Databricks Certified Associate Developer for Apache Spark

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

1. Introduction

1.1 Apache Spark 3 - Databricks Certified Associate Developer.pdf

1. Introducción.html

2. Databricks Certification Preparation

1. Why Apache Spark Certification

2. Certification topics

3. Certification General information

4. Preparation process

5. Tips for passing exam in the first attempt

6. Registration and Certification process

7. Certification questions types

3. Spark Fundamentals

1. Spark Fundamentals

2. How Apache Spark runs

3. Apache Spark ecosystem and official documentation

4. PySpark operation, cluster administration and architecture

4. Basic Functions and RDDs

1. RDDS Fundamentals

2. Initialize PySpark with SparkSession and the SparkContext

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

4. Transformations in RDDs like reduceByKey, groupByKey or sortByKey

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

6. Practical exercise Basic Features and RDDs.html

5. Spark DataFrames and Apache Spark SQL

1. Fundamentals and advantages of DataFrames

2. Characteristics of DataFrames and data sources

3. Creating DataFrames in PySpark

4. Operations with PySpark DataFrames

5. Different types of joins in DataFrames

6. SQL queries in PySpark

7. Advanced features for loading and exporting data in PySpark

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

6. Advanced features in Apache Spark

1. Advanced features and performance optimization

2. BroadCast Join and caching

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

4. Handling and imputation of missing values

5. Partitioning and catalog of APIs

7. Advanced Analytics with Apache Spark

1. Basics of advanced analytics with Spark

2. Data loading and data schema modification

3. Inspect data in PySpark

4. Column transformation in PySpark

5. Advanced missing data imputation in PySpark

6. Data selection with PySpark and PySpark SQL

7. Data visualization and graph generation in PySpark

8. Persist data with PySpark

9. Practical Exercise Advanced Analytics with Apache Spark.html

8. Databricks Platform

1. Importing notebooks, language configuration and markdown

2. Databricks File Dystem (DBFS)

3. Create, manipulate and visualize tables

4. Databricks widgets

9. Spark DataFrame API

1. Column Expressions, operators and methods

2. DataFrame Transformation Methods

3. Subset Rows in Dataframe

10. Dataframe Agregations

1. Grouped data methods

2. Aggregate Functions and Math Functions

11. Datetimes Functions

1. Spark Datetimes functions

12. Built-in function for complex data types

1. String Functions and Collection Functions

2. Aggregate Functions

13. User-Defined Functions

1. User-Defined Function (UDF)

14. Spark Query Optimization

1. Spark Optimization Techniques

2. Lazy Evaluation

3. Wide and Narrow Transformations

4. Parquet file in Spark

5. Parallelism and Partitions

6. Shuffling

7. Caching and Storage Levels

8. Broadcast Hash Join

9. Query Optimization Engine and Adaptative Query Execution

15. Practice Exams

1. Practice Exam 1.html

2. Practice Exam 2.html

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تولید کننده: Udemy-Training