Course Details
Data
Engineering
Excellence
A 2-Day Expert-Level Training on Databricks
Course Overview
Optimize data pipeline development with the Databricks Lakehouse Platform. Use SQL and Python for efficient data extraction, transformation, and loading. Leverage Delta Live Tables for streamlined ingestion and incremental updates. Ensure data integrity and performance with Delta Lake’s ACID transactions and versioning.
In addition, learn to Implement robust data governance using Unity Catalog for metadata management and security. Oversee pipelines to deliver timely results for analytics and dashboards.
Course Modules
1. Understanding the Databricks Lakehouse
- Analyze the synergy between data lakehouses and data warehouses.
- Explore the improvements in data integrity and quality within a lakehouse environment compared to traditional data lakes.
2. Exploring Databricks Platform Architecture
- Gain a comprehensive overview of key architectural components.
- Differentiate between general-purpose clusters and job-specific clusters.
3. Cluster Management and Configuration
- Learn about version management and updates with Databricks Runtime.
- Discover techniques for filtering and accessing specific clusters.
- Understand the implications of cluster termination and identify optimal restart scenarios.
4. Notebook Functionality and Collaboration
- Leverage multiple programming languages within notebooks.
- Execute notebooks programmatically from within other notebooks.
- Explore strategies for sharing and collaborating on notebooks.
5. CI/CD Integration with Databricks Repos
- Implement continuous integration and deployment workflows using Databricks Repos.
- Understand Git operations and their integration with Databricks Repos.
- Compare notebooks version control with Databricks Repos.
1. Data Extraction and Loading Techniques
- Extract data from single files and directory structures.
- Create views, temporary views, and common table expressions (CTEs) for effective data management.
2. Managing External Data Sources
- Interact with non-Delta external tables.
- Explore methods for creating tables from JDBC connections and external CSV files.
3. Data Transformation and Validation
- Apply aggregation functions and handle NULL values.
- Implement strategies for data deduplication and integrity validation.
- Ensure unique values and perform field validations.
4. Data Type Conversion and Parsing
- Cast columns to timestamps and extract temporal data.
- Utilize string operations and dot notation for data extraction.
- Understand the benefits of array functions and JSON parsing.
5. Advanced SQL Techniques
- Analyze join queries and choose between explode and flatten functions.
- Pivot data formats and define SQL User-Defined Functions (UDFs).
- Utilize CASE/WHEN constructs for advanced SQL logic
1. Delta Lake ACID Transactions
- Understand ACID transaction principles and their advantages.
- Evaluate ACID compliance and transaction benefits.
2. Data and Metadata Management
- Distinguish between data and metadata management.
- Compare managed and external tables.
3. Table Management and Version Control
- Create, manage, and inspect tables.
- Analyze Delta Lake directory structures and historical data.
- Roll back tables to previous versions and query specific versions.
4. Data Optimization and Compaction
- Utilize Zordering for data optimization and file compaction.
- Implement generated columns and add metadata annotations.
5. Data Operations and Commands
- Compare CTAS and CREATE OR REPLACE TABLE with INSERT OVERWRITE.
- Identify scenarios for using MERGE and COPY INTO commands.
- Address COPY INTO command issues and troubleshoot effectively.
6. Delta Live Tables (DLT)
- Create and manage Delta Live Tables pipelines.
- Understand triggered versus continuous pipelines.
- Leverage Auto Loader for efficient data ingestion.
- Handle constraint violations and change data capture.
- Analyze event logs and troubleshoot DLT syntax issues.
1. Task Management and Configuration
- Explore the advantages of utilizing multiple tasks within jobs.
- Configure predecessor tasks and identify optimal use cases.
- Review and analyze task execution history.
2. Scheduling and Monitoring Tasks
- Employ CRON expressions for task scheduling.
- Debug and resolve task failures.
- Implement retry policies and notification alerts.
- Configure email notifications for task alerts.
1. Principles of Data Governance
- Explore core components and best practices in data governance.
- Differentiate between metastores and catalogs.
2. Managing Unity Catalog
- Gain an overview of Unity Catalog and its security features.
- Define and utilize service principals.
- Understand security modes compatible with Unity Catalog.
3. Best Practices and Access Control
- Set up UC-enabled clusters and Databricks SQL warehouses.
- Navigate and query three-layer namespaces.
- Implement data object access controls and adhere to best practices.
- Follow best practices for metastore and workspace colocation.
- Use service principals and ensure business unit segregation.
FAQ's
Basic knowledge of SQL, Python, and data engineering concepts. Familiarity with Apache Spark is beneficial but not required.
The course duration typically ranges from 2 to 4 days, depending on the specific program and depth of content.
Participants will learn how to build and manage data pipelines, work with Delta Lake, optimize Spark jobs, and integrate Databricks with various data sources and tools.
Yes, the course includes hands-on labs and practical exercises to help participants apply what they have learned in real-world scenarios.
Participants receive access to course slides, lab exercises, sample data, and additional resources. A certificate of completion is typically awarded at the end of the course
The course can be delivered in various formats, including in-person, virtual, or hybrid. The format may depend on the training provider and organizational requirements.
Yes, many training providers offer customized courses tailored to the specific needs and objectives of an organization.
Course Features
Our course offers hands-on labs using Databricks notebooks, real-world case studies, and training on Delta Lake for reliable data processing. You’ll learn performance optimization techniques for Spark jobs, and how to automate and schedule ETL workflows. By completing the course, you’ll gain expertise in building and managing robust data pipelines, streamlining ETL processes, and ensuring data quality. This practical knowledge will enhance your career prospects, with a certificate of completion to add to your professional credentials.
Integration with Delta Lake
Training on how to use Delta Lake for reliable and scalable data processing.
Hands-On Labs
Practical exercises using Databricks notebooks to reinforce learning.
Data Pipeline Automation
Tools and practices for automating and scheduling ETL workflows.
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.
