Training Cloudera

Training goals dlearning

code: CLOUDERA-DENG-254

This four-day 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.

What you'll learn

During this course, you will learn how 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

What to expect

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.

Conspect Show list

  • 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
Download conspect training as PDF

Additional information

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.
Difficulty level
Duration 4 days
Certificate

The participants will obtain certificates signed by Cloudera (course completion).

Upon completion of the course, attendees are encouraged to continue their study and register for the CDP Data Analyst exam https://www.cloudera.com/about/training/certification/cdp-dataanalyst-exam-cdp-4001.html and/or CDP Data Engineer exam https://www.cloudera.com/about/training/certification/cdp-data-engineer-exam-guide-cdp-3002.html

Certification is a great differentiator. It helps establish you as a leader in the field, providing employers and customers with tangible evidence of your skills and expertise.
Trainer

Certified Cloudera Instructor

Other training Cloudera | Cloudera Data Analyst

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Sessions organised at Compendium CE are usually held in our locations in Kraków and Warsaw, but also in venues designated by the client. The group participating in training meets at a specific place and specific time with a coach and actively participates in laboratory sessions.

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You may participate from at any place in the world. It is sufficient to have a computer (or, actually a tablet, or smartphone) connected to the Internet. Compendium CE provides each Distance Learning training participant with adequate software enabling connection to the Data Center. For more information, please visit dlearning.eu site

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