DENG-254: Preparing with Cloudera Data Engineering Course Overview

To successfully undertake the DENG-254: Preparing with Cloudera Data Engineering course, participants are expected to meet the following prerequisites:

• Basic Linux Experience: Familiarity with Linux commands and the Linux operating environment.
• Programming Knowledge: Basic proficiency in either Python or Scala programming languages.
• Understanding of SQL: Basic knowledge of SQL to facilitate database querying and manipulation.

These foundational skills will help ensure a smooth learning experience as you navigate through the course content focused on Apache Spark, Hive, and additional components in the Cloudera Data Platform. Prior experience with Spark and Hadoop is not necessary, making this course accessible to those new to these technologies but with a solid grounding in the prerequisites listed.

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.