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CDP Certified Data Developer

The CDP Certified Data Developer course is designed for professionals who want to gain a deep understanding of data engineering and development using Cloudera Data Platform (CDP). In this comprehensive training, you will learn how to design, develop, and implement scalable and high-performance data pipelines using tools and frameworks like Apache Spark, Apache Hive, and Apache HBase within the Cloudera Data Platform ecosystem. This course also focuses on developing data solutions for data lakes, data warehouses, and real-time analytics. Upon completing this course, you will be prepared to take the CDP Data Developer Certification Exam and validate your expertise in developing data solutions using CDP.

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Schedule Learners Course Fee (Incl. of all Taxes) Register Your Interest
December 22nd - 26th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 40 Hours)
Guaranteed-to-Run
10% Off
$2,000
$1,800
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January 03rd - 17th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 40 Hours)
20% Off
$2,000
$1,600
January 05th - 09th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 40 Hours)
20% Off
$2,000
$1,600
January 12th - 16th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 40 Hours)
20% Off
$2,000
$1,600
January 19th - 30th
06:00 AM - 10:00 PM (CST)
Live Virtual Classroom (Duration : 40 Hours)
20% Off
$2,000
$1,600
January 26th - 30th
09:00 AM - 05:00 PM (CST)
Live Virtual Classroom (Duration : 40 Hours)
Guaranteed-to-Run
20% Off
$2,000
$1,600

Course Prerequisites

  • Experience with SQL and relational databases
  • Basic understanding of Hadoop and big data technologies
  • Familiarity with Linux/Unix operating systems
  • Knowledge of Apache Spark or similar data processing frameworks is beneficial but not required
  • Experience in programming (Java, Python, or Scala) is helpful

Learning Objectives

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

  • Understand the architecture and components of Cloudera Data Platform (CDP)
  • Develop scalable data pipelines using Apache Spark and Apache NiFi
  • Build and manage data lakes and data warehouses on CDP
  • Implement real-time data processing and analytics using Apache HBase
  • Optimize data jobs for performance, scalability, and reliability
  • Develop security and governance policies in Cloudera Data Platform
  • Prepare for the CDP Certified Data Developer exam with hands-on exercises and exam-focused topics

Target Audience

This course is ideal for individuals looking to build expertise in data engineering and development on the Cloudera Data Platform. It is designed for:

  • Data Engineers
  • Data Developers
  • Big Data Developers
  • ETL Developers
  • Cloud Data Engineers
  • Data Scientists working with large-scale data platforms
  • Cloudera Administrators looking to enhance development skills

Course Modules

  • Introduction to Cloudera Data Platform (CDP)

    • Overview of Cloudera Data Platform and its components
    • Understanding the roles of data developers in the CDP ecosystem
    • Exploring key tools and frameworks: Apache Spark, Hive, HBase, NiFi
    • The architecture of CDP and integration with Hadoop and other big data technologies
  • Data Engineering with Apache Spark

    • Introduction to Apache Spark and its architecture
    • Developing distributed data processing jobs with Spark SQL and Spark Streaming
    • Optimizing Spark jobs for large-scale data processing
    • Integrating Spark with HDFS, Hive, and other CDP tools for data engineering
  • Working with Cloudera Data Warehouse and Data Lakes

    • Implementing data warehouses and lakes in Cloudera Data Platform
    • Working with Apache Hive to manage and analyze large-scale datasets
    • Structuring data pipelines for batch and real-time processing
    • Integrating data lakes with Apache HBase and other storage systems
  • Designing and Developing Data Pipelines

    • Designing scalable ETL (Extract, Transform, Load) pipelines using Apache NiFi and Apache Spark
    • Data transformation techniques using Spark and Hive
    • Best practices for developing and managing data pipelines on CDP
    • Handling data quality, consistency, and error management in data pipelines
  • Advanced Data Processing with Apache Hive

    • Writing complex HiveQL queries to process structured and semi-structured data
    • Implementing partitioning and bucketing for better query performance
    • Optimizing Hive for large datasets and improving execution times
    • Integrating Apache HBase with Hive for real-time analytics
  • Real-Time Data Analytics with Apache HBase

    • Introduction to Apache HBase and its role in real-time data processing
    • Integrating HBase with CDP for real-time, low-latency analytics
    • Managing HBase clusters for optimal performance and scalability
    • Writing HBase APIs to access and process data in real-time
  • Developing Data Solutions with Apache NiFi

    • Using Apache NiFi for data ingestion, integration, and orchestration
    • Automating data movement across systems with NiFi processors
    • Integrating NiFi with other CDP tools to streamline data flow management
    • Developing data flows for real-time analytics and batch processing
  • Data Security and Governance in CDP

    • Implementing data security and access control in Cloudera Data Platform
    • Understanding the role of Apache Ranger and Apache Sentry in data governance
    • Enforcing data privacy and regulatory compliance in CDP environments
    • Managing encryption and auditing for secure data handling
  • Performance Tuning and Optimization in CDP

    • Optimizing data processing jobs for high efficiency and low latency
    • Tuning Spark, Hive, and HBase for better performance in large-scale environments
    • Using Cloudera Manager to monitor cluster health and troubleshoot issues
    • Best practices for managing large datasets and optimizing query performance
  • Preparing for the CDP Data Developer Certification Exam

    • Key topics and exam preparation strategies for the CDP Data Developer certification
    • Understanding exam objectives and preparing through practical exercises
    • Review of sample questions and test-taking tips

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