Delta Lake Operations - 02: Handle Incremental Data
4 Scenarios
1 Hour 10 Minutes
Intermediate
Industry
e-commerce
general
Skills
approach
data-understanding
data-wrangling
batch-etl
data-storage
Tools
databricks
Learning Objectives
Gain insights into handling schema evolution and enforcing data types with Delta Lake.
Understand incremental data processing and its advantages over batch processing.
Learn how to store pre-aggregated results to optimize query performance.
Master the MERGE INTO operation in Delta Lake for updating and inserting records efficiently.
Overview
This module starts with an introduction to key techniques for optimizing data pipelines, including schema evolution, incremental loading, CTAS (Create Table As Select), and the MERGE INTO operation. It covers the need for efficient data processing, focusing on how schema changes, new data, and updates can be handled effectively without reprocessing entire datasets.
The module explores how incremental loading improves pipeline performance, how CTAS can store pre-aggregated results for reuse, and how MERGE INTO enables smooth updates and inserts in Delta tables, all while ensuring schema consistency and data integrity.
Prerequisites
- Basic knowledge of data pipelines and processing approaches.
- Familiarity with SQL queries, aggregation techniques, and Delta Lake operations.
- Understanding of how incremental updates and schema changes impact data processing.