What will you learn?
When you complete Codeinfin Apache Spark course, you’ll be able to:
- Get the complete synopsis of Big Data & Hadoop with HDFS (Hadoop Distributed File System), and YARN (Yet Another Resource Negotiator)
- Understand Scala installation and functional programming.
- Understand Spark Cluster and writing spark application with Spark Streaming
- Understand HDFS , data frames and RDDs.
- Comprehend and execute live Projects in various sectors such as telecommunication, banking, retail etc.
- Understand Hive and Other Spark APIs
Spark is the Primary gizmo for Big data.
With the Big data evolution, Spark has become the fastest growing and widely used Big Data tool used for data processing and analytics. Most of the leading companies across globe has either adopted it already or are migrating into it. An Apache Spark certified professional is bound to have a promising career. If you want to start in pole position, then get a structured training in Apache Spark which is best aligned to Cloudera Hadoop requirements.
Growing demand for Apache Spark Certified Professional:
Wikibon has predicted that Apache Spark will dominate the big data landscapes by 2022. With rampant digitalization, data size is growing exponentially and the best tool to handle the data explosion is Apache Spark. Demand and supply gap for Apache Spark professional is only going to rise in next decade in India and other emerging markets. Beginners can easily fetch a starting salary of 7-8 lacs (glassdoor.com) which grows in geometric progression with the experience.
Codeinfin Apache Spark Online Courses:
Codeinfin Apache Spark Online course is for the learners who have basic understanding of Hadoop ecosystem and sees themselves as a successful Spark developer. This course will give you the in-depth knowledge of Spark internals and you will be creating and deploying real-time Hadoop projects using Apache Spark. Codeinfin Apache Spark Online course has been tailor made by industry experts who have more than 10+ years of experience on Big Data and emerging tools. The real world challenges shared by them will equip you better for a job in Apache Spark domain.
- Understand the difference between Apache Spark and Hadoop
- Scala Installation
- Get deep insights into the functioning of Scala
- Execute Pattern Matching in Scala
- Functional Programming in Scala – Closures, Currying, Expressions, Anonymous Functions
- Know the concepts of classes in Scala
- Object Orientation in Scala – Primary, Auxiliary Constructors, Singleton & Companion Objects
- Traits and Abstract classes in Scala
- Scala Simple Build Tool – SBT
- Building with Maven
- What is Apache Spark?
- Spark Installation
- Spark Configuration
- Spark Context
- Using Spark Shell
- Resilient Distributed Datasets (RDDs) – Features, Partitions, Tuning Parallelism
- Functional Programming with Spark
- RDD Operations – Transformations and Actions
- Types of RDDs
- Key-Value Pair RDDs – Transformations and Actions
- MapReduce and Pair RDD Operations
- A Spark Standalone Cluster
- The Spark Standalone Web UI
- Executors & Cluster Manager
- Spark on YARN Framework
- Spark Applications vs. Spark Shell
- Creating the SparkContext
- Configuring Spark Properties
- Building and Running a Spark Application
- Spark Job Anatomy
- Caching and Persistence
- Caching Overview
- Distributed Persistence
- Shared Variables: Broadcast Variables
- Shared Variables: Accumulators
- Per Partition Processing
- Compression Techniques – Snappy, Zlib, Gzip
- Spark SQL Overview
- Hive Context
- SQL Datatypes
- Dataframes vs RDDs
- Operations on DFs
- Parquet Files with Spark Sql – Read, Write, Partitioning, Merging Schema
- ORC Files
- JSON Files
- Inferring Schema programmatically
- Custom Case Classes
- Temp Tables vs Persistent Tables
- Writing UDFs
- JDBC Support – Examples
- HBase Support – Examples
- Spark Streaming
- Spark Streaming Overview
- Example: Streaming Word Count
- Other Streaming Operations
- Sliding Window Operations