Highlights
- Define Spark’s architectural components
- Describe how DataFrames are transformed, executed, and optimized in Spark
- Apply the DataFrame API to explore, preprocess, join, and ingest data in Spark
- Apply the Structured Streaming API to perform analysis on streaming data
- Use Delta Lake to improve the quality and performance of data pipelines
- prepare to take Databricks Certified Associate Developer for Apache Spark 3.0 Exam with Python (option requiring additional days)
- The course will cover the format and structure of the exam and the skills needed including covering Python programming.
- We will also give best practice advice and tips for passing the exam and give guided tutorials going through official practice examination questions.
- Prior to the training, we will arrange pre-course technical consultation to specify requirement and agree customised content as may be required.
Course Details
Apache Spark Architecture: Distributed Processing
• Distributed Processing: How Apache Spark Runs On A Cluster
• Azure Databricks: How To Create A Cluster
• Databricks Community Edition: How To Create A Cluster
• How does Apache Spark runs on a cluster ?
Apache Spark Architecture: Distributed Data
• Distributed Data: The DataFrame
• How To Define The Structure Of A DataFrame
DataFrame Transformations
• Selecting Columns
• Renaming Columns
• Change Columns data type
How to access columns
• Adding Columns to a DataFrame
• Removing Columns from a DataFrame
• Basics Arithmetic with DataFrame
• Apache Spark Architecture: DataFrame Immutability
• How To Filter A DataFrame
• Apache Spark Architecture: Narrow Transformations
• Dropping Rows
• Handling Null Values Part I - Null Functions
• Handling Null Values Part II - DataFrameNaFunctions
• Sort and Order Rows - Sort & OrderBy
• Create Group of Rows: GroupBy
DataFrame Statistics
• Group and Order
• Joining DataFrames - Inner Join
• Joining DataFrames - Right Outer Join
• Joining DataFrames - Left Outer Join
• Appending Rows to a DataFrame - Union
• Can you Join two DataFrames?
• Caching a DataFrame
• DataFrameWriter Part I
• DataFrameWriter Part II - PartitionBy
• User Defined Functions
• Do you know how to save the result of your work?
Apache Spark Architecture: Execution
• Query Planning
• Execution Hierarchy
• Partioning a DataFrame
• Adaptive Query Execution - An
Who should attend
Attendees should have the following :
- Familiarity with Python and basic programming concepts
- Basic knowledge of SQL, including writing queries
Feedback
4.8 out of 5 average
"Good introduction to Apache Spark. The trainer was great at talking us through the information, specifically optimisation methods. He spoke slowly and concisely which really got his points across. He effectively tailored the course to our specifications which we also appreciated."
RL, Financial Crime Technologist, Apache Spark, April 2021
“JBI did a great job of customizing their syllabus to suit our business needs and also bringing our team up to speed on the current best practices. Our teams varied widely in terms of experience and the Instructor handled this particularly well - very impressive”
Brian F, Team Lead, RBS, Data Analysis Course, 20 April 2022
