Databricks Data Engineer Associate Professional

🌟 Databricks Data Engineer Associate Professional Course 🌟

Course Title:

Mastering Data Engineering with Databricks and Apache Spark

Target Audience:

Data Engineers, Software Engineers, Data Scientists, and IT Professionals who aim to master data engineering using Databricks and Apache Spark.

Course Description:

This comprehensive course is designed to empower individuals with the skills and knowledge necessary to leverage Databricks and Apache Spark for building scalable, high-performance data pipelines. Through a blend of theoretical concepts and practical exercises, you will learn to design, implement, optimize, and manage robust data engineering solutions in a distributed computing environment.

Course Objectives:

By the end of this course, students will be able to:

1. Understand Databricks and Apache Spark: Learn about the architecture and capabilities of Databricks and how it utilizes Apache Spark for data processing.

2. Design and Implement Data Pipelines: Gain the ability to design data ingestion, transformation, and delivery pipelines using Databricks.

3. Optimize Performance: Learn advanced techniques to optimize the performance of data processing jobs, including caching, partitioning, and shuffle operation tuning.

4. Manage Data Storage: Master the use of Delta Lake for ensuring high-velocity data engineering with ACID transactions, schema evolution, and time travel capabilities.

5. Performance Tuning: Understand how to tune Spark jobs and configure clusters effectively to handle large-scale workloads efficiently.

6. Cluster Management: Acquire skills in configuring, scaling, and monitoring Databricks clusters to match task requirements and optimize resource usage.

7. Ensure Data Security and Governance: Learn best practices for securing data within Databricks via access control, permissions, encryption, and audit logging.

8. Collaborate with Databricks Notebooks: Discover how to leverage the full potential of Databricks Notebooks for collaborative development, version control, and documentation.

9. Integrate with Cloud Services: Know how to seamlessly integrate Databricks with leading cloud services like AWS, Azure, and Google Cloud for storing, computing, and managing data at scale.

Course Curriculum:

1. Introduction to Databricks and Apache Spark

   • Overview of the Databricks Platform
   • Apache Spark architecture and primitives
   • Understanding big data processing paradigms

2. Designing Data Pipelines with Databricks

   • Conceptualizing end-to-end data pipelines
   • Data ingestion strategies in Databricks
   • ETL (Extract, Transform, Load) processes implementation

3. Performance Optimization Techniques

   • Caching and persistence strategies for Spark jobs
   • Understanding partitioning and its impact on performance
   • Tuning configurations for optimal Spark performance

4. Data Storage with Delta Lake

   • Introduction to Delta Lake and its benefits
   • Executing transactions, delta tables, and time travel in Delta Lake
   • Data storage optimization techniques like partitioning and compaction

5. Cluster Configuration and Optimization

   • Best practices for cluster configuration and provisioning
   • Auto-scaling clusters based on workload
   • Monitoring performance and troubleshooting issues

6. Data Security, Compliance, and Governance

   • Access control and role-based permissions in Databricks
   • Data encryption mechanisms in transit and at rest
   • Setting up audit logging for better governance

7. Collaborative Development with Notebooks

   • Utilizing notebooks as a collaborative tool
   • Git integration for version control in Databricks
   • Effective documentation and best practices for managing notebooks

8. Integration with Cloud Services

   • Connecting to cloud storage services like S3 or ADLS
   • Leveraging cloud compute resources (AWS, Azure, GCP) for scalable processing
   • Best practices for securing cloud-based data engineering solutions

Learning Outcomes:

Upon completion of this course, students will have a solid understanding of how to implement and manage data pipelines using Databricks. They will be equipped with the skills to optimize these pipelines for performance, ensure high levels of security and governance, and integrate cloud-based services effectively. Students will also gain practical experience through hands-on exercises and real-world scenarios that simulate actual data engineering challenges.

Target Duration:

This is an intensive course designed to be completed over a period of 8-12 weeks, with an estimated total of 50-60 hours of learning activities (videos, reading materials, hands-on exercises, and Quizzes/Klaxons).

Delivery Mode:

The course will be delivered through a combination of video lectures, reading materials, interactive quizzes, hands-on labs, and live workshops led by industry experts. It can be taken online, providing the flexibility to learn at your own pace from anywhere in the world.

Prerequisites:

• Basic understanding of Python or Scala programming languages
• Familiarity with big data concepts and terminology
• Experience with SQL and relational databases is beneficial

Certification:

Upon successful completion of this course, participants will receive a certificate of completion issued by [Your Institution/Organization]. This certification will acknowledge the recipient's expertise in Databricks and Apache Spark for data engineering applications.

Enroll now to embark on your journey to becoming a proficient data engineer with Databricks and Apache Spark!