic mapper or advanced mapper that allows you to visually match columns from your Databricks to fields in Apache Kafka.
Yes, if you integerate Databricks and Apache Kafka using Hightouch, in-warehouse planning is supported.
Great, but what is in-warehouse planning?
Between every sync, Hightouch notices any and all changes in your data model. This allows you to only send updated results to your destination (in this case Apache Kafka). With the baseline setup, Hightouch picks out only the rows that need to be synced by querying every row in your data model before diffing using Hightouch’s infrastructure.
The issue here is this can be slow for large models.
Warehouse Planning allows Hightouch to do this diff directly in your warehouse. Read more on how this workshere.