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DENG-254: Preparing with Cloudera Data Engineering Course Overview

DENG-254: Preparing with Cloudera Data Engineering Course Overview

Join our DENG-254: Preparing with Cloudera Data Engineering course, a comprehensive four-day instructor-led training designed for developers and data engineers. This course equips you with the skills to develop high-performance, parallel applications using Apache Spark, integrated closely with the Cloudera Data Platform (CDP). You'll learn to distribute, store, and process data effectively, write and deploy Spark applications, and utilize tools like Spark SQL and Hive to analyze and manage big data. Engage in hands-on exercises that prepare you for real-world challenges across various industries, enhancing your ability to make data-driven decisions swiftly and efficiently. Perfect for those with basic Linux and programming skills seeking to advance in the field of data engineering.

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Target Audience for DENG-254: Preparing with Cloudera Data Engineering

DENG-254: Preparing With Cloudera Data Engineering offers key concepts in using Apache Spark for developing high-performance, parallel applications on the Cloudera Data Platform.


Targeted Job Roles and Audience:


  • Developers interested in big data and distributed processing
  • Data Engineers seeking to enhance their skills in Cloudera’s ecosystem
  • IT Professionals with a focus on data management and analysis
  • Software Engineers looking to transition into big data roles
  • Technical Architects planning to design scalable big data applications
  • Analysts aiming to improve data-processing jobs using Apache Spark
  • Technology Consultants focusing on data platform solutions in Cloud


Learning Objectives - What you will Learn in this DENG-254: Preparing with Cloudera Data Engineering?

  1. Introduction to the Course’s Learning Outcomes and Concepts: In this four-day course, participants will learn to develop, configure, and optimize big data solutions using Apache Spark, Hive, and Airflow on the Cloudera Data Platform.

  2. List of Learning Objectives and Outcomes:

  • Develop and deploy Apache Spark applications within the Cloudera Data Platform.
  • Utilize HDFS to effectively distribute, store, and process data.
  • Employ Apache Spark and Hive to process and analyze large datasets.
  • Examine and manipulate data using Spark SQL and DataFrames.
  • Create resilient applications with an understanding of Spark’s distributed processing and persistence capabilities.
  • Orchestrate multi-step data processing workflows using Apache Airflow.
  • Enhance application performance and manage workloads through the Data Engineering Service.
  • Address challenges in distributed processing such as shuffle, skew, and order optimizations.
  • Apply best practices in data engineering including data partitioning, bucketing, and handling text and complex data types.
  • Monitor and optimize data operations using Workload XM for better performance and resource management.

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