Course Introduction
Databricks Data Engineering Fundamentals Training Course provides a practical foundation for professionals who want to build, transform, automate, and manage modern data pipelines using the Databricks Data Intelligence Platform. The course introduces core data engineering concepts across Apache Spark, Delta Lake, Auto Loader, Lakeflow pipelines, and workflow orchestration, helping participants understand how these technologies work together to support reliable data processing.
Participants learn how Databricks supports end-to-end data engineering, from ingesting raw information through transformation, validation, storage, and scheduled delivery. The course explores practical approaches for working with batch and streaming data, creating reusable data pipelines, and organizing data processing workloads. Databricks currently positions Lakeflow as an end-to-end data engineering solution covering ingestion, transformation, and orchestration.
The training develops hands-on understanding of Apache Spark within Databricks, including DataFrames, Spark SQL, transformations, actions, joins, aggregations, and efficient processing patterns. Learners also work with Delta Lake, which provides the transactional storage foundation for Databricks lakehouse tables and supports ACID transactions, schema enforcement, historical versions, and integration with batch and streaming workloads.
A major focus is practical data ingestion and pipeline development. Participants explore Auto Loader for incremental file ingestion, schema management, data quality considerations, and reliable processing of continuously arriving data. They also learn how Lakeflow pipelines can simplify the development and operational management of production-oriented batch and streaming pipelines through declarative transformations and managed dependencies.
The course also introduces workflow automation with Lakeflow Jobs, enabling participants to understand how notebooks, pipelines, SQL queries, and other tasks can be coordinated into repeatable workflows. Learners examine scheduling, task dependencies, control flow, monitoring, failure handling, and operational practices for maintaining dependable data engineering processes in Databricks.
By the end of the course, participants will have a practical foundation for designing and implementing Databricks data engineering solutions. Through demonstrations, guided exercises, and scenario-based activities, they will gain experience building ingestion and transformation workflows, working with Delta tables, applying data quality practices, and orchestrating pipelines for dependable downstream analytics and data consumption.
Duration
5 days
Who Should Attend
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Aspiring data engineers seeking a practical introduction to Databricks data engineering.
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Data engineers transitioning from traditional ETL platforms to the Databricks environment.
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Data analysts who need foundational skills for preparing and transforming data in Databricks.
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BI developers working with modern lakehouse data platforms and engineering workflows.
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Database professionals expanding their skills into cloud-based data engineering.
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Software developers who need to build data processing applications using Apache Spark.
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Cloud and platform engineers supporting Databricks data workloads and processing environments.
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Data scientists who need stronger knowledge of data ingestion, preparation, and pipeline development.
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ETL developers interested in modern batch and streaming data engineering architectures.
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Technical professionals preparing for Databricks-focused data engineering projects or responsibilities.
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Solution architects who need foundational knowledge of Databricks data pipelines and lakehouse engineering.
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Project and technical leads who need to understand the components and lifecycle of Databricks data engineering solutions.
Course Objectives
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Understand the fundamental architecture of Databricks and how its data engineering capabilities support modern lakehouse-based data processing and analytics workloads.
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Develop practical skills for using Apache Spark on Databricks to ingest, transform, join, aggregate, filter, and process structured and semi-structured datasets.
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Create and manage Delta Lake tables while understanding transactions, schema enforcement, table history, reliable updates, and the role of Delta in lakehouse architectures.
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Configure incremental data ingestion with Auto Loader and understand how automated file discovery, schema handling, checkpointing, and scalable processing support production pipelines.
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Build foundational Lakeflow pipelines for batch and streaming workloads using declarative transformations, data dependencies, materialized views, streaming tables, and data quality practices.
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Apply practical techniques for cleaning, validating, deduplicating, enriching, and transforming data so that downstream consumers receive consistent and trustworthy datasets.
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Understand how Lakeflow Jobs can orchestrate multiple data engineering tasks through scheduling, dependencies, triggers, conditional logic, and repeatable workflow execution.
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Monitor pipeline and job execution, investigate failures, interpret operational information, and apply basic troubleshooting approaches to maintain dependable data processing.
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Apply data engineering best practices for designing scalable, maintainable, reusable, and operationally reliable Databricks pipelines across development and production environments.
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Complete practical data engineering scenarios that combine ingestion, transformation, Delta Lake storage, data quality, pipeline development, and workflow orchestration into an end-to-end solution.
Comprehensive Course Outline
Module 1: Introduction to Databricks Data Engineering
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Overview of Databricks architecture, lakehouse concepts, and the role of data engineering in modern cloud data platforms.
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Understanding Databricks workspaces, notebooks, compute resources, catalogs, schemas, tables, and fundamental development workflows.
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Exploring the relationship between Apache Spark, Delta Lake, Lakeflow, and other core technologies used for data engineering.
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Reviewing common data engineering lifecycle stages from source ingestion and transformation through storage, orchestration, monitoring, and delivery.
Module 2: Apache Spark Fundamentals on Databricks
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Understanding Apache Spark architecture, distributed processing concepts, Spark sessions, DataFrames, transformations, actions, and execution.
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Working with Spark DataFrames to read datasets, inspect schemas, select columns, filter records, and perform common transformations.
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Using Spark SQL and PySpark to perform joins, aggregations, sorting, grouping, conditional transformations, and analytical data preparation.
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Applying practical Spark development techniques for readable, reusable, and efficient data processing notebooks.
Module 3: Data Ingestion and Source Integration
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Understanding common data ingestion patterns for files, cloud object storage, databases, applications, and other enterprise data sources.
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Designing ingestion workflows that separate raw source data from transformed datasets and support repeatable processing operations.
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Exploring incremental ingestion approaches for continuously arriving files and changing datasets within a Databricks lakehouse environment.
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Applying foundational techniques for handling file formats, schemas, malformed records, metadata, ingestion timestamps, and source-system variations.
Module 4: Delta Lake Fundamentals
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Understanding Delta Lake as the transactional storage foundation for Databricks lakehouse tables and modern data engineering workloads.
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Creating, reading, updating, merging, and managing Delta tables using SQL and Apache Spark DataFrame operations.
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Exploring ACID transactions, schema enforcement, table history, versioning, and reliable data modification capabilities provided by Delta Lake.
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Applying Delta Lake practices for supporting both batch and streaming workloads while maintaining dependable and reusable datasets.
Module 5: Auto Loader and Incremental Data Processing
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Understanding Auto Loader architecture and how it incrementally processes new files arriving in supported cloud storage locations.
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Configuring Auto Loader for scalable ingestion while considering schema inference, schema evolution, checkpoints, and incremental processing behavior.
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Designing ingestion workflows that support reliable processing, recovery, and efficient handling of large numbers of incoming files.
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Applying Auto Loader within practical ETL scenarios to move raw source data into Delta Lake tables for downstream transformation.
Module 6: Lakeflow Pipelines for Data Engineering
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Understanding Lakeflow pipelines and their role in simplifying the development, deployment, and management of batch and streaming data pipelines.
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Building declarative data transformations using SQL and Python while organizing dependencies between source datasets and downstream outputs.
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Working with streaming tables, materialized views, flows, sinks, and other pipeline components used to create structured data processing workflows.
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Applying data quality expectations, transformation logic, and pipeline lifecycle practices to create maintainable production-oriented data pipelines.
Module 7: Data Transformation and Quality Engineering
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Applying filtering, cleansing, standardization, enrichment, joins, aggregations, and deduplication techniques to raw and operational datasets.
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Designing transformation logic that addresses missing values, invalid records, inconsistent formats, duplicate data, and unexpected source changes.
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Implementing practical data quality checks and validation rules to identify unreliable records before they reach downstream consumers.
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Organizing transformation layers to create clean, reusable, and trusted datasets suitable for analytics, reporting, and other business applications.
Module 8: Batch and Streaming Data Engineering
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Comparing batch and streaming processing models and identifying appropriate scenarios for each approach within Databricks data engineering projects.
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Understanding Apache Spark Structured Streaming concepts and how streaming workloads integrate with Delta Lake and incremental ingestion.
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Designing pipelines that process continuously arriving data while considering checkpoints, state, latency, fault tolerance, and output requirements.
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Combining batch and streaming techniques to support flexible lakehouse architectures and evolving organizational data requirements.
Module 9: Lakeflow Jobs and Workflow Orchestration
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Understanding Lakeflow Jobs as the orchestration layer for coordinating repeatable data processing tasks and complex multi-step workflows.
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Creating jobs with multiple tasks, dependencies, schedules, triggers, parameters, and control-flow logic for automated data engineering operations.
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Integrating notebooks, pipelines, SQL queries, and other supported workloads into coordinated workflows that execute consistently.
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Monitoring job runs, investigating task failures, configuring notifications, and applying operational practices for reliable workflow execution.
Module 10: End-to-End Databricks Data Engineering Project
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Designing an end-to-end pipeline that ingests source files, validates incoming records, transforms datasets, and stores results in Delta Lake.
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Building an incremental ingestion workflow using Auto Loader and integrating transformations and quality controls into the processing lifecycle.
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Creating a Lakeflow Job to schedule and orchestrate pipeline activities while incorporating dependencies, monitoring, and failure-handling practices.
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Reviewing the completed solution against scalability, reliability, maintainability, data quality, and operational requirements for practical enterprise use.
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: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill 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: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
