AWS Big Data Specialty Training course detail

Description

In this course, is driven by industry specialists from top associations. This certification tests the candidate on two of the most wanted skills right now – Cloud and Big Data technologies. The AWS Big Data – Specialty certification will not only help you learn some new skills, it can position you for a higher paying job or help you transform your current role into a Big Data and Analytics professional. This course is developed by industry leaders and aligned with the latest best practices.

What will you learn

Key Features
  • 20 Hrs Instructor-led Training
  • 15 Hrs Self-paced Videos
  • 40 Hrs Project Work & Exercises
  • Flexible Schedule
  • 24 x 7 Lifetime Support & Access
  • Certification and Job Assistance

Lessons

  • 5 Lessons
  • • 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

    Hands-on Exercise:

    Creating, Deleting, Managing an AWS Kinesis Stream.

  • • 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

    • Introduction to RDS

    • Basics of RDS

    Hands-on Exercise:

    Creating a table and loading data, Replicating data to another table and backing up, Creating a MySQL database.

  • • Amazon EMR

    • Apache Hadoop

    • Hue with EMR

    • HBase with EMR

    • Spark with EMR

    • AWS Lambda for Big Data Ecosystem

    • Hcatalog

    • Glue

    • Glue Lab

    Hands-on Exercise:

    EMR Cluster creation, Adding steps to EMR, Using Hue with EMR, Using HBase with EMR, Using Spark with EMR, Using HCatalog with Hive on EMR, Using Glue.

  • • What is Amazon Redshift?

    • Data Warehouse System Architecture

    • Redshift Concepts

    • Designing tables

    • Loading Data to Redshift

    • Redshift Workload Management

    • Tuning Query Performance

    • Best Practices using Redshift

    • Amazon Machine Learning

    • Amazon ML Key Concepts

    • Using Amazon ML

    • What is Amazon Athena?

    • When should you use Athena?

    • Running Queries using Athena

    • What Is Amazon Elasticsearch Service?

    • Features of Amazon Elasticsearch Service

    • ES Domains

    Hands-on Exercise:

    Creating a Redshift Cluster, Creating Read Replicas,  Loading data into the cluster, Running queries using the Redshift Query Editor, Backing up the cluster, Running a sample ML model in Amazon ML, Creating a database in Athena and running queries.

  • • What is Amazon QuickSight?

    • How does Amazon QuickSight work?

    • QuickSight SPICE

    • Setting up Amazon QuickSight

    • Data Sources and Data Sets

    • Creating your own Analysis in QuickSight

    • QuickSight Visualization

    • QuickSight Dashboards

    • Security Best Practices

    • EMR Security

    • Redshift Security

    • Introduction to Microstrategy

    Hands-on Exercise:

    Setting up Amazon QuickSight, Creating a Data Set in QuickSight, Creating various Visualizations using the data set, Creating 

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Nitin Singh – 2020-12-18 15:55:16:

It will Very Good.