Data Science & Analytics
Mastering Databricks & Apache Spark: Build ETL Data Pipeline
This course focuses on the end-to-end process of collecting, transforming, processing, and delivering data across enterprise analytics environments.
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Included with Skilldacity Membership
About This Course
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This course focuses on the end-to-end process of collecting, transforming, processing, and delivering data across enterprise analytics environments. The course explores data ingestion, transformation, workflow orchestration, Delta Lake, data quality management, performance optimization, and pipeline automation techniques used in modern data engineering environments.
What You’ll Learn
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- Master Databricks and Apache Spark for modern data engineering
- Build ETL Data Pipelines for large-scale enterprise data environments
- Understand Apache Spark architecture and distributed data processing
- Ingest, transform, and process structured and unstructured data
- Develop scalable ETL and ELT workflows using Databricks
- Work with Spark DataFrames, Spark SQL, and Delta Lake
- Implement data quality validation and governance techniques
- Optimize data pipeline performance and resource utilization
- Automate workflow execution and orchestration processes
- Support analytics, reporting, AI, and machine learning initiatives
- Troubleshoot common data pipeline and processing issues
- Apply industry best practices for cloud-based data engineering
Skills You’ll Gain
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Career Path
Data Science & Analytics
Provider
Skilldacity
Level
Advanced
Certification
Certification Reference
Access
Included with eligible Skilldacity subscription
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