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Description
IMPORTANT before enrolling:
This course is designed to complement your preparation for certification exams, but it is not a substitute for official vendor materials. It is not endorsed by the certification vendor, and you will not receive the official certification study material or a voucher as part of this course.
Unlock the full potential of data engineering with Databricks, the cutting-edge platform designed for handling large-scale data pipelines, ETL processes, and advanced analytics. This comprehensive course is perfect for data engineers, analysts, and anyone looking to enhance their skills in building efficient, scalable data workflows using the Databricks Lakehouse platform.
Whether you’re new to Databricks or looking to deepen your understanding, this course will guide you through the core concepts and advanced techniques required to excel in data engineering.
We begin by introducing Databricks and its key components, explaining how it streamlines data engineering tasks. You’ll learn about the innovative Databricks Lakehouse architecture, which merges the benefits of data lakes and data warehouses, offering a unified approach to data management and analytics.
As we dive deeper into working with data, you’ll explore data ingestion and ETL (Extract, Transform, Load) processes, mastering best practices for preparing and processing data. You’ll gain hands-on experience with Delta Lake, the powerful storage layer that enhances data reliability and performance within Databricks. We’ll cover various data formats and sources, ensuring you’re well-versed in handling formats like Parquet, CSV, and JSON, as well as managing metadata with Hive Metastore and Databricks Catalog.
A key part of the course focuses on Apache Spark, the engine behind Databricks. You’ll discover how Spark simplifies data processing, enabling fast and scalable transformations. You’ll work with DataFrames for data manipulation, explore Spark SQL for querying and transforming data, and learn optimization techniques that ensure efficient data processing, such as predicate pushdown and vectorized I/O.
Moving on to pipeline management, the course covers essential concepts like data engineering workflows, and you’ll learn how to automate these workflows using Databricks Jobs. We’ll introduce Databricks’ workflow orchestration tools, teaching you how to set task dependencies and triggers to ensure seamless pipeline execution.
Data management and governance are vital in any data engineering project. This course will teach you the fundamentals of data governance, including implementing role-based access control (RBAC) to manage permissions. You’ll also learn how to monitor and audit your data pipelines for performance, maintain data versioning, and track lineage using Delta Lake, ensuring data integrity throughout the lifecycle.
Performance optimization is another crucial area we’ll explore. You’ll learn how to configure clusters for different workloads, use caching and data skipping to enhance query performance, and troubleshoot common performance issues. Advanced Delta Lake optimization techniques, such as OPTIMIZE and ZORDER, will help you further enhance the performance of your data operations.
Finally, we’ll delve into advanced topics like streaming data processing with Structured Streaming in Databricks, handling late-arriving data, and ensuring data quality through validations and expectations. This ensures you’re well-prepared for real-time data challenges in today’s fast-paced data environments.
By the end of this course, you’ll be equipped with the skills to build, optimize, and manage scalable data pipelines, master Databricks and Apache Spark, and implement best practices in data governance, performance tuning, and streaming.
Whether you’re preparing for a career in data engineering or seeking to improve your expertise, this course will set you on the path to success.
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