Ace Databricks Certified Associate Developer - Apache Spark

Ace Databricks Certified Associate Developer - Apache Spark - Printable Version

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Ace Databricks Certified Associate Developer - Apache Spark - AD-TEAM - 12-11-2024

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Ace Databricks Certified Associate Developer - Apache Spark
Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.87 GB | Duration: 2h 22m

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.

Master partitions, fault tolerance, caching, persistence, and Spark's optimization mechanisms.

Load, save, and process data in various formats like JSON, CSV, and Parquet.

Understand RDDs and key operations like map and reduce, and learn about broadcast variables and accumulators.

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

|| UNOFFICIAL COURSE ||IMPORTANT NOTICE BEFORE YOU ENROLL:This course is not a replacement for the official materials you need for the certification exams. It is not endorsed by the certification vendor. You will not receive official study materials or an exam voucher as part of this course.This course provides an in-depth exploration of Apache Spark and Databricks, two powerful tools for big data processing. Designed for data engineers, analysts, and developers, this course will take you from the foundational concepts of Spark to advanced optimization techniques, giving you the skills to effectively handle large-scale data in distributed computing environments. I begin by introducing Apache Spark, covering its architecture, the role it plays in modern big data frameworks, and the critical components that make it a popular choice for data processing. You'll also explore the Databricks platform, learning how it integrates with Spark to enhance development workflows, making large-scale data processing more efficient and accessible. Throughout the course, you will dive deep into Spark's core components, including its APIs-RDDs (Resilient Distributed Datasets), DataFrames, and Datasets. These fundamental building blocks will help you understand how Spark handles data in memory and across distributed systems. You'll learn how the Spark driver and executors function, the difference between transformations and actions, and how Spark's lazy evaluation model optimizes computations to boost performance. As the course progresses, you will gain hands-on experience working with Spark DataFrames, exploring operations such as filtering, grouping, and aggregating data. We will also delve into Spark SQL, where you'll see how SQL queries can be used in tandem with DataFrames for structured data processing. For those looking to master advanced Spark concepts, the course covers essential topics like partitioning, fault tolerance, caching, and persistence. You will gain a deep understanding of how Spark optimizes resource usage, ensures data integrity, and maintains performance even in the face of system failures. Additionally, you'll learn how Spark's Catalyst optimizer and Tungsten execution engine work behind the scenes to accelerate queries and manage memory more efficiently. The course also focuses on how to load, save, and manage data in Spark, working with popular file formats such as JSON, CSV, and Parquet. You will explore Spark's schema management capabilities, handling semi-structured data while ensuring data consistency and quality. In the section dedicated to RDDs, you'll gain insight into how Spark processes distributed data, with a focus on operations like map, flatMap, and reduce. You will also learn about broadcast variables and accumulators, which play a key role in optimizing distributed systems by reducing communication overhead. Finally, the course will provide you with the knowledge to manage and tune Spark applications effectively. You will learn how to configure Spark for optimal performance, understand how Spark jobs are executed, and monitor and debug Spark jobs using tools like Spark UI. By the end of this course, you'll have a strong command of both Apache Spark and Databricks, allowing you to design and execute scalable big data solutions in real-world scenarios. Whether you are just starting or looking to enhance your skills, this comprehensive guide will equip you with the practical knowledge and tools needed to succeed in the big data landscape.Thank you

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 10 Introduction to Spark Partitions

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

Lecture 21 Broadcast Variables and Accumulators in Spark

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.

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