Studio Python Task Development

Building Data Processing Pipelines with Zettapark for Python

Data Engineering with Lakehouse Zettapark

Managing Lakehouse Volume Files with Zettapark

Feature Engineering for Customer Features with Zettapark

Data Cleansing with SQL

Building Dynamic Tables for Near Real-Time Incremental Processing

Implementing SCD (Slowly Changing Dimensions) with Streams and Tasks on Lakehouse

Real-time ETL Using Dynamic Table

Table Stream Best Practices Guide

Customer Data Change Tracking: Preserving Complete Change History with Table Stream

Real-time Sales Dashboard: Building a Multi-Layer Incremental Data Warehouse with Dynamic Table

Product Dimension History Tracking: Implementing SCD Type 2 with MERGE INTO

User Behavior Funnel Analysis: Tracking Conversion from Impression to Order

Data Recovery from Mistakes: Using Time Travel to Restore Deleted and Modified Data

New ELT method with Lakehouse

DataOps data security and stable production practice