Cloudera Data Platform Training

Categories Other Courses
Course level:Intermediate

Cloudera Data Platform (CDP) training courses typically cover a wide range of topics aimed at helping users leverage CDP for big data management, analytics, and machine learning tasks. Below is a general outline of the course content that you can expect in Cloudera Data Platform training programs, organized by different roles or focus areas. The training may vary slightly depending on the specific course you are taking (e.g., Data Engineering, Data Science, Administration).

Cloudera Data Platform Training
Cloudera Data Platform Training – Learn Online

Why should you choose Nisa For Cloudera Data Platform Training?

Nisa Trainings is the best online training platform for conducting one-on-one interactive live sessions with a 1:1 student-teacher ratio. You can gain hands-on experience by working on near-real-time projects under the guidance of our experienced faculty. We support you even after the completion of the course and happy to clarify your doubts anytime. Our teaching style at Nisa Trainings is entirely hands-on. You’ll have access to our desktop screen and will be actively conducting hands-on labs on your desktop.

Job Assistance

If you face any problem while working on Cloudera Data Platform Course, then Nisa Trainings is simply a Call/Text/Email away to assist you. We offer Online Job Support for professionals to assist them and to solve their problems in real-time.

The Process we follow for our Online Job Support Service:

  • We receive your inquiry for Online Job
  • We will arrange a telephone call with our consultant to grasp your complete requirement and the tools you’re
  • If our consultant is 100% confident in taking up your requirement and when you are also comfortable with our consultant, we will only agree to provide service. And then you have to make the payment to get the service from
  • We will fix the timing for Online Job Support as mutually agreed by you and our consultant.

Course Information

Cloudera Data Platform Training
Duration: 25 Hours
Timings: Weekdays (1-2 Hours per day) [OR] Weekends (2-3 Hours per day)
Training Method: Instructor Led Online One-on-One Live Interactive
Sessions.

COURSE CONTENT :

1. Cloudera Data Platform (CDP) Overview
  • Introduction to CDP and its components
  • Key features and capabilities of CDP
  • Differences between CDP Private Cloud, CDP Public Cloud, and CDP One
  • Overview of the CDP architecture
  • Data management and governance with CDP
2. Data Engineering with Cloudera Data Platform
  • Apache Hadoop and HDFS (Hadoop Distributed File System) Basics

    • Introduction to Hadoop ecosystem
    • Data storage on HDFS
    • Overview of Hadoop Distributed File System and its components
  • Apache Spark for Data Engineering

    • Introduction to Apache Spark
    • Spark DataFrames and RDDs
    • Running Spark jobs within CDP
    • Spark SQL and optimizations
    • Performance tuning for Spark jobs
  • Apache Hive & Impala

    • Overview of Apache Hive and its role in big data queries
    • HiveQL basics and advanced techniques
    • Introduction to Apache Impala for real-time queries
    • Query optimization and tuning
  • Data Pipelines and ETL in CDP

    • Building and managing data pipelines
    • Using Apache NiFi for data flow management
    • Data integration and transformation tools (e.g., CDP Data Engineering)
    • Hands-on experience with ETL and data workflows
3. Data Science and Machine Learning with CDP
  • Cloudera Data Science Workbench (CDSW)

    • Introduction to the Cloudera Data Science Workbench
    • Setting up a Data Science environment in CDP
    • Using Jupyter notebooks, RStudio, and other tools within CDSW
  • Machine Learning Workflow

    • Introduction to machine learning concepts in CDP
    • Building, training, and deploying machine learning models
    • Using SparkML and H2O.ai for scalable ML model development
    • Model performance evaluation and tuning
  • Big Data Analytics and Advanced Techniques

    • Real-time analytics using Apache Kafka and Spark Streaming
    • Batch vs. stream processing
    • Time series analysis and forecasting
    • NLP (Natural Language Processing) on big data platforms
4. Data Governance and Security in CDP
  • Data Security and Compliance
    • Authentication and authorization (Kerberos, LDAP, SSO)
    • Data encryption in CDP
    • Role-based access control (RBAC)
    • Secure data access with Apache Ranger
  • Data Governance
    • Introduction to Apache Atlas and its role in data governance
    • Metadata management and cataloging
    • Data lineage and impact analysis
    • Data privacy and protection strategies in CDP
5. CDP Administration and Operations
  • Cluster Management and Configuration
    • Installing and configuring CDP clusters
    • Cluster scaling, monitoring, and performance tuning
    • Role of CDP Control Plane and Data Plane
  • Monitoring and Troubleshooting
    • CDP Monitoring tools (e.g., Cloudera Manager)
    • Troubleshooting common issues
    • Log management and diagnostics
  • Data Backup and Recovery
    • Implementing data backup strategies within CDP
    • Recovery plans and data protection in a multi-cloud environment
6. Cloud Integration and Multi-Cloud Architectures
  • Working with CDP in the Cloud
    • Deploying CDP in AWS, Azure, or GCP
    • Hybrid and multi-cloud architecture management
    • Migrating workloads between on-premises and cloud environments
  • Cloud-native Data Services
    • Managing CDP environments with Kubernetes and Docker
    • Leveraging cloud-native tools and services alongside CDP (e.g., AWS S3, Google Cloud BigQuery)
7. CDP Data Warehouse and Data Lakes
  • Managing Data Warehouses in CDP
    • CDP Data Warehouse Overview and benefits
    • Building and managing a data lake in CDP
    • Querying large datasets with SQL engines like Impala and Hive
  • Real-time and Batch Analytics
    • Using CDP for real-time data analytics
    • Integrating batch and stream processing in CDP data pipelines
  • Data Integration with External Systems
    • Data ingestion from various external sources (e.g., RDBMS, flat files, IoT devices)
    • Using CDP tools for external data integration
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