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Big Data on AWS Course Description
Big Data on AWS introduces you to cloud-based big data solutions such as Amazon Elastic MapReduce (EMR), Amazon Redshift, Amazon Kinesis and the rest of the AWS big data platform. In this course, we show you how to use Amazon EMR to process data using the broad ecosystem of Hadoop tools like Hive and Hue. We also teach you how to create big data environments, work with Amazon DynamoDB, Amazon Redshift, and Amazon Kinesis, and leverage best practices to design big data environments for security and cost-effectiveness.
Who should go for this training?
Experienced technology professionals who want to excel in the data engineering space
Data scientists and data analysts interested in learning about big data solutions on AWS
Requirements
Computer or laptop or Smartphone with Highspeed Internet Connection
Participants in this AWS Big Data certification course should have basic knowledge of AWS technical essentials and a fair understanding of big data and Hadoop concepts.
Knowledge of big data technologies such as Pig, Hive, and MapReduce is helpful but not required.
Basic understanding of data warehousing, relational database systems, and database design.
Big Data on AWS Course Syllabus
- Introduction to Big Data
- Big Data tools available in AWS
- Why Big Data on AWS?
- What is AWS Kinesis?
- How Kinesis works?
- Features of AWS Kinesis
- AWS Kinesis Components
- Kinesis Data Streams
- Enhanced Fan-Out in AWS Kinesis
- Kinesis Data Firehose
- Amazon SQS
- AWS Data Pipeline
- What is S3 Glacier?
- Accessing Amazon S3 Glacier
- Glacier Vaults
- Glacier Archives
- What is Amazon DynamoDB?
- How does DynamoDB work?
- Accessing DynamoDB through Portal and CLI
- DynamoDB Tables and Items
- DynamoDB Indexes
- DynamoDB Streams and Replication
- Dynamo Backup and Restore
- DynamoDB Best Practices
- Snowball & AWS BigData
- Data Migration using AWS Snowball
- AWS DMS
- AWS Aurora in BigData
- Storing and Retrieving the Data from DynamoDB
- Introduction to RDS
- Basics of RDS
- AWS Bigdata Processing Services
- Overview of Amazon Elastic MapReduce (EMR)
- EMR Cluster Architecture
- Apache Hadoop Architecture
- Storage Options
- EMR Operations
- AWS Cluster
- Using Hue with EMR
- Setup Hue for LDAP
- Hive on EMR
- Using HBase with EMR
- HBase Architecture
- HBase and EMRFS
- Presto with EMR
- Presto Architecture
- Fundamentals of Apache Spark
- Apache Spark Architecture
- Apache Spark Integration with EMR
- Fundamentals of EMR File System
- Amazon Simple Workflow
- AWS Lambda in Big Data Ecosystem
- AWS Lambda and Kinesis Stream
- AWS Lambda and RedShift
- HCatalog
- Question-Answer Session
- Introduction to AWS Bigdata Analysis Services
- Fundamentals of Amazon Redshift
- Amazon RedShift Architecture
- RedShift in the AWS Ecosystem
- Columnar Databases
- RedShift Table Design
- Choosing the Distribution Style
- Redshift Data types and Loading
- COPY Command for Data Loading
- RedShift Loading Data
- Fundamentals of Machine Learning
- Workflow of Amazon Machine Learning
- Use cases
- Machine learning Algorithms
- Amazon SageMaker
- Machine learning with Amazon Sagemaker
- Elasticsearch
- Amazon Elasticsearch Service
- Zone Awareness
- Logstash and RStudio
- Amazon Athena
- AWS Glue
- Fraud Detection Using Classification Algorithms on AWS Sagemaker
- Question-Answer Session
- Introduction to AWS Bigdata Visualization Services
- Amazon QuickSight
- Amazon QuickSight - Workflow and Use Cases
- Working with data
- Amazon QuickSight: Visualization
- Amazon QuickSight: Dashboard
- Data Visualization: Other Tools
- Kibana
- Create a Dashboard on Kibana
- Introduction to AWS Bigdata Security
- EMR Security
- Roles
- Fundamentals of Redshift Security
- Data Protection and Encryption
- Master Key, Encryption, and Decryption Process
- Amazon Redshift Database Encryption
- Key Management Services(KMS) Overview
- Encryption using Hardware Security Modules
- STS and Cross Account Access
- Cloud Trail
- Question-Answer Session
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