AWS Data Analytics Certification e-learning (Pilvipalvelut) - AWS Certified Big Data - Specialty

AWS Data Analytics Certification e-learning

AWS Certified Big Data - Specialty

Prepares you for the AWS Certified Data Analytics - Specialty (DAS-C01) exam.

The AWS Data Analytics certification training prepares you for all aspects of big data hosting and distributed processing on the AWS platform. Our AWS Data Analytics course is aligned to the AWS Certified Data Analytics Specialty exam and will help you pass it in a single attempt. Developed by industry leaders, this AWS Certified Data Analytics training course explores some interesting topics like AWS QuickSight, AWS lambda and Glue, S3 and DynamoDB, Redshift, Hive on EMR, among others.

AWS Data Analytics Certification course overview

In this AWS Big Data certification course, you will familiarize yourself with the concepts of cloud computing and its deployment models. This AWS Big Data training covers Amazon's AWS cloud platform, Kinesis Analytics, AWS big data storage, processing, analytics, visualization and security services, machine learning algorithms, and much more.

AWS Data Analytics Training key features

• 15 hours of self-study - e-learning
• Interactive learning with Jupyter Notebooks integrated lab.
• 49 (1) industry-based practice projects at the end of the course
• 24/7 support with dedicated project mentoring sessions.
• Dedicated mentoring sessions from our teachers who are industry experts.

Delivery mode: e-learning

Skills covered

• AWS Quicksight
• Kinesis streams
• AWS Lambda and Glue
• s3 and DynamoDB
• Redshift
• Amazon RDS
• Hive on EMR
• HBase with EMR
• AWS Aurora

Key learning outcomes:

Upon completion of this AWS Big Data certification course, you will be able to achieve the following:

• Understand how to use Amazon EMR to process data using tools from the Hadoop ecosystem.
• Understand how to use Amazon Kinesis for processing big data in real time.
• Analyze and transform big data using Kinesis Streams.
• Visualize data and execute queries using Amazon QuickSight.

Target Audience:

This course is best suited for the following professionals:

• Data scientists
• Data engineers
• Solutions architects
• Data analysts

Details and criteria for certification:

• At least 85 percent attendance in a virtual classroom.
• A score of at least 75 percent on the end-of-course assessment.
• Successful evaluation of the project at the end of the course.

Course Syllabus:

Lesson 01 - AWS in Big Data introduction

• Introduction to Cloud Computing
• Cloud Computing Deployments Models
• Amazon Web Services Cloud Platform
• The Cloud Computing Difference
• AWS Cloud Economics
• AWS Virtuous Cycle
• AWS Cloud Architecture Design Principles
• Why AWS for Big Data - Reasons
• Why AWS for Big Data - Challenges
• Databases in AWS
• Relational vs Non-Relational Databases
• Data Warehousing in AWS
• Services for Collecting, Processing, Storing, and Analyzing Big Data
• Amazon Redshift
• Amazon Kinesis
• Amazon EMR
• Amazon DynamoDB
• Amazon Machine Learning
• AWS Lambda
• Amazon Elasticsearch Service
• Amazon EC2 (big data analytics software on EC2 instances)
• Amazon Redshift
• Amazon Kinesis
• Amazon EMR
• Amazon DynamoDB
• Amazon Machine Learning
• AWS Lambda
• Amazon Elasticsearch Service
• Amazon EC2 (big data analytics software on EC2 instances)
• Key Takeaway
• Knowledge Checks
• Lesson End Project

Lesson 02 - Collection

• Objectives
• Amazon Kinesis Fundamentals
• Loading Data into Kinesis Stream
• Kinesis Data Stream High-Level Architecture
• Kinesis Stream Core Concepts
• Kinesis Stream Emitting Data to AWS Services
• Kinesis Connector Library
• Kinesis Firehose
• Transferring Data Using Lambda
• Amazon SQS
• IoT and Big Data
• IoT Framework
• AWS Data Pipeline
• AWS Data Pipeline Components
• Key Takeaway
• Knowledge Checks
• Lesson End Project

Lesson 03 - Storage

• Lesson Objectives
• Introduction to AWS Big Data Storage Services
• Amazon Glacier
• Glacier and Big Data
• DynamoDB Introduction
• The Architecture of the DynamoDB Table
• DynamoDB in AWS Ecosystem
• DynamoDB Partitions
• Data Distribution
• Local Secondary Index (LSI)
• Global Secondary Index (GSI)
• DynamoDB GSI vs LSI
• DynamoDB Stream
• Cross-Region Replication in DynamoDB
• Partition Key Selection
• Snowball & AWS Big Data
• AWS DMS
• AWS Aurora in Big Data
• Key Takeaway
• Knowledge Checks
• Lesson End Project

Lesson 04 - Processing I

• Lesson Objectives
• Introduction to AWS Big Data Processing Services
• Amazon Elastic MapReduce (EMR)
• Apache Hadoop
• EMR Architecture
• Storage Options
• EMR File Storage and Compression
• Supported File Format and File Size
• Single-AZ Concept
• EMR Operations
• EMR Releases
• AWS Cluster
• Launching a Cluster
• Advanced EMR Setting Option
• Choosing Instance Type
• Number of Instances
• Monitoring EMR
• Resizing of Cluster
• Using Hue with EMR
• Setup Hue for LDAP
• Hive on EMR
• Hive Use Cases
• Key Takeaway
• Knowledge Checks
• Lesson End Project

Lesson 05 - Processing II

• HBase with EMR
• HBase Use Cases
• Comparison of HBase with Redshift and DynamoDB
• HBase Architecture HBase on S3
• HBase and EMRFS
• HBase Integration
• HCatalog
• Presto with EMR
• Advantages of Presto
• Presto Architecture
• Spark with EMR
• Spark Use Cases
• Spark Components
• Spark Integration With EMR
• AWS Lambda in AWS Big Data Ecosystem
• Limitations of Lambda
• Lambda and Kinesis Stream
• Lambda and Redshift
• Key Takeaway
• Knowledge Checks
• Lesson End Project

Lesson 06 - Analysis I

• Lesson Objectives
• Introduction to AWS Big Data Analysis Services
• RedShift
• RedShift Architecture
• RedShift in the AWS Ecosystem
• Columnar Databases
• RedShift Table Design
• RedShift Workload Management
• RedShift Loading Data
• RedShift Maintenance and Operations
• Key Takeaway
• Knowledge Checks
• Lesson End Project

Lesson 07 - Analysis II

• Machine Learning
• Machine Learning - Use Cases
• Algorithms
• Amazon SageMaker
• Elasticsearch
• Amazon Elasticsearch Service
• Loading of Data into Elasticsearch
• Logstash
• Kibana
• RStudio
• Characteristics
• Athena
• Presto and Hive
• Integration with AWS Glue
• Comparison of Athena with Other AWS Services
• Lab Run Query on S3 Using Serverless Athena
• Key Takeaway
• Knowledge Checks
• Lesson End Projec

Lesson 08 - Visualization

• Lesson Objectives
• Introduction to AWS Big Data Visualization Services
• Amazon QuickSight
• Amazon QuickSight - Use Cases
• LAB Create an Analysis with a Single Visual Using Sample Data
• Working with Data
• Assisted Practice: TBD
• QuickSight Visualization
• Big Data Visualization
• Apache Zeppelin
• Jupyter Notebook
• Comparison Between Notebooks
• js (Data-Driven Documents)
• MicroStrategy
• Key Takeaway
• Knowledge Checks
• Lesson End Project

Lesson 09 - Security

• Objectives
• Introduction to AWS Big Data Security Services
• EMR Security
• Roles
• Private Subnet
• Encryption At Rest and In Transit
• RedShift Security
• KMS Overview
• SloudHSM
• Limit Data Access
• STS and Cross Account Access
• Cloud Trail
• Key Takeaway
• Knowledge Checks
• Lesson End Project