DENG-254: Preparing with Cloudera Data Engineering

DENG-254: Preparing with Cloudera Data Engineering is an advanced training course designed to provide professionals with the essential skills and knowledge required to excel in Cloudera Data Engineering environments. This course covers the key concepts, tools, and practices necessary to work with large-scale data processing frameworks. Participants will learn how to design, build, and manage robust data pipelines using Apache Hadoop, Apache Spark, and other big data tools integrated into the Cloudera Data Platform (CDP). The course also focuses on best practices for optimizing data workflows, managing complex data systems, and ensuring high-performance processing and analytics in a modern data engineering role.

Schedule & Fee
Learning Objectives
Prerequisites
Target Audience
Course Modules
FAQs

Schedule	Learners	Course Fee (Incl. of all Taxes)	Register Your Interest
December 22^nd - 25^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours) Guaranteed-to-Run		10% Off $1,600 $1,440 Fast Filling! Hurry Up.
December 27^th - 04^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours)		10% Off $1,600 $1,440
January 05^th - 08^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours)		20% Off $1,600 $1,280
January 10^th - 18^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours)		20% Off $1,600 $1,280
January 12^th - 15^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours)		20% Off $1,600 $1,280
January 19^th - 28^th 06:00 AM - 10:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours)		20% Off $1,600 $1,280
January 26^th - 29^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours) Guaranteed-to-Run		20% Off $1,600 $1,280

Course Prerequisites

Basic understanding of Apache Hadoop and Apache Spark
Familiarity with data engineering concepts and distributed computing
Experience with Linux/Unix systems
Basic programming knowledge in languages like Java, Scala, or Python
Knowledge of data integration tools like Apache Kafka and Sqoop is beneficial

Learning Objectives

By the end of this course, participants will be able to:

Design and implement scalable data pipelines using Cloudera Data Engineering tools
Transform and process large datasets with Apache Spark and Hadoop
Manage data storage and integration with HDFS, Kafka, and Apache Nifi
Implement data governance and security best practices in big data environments
Optimize and troubleshoot data engineering workflows for better performance
Build and deploy real-time data processing pipelines with Apache Kafka
Automate and schedule ETL workflows using Apache Airflow and NiFi

Target Audience

This course is intended for individuals looking to gain hands-on experience and advanced skills in Cloudera Data Engineering. The ideal audience includes:

Data Engineers
Big Data Architects
Hadoop/Spark Developers
Data Analysts working with big data
Cloud Engineers responsible for data platforms
IT professionals managing large-scale data infrastructure
Technical leads overseeing big data projects

Course Modules

Introduction to Cloudera Data Engineering
- Overview of Cloudera Data Engineering and its components
- Key concepts in big data engineering: data pipelines, data wrangling, and real-time data processing
- The role of a Data Engineer in the modern data ecosystem
Data Pipelines Design and Architecture
- Best practices for designing scalable and efficient data pipelines
- Working with Apache Hadoop and Apache Spark for data processing
- Building reliable and fault-tolerant pipelines with Cloudera Data Engineering
Managing and Transforming Data with Apache Spark
- Advanced techniques for transforming and processing large datasets using Apache Spark
- Optimizing Spark jobs for batch and stream processing
- Utilizing Spark SQL for complex querying and aggregation tasks
Working with Data Storage and Integration
- Managing distributed data storage with HDFS, HBase, and Apache Parquet
- Integrating data from diverse sources using Apache Kafka, Nifi, and Sqoop
- Best practices for efficient data storage and retrieval in Cloudera environments
Data Governance and Security in Data Engineering
- Implementing data governance frameworks with Apache Atlas and Cloudera Navigator
- Ensuring data security, privacy, and compliance with Cloudera tools
- Managing access control and auditing for sensitive data
Optimization and Performance Tuning for Data Engineering Workflows
- Techniques for optimizing performance of Apache Spark, Hadoop, and Kafka
- Performance tuning for both batch and real-time data processing
- Using Cloudera Manager to monitor and fine-tune system performance
Real-Time Data Processing with Apache Kafka
- Designing and implementing real-time data pipelines with Apache Kafka
- Leveraging Kafka Streams and Kafka Connect for data integration and stream processing
- Optimizing Kafka for low-latency and high-throughput data streaming
ETL Workflows and Automation
- Building automated ETL (Extract, Transform, Load) workflows in Cloudera
- Scheduling and orchestrating data workflows with Apache Airflow
- Leveraging NiFi for data movement and flow management
Big Data Testing and Debugging
- Strategies for testing big data pipelines and ensuring data quality
- Debugging and troubleshooting complex data engineering workflows
- Using Cloudera tools to identify and resolve data pipeline issues

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