Spark Training
Learn Spark that Gather All Useful Tools for Big Data Analysis with Advanced Techniques
THE GOAL OF COURSE
The purpose of the Big Data Training Program is to enable participants to consolidate their knowledge in the their minds with experience sharing, example implementations and case studies, while providing detailed information on key concepts and techniques in the Big Data Ecosystem.
WHAT YOU WILL LEARN
- Learn the terminology and basic concepts behind Spark Architecture and its components, selection criteria and best solution designs,
- Have deep knowledge on the Spark Ecosystem,
- Understand how you should prepare your Spark architecture to production,
- Specialize in the most appropriate cluster configurations and integrations with the hardware and systems within the data center,
- Learn the Spark API, Streaming, MLLib ve GraphX Concepts in detail,
- Develop technical knowledge and competencies with workshops,
- Make a significant contribution to your development in Big Data,
- Be able to find answers to your questions at the points where your head is installed while developing after the program
WHO BENEFITS
- Employees who are doing or planning to make a career in the Big Data place
- Leading teams in technology / IT functions
- Leaders responsible for Digital Transformation.
- Data Scientists and Data Analysts
- New Graduates and University Students
Learn Spring Ecosystem in Depth From Batch Processing to Real Time Data Processing, From Machine Learning to Graph Processing
Theories and practices you will see throughout the program will answer your questions on Spark Ecosystem, also you will learn key techniques to develop the right strategy while implementing your projects
COURSE OUTLINE
- Module-1: Introduction to Spark
- The History of Spark
- The Components of Spark Ecosystem
- Spark Core Concepts
- Installation Spark
- Using Spark Shell Use Cases of Spark
- Module-2: Resilient Distributed Datasets (RDD)
- RDD Concepts
- RDD Life Cycle
- RDD Partitioning and Transformations
- RDD Operations
- Key-Value Pairs Operations
- Cache Operations
- Data Collection with Pair RDDs
- Module-3 Using Spark API
- Understanding SparkConf
- Working with SparkContext
- Using RDD API
- Spark Application Development
- Application Life Cycle
- Cluster Managers
- Logging and Debugging
- Module-4 Working with Spark Streaming
- Streaming Basic Concepts
- Understanding DStreams (Discretized Steams)
- Stateless, Stateful, and Windowed Conversions
- Spark Streaming API
- Real-Time Data Processing with Spark Streaming
- Lab: Example Project Development with Spark Streaming and Kafka
- Module-5 Exploring Spark MLLib and GraphX
- Using Spark MLlib
- Feature Vectors
- Clustering / Grouping, K-Means
- Recommendations
- Classifications
- Spark GraphX Library
- Using Graph API and Graph Creation
- Lab: Classification and Shortest Path Examples
- Module-6 Performance Optimization and Best Practices
- General Performance Issues
- Diagnosing Performance Problems
- Broadcast Variant and Accumulator Usage
- Cache Usage
- Narrow and Wide Dependencies
- Spark UI Usage
- Production Setup and Performance Tips
- Lab: You will learn on the sample data set by applying lots of examples on Spark Components