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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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