In this course, students will build upon their existing knowledge of Apache Spark, Structured Streaming, and Delta Lake to unlock the full potential of the data lakehouse by utilizing the suite of tools provided by Databricks. This course places a heavy emphasis on designs favoring incremental data processing, enabling systems optimized to continuously ingest and analyze ever-growing data. By designing workloads that leverage built-in platform optimizations, data engineers can reduce the burden of code maintenance and on-call emergencies, and quickly adapt production code to new demands with minimal refactoring or downtime.
The topics in this course should be mastered prior to attempting theDatabricks Certified Data Engineer Professional exam.
Languages Available: English | 日本語| Português BR|한국어
Skill Level
Professional
Duration
16h
Prerequisites
- Experience using PySpark APIs to perform advanced data transformations
- Familiarity implementing classes with Python
- Experience using SQL in production data warehouse or data lake implementations
- Experience working in Databricks notebooks and configuring clusters
- Familiarity with creating and manipulating data in Delta Lake tables with SQL
The prerequisites listed above can be learned by taking the Data Engineering with Databricks and Apache Spark Programming with Databricks instructor-led courses (can be taken in either order) and validated by passing the Databricks Certified Data Engineer Associate and Databricks Certified Associate Developer for Apache Spark certification exams.
Outline
Day 1
- The Lakehouse Architecture
- Optimizing Data Storage
- Understanding Delta Lake Transactions
- Delta Lake Isolation with Optimistic Concurrency
- Streaming Design Patterns
- Clone for Development and Data Backup
- Auto Loader and Bronze Ingestion Patterns
- Streaming Deduplication and Quality Enforcement
- Slowly Changing Dimensions
- Streaming Joins and Statefulness
Day 2
- Stored and Materialized Views
- Storing Data Securely
- Granting Privileged Access to PII
- Deleting Data in the Lakehouse
- Orchestration and Scheduling with Multi-Task Jobs
- Monitoring, Logging, and Handling Errors
- Promoting Code with Databricks Repos
- Programmatic Platform Interactions (Databricks CLI and REST API)
- Managing Costs and Latency with Streaming Workloads
Upcoming Public Classes
Date | Time | Language | Price |
|---|---|---|---|
Jul 08 - 11 | 01 PM - 05 PM (Australia/Sydney) | English | $1500.00 |
Jul 08 - 09 | 09 AM - 05 PM (Asia/Kolkata) | English | $1500.00 |
Jul 10 - 11 | 09 AM - 05 PM (Europe/Paris) | English | $1500.00 |
Jul 15 - 16 | 09 AM - 05 PM (Europe/London) | English | $1500.00 |
Jul 22 - 25 | 09 AM - 01 PM (America/Los_Angeles) | English | $1500.00 |
Jul 29 - 30 | 09 AM - 05 PM (Australia/Sydney) | English | $1500.00 |
Jul 30 - Aug 02 | 08 AM - 12 PM (Asia/Singapore) | English | $1500.00 |
Aug 05 - 06 | 09 AM - 05 PM (America/Chicago) | English | $1500.00 |
Aug 07 - 08 | 09 AM - 05 PM (Europe/Paris) | English | $1500.00 |
Aug 12 - 13 | 09 AM - 05 PM (Europe/London) | English | $1500.00 |
Aug 19 - 20 | 09 AM - 05 PM (Australia/Sydney) | English | $1500.00 |
Aug 26 - 29 | 09 AM - 01 PM (Asia/Kolkata) | English | $1500.00 |
Sep 03 - 04 | 09 AM - 05 PM (Europe/Paris) | English | $1500.00 |
Sep 16 - 19 | 01 PM - 05 PM (Australia/Sydney) | English | $1500.00 |
Oct 08 - 09 | 09 AM - 05 PM (Europe/London) | English | $1500.00 |
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