Course Overview
Advanced DataEngineering with Databricks provides an in-depth exploration of advanced data processing, modeling, and Databricks tools. It focuses on optimizing partitioning strategies, implementing incremental data processing with Delta Lake and Structured Streaming, and applying best practices for data transformation and quality. Participants will gain expertise in leveraging Databricks tools for robust security, performance monitoring, and efficient testing and deployment of data pipelines, ensuring effective management and governance within a Databricks environment.
Course Modules
1. Partitioning Strategies and Performance Optimization
- Differentiate between partitioning strategies: coalesce, repartition, repartition by range, and rebalance
- Evaluate and select optimal partitioning columns for various data scenarios
- Analyze the effects of file size management and over-partitioning on Spark query performance
2. Data Frame Management and Manipulation
- Configure PySpark DataFrames to control file size during disk writes
- Implement strategies for updating records in Spark tables (Type 1 updates)
3. Streaming and Delta Lake Integration
- Apply design patterns for Structured Streaming and Delta Lake integration
- Optimize state management with stream-static joins and Delta Lake
- Implement stream-static joins and deduplication techniques within Spark Structured Streaming
- Activate Change Data Feed (CDF) on Delta Lake tables and adapt processing workflows for CDC
- Utilize CDF for efficient data propagation and deletion
- Demonstrate effective data partitioning strategies for archiving and data deletion
1. Transformation and Quality Assurance
- Outline data transformation objectives during the transition from bronze to silver layers
- Examine how Change Data Feed (CDF) resolves update and delete propagation issues within Lakehouse architecture
- Utilize Delta Lake cloning to understand the interaction of shallow and deep clones with source and target tables
2. Table Design and Implementation
- Design multiplex bronze tables to address challenges in scaling streaming workloads
- Implement best practices for streaming data from multiplex bronze tables
- Apply incremental processing, data quality enforcement, and deduplication from bronze to silver layers
- Assess data quality enforcement methods based on Delta Lake capabilities
- Address the absence of foreign key constraints in Delta Lake tables
- Implement constraints to maintain data integrity in Delta Lake tables
- Develop lookup tables and evaluate trade-offs for normalized data models
- Design architectures for Slowly Changing Dimension (SCD) tables using Delta Lake for both streaming and batch workloads
- Implement SCD Types 0, 1, and 2 tables
1. Delta Lake Fundamentals
- Explain Delta Lake’s transaction log and cloud object storage for ensuring atomicity and durability
- Describe Delta Lake’s Optimistic Concurrency Control for transaction isolation and conflict resolution
- Detail the functionality of Delta Lake’s cloning features
2. Optimization and Indexing
- Apply Delta Lake indexing techniques, including partitioning, Z-order indexing, bloom filters, and file size management
- Optimize Delta tables for performance in Databricks SQL service
1. Dynamic Data Access Control
- Implement dynamic views for data masking and access control
- Utilize dynamic views to manage row and column-level access within the data environment
1. System Performance Analysis
- Analyze Spark UI elements for performance evaluation, application debugging, and optimization
- Monitor event timelines and metrics for job stages within the cluster
- Derive insights from Spark UI, Ganglia UI, and Cluster UI to address performance issues and debug applications
2. Job Management and Deployment
- Design systems to manage cost and latency SLAs for production streaming jobs
- Deploy and oversee streaming and batch job execution
1. Notebook and Code Management
- Adapt notebook dependency patterns to integrate Python file dependencies
- Convert Python code maintained as Wheels for direct imports using relative paths
- Troubleshoot and resolve failed jobs
2. Job Creation and CLI Configuration
- Design Jobs based on common use cases and establish multi-task job dependencies
- Configure Databricks CLI for workspace and cluster interaction
- Execute CLI commands for job deployment and monitoring
- Utilize REST API for job cloning, run triggering, and output export
FAQ's
Topics include advanced ETL pipeline design, performance tuning, Delta Live Tables, and handling complex data workflows.
Participants should have a solid understanding of data engineering principles, experience with Apache Spark, and familiarity with Databricks.
The course is typically offered over 2 to 4 days and includes a combination of lectures, practical labs, and case studies.
Some courses may include quizzes or practical assessments to evaluate understanding and application of the concepts covered.
Post-course support may include access to online forums, additional resources, or follow-up sessions for clarifying any doubts or challenges.
The course is often available both in-person and online, depending on the training provider and course schedule.
Registration details can typically be found on the training provider’s website. Participants can register directly online or contact the provider for further information
Course Features
Our course covers advanced ETL techniques, Delta Live Tables for managing data pipelines, and performance tuning strategies for large-scale data processing. You’ll gain insights into scalable data architecture design and work on hands-on projects that tackle real-world challenges. By the end of the course, you’ll master complex data pipelines, optimize performance for large datasets, and unify data management with Delta Live Tables. With these advanced skills, you’ll position yourself as a data engineering expert and earn a certificate to boost your professional recognition.
Advanced ETL Techniques
In-depth coverage of complex ETL patterns and best practices.
Hands-On Projects
Practical projects that involve real-world data engineering challenges.
Scalable Architecture
Insights into designing and implementing scalable and efficient data architectures.
Elevate Your Skills
Join our courses to enhance your expertise in data engineering, machine learning, and advanced analytics. Gain hands-on experience with the latest tools and techniques that are shaping the future of data.
