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Databricks Certified Associate Developer for Apache Spark
Databricks Certified Associate Developer - Apache Spark 2022

Databricks Certified Associate Developer - Apache Spark 2022

Loại khoá học: IT Certifications

A Step by Step Hands-on Guide to prepare for Databricks Certified Associate Developer for Apache Spark using Pyspark

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50.000 VND
99 VND
Đầy Đủ Bài Giảng
Học Online Tiện Lợi
Kích Hoạt Nhanh 2-5 Phút
Thanh toán tự động
Được phép tải xuống

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Nội dung khoá học

Đánh giá

Mô tả

The Databricks Certified Associate Developer for Apache Spark is one of the most challenging exams. It's great at assessing how well you understand not just Data Frame APIs, but also how you make use of them effectively as part of implementing Data Engineering Solutions, which makes Databricks Associate certification incredibly valuable to have and pass. Rest assured, I've passed it myself with a score of 90%.

This is going to be a long journey, but passing the Databricks Certified Associate Developer for Apache Spark exam will be worth it!

This Databricks Certified Associate Developer for Apache Spark is different from the other ones you'll find on Udemy. You are the better judge.

  • First, we will make sure we have the right environment based on Databricks to practice. If you do not have the environment don't worry, you will be guided to set up the environment using Azure

  • Once the environment is ready you will be guided to upload the material and also data sets for practice

  • It covers in-depth all the new topics on the Databricks Certified Associate Developer for Apache Spark exam using Pyspark.

  • It's packed with practical knowledge on how to use Pyspark Data Frame APIs inside and out as a Data Engineer using Databricks Platform

  • It teaches you how to prepare for the Databricks exam AND how to prepare for the real world leveraging Pyspark Data Frame APIs.

  • It's a logical progression of topics, not a laundry list of random services

  • It's fast-paced and precise to the point

  • All the material will be made available as a Databricks Archive. Just upload it into your platform and start using it

Concretely, here's what we'll learn to pass the Databricks Certified Associate Developer for Apache Spark exam:

  • Setup of Databricks Environment using Azure

  • The Databricks CLI and important commands to interact with DBFS. It will be used to quickly set up the data for practice.

  • In-Depth Data Frame APIs using Pyspark that are relevant for the exam.

  • All Important and Commonly used Data Frame APIs for selecting, renaming, and manipulating columns leveraging Pyspark

  • Pyspark Data Frame APIs for filtering, dropping, sorting, and aggregating rows

  • Pyspark Data Frame APIs for joining, reading, writing, and partitioning DataFrames

  • Working with UDFs using Pyspark and Spark SQL Functions

  • Apache Spark Architectural Concepts which are very important for the exam

  • Adaptive Query Execution which been introduced in recent times.

  • Tips and Key Strategies to ROCK the exam

This Databricks Certified Associate Developer for Apache Spark course is full of opportunities to apply your knowledge:

  • There are many hands-on lectures in every section

  • There's a Databricks Certified Associate Developer for Apache Spark tips and strategies using Mock Test provided by Databricks at the end of the course

  • We'll be using the Databricks single node cluster most of the time. It will cost you some money based on your usage.

  • I'll be showing you how to go beyond the Single Node for some sections (you know... the real world!)

Bạn sẽ học được gì

Databricks Certified Associate Developer for Apache Spark exam details

Setting up Databricks Platform for practice to also to prepare for Databricks Certified Associate Developer for Apache Spark Exam

Selecting, renaming and manipulating columns using Spark Data Frame APIs

Filtering, dropping, sorting, and aggregating rows using Spark Data Frame APIs

Joining, reading, writing and partitioning DataFrames using Spark Data Frame APIs

Working with UDFs and Spark SQL functions using Spark Data Frame APIs

Spark Architecture and Adaptive Query Execution (AQE)

Yêu cầu

  • Basic Programming using Python to understand the questions in Databricks Certified Associate Developer for Apache Spark Exam
  • Decent Laptop with stable internet connection to take the course and prepare for also to prepare for Databricks Certified Associate Developer for Apache Spark Exam
  • Valid Databricks Account using AWS or Azure or GCP is highly desired to also to prepare for Databricks Certified Associate Developer for Apache Spark Exam

Nội dung khoá học

16 sections

Getting Started with Databricks Certified Associate Developer for Apache Spark

7 lectures
Introduction to Databricks Certified Associate for Apache Spark Developer Course
02:13
Sign up for Databricks Academy Website
01:49
Get Details related to Databricks Certified Associate exam for Spark Developer
01:54
Overview of Databricks Certified Associate for Apache Spark Curriculum
03:07
Resources to prepare for Databricks Certified Associate Spark Developer Exam
02:37
Exam Details for Databricks Certified Associate Developer for Apache Spark
02:03
Registering for Databricks Certified Associate Developer for Apache Spark
02:28

Setup Databricks Environment using Azure

11 lectures
Sign up for Azure Portal
01:23
Setup Databricks Platform using Azure
04:05
Prerequisites for the Databricks Spark Developer Certification
02:23
Create Single Node Cluster to explore Spark APIs
04:02
Getting Started with Databricks Notebooks
02:04
Setup Databricks Certification Course Material
02:52
Quick Tour of Course Material using Databricks Notebooks
06:37
Install and Configure Databricks CLI
02:56
Interacting with File System using CLI
09:34
Setup Retail Datasets using Databricks CLI
06:40
Validate Data Sets using Databricks Notebooks
03:30

Create Spark Dataframes using Python Collections and Pandas Dataframes

16 lectures
Create Spark Dataframes using Python Collections and Pandas Dataframes
01:22
Create Single Column Spark Dataframe using List
06:18
Create Multi Column Spark Dataframe using List
04:32
Overview of Spark Row
04:55
Convert List of Lists into Spark Dataframe using Row
07:26
Convert List of Tuples into Spark Dataframe using Row
04:08
Convert List of Dicts into Spark Dataframe using Row
10:07
Overview of Basic Data Types in Spark
05:08
Specifying Schema for Spark Dataframe using String
05:36
Specifying Schema for Spark Dataframe using List
02:22
Specifying Schema using Spark Types
07:00
Create Spark Dataframe using Pandas Dataframe
02:50
Overview of Special Data Types in Spark
01:00
Array Type Columns in Spark Dataframes
06:33
Map Type Columns in Spark Dataframes
08:57
Struct Type Columns in Spark Dataframes
05:10

Selecting and Renaming Columns in Spark Data Frames

14 lectures
Selecting and Renaming Columns in Spark Data Frames - Introduction
00:57
Creating Spark Data Frame to Select and Rename Columns
01:45
Overview of Narrow and Wide Transformations
03:53
Overview of Select on Spark Data Frame
06:30
Overview of selectExpr on Spark Data Frame
06:48
Referring Columns using Spark Data Frame Names
05:03
Understanding col function in Spark
08:12
Invoking Functions using Spark Column Objects
06:33
Understanding lit function in Spark
07:51
Overview of Renaming Spark Data Frame Columns or Expressions
02:26
Naming derived columns using withColumn
07:44
Renaming Columns using withColumnRenamed
03:32
Renaming Spark Data Frame columns or expressions using alias
09:09
Renaming and Reordering multiple Spark Data Frame Columns
06:54

Manipulating Columns in Spark Data Frames

20 lectures
Manipulating Columns in Spark Data Frames - Introduction
02:18
Predefined Functions using Spark Data Frame APIs
08:03
Create Dummy Data Frame
04:32
Categories Of Functions to Manipulate Columns in Spark Data Frames
05:05
Getting Help on Spark Functions
06:36
Special Functions col and lit using Spark
17:07
Common String Manipulation Functions
09:46
Extracting Strings using substring from Spark Data Frame Columns
08:02
Extracting Strings using split from Spark Data Frame Columns
09:38
Padding Characters around strings in Spark Data Frame Columns
04:59
Trimming Characters from strings in Spark Data Frame Columns
05:47
Date and Time Manipulation Functions using Spark Data Frames
05:04
Date and Time Arithmetic using Spark Data Frames
09:33
Using date and time trunc functions on Spark Data Frames
05:27
Date and Time Extract Functions on Spark Data Frames
03:14
Using to_date and to_timestamp on Spark Data Frames
08:32
Using date_format Function on Spark Data Frames
06:47
Dealing with Unix Timestamp in Spark Data Frames
06:43
Dealing with nulls in Spark Data Frames
11:08
Using CASE and WHEN on Spark Data Frames
06:49

Filtering Data from Spark Data Frames

13 lectures
Filtering Data from Spark Data Frames - Introduction
01:04
Creating Spark Data Frame for Filtering
01:23
Overview of Filter or Where Function on Spark Data Frame
04:15
Overview of Conditions and Operators related to Spark Data Frames
01:50
Filter using Equal Condition on Spark Data Frames
14:41
Filter using Not Equal Condition on Spark Data Frames
07:45
Filter using Between Operator on Spark Data Frames
10:13
Dealing with Null Values while Filtering Data in Spark Data Frames
05:47
Overview of Boolean Operations
02:05
Boolean OR on same column of Spark Data Frame and IN Operator
10:18
Filtering with Greater Than and Less Than on Spark Data Frames
10:28
Boolean AND Condition on Spark Data Frames
07:33
Boolean OR on different columns of a Spark Data Frame
07:00

Dropping Columns from Spark Data Frames

8 lectures
Dropping Columns from Spark Data Frames - Introduction
00:59
Creating Spark Data Frame for Dropping Columns
00:49
Overview of Spark Data Frame drop function
01:30
Dropping a Single Column from a Spark Data Frame
02:10
Dropping Multiple Columns from a Spark Data Frame
02:18
Dropping List of Columns from a Spark Data Frame
02:56
Dropping Duplicate Records from Spark Data Frames
05:24
Dropping Null based Records from Spark Data Frames
08:27

Sorting Data in Spark Data Frames

8 lectures
Sorting Data in Spark Data Frames - Introduction
00:39
Creating Spark Data Frame for Sorting the Data
00:34
Overview of Sorting a Spark Data Frame
07:18
Sort Spark Data Frame in Ascending Order by a given column
06:27
Sort Spark Data Frame in Descending Order by a given column
09:29
Dealing with Nulls while sorting Spark Data Frame
05:33
Composite Sorting of a Data Frame
06:59
Prioritized Sorting of a Spark Data Frame
06:13

Performing Aggregations on Spark Data Frames

8 lectures
Performing Aggregations on Spark Data Frames - Introduction
01:07
Validate Data Sets for Aggregations using Spark
03:00
Common Spark Aggregate Functions
02:12
Total Aggregations on a Spark Data Frame
06:59
Getting Count of a Spark Data Frame
03:16
Overview of groupBy on Spark Data Frame
04:23
Perform Grouped Aggregations using direct functions on a Spark Data Frame
08:38
Perform Grouped Aggregations using Agg on a Spark Data Frame
10:42

Joining Spark Data Frames

11 lectures
Joining Spark Data Frames - Introduction
00:45
Setup Data Sets to perform joins
05:13
Overview of Joins using Spark Data Frames
06:23
Define Aliases for Spark Data Frames
03:43
Performing Inner Join on Spark Data Frames
08:23
Performing Outer Join using left between Spark Data Frames
09:46
Performing Outer Join using right between Spark Data Frames
02:27
Difference between Left Outer Join and Right Outer Join
01:27
Performing Full Outer Join between Spark Dataframes
07:53
Overview of Broadcast Join in Spark
07:49
Performing Cross Join using Spark Data Frames
02:28

Reading Data from Files into Spark Data Frames

16 lectures
Reading Data from Spark Data Frames into Files - Introduction
01:56
Validate Data Sets for Reading from Files using Spark APIs
04:23
Convert JSON Data to Parquet using Spark APIs
11:37
Convert Comma Separated Files to Pipe Separated Files using Spark
03:47
Overview of Reading Data Files into Spark Data Frames
03:52
Steps to follow to read data from files into Spark Data Frame
08:42
Reading Data from CSV files into Spark Data Frame
02:33
Specifying Schema while reading CSV data into Data Frame
08:11
Using toDF and inferSchema using CSV to create Spark Data Frame
03:53
Specifying Delimiter while using CSV to create Spark Data Frame
03:10
Using Options while reading CSV Files into Spark Data Frame
07:59
Reading JSON Files into Spark Data Frame
02:13
Specifying Schema while reading JSON Files into Data Frame
04:18
Side effects of inferring schema while creating Spark Data Frame
02:32
Reading Parquet Files into Spark Data Frame
03:32
Specifying Schema while reading Parquet Files into Data Frame
09:02

Writing Data from Spark Data Frames into Files

15 lectures
Writing Data from Spark Data Frames into Files - Introduction
01:20
Validate Data Sets for Writing into Files using Spark APIs
01:48
Overview of Writing Spark Data Frames into Files
07:22
Steps to follow to write Spark Data Frames into Files
03:42
Writing Spark Data Frames into CSV files
05:44
Specifying Header while writing Spark Data Frame into CSV files
04:42
Using Compression while writing Spark Data Frame into CSV Files
04:57
Specifying Delimiter while writing Spark Data Frame into CSV Files
07:59
Using Options while writing Spark Data Frame into CSV Files
08:49
Writing Spark Data Frames into JSON Files
03:54
Compression while writing Spark Data Frames into JSON Files
04:12
Writing Spark Data Frames into Parquet Files
04:01
Compression while writing Spark Data Frames into Parquet Files
05:57
Different Modes to write Spark Data Frame into Files
06:35
Coalesce and Repartitioning of Spark Data Frames
09:02

Partitioning Spark Data Frames

6 lectures
Partitioning Spark Data Frames - Introduction
01:08
Overview of Partitioning Data Frames
03:08
Partition Spark Data Frame By Single Column
07:08
Partition Spark Data Frame By Multiple Columns
02:34
Setup Data Set for Partition Pruning
02:37
Reading Data into Spark Data Frames using Partition Pruning
09:28

Working with Spark SQL Functions

5 lectures
Overview of Spark User Defined Functions
00:59
Registering Spark User Defined Functions
01:40
Using Spark UDFs as part of Data Frame APIs
03:24
Using Spark UDFs as part of Spark SQL
05:01
Create Spark UDF to cleanse data in Spark Data Frame
04:33

Spark Architecture

10 lectures
Setup Multinode Spark Cluster using Databricks Platform
01:49
Review important Databricks Spark User Interfaces
04:54
Overview of Spark Cores and Slots
01:46
Submit Basic Application to understand tasks
04:10
Run Spark Application to understand the Execution Life Cycle
03:54
Review Properties related to Spark Adaptive Execution
02:29
Disabling Adaptive Execution and Running Spark Application
03:44
Review Details of Spark Application with out Adaptive Execution
04:54
Run Spark Application with Adaptive Execution
03:38
Review Spark Adaptive Query Execution Properties
03:59

Tips and Strategies to take the Databricks Certified Associate Developer for

4 lectures
Overview of the Material Provided for Databricks Apache Spark Exam
06:05
Accessing the Mock Test
01:22
Understanding Mock Test for Databricks Certified Associate Spark Exam
02:57
Spark Coding Practice Tests for Databricks Certification Exam
00:57

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