Requirements

• Basic Knowledge of Data Engineering: Familiarity with concepts like data pipelines
• ETL (Extract
• Transform
• Load) processes
• and data transformation.
• Experience with SQL: Knowledge of SQL (Structured Query Language) for querying and manipulating data. This is essential for working with Databricks and Spark SQL for data transformations.
• Familiarity with Cloud Platforms: Basic understanding of cloud services (such as AWS
• Azure
• or Google Cloud)
• as Databricks integrates with these platforms for storage and compute resources.

Description

The Databricks Professional Data Engineer course is designed to provide data engineers with the knowledge and practical skills required to excel in the modern data landscape. This course focuses on building, optimizing, and managing scalable data pipelines using Databricks and Apache Spark, empowering professionals to design sophisticated data solutions that meet the demands of today's big data environments. As an industry-leading platform for big data processing, Databricks brings together the power of Apache Spark, cloud computing, and Delta Lake to deliver reliable, high-performance data workflows.

Whether you're an experienced data engineer or someone transitioning into the field, this course offers in-depth coverage of advanced data engineering concepts, including real-time data processing, cloud integration, performance tuning, and data governance. Through hands-on labs, practical exercises, and real-world case studies, this course provides a comprehensive and applied understanding of how to leverage Databricks for big data processing.

Course Overview

The Databricks Professional Data Engineer course goes beyond introductory concepts and dives deep into the intricacies of working with Databricks and Spark in large-scale, cloud-based data ecosystems. You will learn how to create optimized data pipelines, integrate with cloud storage and compute resources, use Delta Lake for reliable data management, and fine-tune data workflows for performance and scalability. By the end of the course, you will be equipped to tackle complex data engineering challenges and build high-quality data solutions that support data-driven decision-making in your organization.

Key Concepts Covered

1. Advanced Databricks and Apache Spark A solid understanding of Apache Spark is fundamental for a data engineer, and this course provides in-depth coverage of Spark’s advanced capabilities. You will learn how to work with RDDs (Resilient Distributed Datasets), DataFrames, and Datasets, including their performance considerations and optimization strategies. In addition, the course addresses cluster management and tuning, helping you maximize the performance of Spark jobs in Databricks. Key topics include:

   • Understanding Spark's architecture and execution engine

   • Performance optimizations and job tuning techniques

   • Managing Spark clusters effectively for scalable data processing

2. Building Complex Data Pipelines One of the core responsibilities of a data engineer is building data pipelines. This course covers the creation of complex, efficient ETL (Extract, Transform, Load) workflows using Databricks. You will explore data transformations, scheduling workflows, and incorporating error handling and fault tolerance into your pipelines. Furthermore, the course will introduce you to Spark Streaming for processing real-time data, enabling you to build pipelines that handle both batch and streaming data. Topics include:

   • Designing and building scalable ETL pipelines

   • Using Databricks notebooks for pipeline orchestration

   • Implementing real-time data processing with Spark Streaming

   • Integrating third-party data sources (e.g., Kafka, Kinesis, Azure Event Hubs)

3. Delta Lake and Data Management Delta Lake is an integral part of the Databricks platform, enabling reliable, performant data lakes with ACID (Atomicity, Consistency, Isolation, Durability) transactions. The course will introduce you to Delta Lake’s architecture, covering how it allows you to manage large-scale datasets efficiently while ensuring data quality. You will learn how to implement schema enforcement, time travel, and other powerful features of Delta Lake for data management. Key topics include:

   • Understanding the fundamentals of Delta Lake

   • Implementing schema enforcement and evolution

   • Performing time travel with Delta Lake

   • Optimizing Delta Lake performance (e.g., partitioning, file formats)

4. Performance Optimization and Tuning As data pipelines grow in size and complexity, performance becomes a critical consideration. In this section, you will learn how to optimize the performance of your Spark jobs and Databricks clusters. You will explore various performance-tuning techniques, such as partitioning, caching, and resource management, and discover how to troubleshoot and resolve performance bottlenecks. Topics include:

   • Optimizing Spark job performance through proper configurations

   • Understanding and managing Spark partitions and shuffling

   • Tuning Databricks clusters for high performance

   • Best practices for memory management and job scheduling

5. Cloud Integration and Management Cloud platforms, such as AWS, Azure, and Google Cloud, are increasingly central to modern data engineering workflows. In this course, you will learn how to integrate Databricks with cloud services for scalable storage and compute capabilities. The course covers how to connect Databricks to cloud-based storage systems like Amazon S3, Azure Blob Storage, and Google Cloud Storage, and how to use cloud compute resources to scale your data processing jobs. You will also learn best practices for cloud security and cost optimization. Topics include:

   • Integrating Databricks with cloud storage (e.g., AWS S3, Azure Blob)

   • Managing cloud compute resources for Databricks jobs

   • Ensuring data security and compliance in the cloud

   • Optimizing costs and performance when using cloud services

6. Data Governance and Security Data governance is essential for maintaining the integrity, security, and compliance of data pipelines. This section of the course focuses on implementing data governance strategies within Databricks, such as auditing, lineage tracking, and access control. You will learn how to ensure data privacy and security, implement role-based access control (RBAC), and use encryption for sensitive data. Topics include:

   • Implementing data lineage and auditing mechanisms

   • Configuring role-based access control (RBAC) for data protection

   • Data encryption for both storage and transit

   • Ensuring compliance with regulations (e.g., GDPR, HIPAA)

7. Collaboration and Monitoring Effective collaboration is essential for modern data engineering teams. This course will show you how to use Databricks notebooks to collaborate with team members and share code, insights, and results. You will also learn how to monitor and track the performance of your data pipelines, set up alerts for job failures or anomalies, and troubleshoot any issues that arise. Key topics include:

   • Using Databricks notebooks for collaboration and version control

   • Setting up monitoring and logging for data pipelines

   • Troubleshooting and resolving errors in data workflows

   • Creating automated alerts and notifications for critical issues

Who this course is for:

• Data Engineer
• Big Data Developers
• Cloud Data Engineers