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Data Ingestion with Lakeflow Connect

This course provides a comprehensive introduction to Lakeflow Connect as a scalable and simplified solution for ingesting data into Databricks from a variety of data sources. You will begin by exploring the different types of connectors within Lakeflow Connect (Standard and Managed), learn about various ingestion techniques, including batch, incremental batch, and streaming, and then review the key benefits of Delta tables and the Medallion architecture.


From there, you will gain practical skills to efficiently ingest data from cloud object storage using Lakeflow Connect Standard Connectors with methods such as CREATE TABLE AS (CTAS), COPY INTO, and Auto Loader, along with the benefits and considerations of each approach. You will then learn how to append metadata columns to your bronze level tables during ingestion into the Databricks data intelligence platform. This is followed by working with the rescued data column, which handles records that don’t match the schema of your bronze table, including strategies for managing this rescued data.


The course also introduces techniques for ingesting and flattening semi-structured JSON data, as well as enterprise-grade data ingestion using Lakeflow Connect Managed Connectors.


Finally, learners will explore alternative ingestion strategies, including MERGE INTO operations and leveraging the Databricks Marketplace, equipping you with foundational knowledge to support modern data engineering ingestion.

Skill Level
Associate
Duration
4h
Prerequisites

• Basic understanding of the Databricks Data Intelligence platform, including Databricks Workspaces, Apache Spark, Delta Lake, the Medallion Architecture and Unity Catalog.

• Experience working with various file formats (e.g., Parquet, CSV, JSON, TXT).

• Proficiency in SQL and Python.

• Familiarity with running code in Databricks notebooks.

Outline

  • Introduction to Data Engineering in Databricks

- Data Engineering in Databricks

- What is Lakeflow Connect?

- Delta Lake Review

- Exploring the Lab Environment


  • Cloud Storage Ingestion with LakeFlow Connect Standard Connectors

- Introduction to Data Ingestion from Cloud Storage

- Appending Metadata Columns on Ingest

- Working with the Rescued Data Column

- Ingesting Semi-Structured Data: JSON


  • Enterprise Data Ingestion with LakeFlow Connect Managed Connectors

- Ingesting Enterprise Data into Databricks Overview

- Enterprise Data Ingestion with Lakeflow Connect


  • Ingestion Alternatives

- Ingesting Data with Databricks Marketplace

- Ingesting into Existing Delta Tables

- Data Ingestion with MERGE INTO

Upcoming Public Classes

Date
Time
Your Local Time
Language
Price
Jun 01
01 PM - 05 PM (Europe/London)
-
English
$750.00
Jun 02
09 AM - 01 PM (Australia/Sydney)
-
English
$750.00
Jul 02
08 AM - 12 PM (Asia/Kolkata)
-
English
$750.00
Jul 02
09 AM - 01 PM (America/New_York)
-
English
$750.00

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Upcoming Public Classes

Machine Learning Practitioner

Advanced Machine Learning with Databricks

This course is aimed at data scientists and machine learning practitioners and consists of two, four-hours modules. 

Machine Learning at Scale

In this course, you will gain theoretical and practical knowledge of Apache Spark’s architecture and its application to machine learning workloads within Databricks. You will learn when to use Spark for data preparation, model training, and deployment, while also gaining hands-on experience with Spark ML and pandas APIs on Spark. This course will introduce you to advanced concepts like hyperparameter tuning and scaling Optuna with Spark. This course will use features and concepts introduced in the associate course such as MLflow and Unity Catalog for comprehensive model packaging and governance.

Advanced Machine Learning Operations

In this course, you will be provided with a comprehensive understanding of the machine learning lifecycle and MLOps, emphasizing best practices for data and model management, testing, and scalable architectures. It covers key MLOps components, including CI/CD, pipeline management, and environment separation, while showcasing Databricks’ tools for automation and infrastructure management, such as Databricks Asset Bundles (DABs), Workflows, and Mosaic AI Model Serving. You will learn about monitoring, custom metrics, drift detection, model rollout strategies, A/B testing, and the principles of reliable MLOps systems, providing a holistic view of implementing and managing ML projects in Databricks.

Paid
8h
Lab
instructor-led
Professional

Questions?

If you have any questions, please refer to our Frequently Asked Questions page.