Curs Big Data Intermediate

Acest curs de instruire de cinci zile este dedicat sustinerii examenului Big Data Intermediate.

PRET DE LISTA
contacteaza-ne
DURATA CURS
5 zile x 8 ore/zi
CERTIFICARE CURS
Beingcert

Agenda cursului Big Data Intermediate

Module 1: Introduction

Lessons

  • Introduction of Big Data and its significance
  • Numerous uses of Big Data in different industries like Telecom, E‐commerce, Finance, Insurance etc.
  • Issues being faced by traditional large‐scale industries

Module 2:

Lessons

  • Introduction to Hadoop
  • Diverse projects of Apache
  • Projects Role in Hadoop’s environment
  • Big Data requirements through important technology foundations
  • Conditions and solutions of existing Data Analytics Architecture
  • Differentiation between traditional data management systems and Big Data management systems
  • Analyzing the key framework requirements for Big Data Analytics
  • Importance of Real Time Data
  • Usage of Big and Real Time Data as a Business Planning Tool
  • Hadoop Ecosystem & Hadoop 2.x core components

Module 3:

Lessons

  • Hadoop Master‐Slave design
  • Distributed File System for Hadoop – notion of data storage
  • Various types of cluster set ups – fully distributed, PSEUDO etc.
  • Installing Hadoop cluster set up
  • Hadoop 2.x cluster designing
  • Hadoop cluster modes
  • Going through cluster management tools like Cloudera manager/Apache ambari

Module 4:

Lessons

  • Understanding HDFS and HDFS data storage
  • Getting the data from local machine to Hadoop and vice versa
  • Understanding of MapReduce and understanding of Mapper and Reducer (Traditional way vs MapReduce way)
  • Get to know about the framework of MapReduce program
  • Use of basic JAVA for developing MapReduce Program
  • Use of streaming basic API for developing MapReduce Program

Module 5:

Lessons

  • Merging of Hadoop with an existing Enterprise
  • Usage of Sqoop for loading data from RDBMS into HDFS
  • Usage of Flume for managing real time data
  • Using Legacy systems for accessing HDFS

Module 6:

Lessons

  • Data Analysis Tools introduction
  • Apace PIG – MapReduce vs PIG, PIG Use cases
  • Data Model of PIG
  • Streaming of PIG
  • PIG Latin program and its execution
  • Understanding of PIG Latin – Relational Operators, File Loaders, Group Operator, Diagnostic
  • Operators, PIG UDF, Joins and COGROUP
  • Composing JAVA UDF’s
  • Embedded PIG in JAVA
  • PIG Macros
  • Substitution Parameter
  • Automating the design using PIG and implementing MapReduce applications
  • Usage of PIG for applying a structured data to an unstructured Big Data

Module 7:

Lessons

  • Apache Hive – Hive vs PIG, Hive use cases
  • Understanding the HIVE data storage principle
  • Understanding the File Formats and the Records Formats supported by the Hive environment
  • Perform operations with data in Hive
  • Hive QL – Joining Tables, Dynamic Partitioning, Custom Map, Map/Reduce Scripts
  • Hive UDF, Hive Script
  • Methods of loading data in Hive
  • Serialization and Deserialization
  • Hive Persistence formats
  • Using Hive for handling Text Data
  • Merging external BI tools with Hadoop Hive

Module 8:

Lessons

  • Introduction of Impala and its format
  • Execution of queries through Impala and its relevance
  • Hive vs PIG Vs Impala
  • User defined functions for extending Impala

Module 9:

Lessons

  • NoSQL Database – Hbase
  • Introduction to oozie

Module 10:

Lessons

  • Understanding Apace Spark
  • In Memory Data vs the Streaming Data
  • Spark Vs MapReduce
  • Different modes of Spark
  • Demonstration of Spark Installation
  • Understanding Spark on a cluster
  • Spark Standalone Cluster

Module 11:

Lessons

  • Invoking Spark Shell
  • Establishing the Spark Context
  • Getting a file loaded in shell
  • Conducting basic operations on files in Spark Shell
  • Overview of Cache
  • Distributed Persistence
  • Overview of Spark Streaming

Module 12:

Lessons

  • Scrutinize Hive and Spark SQL format
  • Analyzing Spark SQL
  • Spark SQL Context
  • Merging Hive and Spark SQL
  • Supporting JSON and Parquet File Formats
  • Implementing Data Visualization in Spark
  • Data Loading
  • Queries of Hive Through Spark
  • Spark’s Performance Tuning Tips
  • Shared Variables – Broadcast Variables and Accumulators

Module 13:

Lessons

  • Usage of Spark Streaming for extracting and analyzing data from twitter
  • Understanding of comparison between Spark and Storm

Module 14:

Lessons

  • Understanding of GraphX module in Spark
  • Designing graphs with GraphX

Module 15:

Lessons

  • Overview of Machine Learning Framework
  • Implementing few of the Machine Learning algorithms using Spark MLLib

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