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DP-750: Implementing Data Engineering Solutions with Azure Databricks
Handling large volumes of data efficiently is one of the central challenges for modern organizations. Azure Databricks provides a unified cloud platform that brings together data engineering, analytics, and machine learning – enabling teams to build and scale powerful data solutions.
Key topics
- Designing and implementing high-throughput data flows
- Transforming and analyzing large-scale datasets using Apache Spark
- Structuring efficient lakehouse environments with Delta Lake
- Connecting diverse data sources into unified pipelines
- Improving processing speed and ensuring data quality
- Orchestrating and automating cloud-native data workflows
- Handling heterogeneous data across structured and unstructured formats
- Processing continuous data streams in real time
Prerequisite
Familiarity with SQL and cloud services allows you to apply the content right away. A background in database concepts or data processing is an advantage, though not required.
Target group
This course is aimed at anyone who wants to leverage Azure Databricks for real-world data projects and expand their cloud engineering skills – from pipeline design to analytics.
Azure Databricks is one of the leading platforms in cloud-based data engineering. The DP-750 course gives you the skills to take on complex data challenges and advance your career in a field with growing demand.
Course Content
Setting Up Compute Resources in the Databricks Workspace
- Identifying and selecting the right compute type
- Defining performance and scaling parameters
- Configuring Photon, Runtime, Spark, and ML
- Installing and managing libraries
- Setting up and maintaining access permissions
Building and Organizing Objects in Unity Catalog
- Following consistent naming conventions
- Building catalogs, schemas, and namespaces
- Creating volumes in Unity Catalog
- Setting up tables, views, and materialized views
- Connecting and configuring external catalogs
- Applying DDL operations to tables
- Setting up AI/BI Genie for Data Discovery
Protecting and Securing Unity Catalog Objects
- Managing access permissions for Unity Catalog objects
- Implementing table, column, and row-level access controls
- Leveraging Azure Key Vault keys in Azure Databricks
- Configuring authentication via service principals
- Securing resource access with managed identities
Maintaining and Administering Unity Catalog Objects
- Keeping table and column definitions up to date
- Configuring ABAC using tags and policies
- Implementing row filters and column masking
- Defining and enforcing data retention policies
- Tracking data lineage in Catalog Explorer
- Configuring and reviewing audit logs
- Designing secure Delta Sharing strategies
Designing and Realizing Data Models for Unity Catalog
- Capturing data sources and configuring ingestion
- Choosing the right ingestion tool (e.g., Lakeflow Connect, Notebooks, Azure Data Factory)
- Deciding between batch and streaming ingestion
- Determining the appropriate table format (Parquet, Delta, CSV, JSON, Iceberg)
- Establishing partitioning strategies
- Choosing the right SCD type
- Defining data granularity precisely
- Capturing the history of changes in datasets
- Setting a clustering approach (Liquid Clustering, Z-Ordering, Deletion Vectors)
- Deciding between managed and unmanaged tables
Ingesting Data into Unity Catalog
- Ingesting data with Lakeflow Connect (Batch & Streaming)
- Using notebooks for data collection (Batch & Streaming)
- Loading data via SQL (CTAS, CREATE OR REPLACE, COPY INTO)
- Leveraging CDC feeds for continuous data integration
- Using Spark Structured Streaming for real-time ingestion
- Connecting Azure Event Hubs as a streaming source
- Deploying Lakeflow Spark Declarative Pipelines with Auto Loader
Preparing, Transforming, and Loading Data into Unity Catalog
- Creating data profiles and analyzing distributions
- Choosing the right data types for columns
- Removing duplicates, missing values, and nulls
- Applying filters, groupings, and aggregations
- Combining datasets with Join, Union, and Intersect
- Pivoting and denormalizing datasets
- Using Merge, Insert, and Append operations for loading
Ensuring and Controlling Data Quality in Unity Catalog
- Setting up checks for null values, cardinality, and value ranges
- Configuring data type validations
- Controlling schemas and handling deviations
- Using pipeline expectations in Lakeflow Spark Declarative Pipelines for quality control
Designing and Building Data Pipelines
- Defining the logic and flow of data pipelines
- Deciding between notebook-based and Lakeflow pipeline approaches
- Developing and configuring task logic for Lakeflow
- Implementing error handling strategies for pipelines and jobs
- Building notebook-based data pipelines
- Using Lakeflow Pipelines for data processing
Building and Operating Lakeflow Jobs
- Setting up and configuring jobs from scratch
- Defining and activating job triggers
- Planning and scheduling project workflows
- Setting up job notifications
- Configuring automatic restarts for jobs and pipelines
Using Azure Databricks for Professional Development Workflows
- Applying version control with Git in a structured way
- Maintaining branches, coordinating pull requests, and resolving conflicts
- Applying test concepts (component, integration, E2E, and UAT tests)
- Setting up and packaging Databricks Asset Bundles
- Deploying bundles via the Azure Databricks CLI
- Deploying bundles via the REST API
Monitoring, Analyzing, and Optimizing Workloads
- Controlling cluster utilization for performance and cost efficiency
- Debugging Lakeflow jobs (repair, restart, pause, and run)
- Resolving performance issues in Apache Spark jobs and notebooks
- Analyzing caching, skew, spill, and shuffle issues with DAG, Spark UI, and Query Profile
- Optimizing Delta tables with OPTIMIZE and VACUUM
- Setting up log streaming with Log Analytics in Azure Monitor
- Configuring alert rules in Azure Monitor
Up to 100% funding for your continuing education
Attractive funding opportunities are available for numerous continuing education programs – depending on the funding program, up to 100% funding is possible.
The requirement is that you live or work in Germany. Our funding opportunities are not available outside of Germany.
Funding opportunities are available for career starters, job seekers, career changers, startups, experienced professionals, those seeking career advancement, and specialists.
What practical skills does the DP-750 course cover?
- Building data flows, handling large datasets, and deploying scalable solutions with Azure Databricks in production environments.
What makes the DP-750 course especially valuable for data engineers?
- In the training, you learn how real-time processing, analytics, and cloud workflows can be effectively combined within a single platform.
What technologies are used in the DP-750 training?
- Apache Spark, Delta Lake, and Azure Databricks form the technological foundation for production-ready data engineering workflows in the cloud.
What measurable benefits does Azure Databricks deliver in projects?
- Higher processing speeds, automated data flows, and robust analytics capabilities for large-scale datasets.
Who is the DP-750 course best suited for?
- Ideal for anyone looking to build Azure-based data engineering practices systematically or deepen their existing cloud knowledge in a targeted way.
How does the DP-750 prepare you for big data projects?
- Scalable architecture patterns, high-performance processing approaches, and hands-on Databricks solutions are at the center of the training.
What do you learn about Delta Lake in the DP-750 training?
- Through the use of modern lakehouse technologies, data quality, pipeline reliability, and analytics speed all improve.
What strategic advantage does Azure Databricks offer organizations?
- Azure Databricks seamlessly unites data integration, AI applications, and analytics in one platform, creating a highly efficient ecosystem.
