Ace Databricks Certified Associate Developer – Apache Spark

Ace Databricks Certified Associate Developer – Apache Spark
Published 11/2024
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

Databricks and Apache Spark Mastery: Streamline Big Data Workflows, dvanced Data Processing, Apache Spark Prep and Tips.

What you’ll learn

Understand the architecture, components, and role of Apache Spark in big data processing.

Explore Databricks’ features and its integration with Spark for efficient data engineering workflows.

Learn the differences between RDDs, DataFrames, and Datasets, and when to use each.

Gain a deep understanding of the Spark driver, executors, transformations, actions, and lazy evaluation.
Perform filtering, grouping, and aggregating data using Spark DataFrames and Spark SQL.

Configure and optimize Spark applications, monitor job execution, and use Spark’s debugging tools.

and much more

Requirements

Willingness or Interest to learn about Databricks Certified Associate Developer for Apache Spark.

Description

Overview

Section 1: Introduction to Apache Spark and Databricks

Lecture 1 Overview of Apache Spark

Lecture 2 Introduction to Databricks Platform

Lecture 3 Spark API Overview

Section 2: Spark Core Concepts

Lecture 4 Spark Driver and Executors

Lecture 5 Transformations and Actions in Spark
Lecture 6 Lazy Evaluation in Spark

Section 3: Working with Spark DataFrames

Lecture 7 Introduction to Spark DataFrames

Lecture 8 DataFrame Operations

Lecture 9 Spark SQL and DataFrames

Section 4: Advanced Spark Concepts

Lecture 11 Fault Tolerance in Spark

Lecture 12 Caching and Persistence in Spark

Section 5: Spark Optimization Techniques

Lecture 13 Spark Catalyst Optimizer

Lecture 14 Tungsten Execution Engine

Lecture 15 Spark Shuffle Mechanism

Section 6: Handling Data in Spark

Lecture 16 Loading and Saving Data in Spark

Lecture 17 Working with JSON, CSV, and Parquet Files

Lecture 18 Schema Management in Spark

Section 7: Distributed Data Processing with RDDs

Lecture 19 Introduction to RDDs (Resilient Distributed Datasets)

Lecture 20 Key RDD Operations: Map and Reduce

Section 8: Managing and Tuning Spark Applications

Lecture 22 Configuring Spark Applications

Lecture 23 Understanding Spark Job Execution

Lecture 24 Monitoring and Debugging Spark Jobs

Data Engineers who want to master Apache Spark and Databricks for building scalable data processing pipelines.,Data Analysts looking to expand their skills in big data processing and analysis using Spark and Databricks.,Developers interested in learning how to implement distributed data processing systems and optimize performance.,Big Data Enthusiasts eager to understand Spark’s role in modern data frameworks and how to handle large datasets efficiently.,IT Professionals who need to design and manage Spark-based solutions in distributed environments.,Anyone aiming to enhance their career in big data, cloud computing, or data engineering roles.