Databricks Fundamentals & Apache Spark Core
Databricks Fundamentals & Apache Spark Core, available at $69.99, has an average rating of 4.55, with 76 lectures, based on 2260 reviews, and has 24321 subscribers.
You will learn about Databricks Apache Spark Architecture Apache Spark DataFrame API Apache Spark SQL Selecting, and manipulating columns of a DataFrame Filtering, dropping, sorting rows of a DataFrame Joining, reading, writing and partitioning DataFrames Aggregating DataFrames rows Working with User Defined Functions Use the DataFrameWriter API This course is ideal for individuals who are Software developers curious about big-data, data engeneering and data science or Beginner data engineer who want to learn how to do work with databricks or Beginner data scientist who want to learn how to do work with databricks It is particularly useful for Software developers curious about big-data, data engeneering and data science or Beginner data engineer who want to learn how to do work with databricks or Beginner data scientist who want to learn how to do work with databricks.
Enroll now: Databricks Fundamentals & Apache Spark Core
Summary
Title: Databricks Fundamentals & Apache Spark Core
Price: $69.99
Average Rating: 4.55
Number of Lectures: 76
Number of Published Lectures: 72
Number of Curriculum Items: 76
Number of Published Curriculum Objects: 72
Original Price: €99.99
Quality Status: approved
Status: Live
What You Will Learn
- Databricks
- Apache Spark Architecture
- Apache Spark DataFrame API
- Apache Spark SQL
- Selecting, and manipulating columns of a DataFrame
- Filtering, dropping, sorting rows of a DataFrame
- Joining, reading, writing and partitioning DataFrames
- Aggregating DataFrames rows
- Working with User Defined Functions
- Use the DataFrameWriter API
Who Should Attend
- Software developers curious about big-data, data engeneering and data science
- Beginner data engineer who want to learn how to do work with databricks
- Beginner data scientist who want to learn how to do work with databricks
Target Audiences
- Software developers curious about big-data, data engeneering and data science
- Beginner data engineer who want to learn how to do work with databricks
- Beginner data scientist who want to learn how to do work with databricks
Welcome to this course on Databricks and Apache Spark 2.4 and 3.0.0
Apache Spark is a Big Data Processing Framework that runs at scale.
In this course, we will learn how to write Spark Applications using Scala and SQL.
Databricks is a company founded by the creator of Apache Spark.
Databricks offers a managed and optimized version of Apache Spark that runs in the cloud.
The main focus of this course is to teach you how to use the DataFrame API & SQL to accomplish tasks such as:
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Write and run Apache Spark code using Databricks
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Read and Write Data from the Databricks File System – DBFS
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Explain how Apache Spark runs on a cluster with multiple Nodes
Use the DataFrame API and SQL to perform data manipulation tasks such as
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Selecting, renaming and manipulating columns
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Filtering, dropping and aggregating rows
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Joining DataFrames
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Create UDFs and use them with DataFrame API or Spark SQL
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Writing DataFrames to external storage systems
List and explain the element of Apache Spark execution hierarchy such as
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Jobs
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Stages
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Tasks
Course Curriculum
Chapter 1: Setup
Lecture 1: Introduction
Lecture 2: Create a Databricks community account
Lecture 3: Install the Dataset
Lecture 4: Overview of the dataset
Lecture 5: Install the notebooks
Chapter 2: Introduction to Databricks and Apache Spark
Lecture 1: Introduction to databricks
Lecture 2: Write your first Apache Spark Code
Lecture 3: Apache Spark Architecture: How Apache Spark runs on a cluster
Lecture 4: Practice: Find customer with the same birthday as you
Chapter 3: The DataFrame API: Basics
Lecture 1: Create a DataFrame from a CSV file
Lecture 2: Configure options to read a CSV file
Lecture 3: How to select columns from a DataFrame
Lecture 4: How to reference columns of a DataFrame
Lecture 5: Understand the DataFrame Schema: Part 1
Lecture 6: Understand the DataFrame Schema: Part 2
Lecture 7: Specify a DataFrame Schema using a DDL-formatted string : Part 1
Lecture 8: Specify a DataFrame Schema using a DDL-formatted string : Part 2
Lecture 9: Spark Architecture: The Organization of a DataFrame
Chapter 4: The DataFrame API: Transforming Data
Lecture 1: Adding columns to a DataFrame
Lecture 2: Renaming columns of a DataFrame
Lecture 3: Removing columns from a DataFrame
Lecture 4: Filtering rows from a DataFrame
Lecture 5: Joining multiple DataFrames: Part 1
Lecture 6: Joining multiple DataFrames: Part 2
Lecture 7: Aggregation: Count
Lecture 8: Aggregation: Count Distinct
Lecture 9: Aggregation: Get the Min value
Lecture 10: Aggregation: Get the Max value
Lecture 11: Aggregation: Get the Sum and SumDistinct
Lecture 12: Aggregation: Average and Mean
Lecture 13: Aggregation: Grouping data – Part 1
Lecture 14: Aggregation: Grouping data – Part 2
Lecture 15: Practice: Business Query 1
Lecture 16: Practice: Business Query 2
Lecture 17: Apache Spark Architecture: How Apache Spark Transforms data Internally
Lecture 18: User Defined Function
Chapter 5: Spark SQL & SQL Fundamentals
Lecture 1: Run SQL on a DataFrame: TempView
Lecture 2: Run SQL on a DataFrame: GlobalView
Lecture 3: Databases: List, Create, Delete, Select
Lecture 4: Tables: Unmanaged
Lecture 5: Tables: Managed
Lecture 6: SQL Fundamentals: Select Clause & Select Expression
Lecture 7: SQL Fundamentals: Where Clause, Equality Checks
Lecture 8: SQL Fundamentals: Handling NULLs in Where Clause
Lecture 9: SQL Fundamentals: Aggregations – Sum, Count, AVG, Mean
Lecture 10: SQL Fundamentals: Group By Clause
Lecture 11: SQL Fundamentals: Having Clause
Lecture 12: SQL Fundamentals: Order By Clause
Lecture 13: SQL Fundamentals: Inner Joins
Lecture 14: SQL Fundamentals: Left Outer Joins
Lecture 15: SQL Fundamentals: Right Outer Joins
Lecture 16: SQL Fundamentals: Predicates and Operators, like predicate
Lecture 17: SQL Fundamentals: Case Expressions
Lecture 18: Practice : Business Query 3
Lecture 19: Practice: Business Query 4
Lecture 20: Practice: Business Query 5
Chapter 6: Working with different type of data
Lecture 1: Specify the Schema of a DataFrame with StructType
Lecture 2: Converting literals to Spark Types: The lit function
Lecture 3: Working with booleans
Lecture 4: Working with numbers
Lecture 5: Working with strings
Lecture 6: Working with dates and timestamps
Lecture 7: Complex Types: Structs
Lecture 8: Complex Types: Arrays
Lecture 9: Complex Types: Maps
Lecture 10: Handling NULL Values: Drop NULL Values
Lecture 11: Handling NULL Values: Replace NULL Values
Chapter 7: Data Sources
Lecture 1: DataFrameReader: Read CSV Files
Lecture 2: DataFrameReader: Read JSON Files
Lecture 3: DataFrameWriter: Write Data
Lecture 4: Create DataFrame manually
Chapter 8: Become Apache Spark Certified
Lecture 1: Bonus Lecture
Instructors
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Wadson Guimatsa
Data Engineer
Rating Distribution
- 1 stars: 15 votes
- 2 stars: 22 votes
- 3 stars: 271 votes
- 4 stars: 852 votes
- 5 stars: 1100 votes
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