ETL Basics
Design and implement extract-transform-load pipelines for structured data.
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After this skill you can…
- Write a Python ETL pipeline with pandas
- Handle schema changes and bad data gracefully
- Log pipeline runs and alert on failures
Prerequisites
Watch (10 videos)
Analytics in 15: Save Time! Try No-Code Data Movement and Transformation
→ Use no-code interface for ETL→ Generate ETL code automatically
Learn ETL Pipelines in Databricks in Under 1 Hour | Data Engineering in Databricks
→ Build ETL pipelines in Databricks→ Orchestrate jobs for data automation
Full End-to-End Data Engineering Project in Databricks
→ Build an ETL pipeline in Databricks→ Create a data engineering project
Designing ETL Pipelines with Medallion Architecture in Azure Synapse
→ Build ETL pipelines in Azure Synapse→ Implement Medallion Architecture
Building reliable ETL pipelines with built-in observability - Data Engineering with Databricks
→ Build ETL pipelines→ Monitor data pipelines→ Optimize data pipeline performance
ETL in SQL Explained | Extract, Transform, Load Process in SQL | SQL Interview Questions & Answers
→ Extract data from a database→ Transform data using SQL→ Load data into a database
Simplify and Fast-Track ETL Modernization with AWS Glue - AWS Online Tech Talks
→ Build ETL pipelines with AWS Glue→ Migrate legacy ETL tools to cloud-based solutions
Building Data Quality in ETL pipelines using AWS Glue Data Quality
→ Build data quality in ETL pipelines→ Use AWS Glue Data Quality
Dataflow for Real-time ETL and Integration
→ Build real-time ETL pipelines with Dataflow→ Deploy ETL pipelines to Google Cloud
SQL-Based ETL: Options for SQL-Only Databricks Development
→ Build SQL-based ETL pipelines→ Create data pipelines using Databricks
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