Master Databricks Certified Data Engineer Associate Training

Databricks for Data Engineers: ETL, Delta Lake, and Apache Spark, Build Pipelines and Workflows for Success. UNOFFICIAL

What you'll learn

Fundamentals of Databricks and its role in data engineering.

How to work with the Databricks Lakehouse platform, combining data lakes and data warehouses.

Best practices for data ingestion and ETL processes.

Delta Lake features for ensuring data reliability and performance.

How to handle various data formats like Parquet, CSV, and JSON.

Metadata and catalog management using Hive Metastore and Databricks Catalog.

The basics of Apache Spark and its use for data transformations.

Working with DataFrames and Spark SQL for querying and manipulating data.

Techniques to optimize data transformations and performance.

How to automate workflows and pipelines using Databricks Jobs and Workflows.

Implementing data governance, access control, and monitoring pipelines.

Performance tuning techniques such as caching, data skipping, and cluster optimization.

Streaming data processing using Structured Streaming in Databricks.

Ensuring data quality through validations and expectations.

and much more

Requirements

Willingness or Interest to learn about Databricks Certified Data Engineer Associate for Success.

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.Thank you

Overview

Section 1: Introduction to Databricks and Data Engineering

Lecture 1 What is Databricks?

Lecture 2 Introduction to the Databricks Lakehouse Platform

Section 2: Working with Data on Databricks

Lecture 3 Data Ingestion and ETL Concepts

Lecture 4 Understanding Delta Lake

Lecture 5 Data Sources and Formats in Databricks

Lecture 6 Managing Metadata and Catalogs

Section 3: Transforming Data with Apache Spark

Lecture 7 Introduction to Apache Spark for Data Engineering

Lecture 8 Working with DataFrames

Lecture 9 Optimizing Data Transformations

Lecture 10 Understanding Spark SQL

Section 4: Managing Pipelines and Workflows

Lecture 11 Introduction to Data Engineering Workflows

Lecture 12 Using Databricks Jobs for Pipeline Automation

Lecture 13 Orchestrating Workflows with Databricks Workflows

Lecture 14 Introduction to Task Dependencies and Triggers

Section 5: Data Management and Governance

Lecture 15 Data Governance Fundamentals

Lecture 16 Implementing Access Controls

Lecture 17 Monitoring and Auditing Data Pipelines

Lecture 18 Data Versioning and Lineage with Delta Lake

Section 6: Performance Optimization and Troubleshooting

Lecture 19 Optimizing Cluster Configuration

Lecture 20 Understanding Caching and Data Skipping

Lecture 21 Troubleshooting Common Performance Issues

Lecture 22 Delta Lake Optimization Techniques

Section 7: Advanced Concepts in Data Engineering

Lecture 23 Introduction to Streaming Data with Structured Streaming

Lecture 24 Handling Late Data and Watermarking

Lecture 25 Ensuring Data Quality with Expectations and Validations

Data Engineers looking to enhance their skills in building scalable, efficient data pipelines using Databricks.,Data Analysts who want to expand their knowledge of data engineering and processing large-scale datasets.,Developers working with big data platforms who need to understand the tools and workflows within Databricks.,Business Intelligence Professionals seeking to leverage Databricks for more advanced analytics and ETL processes.,Anyone interested in Databricks who wants to learn how to manage data pipelines, optimize performance, and implement data governance.,Whether you’re new to Databricks or looking to deepen your expertise, this course will provide you with the tools and techniques to excel in data engineering.