Preparing with Cloudera Data Engineering
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17 Jun 2024 - 20 Jun 2024 | Preparing with Cloudera Data Engineering |
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18 Nov 2024 - 21 Nov 2024 | Preparing with Cloudera Data Engineering |
Description
This hands-on training course delivers the key concepts and knowledge developers need to use Apache Spark to develop high-performance, parallel applications on the Cloudera Data Platform (CDP).
Hands-on exercises allow students to practice writing Spark applications that integrate with CDP core components. Participants will learn how to use Spark SQL to query structured data, how to use Hive features to ingest and denormalize data, and how to work with “big data” stored in a distributed file system.
After taking this course, participants will be prepared to face real-world challenges and build applications to execute faster decisions, better decisions, and interactive analysis, applied to a wide variety of use cases, architectures, and industries.
Audience and prerequisites
This course is designed for developers and data engineers. All students are expected to have basic Linux experience and basic proficiency with either Python or Scala programming languages. Basic knowledge of SQL is helpful. Prior knowledge of Spark and Hadoop is not required.
Objectives
Students who successfully complete this course will be able to:
- Distribute, store, and process data in a CDP cluster.
- Write, configure, and deploy Apache Spark applications.
- Use the Spark interpreters and Spark applications to explore, process, and analyze distributed data.
- Query data using Spark SQL, DataFrames, and Hive tables.
- Deploy a Spark application on the Data Engineering Service.
Topics
HDFS Introduction
- HDFS Overview.
- HDFS Components and Interactions.
- Additional HDFS Interactions.
- Ozone Overview.
- Exercise: Working with HDFS.
YARN Introduction
- YARN Overview.
- YARN Components and Interaction.
- Working with YARN.
- Exercise: Working with YARN.
Working with RDDs
- Resilient Distributed Datasets (RDDs).
- Exercise: Working with RDDs.
Working with DataFrames
- Introduction to DataFrames.
- Exercise: Introducing DataFrames.
- Exercise: Reading and Writing DataFrames.
- Exercise: Working with Columns.
- Exercise: Working with Complex Types.
- Exercise: Combining and Splitting DataFrames.
- Exercise: Summarizing and Grouping DataFrames.
- Exercise: Working with UDFs.
- Exercise: Working with Windows.
- Introduction to Apache Hive.
- About Hive.
- Transforming data with Hive QL.
Working with Apache Hive
- Exercise: Working with Partitions.
- Exercise: Working with Buckets.
- Exercise: Working with Skew.
- Exercise: Using Serdes to Ingest Text Data.
- Exercise: Using Complex Types to Denormalize Data.
Hive and Spark Integration
- Hive and Spark Integration.
- Exercise: Spark Integration with Hive.
Distributed Processing Challenges
- Shuffle.
- Skew.
- Order.
Spark Distributed Processing
- Spark Distributed Processing.
- Exercise: Explore Query Execution Order.
Spark Distributed Persistence
- DataFrame and Dataset Persistence.
- Persistence Storage Levels.
- Viewing Persisted RDDs.
- Exercise: Persisting DataFrames.
Data Engineering Service
- Create and Trigger Ad-Hoc Spark Jobs.
- Orchestrate a Set of Jobs Using Airflow.
- Data Lineage using Atlas.
- Auto-scaling in Data Engineering Service.
Workload XM
- Optimize Workloads, Performance, Capacity.
- Identify Suboptimal Spark Jobs.
Appendix: Working with Datasets in Scala
- Working with Datasets in Scala.
- Exercise: Using Datasets in Scala.
Open calls
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17 Jun 2024 - 20 Jun 2024 | Preparing with Cloudera Data Engineering |
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18 Nov 2024 - 21 Nov 2024 | Preparing with Cloudera Data Engineering |
