This associate-level e-learning course introduces Lakeflow Connect as a streamlined way to ingest data from diverse sources into the Databricks Data Intelligence Platform. You’ll move from core concepts and architectures to practical techniques for batch, incremental and streaming ingestion using Delta Lake and the Medallion architecture, all with a focus on real-world data engineering workflows.
What you’ll learn:
- How Lakeflow Connect simplifies and scales ingestion from cloud object storage and enterprise systems
- The role and benefits of Delta tables and the Medallion architecture in modern ingestion design
- Practical patterns for batch, incremental batch and streaming ingestion using CREATE TABLE AS, COPY INTO and Auto Loader
- How to capture file-level metadata and use rescued data columns to handle malformed or schema-mismatched records
- Techniques for ingesting and flattening semi-structured JSON data at scale
- Enterprise-grade ingestion using Lakeflow Connect Managed Connectors
- Alternative strategies like MERGE INTO and using Databricks Marketplace for data onboarding
Course highlights:
- Free, self-paced e-learning in English with slides, notes and selected demo videos
- Approx. 2 hours of content organized into 4 focused sections, plus quiz and resources
- Hands-on focus on cloud object storage ingestion, metadata enrichment, and rescued data handling
- Coverage of both Standard and Managed Connectors for Lakeflow Connect
- Designed for data engineers with SQL/Python experience and familiarity with Databricks, Delta Lake and the Medallion architecture
Ready to modernize your ingestion pipelines and standardize how data lands in your lakehouse with Lakeflow Connect? Enroll in “Data Ingestion with Lakeflow Connect” today and start building scalable, resilient ingestion patterns for your data engineering workloads.
Earn a completion certificate you can showcase on your LinkedIn profile or include in your resume.
Over to our community:
If you’re already ingesting data into Databricks today, what’s the biggest challenge you face - schema drift, real-time requirements, managing multiple sources, or something else?
