
Big Data Analytics Online Training
Big Data Training online training
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Big Data Analytics Online Training Course Content

Big Data Analytics Course Syllabus
Big Data Analytics Course Content
1. Overview of Big Data
This includes topics such as history of big data, its
elements, career related knowledge, advantages, disadvantages and similar
topics.
2. Using Big Data in Businesses
This module should focus on the application perspective of
Big Data covering topics such as using big data in marketing, analytics,
retail, hospitality, consumer good, defense etc.
3. Technologies for Handling Big Data
Big Data is primarily characterized by Hadoop. This module
cover topics such as Introduction to Hadoop, functioning of Hadoop, Cloud
computing (features, advantages, applications) etc
4. Understanding Hadoop Ecosystem
This includes learning about Hadoop and its ecosystem which
includes HDFS, MapReduce, YARN, HBase, Hive, Pig, Sqoop, Zookeeper, Flume,
Oozie etc.
5. Dig Deep to understand the fundamental of MapReduce and
HBase
This module should cover the entire framework of MapReduce
and uses of mapreduce.
6. Understanding Big Data Technology Foundations
This module covers the big data stack i.e. data source
layer, ingestion layer, source layer, security layer, visualization layer,
visualization approaches etc.
7. Databases and Data Warehouses
This module should cover all about databases, polygot
persistence and their related introductory knowledge
8. Using Hadoop to store data
This includes an entire module of HDFS, HBase and their
respective ways to store and manage data along with their commands.
9. Learn to Process Data using Map Reduce
This emphasizes on developing simple mapreduce framework and
the concepts applied to it.
10. Testing and Debugging Map Reduce Applications
After the applications are developed, the next step is to
test and debug it. This modules imparts this knowledge.
11. Learn Hadoop YARN Architechture
This module covers the background of YARN, advantages of
YARN, working with YARN, backward compatibility with YARN, YARN Commands, log
management etc.
12. Exploring Hive
This modules introduces you with all the necessary knowledge
of Hive.
13. Exploring Pig
This modules introduces you with all the necessary knowledge
of PIG.
14. Exploring Oozie
This modules introduces you with all the necessary knowledge
of Oozie.
15. Learn NoSQL Data Management
This modules covers all about NoSQL including document
databases, relationships, graph databases, schema less databases, CAP Theorem
etc.
16. Integrating R and Hadoop and Understanding Hive in
Detail
This module introduces you to RHadoop, ways to do text mining and related knowledge
Course Outline
Module 0. Introduction and Setup:
- How
to start Spark and Zeppelin services in Ambari
- How
to login to Spark using Python and Scala
Module 1. Spark Architecture:
- What
is Apache Spark?
- Spark
processing (Jobs, Stages, Tasks)
- Spark
components (Driver, Context, Yarn, HDFS, Workers, Executors)
Module 2. Getting Started with RDDs:
- Running
queues in Python, Scala and Zeppelin
- Queries
using most popular Transformations and Actions
- Creating
RDDs
Module 3. Pair RDDs:
- Difference
between RDDs and Pair RDD
- 1
Pair Actions, 1 Pair Transformations and 2 Pair Transformations
Module 4. Spark SQL:
- Working
with DataFrames and Tables and DataSets
- Catalyst
optimizer overview
Module 5. Spark Streaming):
- Working
with DStreams
- Stateless
and Stateful Streaming labs using HDFS and Sockets
Module 6. Visualizations using
Zeppelin:
- Creating
various Charts using DataFrames and Tables
- How
to create Pivot charts and Dynamic forms
Module 7. Spark UI
- Overview
of Job, Stage and Tasks
- Monitoring
Spark jobs in Spark UI
Module 8. Performance Tuning:
- Caching,
Checkpoint, Accumulators and Broadcast Variables
- Hashed
Partitions, Tungsten, Executor memory and Serialization
Module 9. Spark Applications
- Creating
an application via spark-submit
- Parameter
configurations (number executors, driver memory, executor cores, etc.)
Module 10. Spark 2.0 Machine Learning
(ML)
- How
ML Pipelines work
- Making
Predictions using Decision Tree
Module 1. Datasets and Catalog:
- What
is a Dataset?
- When
to use which object
- Encoders
and semi-structured data
- Common
ways to create DS
- Cannot
create DS these ways
- Casting
DS and convert DS to DF to RDD
- Review
questions: Datasets / Catalog
- Dataset
versus SQL/DataFrames
- Serialization
performance using Encoders
- Dataset
caching
- Creating
DS from an RDD
- Casting
DS and convert DS these ways
- Hive
list Catalog
- In
Review: Datasets / Catalog
Module 2. Catalyst and Tungsten
functionalities:
- Before
we begin: Open Zeppelin note
- DataFrames,
Datasets and Views use Catalyst / Tungsten
- Catalyst
optimizer overview
- Catalyst:
Join on 2 Spark views demo
- But
RDDs can’t use Catalyst
- Loading
data in Spark 2.x and Catalyst
- Loading
data in Spark 2.x and Catalyst
- Load
data (old way), then join
- Execution
Plan from ‘old way’ loading
- DataFrameReader:
Load / Execution plan
- Dropping
hints to Catalyst
- Catalyst:
column pruning demo
- Catalyst:
Column (& Partition) pruning
- Catalyst:
Predicate pushdown concepts
- Tungsten
overview
- Binary
processing
- Improved
Memory usage
- Improved
caching demo
- Whole-stage
code gen
- Whole-stage
code gen demo
- Whole-stage
code gen Vectorization
- Review
questions: Catalyst / Tungsten
- In
Review: Catalyst / Tungsten
Module 3. Performance Tuning:
- 2
types of Machine Learning
- How
Models are Created
- Four
Common MLlib functions
- What
is Supervised Learning?
- Spark
Superbised Learning Workflow
- Unsupervised
Learning
- RDD
– Machine Learning (MLlib)
- KMeans
scenario
- Load
data
- Create
Model and Predict
- Compare
Actual to Predict
- Collaborative
Filtering (CF) recommender
- Lab:
Will We like Star Wars?
- Classification
Functions (Supervised)
- Classification
uses LabelPoint
- CASTing
X-var and Y-vars for LabelPoint
- Logistic
regression, Support Vector Machines, NaiveBayes and Decision Tree
(Supervised)
- ML
Pipeline terminology
- How
ML Pipeline works
- Cleaning
the data
- Train
ML pipeline – The Big Picture
- Imprving
the Model
- Lab:
Predict Titanic Survivors (Random Forest)
- Review
Questions: Machine Learning
- In
Review: Machine Learning
- But
wait, there’s more (for MLlib) (Appendix)
- Linear
Regression on scenario (Supervised)