Big Data Training online training
QTEC provides real-time and placement oriented Big Data Analytics Training online training, online training. Our Big Data Analytics Training course includes basic to advanced level and our Big Data Analytics Training course is designed to get the placement in major MNC companies online training as soon as you complete the Big Data Analytics Training online training from our institute.
Our Big Data Analytics Training online training trainers are certified in Big Data Analytics Training online training technology and are experienced working professionals with hands-on real-time Big Data Analytics Training online training project exposure. We have designed our Big Data Analytics Training online training course content and syllabus based on students requirement to achieve everyone's career goal. In our Big Data Analytics Training online training, online training training program, you will learn all the advanced concepts based on real-time scenarios we provide job oriented Big Data Analytics Training online training placement training.
QTEC offers Big Data Analytics Training online training training with choice of multiple training locations across online training. Our Big Data Analytics Training online training, online training training centers are well equipped with advanced lab facilities and excellent infrastructure. We also provide Big Data Analytics Training online training certification training. Through our associated Big Data Analytics Training online training training centers, we have trained more than 500+ students and provided placement. Our Big Data Analytics Training online training training course fee is value for money and tailor-made course fee based on the each student's training requirements. Big Data Analytics Training online training which provides the training course online training is flexible to meet studentsâ€™ needs and conducted on daytime classes, weekend training classes, evening batch classes, and fast-track training classes.
QTEC provides real-time Big Data Analytics Online Training. We Provide 100% Placement Assistance and ensures that each and every student gets all kind of support from us.
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
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 canâ€™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 â€˜old wayâ€™ 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 â€“ 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 â€“ The Big Picture
- Imprving the Model
- Lab: Predict Titanic Survivors (Random Forest)
- Review Questions: Machine Learning
- In Review: Machine Learning
- But wait, thereâ€™s more (for MLlib) (Appendix)
- Linear Regression on scenario (Supervised)