AWS BIG DATA TRAINING Duration: 15 Hrs

Topics: 

• Cloud computing overview 
• Different models available in Cloud computing 
•  AWS Fundamentals 
• Different Cloud Computing Services 
• AWS Cloud Economics
• AWS Cloud Architecture design principles 
• AWS Virtuous Cycle 
•  Why AWS for Big Data 
• Data warehousing in AWS 
• Database in AWS 
• AWS Cloud Architecture standards 
• Process to deploy a data warehouse using Amazon Redshift 
• Comparison between relational and non-relational databases 

Learning Outcome: Upon the completion of this chapter you will gain basic knowledge of AWS and BIg data and their relation. 

Topics: 

• Big Data Collection Services 
• Basics of Amazon Kinesis 
• Data loading into Amazon Storage 
• Kinesis Datastream architecture 
• Kinesis data firehouse 
• Kinesis Connector library 
• Data transfer using AWS Lambda 
• Using AWS Lam to break up data into Amazon S3
• Data streaming with Amazon Kinesis data analytics 

Learning outcome: By the completion of this chapter you will gain an in-depth working knowledge of the various Big Data collection services available, and Basics of Kinesis. 

Topics: 

• AWS Big Data Storage services 
• Data Management 
• Data lakes and analytics
•  Data lifecycle 
• Glacier and Big Data 
• Basics of Amazon Glacier 
• DynamoDB Introduction and its components 
• DynamoDB in AWS Ecosystem 
• DynamoDb GSI and LSI 
• Data Distribution 
• DynamoDB partitions 
• Cross-region replication 
• DynamoDB performance 
• Snowball & AWS Big Data
• Create and Modify Aurora DB cluster 
• Storing and retrieving data from DynamoB

Learning Outcome: By the completion of this module you will gain hands-on experience in storage and various components available in it.  

Topics: 

• AWS Big Data processing Services
• EMR cluster architecture 
• Amazon Elastic MapReduce (EMR) 
• The architecture of the EMR cluster 
• Apache Hadoop and its architecture 
• AWS Cluster 
• EMR Operations 
• Storage options 
• Setup Hue for LDAP
• Hive on EMR 

Learning outcome: By the completion of this module, you will gain in-depth knowledge of above-specified concepts and understand their role in the data processing. 

Topics: 

• Introduction to HBase
• HBase Architecture 
• Cluster creation with HBase 
• Presto Architecture 
• Presto with EMR 
• Apache Spark architecture 
• Basics of Apache Spark 
• Cluster creation with Spark 
• Amazon Simple Workflow 
• Fundamentals of EMR file system
• Lambda role in AWS Big data 
• Kinesis and Lambda stream 
• Catalogue 
• AWS Lambda and RedShift 

Learning Outcome:  By the completion of this module you will gain an overall knowledge of the above concepts and their role in processing huge volumes of data.  

Topics: 

• Overview of AWS Big Data Analysis Services 
• Amazon RedShift Architecture 
• Basics of Amazon RedShift 
• RedShift in the AWS Ecosystem 
• RedShift table Design 
• Columnar Databases 
• Data types in RedShift 
• Selection of Distributed style 
• Copy command for loading data 
• RedShift Data Loading 

Learning Outcome: By the completion of this module you will gain an overall knowledge of ETL with RedShift and multiple concerts associated with it. 

Topics: 

• Machine Learning fundamentals 
• Workflow structure of Amazon machine learning 
• Introduction to Amazon Sagemaker 
• Machine Learning Algorithms 
• Elasticsearch 
• Zone Awareness
• Amazon Elasticsearch Service 
• RStudio 
• Logstash 
• Amazon Athena 
• AWS Glue 
• Fraud detection using AWS 

Learning Outcome: By the completion of this chapter you will gain hands-on experience in handling various tasks of machine learning and its role in AWS. 

Topics: 

• Introduction to Visualization Services  
• Amazon QuickSight 
• Workflows and use cases of Amazon Quickstart 
• Conduct analysis of a Marketing campaign 
• Working with data 
• Amazon QuickSight Visualization  
• Amazon QuickSight Stories 
• Data Visualization 
• Kibana 
• Create a Dashboard on Kibana 

Learning outcome: By the completion of this module, you will gain all the knowledge to perform Visualization and Analysis on AWS Big Data AWS. 

Topics: 

• Introduction to AWS Big Data Security 
• Introduction to EMR Security and best practices to follow 
• Fundamentals of RedShift Security 
• Data Encryption and protecting. 
• RedShift Security 
• Data Encryption and Protection Principles
• Cloud Trail 
• STS and Cross account Status
• Key Management Services 
• Encryption, Master Key, and Decryption process 

Learning Outcome: Upon the completion of this module you will gain expertise in implementing Security services on AWS Big Data.  

Every single class has been recorded so you will get the daily class recordings everyday. 

We tied up with some DevOps Companies. We forward your profile when they required developer. But we can’t assure 100% job guarantee

Yes, for every training session there is a demo class before going to start training. 

Opstrainers choose a real time team as a trainer who is working on that particular module only.

Yes, we are providing 100% practical knowledge on each module with real time scenarios.

Yes, course material and recording of all your classes is accessible lifetime.

Most of the students get satisfied with our training, if you are not then we provide a specialised training in return.

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