Databricks Certified Associate Developer for Apache Spark

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1. Introduction

1.1 Apache Spark 3 - Databricks Certified Associate Developer.pdf

1. Introducción.html

2. Databricks Certification Preparation

1. Why Apache Spark Certification.mp4

02:25

2. Certification topics.mp4

03:16

3. Certification General information.mp4

02:31

4. Preparation process.mp4

01:50

5. Tips for passing exam in the first attempt.mp4

03:40

6. Registration and Certification process.mp4

03:28

7. Certification questions types.mp4

02:17

3. Spark Fundamentals

1. Spark Fundamentals.mp4

01:37

2. How Apache Spark runs.mp4

01:57

3. Apache Spark ecosystem and official documentation.mp4

04:59

4. PySpark operation, cluster administration and architecture.mp4

03:38

4. Basic Functions and RDDs

1. RDDS Fundamentals.mp4

03:19

2. Initialize PySpark with SparkSession and the SparkContext.mp4

04:06

3. Transformations in RDDs like map, filter, flatMap and distinct.mp4

02:25

4. Transformations in RDDs like reduceByKey, groupByKey or sortByKey.mp4

04:36

5. RDD actions such as count, first, collect or take.mp4

02:43

6. Practical exercise Basic Features and RDDs.html

5. Spark DataFrames and Apache Spark SQL

1. Fundamentals and advantages of DataFrames.mp4

03:27

2. Characteristics of DataFrames and data sources.mp4

02:24

3. Creating DataFrames in PySpark.mp4

03:07

4. Operations with PySpark DataFrames.mp4

04:42

5. Different types of joins in DataFrames.mp4

02:43

6. SQL queries in PySpark.mp4

02:26

7. Advanced features for loading and exporting data in PySpark.mp4

03:35

8. Practical Exercise Spark DataFrames and Apache Spark SQL.html

6. Advanced features in Apache Spark

1. Advanced features and performance optimization.mp4

03:39

2. BroadCast Join and caching.mp4

04:49

3. User Defined Functions (UDF) and advanced SQL functions.mp4

04:36

4. Handling and imputation of missing values.mp4

02:51

5. Partitioning and catalog of APIs.mp4

04:32

7. Advanced Analytics with Apache Spark

1. Basics of advanced analytics with Spark.mp4

02:15

2. Data loading and data schema modification.mp4

04:03

3. Inspect data in PySpark.mp4

02:46

4. Column transformation in PySpark.mp4

01:58

5. Advanced missing data imputation in PySpark.mp4

05:32

6. Data selection with PySpark and PySpark SQL.mp4

06:23

7. Data visualization and graph generation in PySpark.mp4

05:17

8. Persist data with PySpark.mp4

01:50

9. Practical Exercise Advanced Analytics with Apache Spark.html

8. Databricks Platform

1. Importing notebooks, language configuration and markdown.mp4

03:00

2. Databricks File Dystem (DBFS).mp4

01:59

3. Create, manipulate and visualize tables.mp4

02:00

4. Databricks widgets.mp4

01:57

9. Spark DataFrame API

1. Column Expressions, operators and methods.mp4

03:12

2. DataFrame Transformation Methods.mp4

02:54

3. Subset Rows in Dataframe.mp4

01:57

10. Dataframe Agregations

1. Grouped data methods.mp4

02:22

2. Aggregate Functions and Math Functions.mp4

01:23

11. Datetimes Functions

1. Spark Datetimes functions.mp4

04:51

12. Built-in function for complex data types

1. String Functions and Collection Functions.mp4

03:38

2. Aggregate Functions.mp4

03:05

13. User-Defined Functions

1. User-Defined Function (UDF).mp4

03:43

14. Spark Query Optimization

1. Spark Optimization Techniques.mp4

02:24

2. Lazy Evaluation.mp4

01:31

3. Wide and Narrow Transformations.mp4

01:17

4. Parquet file in Spark.mp4

01:32

5. Parallelism and Partitions.mp4

03:37

6. Shuffling.mp4

03:07

7. Caching and Storage Levels.mp4

03:18

8. Broadcast Hash Join.mp4

01:52

9. Query Optimization Engine and Adaptative Query Execution.mp4

04:03

15. Practice Exams

1. Practice Exam 1.html

2. Practice Exam 2.html

Description

A Step by Step Hands-on guide to prepare Databricks Associate certification and prove your skills as data professional

What You'll Learn?

How to prepare for the Databricks Certified Associate Developer For Apache Spark 3 Certification Exam
Learn how Apache Spark runs on a cluster of computer
Learn the different techniques to select the columns of a DataFrame
How to use Databricks Community Edition to write Apache Spark Code
Understanding the basics of the Spark architecture, including Adaptive Query Execution
Apply the Spark DataFrame API to complete individual data manipulation task

Who is this for?

Any Developer who wants to start using Apache Spark in their career

Beginner Spark Developer seeking Big Data Certification

Anyone that want to pass Databricks Certified Associate Developer for Apache Spark in the first attempt

More details

Description
If you are looking for a certification-oriented, hands-on and comprehensive course to prepare for the Databricks Certified Associate Developer for Apache Spark certification, you have come to the right place.
This course is designed to prepare you to learn everything related to the Databricks Certified Associate Developer for Apache Spark certification.
In todayâ€™s data-driven world, Apache Spark has become the standard big-data cluster processing framework. And Databricks have become the reference tool for Big Data. For this reason, Databricks is one of the most valuable skills today. Having the Databricks Certified Associate Developer for Apache Spark certification will allow you to position yourself in the Big Data job market. Get certified and advance your Big Data career.

With theoretical training, downloadable study guides, hands-on exercises, and mock exams, this is the only course you'll need to learn Apache Spark in Databricks and get certified. The exam consists of 60 multiple-choice questions. Candidates will have 120 minutes to complete the exam.

Topics covered in the course:
Certification preparation.
This course teaches you how to prepare for the Databricks exam. Including tips, proved preparation methodology, hands-on lectures in every section and tips and strategies using Mock Test.

Spark Architecture â€” Conceptual
Â· Cluster architecture: nodes, drivers, workers, executors, slots, etc.
Â· Spark execution hierarchy: applications, jobs, stages, tasks, etc.
Â· Shuffling
Â· Partitioning
Â· Lazy evaluation
Â· Transformations vs Actions
Â· Narrow vs Wide transformations

Spark Architecture â€” Applied
Â· Execution deployment modes
Â· Stability
Â· Storage levels
Â· Repartitioning
Â· Coalescing
Â· Broadcasting
Â· DataFrames

Spark DataFrame API
Â· Subsetting DataFrames (select, filter, etc.)
Â· Column manipulation (casting, creating columns, manipulating existing columns, complex column types)
Â· String manipulation (Splitting strings, regex)
Â· Performance-based operations (repartitioning, shuffle partitions, caching)
Â· Combining DataFrames (joins, broadcasting, unions, etc)
Â· Reading/writing DataFrames (schemas, overwriting)
Â· Working with dates (extraction, formatting, etc)
Â· Aggregations
Â· Miscellaneous (sorting, missing values, typed UDFs, value extraction, sampling)

Finally, we will conclude with a complete, comprehensive set of realistic, high-quality questions to practice for the Databricks Certified Developer for Apache Spark 3.0 exam in Python. These up-to-date practice exams provide you with the knowledge and confidence you need to pass the exam with excellence. Questions are based on the actual distribution of topics in the real exam. We include also real exam questions.The questions cover all themes being tested for in the exam, including specifics to Python and Apache Spark 3.0.

If you're ready to sharpen your skills, increase your career opportunities, and become a Big Data expert, join today and get immediate and lifetime access to:
â€¢ Complete guide to Databricks Certified Associate Developer for Apache Spark guide (PDF e-book)
â€¢ Downloadable Spark project files
â€¢ Practical exercises
â€¢Quizzes and mock exams
â€¢ Spark resources like Cheatsheets and Summaries
â€¢ 1 to 1 expert support
â€¢ Course question and answer forum

See you there!
Who this course is for:
Any Developer who wants to start using Apache Spark in their career
Beginner Spark Developer seeking Big Data Certification
Anyone that want to pass Databricks Certified Associate Developer for Apache Spark in the first attempt

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