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BIG DATA

Big data analytics is the process of examining large amounts of data of a variety of types to uncover hidden patterns, unknown correlations, and other useful information. Such information can provide competitive advantages between similar organizations and result in business benefits, such as more effective marketing and increased revenue.

New methods of working with big data, such as Hadoop and Map Reduce, offer alternatives to traditional data warehousing. Big Data Analytics with R and Hadoop is focused on the techniques of integrating R and Hadoop by various tools such as RHIPE and R-Hadoop.

A powerful data analytics engine can be built and process analytics algorithms over a large scale dataset in a scalable manner. This can be implemented through data analytics.

Upskill and get certified in Big Data with our one-of-a-kind big data training course, crafted to help you become adept at Big Data Technology.


Module List

  • HADOOP Architecture
  • Map Reduce Architecture
  • HADOOP Developer Tasks
  • BHADOOP Administrative Tasks
  • ACL (Access control list) Upgrading HADOOP
  • Hive Architecture
  • Pig Architecture
  • SQOP Architecture
  • Mini Project / POC (Proof of Concept)

Course Details

    LINUX

    • What is File System?
    • Block, Linux commands
    • How is the system working?
    • File system, OS, CPU, Applications
    • Shell Script basics
    • How to automate Sqoop
    • hive using a shell script.

  • Installation
  • Java 8
  • Scala 2.11.8
  • Intelli-J
  • What is JVM?
  • Java Vs Scala
  • Scala basics
  • Primitive DataTypes
  • Scala Type Hierarchy
  • Strings, List
  • Arrays
  • tuples for loop match
  • switch
  • functions
  • recursive functions
  • nested functions
  • Higher-order functions
  • anonymous function
  • most frequently used functions in Spark
  • map, flat map,
  • distinct group by the filter

  • What is data?
  • Why Big Data came into the picture?
  • Common Big Data problems.
  • Normal file system
  • bigdata ecosystems functioning.
  • Storage problems
  • processing problems.
  • Frameworks – programming languages.
  • Different type of popular Big Data frameworks.
  • Where Hadoop recommended?
  • Hadoop & ecosystems.
  • Hadoop internal concepts.

  • HDFS Daemons (Name node, Data node, Secondary Name node),
  • HDFS Block Vs Unix file system
  • Block,
  • HDFS Replication importance,
  • HDFS File Write Process,
  • HDFS File Read Process,
  • Secondary name node,
  • Namenode High Availability,
  • Why Namenode very Fast.
  • HDFS Shell Commands.
  • WebHDFS REST API.
  • Input split. Why MapReduce slow?
  • How HDFS solve Storage Problems.

  • Introduction – YARN
  • Daemons (Resource Manager, NodeManager)
  • Why YARN needed?
  • Responsibilities of Resource Manager
  • Node Manager and Application Master on YARN Cluster
  • What is Container in YARN?
  • Mapreduce input split,
  • speculative execution,
  • debug yarn logs.
  • Creating JAR file in IDE Block Vs Split – Partition
  • Hash Partitioner
  • Custom Partitioner – Combiner
  • How Application Master functioning.
  • Yarn life cycle.
  • Mapreduce Vs Spark.
  • Why MapReduce is very Slow.
  • YARN Vs MESOS

  • Introduction to HIVE
  • Hive Vs RDBMS
  • Data types (Primitive, Collection)
  • Create Tables (Managed, external)
  • DML operations (load,insert,export)
  • Managed Vs external tables
  • QL Queries (select, where, group by, having, sort by, order by)
  • File Formats (Text, ORC, Parquet)
  • Partitioning,
  • Bucketing,
  • Partitioning Vs Bucketing-Views
  • different types of joins (inner, outer)
  • Map Side join
  • Bucketing join
  • Series (CSVSerde, JsonSerde, MultiDelimitSerde)
  • How to update data using Hive.
  • Automate Hive script using Shell

  • Import data from Oracle
  • MySQL
  • MsSQL data using Sqoop.
  • Change the delimiter and file format of data during import using Sqoop.
  • Compression techniques
  • Store data in Hive optimization
  • Best practice.
  • Export structure data to Sqoop.
  • Limitations, problems in Sqoop.

  • Kafka producer API
  • Consumer API
  • Streaming API
  • Kafka Topics
  • Zookeeper
  • Brokers
  • leaders
  • partitions
  • Kafka installation in windows.
  • Kafka wordcount program.
  • How to process JSON
  • Avro file using Kafka.
  • Spark Kafka integration.
  • Hbase: How to create a table
  • Column family
  • load data, get, put, scan.
  • Update data.
  • Phoenix: Create a table and run SQL commands on top of Hbase using Phoenix. Run OLAP, OLTP commands using Phoenix.
  • Spark -Phoenix integration run final year project.
  • Cassandra installation and run SQL queries,
  • problems using Cassandra.

  • What is Cloud Computing?
  • AWS vs Azure.
  • Different type of AWS Services.
  • RDS: Create Oracle, MySQL, MsSQL, insert data and run DML & DDL queries. EC2: Create windows and Ubuntu OS.
  • Image (AMI) importance.
  • Install different software in ubuntu.
  • EMR & S3: Run Hadoop, Hive, Spark, Sqoop command using EMR.
  • Access S3 data using S3 in CLI. Buckets.
  • IAM: Groups, users, policies.

  • Spark installation in windows.
  • Why & Where use Spark?
  • Hadoop Vs Spark.
  • RDD API – DAG Graph
  • Lineage Graph
  • Transformations
  • Actions, Word count program.
  • RDD vs Hadoop input Split
  • Key-pair RDDs, Properties
  • lineage in-memory Different ways to create RDD.
  • Joins, tuning Hands-on.
  • Convert rdd to dataframe api
  • Catalyst optimizer data frame API.
  • DSL commands: select, filter, group by, join, distinct, order by, where.
  • Run SQL queries on top of multiple datatype datasets (JSON, CSV, XML, parquet, orc) Get data from Oracle, MySQL, MsSQL, optimization techniques.
  • Submit a project in the production environment.
  • check logs and analyze using web UI.
  • Process Hbase.
  • Cassandra and Redshift data using Spark.
  • DataSet API importance in spark.
  • Case classes.
  • process complex JSON data.
  • Spark Streaming: Wordcount program
  • Datastream API. How to get logs from the webserver.
  • How to run SQL queries on top of streaming data.
  • Structure streaming using spark.
  • Get twitter data using Spark.
  • Process and implement sentiment analysis using Spark.
  • Get data from different sources using Spark.

  • Installation of FLINK
  • Spark Vs Flink
  • Flink Table library
  • DataSet API
  • Get oracle data using FLINK.
  • Process CSV,
  • JSON data using Flink.
  • Performance optimization
  • Encoder

  • 3 POC (proof of concept)
  • Profile Upgradation
  • Interview tips and Clouder Certification guidance.
  • How to learn other technologies like artificial intelligence, IoT,
  • How to get sample code.

Training Advantages
35 contact hours
Industry Case Studies
Industry case studies
Real time training

BIG DATA FAQ'S

    There are various indications in real-time scenarios to show the demand for Big Data in today’s IT world. Obviously, the amount of data generated in each and every organization is extremely large. Therefore, it has become inevitable to preserve such a big volume of data. Big Data comes into the picture to handle this kind of heavy data load and has been aiding a plethora of IT organizations around the world in the case of valuable decision making, business improvement and also to establish a great leap in data management against their competitors.

    In reality, many organizations in the United States find it very hard to get qualified candidates in Big Data. Skill Gap has become a predominant issue in Big Data due to lack of training.

    As of the latest survey, there are nearly 190,000 data scientists and 1.5 million managers are in demand in the United States. If you are interested or passionate about Big Data, it is highly recommended to take up Big Data training, which will add value to your profile.

    Based on the analytical and practical skills of the candidates, we have five different types of job roles in Big Data analytics as follows,

    1. Solutions expert
    2. Analytics salesperson
    3. Storyteller
    4. Expert programmer or Tools expert
    5. Expert Modeler

    Two simple ways to learn Big Data are

    1. In-class training and
    2. Online training

    The reason to suggest these training modes is, both the above training methods will give a comprehensive understanding and knowledge of the Big Data topic you are interested in. Yet another important thing to keep in mind is the training institute or mentors you choose to learn Big Data. You have a list of established Big Data training institutes in the United States here. Depending upon your convenience, you could either choose, In-class training or online training.

    Big Data concepts are large and quite complex in certain aspects. With respect to the type of big data course, your pre-requisites will be sought. For instance, to learn Hadoop, a basic knowledge of Linux/GNU operating system and programming languages such as Scala, Java or Python are recommended.