Cloudera Developer
This hands-on training course delivers the key concepts and expertise developers need to develop high-performance parallel applications with Apache Spark 2. Participants will learn how to use Spark SQL to query structured data and Spark Streaming to perform real-time processing on streaming data from a variety of sources. Developers will also practice writing applications that use core Spark to perform ETL processing and iterative algorithms. The course covers how to work with large datasets stored in a distributed file system and execute Spark applications on a Hadoop cluster. After taking this course, participants will be prepared to face real-world challenges and build applications to execute faster decisions, better decisions, and interactive analysis, applied to a wide variety of use cases, architectures, and industries.
With this course update, we streamlined the agenda to help you quickly become productive with the most important technologies, including Spark 2.
Get hands-on experience
Hands-on exercises take place on a live cluster, running in the cloud. A private cluster will be built for each student to use during the class.
Through instructor-led discussion and interactive, hands-on exercises, participants will navigate the Hadoop ecosystem, learning how to::
- Distribute, store, and process data in a Hadoop cluster
- Write, configure, and deploy Spark applications on a cluster
- Use the Spark shell for interactive data analysis
- Process and query structured data using Spark SQL
- Use Spark Streaming to process a live data stream
What to expect
This course is designed for developers and engineers who have programming experience, but prior knowledge of Hadoop and/or Spark is not required.
- Apache Spark examples and hands-on exercises are presented in Scala and Python. The ability to program in one of those languages is required.
- Basic familiarity with the Linux command line is assumed.
- Basic knowledge of SQL is helpful
Introduction to Apache Hadoop and the Hadoop Ecosystem
- Introduction to Apache Hadoop and the Hadoop Ecosystem
- Apache Hadoop Overview
- Data Ingestion and Storage
- Data Processing
- Data Analysis and Exploration
- Other Ecosystem Tools
- Introduction to the Hands-On Exercises
Apache Hadoop File Storage
- Apache Hadoop Cluster Components
- HDFS Architecture
- Using HDFS
Distributed Processing on an Apache Hadoop Cluster
- YARN Architecture
- Working With YARN
Apache Spark Basics
- What is Apache Spark?
- Starting the Spark Shell
- Using the Spark Shell
- Getting Started with Datasets and DataFrames
- DataFrame Operations
Working with DataFrames and Schemas
- Creating DataFrames from Data Sources
- Saving DataFrames to Data Sources
- DataFrame Schemas
- Eager and Lazy Execution
Analyzing Data with DataFrame Queries
- Querying DataFrames Using Column Expressions
- Grouping and Aggregation Queries
- Joining DataFrames
RDD Overview
- RDD Overview
- RDD Data Sources
- Creating and Saving RDDs
- RDD Operations
Transforming Data with RDDs
- Writing and Passing Transformation Functions
- Transformation Execution
- Converting Between RDDs and DataFrames
Aggregating Data with Pair RDDs
- Key-Value Pair RDDs
- Map-Reduce
- Other Pair RDD Operations
Querying Tables and Views with Apache Spark SQL
- Querying Tables in Spark Using SQL
- Querying Files and Views
- The Catalog API
- Comparing Spark SQL, Apache Impala, and Apache Hive-on-Spark
Working with Datasets in Scala
- Datasets and DataFrames
- Creating Datasets
- Loading and Saving Datasets
- Dataset Operations
Writing, Configuring, and Running Apache Spark Applications
- Writing a Spark Application
- Building and Running an Application
- Application Deployment Mode
- The Spark Application Web UI
- Configuring Application Properties
Distributed Processing
- Review: Apache Spark on a Cluster
- RDD Partitions
- Example: Partitioning in Queries
- Stages and Tasks
- Job Execution Planning
- Example: Catalyst Execution Plan
- Example: RDD Execution Plan
Distributed Data Persistence
- DataFrame and Dataset Persistence
- Persistence Storage Levels
- Viewing Persisted RDDs
Common Patterns in Apache Spark Data Processing
- Common Apache Spark Use Cases
- Iterative Algorithms in Apache Spark
- Machine Learning
- Example: k-means
Apache Spark Streaming: Introduction to DStreams
- Apache Spark Streaming Overview
- Example: Streaming Request Count
- DStreams
- Developing Streaming Applications
Apache Spark Streaming: Processing Multiple Batches
- Multi-Batch Operations
- Time Slicing
- State Operations
- Sliding Window Operations
- Preview: Structured Streaming
Apache Spark Streaming: Data Sources
- Streaming Data Source Overview
- Apache Flume and Apache Kafka Data Sources
- Example: Using a Kafka Direct Data Source
What if I miss one (or) more class?
No need to worry about the classes you missed. We will definitely guide you by having optional classes or by having classes with other batches with the same topic you missed previous classes.
Who is my instructor?
IT professionals who have strong knowledge in technical know how to convey things with the real-time example. Even a layman could understand the concepts which given by our experts.
What are the modes of training offered for this course?
We offer this course in “Live Instructor-Led Online Training” mode. Through this way you won’t mess anything in your real-life schedule. You will be shared with live meeting access while your session starts.
What are the system requirements to work?
Minimum 2GB RAM and i3 processor is required
Can I attend a demo session?
You can get a sample class recording to ensure you are in right place. We ensure you will be getting complete worth of your money by assigning a best instructor in that technology.
How about group discounts (or) corporate training for our team?
We are absolutely loved to talk in-person about group training (or) corporate training. So, please get in touch with our team through “Quick Enquiry”, “Live Chat” or “Request Call-back” channels.
Where do Our Online learners and Trainer’s come from
We are providing online training, One-to-One training with the help of experts. Our learners and trainers are frequently coming from different countries like USA, India, UK, Australia, New Zealand, Canada and UAE. To specify in cities London, Bangalore, California, New York, Pune, Mumbai, Chennai, New Delhi, San Francisco, New Jersey, Texas, Florida, Kolkata, Gurgaon, Berlin and Hyderabad among many.
I have more queries?
If you want to know More Details about Online Training Please Contact us. Or you can share your quires through hr@jobmasterss.com Estimated turnaround time will be 24 hours for mails.
Industry-Aligned Curriculum
Co-created with product leaders from top companies to match real-world demands.
Hands-On Projects
Build real product strategies, mockups, and roadmaps to showcase in your portfolio.
Expert Mentorship
Learn directly from experienced product managers and get personalized guidance.
Flexible Online Format
Attend live instructor-led sessions or learn at your own pace with recorded modules.
Job-Oriented Learning
Includes interview prep, resume reviews, and mock interviews tailored for PM roles.
Certification Support
Gain a course completion certificate and get support to crack certifications
Contact us
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- Learning strategies that are appropriate and tailored to your company’s requirements.
- Live projects guided by instructors are a characteristic of the virtual learning environment.
- The curriculum includes of full-day lectures, practical exercises, and case studies.
Got more questions?
Talk to our team directly. A program advisor will get in touch with you shortly.
We’re happy to answer any questions you may have and help you determine which of our services best fit your needs.