Accelerate data science at scale with secure, reliable data pipelines
Fivetran’s automated data replication from 400+ sources and data lake and lakehouse integrations enable you to find statistical significance faster and stop maintaining pipelines.
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Meet Kiva’s Melissa Fabros
Having a steady stream of data has freed up resources at Kiva, enabling engineers to build microfinance products rather than monitor its ETL process. By using Fivetran to combine data from disparate sources, Kiva can see where people are motivated to put their funds, measure social impact and better serve its mission.
Data movement that is powerful and simple
Fivetran automates important workloads for AI & ML — so you can connect your data lake or lakehouse in just minutes.
Full historical loads for every source
Efficient incremental updates, including automated schema drift
Comprehensive object support
Accurately mapped data types
Support for your chosen destinations
Data lakehouse formats that maintain transactional integrity
Application events for downstream orchestration
Automate data pipelines to fuel your AI and ML workflows
Compatible with your ideal data flow tooling
We use the native Delta Lake file format to load data into Databricks through their SQL API. This format helps support data governance with built-in transaction integrity features, schema changes and more.
Bring governance to your data lake
With AWS S3 targets, we’ve chosen to use the Iceberg format to bring your lakehouse closer to your database in terms of data governance features.
"Instead of hiring the kind of traditional engineering headcount, Fivetran allows us to focus on business value, hiring analysts, dashboard builders, people who are experts in web analytics and paid media. Our infrastructure is a lot broader and more advanced than it was a year or two ago.”
Chris Klaczynski, Marketing Analytics Manager, Databricks
Related resources
ASICS focuses on digital innovation, not ELT with Fivetran
Autodesk is all in on the modern data stack
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