AWS certified data analytics study guide specialty (DAS-C01) exam
This comprehensive study guide will help assess your technical skills and prepare for the updated AWS Certified Data Analytics exam. Earning this AWS certification will confirm your expertise in designing and implementing AWS services to derive value from data. The AWS Certified Data Analytics Study...
| Main Author: | |
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| Format: | eBook |
| Language: | English |
| Published: |
Indianapolis
Sybex, a Wiley brand
2020
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| Online Access: | |
| Collection: | O'Reilly - Collection details see MPG.ReNa |
Table of Contents:
- Cover
- Title Page
- Copyright Page
- Acknowledgments
- About the Author
- About the Technical Editor
- Contents at a Glance
- Contents
- Introduction
- What Does This Book Cover?
- Preparing for the Exam
- Registering for the Exam
- Studying for the Exam
- The Night before the Exam
- During the Exam
- Interactive Online Learning Environment and Test Bank
- Exam Objectives
- Assessment Test
- Chapter 1 History of Analytics and Big Data
- Evolution of Analytics Architecture Over the Years
- The New World Order
- Analytics Pipeline
- Data Sources
- Collection
- Storage
- Includes bibliographical references and index
- Using Lake Formation to Build a Data Lake on AWS
- Exam Objectives
- Objective Map
- Assessment Test
- References
- Chapter 2 Data Collection
- Exam Objectives
- AWS IoT
- Common Use Cases for AWS IoT
- How AWS IoT Works
- Amazon Kinesis
- Amazon Kinesis Introduction
- Amazon Kinesis Data Streams
- Amazon Kinesis Data Analytics
- Amazon Kinesis Video Streams
- AWS Glue
- Glue Data Catalog
- Glue Crawlers
- Authoring ETL Jobs
- Executing ETL Jobs
- Change Data Capture with Glue Bookmarks
- Use Cases for AWS Glue
- Amazon SQS
- Amazon Data Migration Service
- What Is AWS DMS Anyway?
- What Does AWS DMS Support?
- AWS Data Pipeline
- Pipeline Definition
- Pipeline Schedules
- Task Runner
- Large-Scale Data Transfer Solutions
- AWS Snowcone
- AWS Snowball
- AWS Snowmobile
- AWS Direct Connect
- Summary
- Review Questions
- References
- Exercises & Workshops
- Chapter 3 Data Storage
- Introduction
- Amazon S3
- Amazon S3 Data Consistency Model
- Data Lake and S3
- Data Replication in Amazon S3
- Server Access Logging in Amazon S3
- Partitioning, Compression, and File Formats on S3
- Amazon S3 Glacier
- Vault
- Archive
- Processing and Analysis
- Visualization, Predictive and Prescriptive Analytics
- The Big Data Reference Architecture
- Data Characteristics: Hot, Warm, and Cold
- Collection/Ingest
- Storage
- Process/Analyze
- Consumption
- Data Lakes and Their Relevance in Analytics
- What Is a Data Lake?
- Building a Data Lake on AWS
- Step 1: Choosing the Right Storage
- Amazon S3 Is the Base
- Step 2: Data Ingestion
- Moving the Data into the Data Lake
- Step 3: Cleanse, Prep, and Catalog the Data
- Step 4: Secure the Data and Metadata
- Step 5: Make Data Available for Analytics
- Amazon DynamoDB
- Amazon DynamoDB Data Types
- Amazon DynamoDB Core Concepts
- Read/Write Capacity Mode in DynamoDB
- DynamoDB Auto Scaling and Reserved Capacity
- Read Consistency and Global Tables
- Amazon DynamoDB: Indexing and Partitioning
- Amazon DynamoDB Accelerator
- Amazon DynamoDB Streams
- Amazon DynamoDB Streams
- Kinesis Adapter
- Amazon DocumentDB
- Why a Document Database?
- Amazon DocumentDB Overview
- Amazon Document DB Architecture
- Amazon DocumentDB Interfaces
- Graph Databases and Amazon Neptune
- Amazon Neptune Overview
- Amazon Neptune Use Cases