AWS Certified Data Analytics

About Course
AWS Certified Data Analytics – Specialty (DAS-C01) Training is a specialized program designed for data professionals aiming to excel in the field of data analytics within the Amazon Web Services (AWS) ecosystem. This training covers a wide array of data analytics topics, including data collection, storage, processing, visualization, and machine learning. Participants learn to leverage AWS services such as Amazon Redshift, Kinesis, and QuickSight to build scalable and efficient data solutions. The course prepares individuals for the AWS Certified Data Analytics – Specialty exam, a globally recognized certification that validates their expertise in AWS data analytics services, making them valuable assets in the data-driven decision-making landscape.
What Will You Learn?
- 1. Specialized Expertise
- 2. Certification
- 3. Career Advancement
- 4. Effective Data Solutions
- 5. Data Visualization
- 6. Machine Learning Integration
- 7. In-Demand Skills
Course Content
Module 01: Introduction
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1.01 Course Overview
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1.02 Introducing Hands – On Case Study: Cadabra. com
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1.03 Cost of the Course and Amazon Web Services (AWS) Budget Setup
Module 02: Domain 01 – Collection
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2.01 Collection Section Introduction
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2.02 Amazon Kinesis Data Streams Overview
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2.03 Kinesis Producers
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2.04 Kinesis Consumers
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2.05 Kinesis Enhanced Fan Out
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2.06 Kinesis Scaling
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2.07 Kinesis – Handling Duplicate Records
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2.08 Kinesis Security
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2.09 Kinesis Data Firehose
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2.10 CloudWatch Subscription Filter with Kinesis
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2.11 [Exercise] Kinesis Firehose – Part One
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2.12 [Exercise] Kinesis Firehose – Part Two
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2.13 [Exercise] Kinesis Firehose – Part Three
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2.14 [Exercise] Amazon Kinesis Data Streams
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2.15 Amazon Simple Queue Service (Amazon SQS) Overview
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2.16 Kinesis Data Streams Versus Amazon Simple Queue Service (Amazon SQS)
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2.17 Internet of Things (IoT) Overview
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2.18 Internet of Things (IoT) Components Deep Dive
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2.19 Amazon Web Services (AWS) Database Migration Service (DMS)
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2.20 Amazon Web Services (AWS) Direct Connect
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2.21 Amazon Web Services (AWS) Snow Family
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2.22 Amazon MSK: Managed Streaming for Apache Kafka
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2.23 Kinesis Versus MSK
Module 03: Domain 02 – Storage
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3.01 Amazon Simple Storage Service (Amazon S Three) Overview
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3.02 Amazon Simple Storage Service (Amazon S Three) Hands On
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3.03 Amazon Simple Storage Service (Amazon S Three) Storage Classes
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3.04 Amazon Simple Storage Service (Amazon S Three) Storage Classes Hands On
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3.05 Amazon Simple Storage Service (Amazon S Three) Lifecycle Rules
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3.06 Amazon Simple Storage Service (Amazon S Three) Lifecycle Rules Hands On
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3.07 Amazon Simple Storage Service (Amazon S Three) Versioning
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3.08 Amazon Simple Storage Service (Amazon S Three) Versioning Hands On
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3.09 Amazon Simple Storage Service (Amazon S Three) Replication
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3.10 Amazon Simple Storage Service (Amazon S Three) Replication Hands On
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3.11 Amazon Simple Storage Service (Amazon S Three) Performance
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3.12 Amazon Simple Storage Service (Amazon S Three) Encryption
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3.13 Amazon Simple Storage Service (Amazon S Three) Encryption Hands On
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3.14 Amazon Simple Storage Service (Amazon S Three) Security and Bucket Policies
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3.15 Amazon Simple Storage Service (Amazon S Three) Security and Bucket Policies Hands On
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3.16 Amazon Simple Storage Service (Amazon S Three) and Glacier Select
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3.17 Amazon Simple Storage Service (Amazon S Three) Event Notifications
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3.18 Amazon Simple Storage Service (Amazon S Three) Event Notifications Hands On
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3.19 Amazon DynamoDB Overview
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3.20 Amazon DynamoDB Provisioned Throughput
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3.21 Amazon DynamoDB Partitions
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3.22 Amazon DynamoDB APIs
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3.23 Amazon DynamoDB Indexes: Local Secondary Index (LSI) and Global Secondary Index (GSI)
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3.24 Amazon DynamoDB Accelerator (DAX)
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3.25 Amazon DynamoDB Streams
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3.26 Amazon DynamoDB Time to Live (TTL)
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3.27 Amazon DynamoDB Security
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3.28 Amazon DynamoDB – Storing Large Objects
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3.29 [Exercise] Amazon DynamoDB
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3.30 Amazon ElastiCache Overview
Module 04: Domain 03 – Processing
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4.01 Section Introduction: Processing
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4.02 What is AWS Lambda?
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4.03 Lambda Integration – Part One
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4.04 Lambda Integration – Part Two
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4.05 Lambda Costs, Promises, and Anti – Patterns
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4.06 [Exercise] AWS Lambda
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4.07 What is Glue and Partitioning your Data Lake
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4.08 Glue Hive and Extract Transform and Load (ETL)
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4.09 Glue ETL: Developer Endpoints, Running ETL Jobs with Bookmarks
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4.10 Glue Costs and Anti – Patterns
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4.11 AWS Glue Studio
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4.12 AWS Glue DataBrew
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4.13 AWS Glue Elastic Views
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4.14 AWS Lake Formation
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4.15 Amazon Elastic MapReduce (EMR) Architecture and Usage
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4.16 Amazon Elastic MapReduce (EMR) Amazon Web Services (AWS) Integration and Storage
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4.17 Amazon Elastic MapReduce (EMR) Promises and Introduction to Hadoop
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4.18 Introduction to Apache Spark
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4.19 Spark Integration with Kinesis and Redshift
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4.20 Hive on Amazon Elastic MapReduce (EMR)
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4.21 Apache Pig on Amazon Elastic MapReduce (EMR)
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4.22 Apache HBase on Amazon Elastic MapReduce (EMR)
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4.23 Presto on Amazon Elastic MapReduce (EMR)
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4.24 Apache Zeppelin and Amazon Elastic MapReduce (EMR) Notebooks
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4.25 Hue Splunk and Flume
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4.26 S ThreeDistCp and Other Services
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4.27 Amazon Elastic MapReduce (EMR) Security and Instance Types
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4.28 [Exercise] Elastic MapReduce – Part One
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4.29 [Exercise] Elastic MapReduce – Part Two
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4.30 Amazon Web Services (AWS) Data Pipeline
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4.31 Amazon Web Services (AWS) Step Functions
Module 05: Domain 04 – Analysis
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5.01 Section Introduction: Analysis
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5.02 Introduction to Kinesis Analytics
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5.03 Kinesis Analytics Costs and RANDOM CUT FOREST
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5.04 [Exercise] Kinesis Analytics – Part One
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5.05 [Exercise] Kinesis Analytics – Part Two
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5.06 Introduction to Amazon Elasticsearch
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5.07 Amazon Elasticsearch Service
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5.08 Amazon Elasticsearch Service Performance
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5.09 [Exercise] Amazon Elasticsearch Service
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5.10 Introduction to Amazon Athena
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5.11 Athena and Glue, Costs, and Security
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5.12 Athena Performance
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5.13 [Exercise] Amazon Web Services (AWS) Glue and Athena
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5.14 Amazon Redshift Introduction and Architecture
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5.15 Redshift Spectrum and Performance Tuning
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5.16 Amazon Redshift Durability and Scaling
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5.17 Amazon Redshift Distribution Styles
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5.18 Amazon Redshift Sort Keys
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5.19 Amazon Redshift Data Flows and the COPY Command
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5.20 Amazon Redshift Integration/Workload Management (WLM)/Vacuum/Anti-Patterns
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5.21 Amazon Redshift Resizing (Elastic vs. Classic) and new Redshift features in Two Thousand and Twenty
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5.22 Amazon Redshift Security Concerns
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5.23 [Exercise] Redshift Spectrum – Part One
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5.24 [Exercise] Redshift Spectrum – Part Two
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5.25 Amazon Relational Database Service (RDS) and Aurora
Module 06: Domain 05 – Visualization
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6.01 Section Introduction: Visualization
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6.02 Introduction to Amazon QuickSight
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6.03 Amazon QuickSight Pricing and Dashboards
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6.04 Choosing Visualization Types
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6.05 [Exercise] Amazon QuickSight
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6.06 Other Visualization Tools (HighCharts D Three and so on)
Module 07: Domain 06 – Security
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7.01 Encryption Hundred and One
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7.02 S Three Encryption (Reminder)
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7.03 Amazon Web Services Key Management Service (AWS KMS) Overview
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7.04 Amazon Web Services Key Management Service (AWS KMS) Key Rotation
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7.05 Amazon Web Services (AWS) CloudHSM Overview
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7.06 Amazon Web Services AWS Security Features Deep Dive – Part One
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7.07 Amazon Web Services AWS Security Features Deep Dive – Part Two
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7.08 Amazon Web Services AWS Security Features Deep Dive – Part Three
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7.09 Amazon Web Services Security Token Service (AWS STS) and Cross – Account Access
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7.10 Identity Federation
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7.11 Policies – Advanced
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7.12 Amazon Web Services (AWS) CloudTrail
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7.13 Virtual Private Cloud (VPC) Endpoints
Module 08: Everything Else
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8.01 Amazon Web Services (AWS) Service Integrations
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8.02 Instance Types for Big Data
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8.03 Amazon Elastic Compute Cloud (Amazon EC Two) for Big Data
Module 09: Preparing for the Exam
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9.01 Exam Tips
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9.02 State of Learning Checkpoint
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9.03 Exam Walkthrough and Signup
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9.04 Save Fifty percent on your AWS Exam Cost
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9.05 Get an Extra Thirty Minutes on your AWS Exam – Non – Native English Speakers Only
Module 10: Appendix – Machine Learning Topics for the Amazon Web Services AWS Certified Big Data Exam
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10.01 Machine Learning Hundred and One
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10.02 Classification Models
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10.03 Amazon Machine Learning Service
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10.04 Amazon SageMaker
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10.05 Deep Learning Hundred and One
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10.06 [Exercise] Amazon Machine Learning – Part One
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10.07 [Exercise] Amazon Machine Learning – Part Two
Module 11: Wrapping Up
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11.01 Congratulations Now make sure you are ready