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Big Data Classes in BTM Layout Bangalore

Best Big Data Training in

Big Data Training in BTM Layout & Best Big Data Classes in Bangalore

Ecare Technologies offers the best Big Data Training in BTM Layout with most experienced professionals. We conduct all our students from the Basic Level of Big Data Classes to Advanced level. All these Classes are undergone not only theoretically, but also are executed on the real-time basis. Our Trainers are Real-time Working professionals in the Big Data Field from many years with hands on real time Big Data Project Knowledge.

Ecare Technologies is a highly experienced Big Data Training Institute in BTM Layout. This has helped students to get placed in top MNCs. We Offer Big Data Classes in BTM Layout for working professionals and students. we offer Regular training classes in Morning Batches and Evening Batches we also offer Weekend Training classes and Fast-track Training Classes for Big Data Course.

Hadoop Bigdata Course Content:

 Course Duration: 25 days (60 hours duration).

Bigdata Fundamentals

Day1: (2hours)

1. Understanding BigData.

a. What is Big Data?

b. Big-Data characteristics.

c. Challenges with the traditional Data Base Systems and Distributed Systems.

2. Hadoop Distributions:

a. Hortonworks

b. Cloudera

c. Pivotal HD

d. Greenplum.

Day2: (2hours)

3. Introduction to Apache Hadoop.

a. Flavors of Hadoop: Big-Insights, Google Query etc..

4. Hadoop Eco-system components: Introduction

a. MapReduce

b. HDFS

c. Apache Pig

d. Apache Hive

e. HBASE

f. Apache Oozie

g. FLUME

h. SQOOP

i. Spark.

j. Kafka

k. Crunch

Day3: (2hours)

5. Understanding Hadoop Cluster

6. Hadoop Core-Components.

a. NameNode.

b. JobTracker.

c. TaskTracker.

d. DataNode.

e. SecondaryNameNode.

7. HDFS Architecture

f. Why 64MB?

g. Why Block?

h. Why replication factor 3?

Hadoop

 Course Duration: 25 days (60 hours duration).

Day4: (2hours)

8. Discuss NameNode and DataNode.

9. Discuss JobTracker and TaskTracker.

10. Typical workflow of Hadoop application

11. Rack Awareness.

a. Network Topology.

b. Assignment of Blocks to Racks and Nodes.

c. Block Reports

d. Heart Beat

e. Block Management Service.

Day5: (4hours)

12. Anatomy of File Write.

13. Anatomy of File Read.

14. Heart Beats and Block Reports

15. Map Reduce Overview

16. Cluster Configuration

a. Core-default.xml

b. Hdfs-default.xml

c. Mapred-default.xml

d. Yarn-site.xml

e. Hadoop-env.sh

f. Slaves

g. Masters

17. Map Reduce Framework

18. Why Map Reduce?

19. Use cases where Map Reduce is used.

20. YARN Architecture

21. Hadoop Classic vs YARN

22. YARN Demo

Day6: (2hours)

23. MR Practicals

a. Setup environment for the programs.

b. Possible ways of writing Map Reduce program with sample codes find the best code and

discuss.

c. Configured, Tool, GenericOptionParser and queues usage.

24. Limitations of traditional way of solving word count with large dataset.

Hadoop

 Course Duration: 25 days (60 hours duration).

Day7: (2hours)

25. Map Reduce way of solving the problem.

26. Complete overview of MapReduce.

27. Unit testing of mapreduce programs using Junit, MRUnit frameworks.

28. Challenges in Hadoop Testing and options available

29. Manual testing of MapReduce programs

Day8: (2hours)

30. Split Size

31. Combiners

32. Multi Reducers

33. Parts of Map Reduce

34. Shuffle, Sort and Merge phases

35. Map Reduce Design Patterns

Day 9: (2hours)

1. Cloudera Distribution of Hadoop(CDH) – VM Setup

2. HDFS Practicals (HDFS Commands)

3. Map Reduce Anatomy

a. Job Submission.

b. Job Initialization.

c. Task Assignments.

d. Task Execution.

Day10: (4hours)

4. Schedulers

5. Map Reduce Failure Scenarios

6. Speculative Execution

7. Sequence File

8. Input File Formats

9. Output File Formats

10. Writable DataTypes

11. Custom Input Formats

12. Example List, show and run examples in map reduce.

13. Debugging Map Reduce Programs

14. Error Tracing of Map Reduce programs

15. Discussion on most common issues in MR.

16. Calculating the stats of MR Programs

Day11: (2hours)

Map Reduce Advance Concepts with usecases(Hands On):

17. Partitioning and Custom Partitioner

18. Joins

Hadoop

 Course Duration: 25 days (60 hours duration).

19. Multi outputs

20. Counters

21. MR unit testcases

22. MR Design patterns

23. Distributed Cache

a. Command line implementation

24. MapReduce API implementation

Day12: (2hours)

Sqoop:

1. Sqoop Theory

2. Demo for Sqoop and Practicals.

3. Sqoop Imports and Exports

4. Sqoop Tuning.

Day 13: (2hours)

Hive:

1. Hive Background.

2. What is Hive?

3. Where to Use Hive?

4. Hive Architecture

5. Metastore

6. Hive execution modes.

7. External, Managed, External tables.

Day 14: (2hours)

8. Hive Partitioning

9. Hive Bucketing

10. Hive Data Model

11. Hive Data Types

a. Primitive

b. Complex

12. Queries:

c. Create Managed Table

d. Load Data

e. Insert overwrite table

f. Insert into Local directory.

g. CTAS.

h. Insert Overwrite table select.

13. Joins

a. Inner Joins

b. Outer Joins

Hadoop

 Course Duration: 25 days (60 hours duration).

c. Skew Joins

Day 15: (4hours)

14. Hive Sort By, Order By

15. Multi-table Inserts

16. Multiple files, directories, table inserts.

17. Serde

a. RegexSerde

b. AvroSerde

18. Storing in Sequence and ORC File Format

19. UDF

20. Hive through CLI, Batch and Hue

21. Hive Practical’s and Usecases

22. Hive Configuration and Hive-site.xml

23. Optimizing hive queries

24. Best Practices in Hive

25. Debugging Hive Scripts and Error Tracing.

a. Common Issues in Hive.

b. Hive Optimization Techniques and Best Practices

Day 16: (2hours)

Pig:

1. Need of Pig?

2. Why Pig Created?

3. Introduction to skew Join.

4. Why go for Pig when Map Reduce is there?

5. Pig use cases.

6. Pig built in operators

7. Pig store schema.

Day 17: (2hours)

8. Operators:

a. Load

b. Store

c. Dump

d. Filter.

e. Distinct

f. Group

g. CoGroup

h. Join

i. Stream

j. Foreach Generate

k. Parallel.

l. Distinct

Hadoop

 Course Duration: 25 days (60 hours duration).

m. Limit

n. ORDER

o. CROSS

p. UNION

q. SPLIT

r. Sampling

Day 18: (2hours)

9. Dump Vs Store

10. DataTypes

a. Complex

i. Bag

ii. Tuple

iii. Atom

iv. Map

b. Primitives.

v. Integers

vi. Float

vii. Chararray

viii. byteArray

ix. Double

Day 19: (2hours)

11. Diagnostic Operators

c. Describe

d. Explain

e. Illustrate

12. UDFs.

13. Physical and Logical Execution Plans

14. Storage Handlers.

15. Pig Practicals and Usecases.

16. Pig vs Hive

17. Testing suite’s for Pig like Pig Lipstick, Pig Penny for debugging and error tracing.

18. Data loading using HCatalog Loader

19. Pig Debugging using Explain and Illustrate commands

20. Pig Stats

Day 20: (4hours)

Impala:

1. Impala Architecture

2. ImpalaD Deamon

3. Impala StateStore

4. Impala Catalog

Hadoop

 Course Duration: 25 days (60 hours duration).

5. MPP Architecture

6. Impala Practicals

7. Adhoc Querying in Impala.

8. Impala integration with Hive

Day 21: (2hours)

Hadoop File Formats:

1. Sequence File

2. Avro File

3. ORC File Format

4. Parquet File Format

5. Storing Hive data in these File Formats

6. Comparing File Formats

7. Compression techniques like snappy, lzo, bgzip,etc

8. AVRO Shemas

Comparing BigData Execution Engines (Tez, MR, DAG, RDD, MPP)

Day 22: (2hours)

Introduction to NOSQL Databases:

1. Problem with RDBMS

2. Row Oriented vs Column Oriented

3. Introduction to NOSQL DB’s

4. CAP Theorem

Day 23: (2hours)

HBase:

1. Introduction to NOSQL Databases.

2. NOSql Landscapes

3. Introduction to HBASE

4. HBASE vs RDBMS

5. Create Table on HBASE using HBASE shell

6. Where to use HBASE?

7. Where not to use HBASE?

8. Write Files to HBASE.

9. Major Components of HBASE.

a. HBase Master.

b. HRegionServer.

c. HBase Client.

d. Zookeeper.

e. Region.

10. Compactions

11. HBase Practicals

Hadoop

 Course Duration: 25 days (60 hours duration).

12. Bulk Loads

13. HBase Command Line

14. Using Map Reduce for HBase Operations

15. HBase Java Client Programming

Day 24: (2hours)

Flume:

1. Flume Architecture

2. Real time streaming in Flume

3. Defining and Deploying Flume Agents

4. Access data from multiple sources to collectors.

5. Different types of channels

6. Configuring Flume Agents

7. Running a Usecase

Day 25: (4hours)

Kafka:

1. Learn how to Develop Game-Changing Real Time Applications

2. Master kafka & its components

3. Understand architecture of kafka

4. Install kafka on single node as well as on multi-node cluster

5. Configure consumer, producer and brokers

6. Perform various Kafka Operations like adding and removing topics, modifying topics etc.

Oozie:

1. Oozie Architecture

2. Workflow designing in Oozie

3. Scheduling workflows in Oozie

4. Oozie practicals.

5. Automate the testing process using Oozie


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Big Data Training trainer Profile & Placement

  • More than 10 Years of experience in Big Data Training
  • Has worked on multiple realtime Big Data Training
  • Working in a top MNC company in
  • Trained 2000+ Students so far in Big Data Training .
  • Strong Theoretical & Practical Knowledge
  • Certified Professionals

Big Data Training Placement in

  • More than 2000+ students Trained in Big Data Training
  • 92% percent Placement Record
  • 1000+ Interviews Organized

Big Data Training batch size in


Regular Batch ( Morning, Day time & Evening)

  • Seats Available : 8 (maximum)

Big Data Training Weekend Training Batch( Saturday, Sunday & Holidays)

  • Seats Available : 8 (maximum)

Big Data Training Fast Track batch

  • Seats Available : 5 (maximum)

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Big Data Training  in Bangalore - Marathahalli
Ecare Technologies

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I had Recently completed Big Data Training in Bangalore from Ecare Technologies. Teaching faculty was awesome they helped me to understand all the concepts very easily


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