In the era of big data and cloud computing, mastering Data Engineering with Databricks is a crucial skill for building scalable, reliable, and efficient data pipelines that power modern analytics and machine learning applications, as the Databricks Lakehouse platform unifies data warehousing and data lakes to simplify complex data architectures. This specialized knowledge is essential for data engineers seeking to streamline Extract, Transform, Load (ETL) processes, ensure data quality, and accelerate data delivery to business stakeholders. This comprehensive training course is meticulously designed to equip data engineers, data scientists, and cloud architects with the advanced knowledge and practical strategies required to leverage Databricks' full potential, from building a robust Delta Lake foundation to orchestrating complex data workflows and implementing production-grade data solutions. Without robust expertise in Data Engineering with Databricks, organizations risk inefficient data processing, data quality issues, and a fragmented data ecosystem that hinders innovation and timely decision-making, underscoring the vital need for specialized expertise in this critical domain.

Duration: 10 Days

Target Audience

• Data Engineers and ETL Developers
• Data Scientists working with large datasets
• Cloud Architects and DevOps Engineers
• Business Intelligence (BI) Developers
• Database Administrators (DBAs)
• IT Managers and Technical Leaders
• Analytics Professionals
• Software Engineers with an interest in data infrastructure
• Anyone involved in designing, building, or managing data pipelines on the cloud.

Objectives

• Understand the Databricks Lakehouse Platform and its core components.
• Learn about the Delta Lake format and its advantages for data engineering.
• Acquire skills in using Apache Spark for large-scale data processing in Databricks.
• Comprehend techniques for building reliable and scalable ETL/ELT pipelines.
• Explore strategies for managing data quality and data governance with Databricks.
• Understand the importance of structured streaming for real-time data ingestion.
• Gain insights into orchestrating and automating data workflows using Databricks Jobs.
• Develop a practical understanding of CI/CD practices for Databricks projects.
• Master the use of Databricks notebooks, Repos, and the collaborative workspace.
• Acquire skills in optimizing Spark jobs and managing cluster resources.
• Learn to apply best practices for security and access control in Databricks.
• Comprehend techniques for migrating existing data pipelines to the Databricks platform.
• Explore strategies for MLOps and preparing data for machine learning.
• Understand the importance of using Databricks SQL for business analytics.
• Develop the ability to lead and implement production-ready Data Engineering with Databricks solutions.

Course Content

Module 1: Introduction to the Databricks Lakehouse Platform

• Overview of the Lakehouse architecture and its benefits.
• The Databricks workspace: notebooks, clusters, and the Web UI.
• Core components: Delta Lake, Apache Spark, and Databricks Runtime.
• The role of Databricks in the modern data stack.
• Setting up a Databricks environment and workspace.

Module 2: Delta Lake Fundamentals

• Introduction to Delta Lake: what it is and why it's important.
• Key features: ACID transactions, time travel, and schema enforcement.
• Creating, reading, and writing to Delta tables.
• Using Delta Lake for data versioning and auditing.
• Optimizing Delta tables: VACUUM and OPTIMIZE commands.

Module 3: Apache Spark for Data Engineering

• Spark architecture review: RDDs, DataFrames, and the Catalyst Optimizer.
• Using Spark SQL for data manipulation and querying.
• Core Spark transformations and actions.
• Partitioning data for performance.
• Using Databricks notebooks for interactive Spark development.

Module 4: Building ETL Pipelines with Databricks

• The medallion architecture: Bronze, Silver, and Gold tables.
• Designing and implementing a simple ETL pipeline.
• Reading data from various sources (S3, ADLS, JDBC).
• Transforming data using Spark DataFrames.
• Writing clean, transformed data to Delta tables.

Module 5: Data Ingestion and Structured Streaming

• Introduction to Structured Streaming for real-time data.
• Reading data from streaming sources (Kafka, Kinesis).
• Performing stateful and stateless transformations on streams.
• Writing stream-processed data to Delta tables.
• Monitoring and managing streaming jobs.

Module 6: Advanced ETL with Databricks

• Using MERGE statements for Upserts and Slowly Changing Dimensions (SCD).
• Handling common data quality issues and data validation.
• Implementing data lineage and data cataloging.
• Error handling and fault tolerance in ETL pipelines.
• Best practices for production-ready ETL code.

Module 7: Data Quality and Governance with Databricks

• Data quality frameworks and their implementation.
• Schema evolution and enforcement in Delta Lake.
• Using Databricks Unity Catalog for unified governance.
• Managing access control and permissions for data and notebooks.
• Auditing and compliance features in the Databricks platform.

Module 8: Orchestration and Automation with Databricks

• Introduction to Databricks Jobs for scheduling and orchestration.
• Creating multi-task workflows with dependencies.
• Monitoring job runs and setting up notifications.
• Parameterizing notebooks and jobs for reusability.
• Integrating with external orchestrators like Airflow and Azure Data Factory.

Module 9: MLOps and Data Preparation for Machine Learning

• The role of data engineers in the machine learning lifecycle.
• Feature engineering and data preprocessing for models.
• Creating feature tables with Delta Lake.
• Using Databricks AutoML for feature selection.
• Serving data to ML models from the Lakehouse.

Module 10: Performance Tuning and Optimization

• Optimizing Spark configurations for job performance.
• Troubleshooting slow Spark jobs using the Spark UI.
• Best practices for data layouts and file sizes.
• Caching and broadcasting for performance gains.
• Techniques for cost optimization and resource management.

Module 11: Security and Operations Management

• Securing the Databricks workspace and clusters.
• Identity management and single sign-on (SSO).
• Networking and private link configurations.
• Monitoring cluster health and performance.
• CI/CD practices for Databricks notebooks and projects.

Module 12: Databricks SQL for Analytics and BI

• Introduction to Databricks SQL: what it is and who it's for.
• Using Databricks SQL warehouses for high-performance queries.
• Connecting BI tools (Tableau, Power BI) to Databricks.
• Creating dashboards and visualizations with Databricks SQL.
• Governance and security for SQL analytics.

Module 13: Migrating Data Pipelines to Databricks

• Strategies for migrating on-premise or legacy data warehouses.
• Migrating ETL jobs from other platforms (e.g., Hive, Informatica).
• Tools and scripts for data migration.
• Planning for a seamless transition and minimizing downtime.
• Case studies of successful migration projects.

Module 14: Advanced Databricks Features

• Using Databricks Repos for Git integration and version control.
• Databricks Connect for developing locally.
• The Databricks APIs for programmatic access.
• Managing ML models and experiments with MLflow.
• The Databricks Marketplace and solutions.

Module 15: Practical Workshop: Building an End-to-End Project

• Participants work in teams on a real-world data engineering project.
• Exercise: Design a Lakehouse architecture for the project.
• Build an ETL pipeline from raw data to a Gold table.
• Orchestrate the pipeline using Databricks Jobs.
• Present the final project and discuss key design decisions.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.