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Apache Spark With Scala/Python And Apache Storm Certification Training Course

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Course Duration

50 Days.

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ABOUT COURSE

Apache Spark With Scala/Python And Apache Storm online training is an open source cluster computing big data framework. It provides faster and more general data processing platform engine. It is basically designed for fast computation. It works with file system to distribute data cluster and process that data in parallel. It covers wide range of workloads like batch applications, iterative algorithms, interactive queries, complex analytics and streaming.

Apache Spark With Scala/Python And Apache Storm Certification is the 2nd great framework of Big Data analytics. The popularity of Spark and Scala is gradually increases, which increase its demand. It is mainly used for data processing, querying, and generating analytics reports in a faster way. As compared to MapReduce, Apache Spark has high speed In-memory data processing engine.

BIGDATA TRAINING

Benefits of Attending Apache Spark With Scala/Python And Apache Storm Training

  • It can help big data scientist to create complex workflow easily with its high-level libraries.
  • Better-integrated framework, supports all the formats like text data, batch data, real-time streaming, graphical etc.
  • Apache Spark is a general-purpose & lightning fast cluster computing system (a Framework) whereas Scala is a high-level programming language in which Spark is written.
  • Scala is comparatively new to the programming scene, but has become popular very quickly.
  • Apache Spark With Scala/Python And Apache Storm Courses:
  • Spark’s big data abstraction RDD
  • Scala language in details
  • Structured Data – SQL, DataFrames and Datasets
  • Spark Streaming

Apache Spark is an open-source data processing engine to store and process data in real-time across various clusters of computers using simple programming constructs. It has fewer lines of code and supports authentication via a shared secret. It can also run on YARN leveraging the capability of Kerberos. In general spark is a fast data processing engine. The main reason of spark being faster is that it process the data in-memory.

Spark processes data 100 times faster than MapReduce as it is done in-memory. important features of Apache Spark Fast processing, In-memory computing, Fault-tolerant and Flexible in nature. when you submit your job to spark, it takes all the data in memory from the disk and performs all the jobs tasks and clean the memory once all tasks complete.

Apache Spark has the following key features:

  • Apache Spark is a distributed processing system which used for big data workloads.
  • Spark can be used for multiple things like for running distributed SQL, for creating data pipelines, ingesting data into a database, running Machine Learning algorithms and working with graphs or data streams.
  • It process the data in-memory and use caching and optimized query execution for fast queries against data of any size.
  • Apache Spark is an open source, general purpose, distributed cluster computing system which provides faster analytics than Apache Hadoop.
  • Spark was written in Scala, which is considered the primary language for interacting with the Spark Core engine.
  • The Spark core consists of the basic functionalities of Spark such as memory management, fault recovery, task scheduling.
  • Spark not only processes Big Data, but can process structured data as well. This is done using Spark SQL.
  • To simplify graph analytical problems, Spark provides a library which helps in, manipulating graphs and enabling graph parallel-computation.

Why Kasha Training Education?

  • Kasha Training Education provides the best Apache Spark With Scala/Python And Apache Storm certification training course in collaboration with industry experts with certified trainers.
  • Kasha Training provide flexibility in learning, one can undergo training at his own comfort, pace and anywhere anytime. It is also cost effective and eco-friendly because no travelling costs and course once developed can be used any number of times.
  • Through Apache Spark With Scala/Python And Apache Storm courses students have access to a quality of Apache Spark With Scala/Python And Apache Storm Training material and lectures. This is really helpful as now you have the chance to take a course from a faculty whose teaching frequency matches with my learning frequency.
  • Our training for Apache Spark With Scala/Python And Apache Storm Online Training focuses on improving participants skills by emphasizing importance on key objectives, milestones, deliverables to work within the constraints of scope, time, cost and quality of the project.

Feedback from our Participants

“I was very satisfied with the trainer interactions and overall process of learning online. “Apache Spark With Scala/Python And Apache Storm online training is providing excellent coaching, I am 100 % satisfied. I will definitely advise my friends to go for Kasha Training”
DeepDubai
Apache Spark With Scala/Python And Apache Storm Certification is one of the wonderful trainers I had ever seen in Kasha Training. “The trainer has been fantastic. His knowledge on the subject matter is sound and it’s good he has practical experience”
VartikaThailand
Trainer almost left no room for asking a doubt, he himself brought up the doubt what could erupt in our mind to discussion. May be he has a lot of experience to teach, so he know where a doubt can erupt in our mind”
SmritiIndia
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COURSE CURRICULUM

  • What is Apache Spark
  • Understanding Lambda Architecture for Big Data Solutions
  • Role of Apache Spark in an ideal Lambda Architecture
  • Understanding Apache Spark Stack
  • Spark Versions
  • Storage Layers in Spark
  • Downloading Apache Spark
  • Installing Spark in a Single Node
  • Understanding Spark Execution Modes
  • Batch Analytics
  • Real Time Analytics Options
  • Exploring Spark Shells
  • Introduction to Spark Core
  • Setting up Spark as a Standalone Cluster
  • Setting up Spark with Hadoop YARN Cluster
  • Basics of Python
  • Basics of Scala
  • Understanding the Basic component of Spark -RDD
  • Creating RDDs
  • Operations in RDD
  • Creating functions in Spark and passing parameters
  • Understanding RDD Transformations and Actions
  • Understanding RDD Persistence and Caching
  • Examples for RDDs
  • Installation of Anaconda Python
  • Installation of Jupiter Notebook
  • Working with Jupiter Notebook
  • Installation of Zeppelin
  • Working with Zeppelin notebooks
  • Anatomy of Hadoop Cluster, Installing and Configuring Plain Hadoop
  • Batch v/s Real time
  • Limitations of Hadoop
  • Understanding the Key/Value Pair Paradigm
  • Creating a Pair RDD
  • Understanding Transformations on Pair RDDs
  • Understanding Actions on Pair RDDs
  • Understanding Data Partitioning in RDDs
  • Understanding Default File Formats supported in Spark
  • Understanding File systems supported by Spark
  • Loading data from the local file system
  • Loading data from HDFS using default Mechanism
  • Spark Properties
  • Spark UI
  • Logging in Spark
  • Checkpoints in Spark
  • Creating a HiveContext
  • Inferring schema with case classes
  • Programmatically specifying the schema
  • Understanding how to load and save in Parquet, JSON, RDBMS and any arbitrary source ( JDBC/ODBC)
  • Understanding DataFrames
  • Working with DataFrames
  • Understanding the role of Spark Streaming
  • Batch versus Real-time data processing
  • Architecture of Spark Streaming
  • First Spark Streaming program in Java with packaging and deploying
  • Anatomy of Hadoop Cluster, Installing and Configuring Plain Hadoop
  • What is Big Data Analytics
  • Batch v/s Real time
  • Limitations of Hadoop
  • Storm for Real Time Analytics
  • Installation of Storm
  • Components of Storm
  • Properties of Storm
  • Storm Running Modes
  • Creating First Storm Topology
  • Topologies in Storm
  • Getting Data
  • Bolt Lifecycle
  • Bolt Structure
  • Reliable vs Unreliable Bolts

WHO CAN LEARN?

  • IT Developers and Testers
  • Data Scientists
  • Analytics Professionals
  • Research Professionals
  • BI and Reporting Professionals
  • Students who wish to gain a thorough understanding of Apache Spark
  • Professionals aspiring for a career in field of real-time Big Data Analytics

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