Video description
Expert tips, techniques, and best practices to pass the AWS Certified Big Data - Specialty exam
About This Video
- Understand the importance of the AWS Certified Big Data - Specialty Certification exam for advancing in your career
- Get to grips with the exam pattern and exam syllabus
- Discover different types of cloud computing and their advantages
In Detail
This course covers all aspects of hosting big data on the Amazon Web Services (AWS) platform, and will prepare you to confidently perform distributed processing.
The course begins with an overview of exam details and the recommended AWS knowledge you need before starting the course. It then takes you through topics relating to big data on AWS such as cloud computing and deployment, databases and data warehousing in AWS, and AWS services for big data. Next, you’ll move on to learn about data collection within big data on AWS which will cover data producers and consumers, IoT and big data, and Kinesis Firehose. As you advance, you’ll get to grips with the storage and processing aspects of big data on AWS, covering DynamoDB, AWS aurora in big data, and Amazon EMR. Finally, you’ll delve into visualization and security, and create a project for analyzing large datasets.
By the end of this course, you will have learned about cloud-based big data solutions, and be able to use AWS Elastic MapReduce to process data and create big data environments.
Audience
This course is for anyone with the cloud practitioner or associate-level AWS certification and a minimum of 2 years’ experience in performing complex big data analysis, including solutions architects, SysOps administrators, data scientists, and data analysts. The course assumes an understanding of AWS security best practices and AWS service integration.
Publisher resources
Table of contents
- Chapter 1 : Exam Details
-
Chapter 2 : Big Data on AWS Introduction
- Learning Objectives
- Cloud Computing Introduction, Advantages, and Types
- Cloud Deployment Models
- Cloud Service Categories
- AWS Cloud Platform
- AWS Cloud Architecture Design Principles - Part I
- AWS Cloud Architecture Design Principles - Part II
- Why AWS for Big Data - Reasons and Challenges
- Databases in AWS
- Data Warehousing in AWS
- Redshift, Kinesis, and EMR
- DynamoDB, Machine Learning, and Lambda
- Elastic Search Services and EC2
- Key Takeaways
-
Chapter 3 : Big Data on AWS - Collection
- Learning Objective
- Amazon Kinesis and Kinesis Stream
- Kinesis Data Stream Architecture and Core Components
- Data Producer
- Data Consumer
- Kinesis Stream Emitting Data to AWS Services and Kinesis Connector Library
- Kinesis Firehose
- Demo - Put and Get Records from Kinesis Data Stream
- Transferring Data Using Lambda
- Amazon SQS Lifecycle and Architecture
- IoT and Big Data
- IoT Framework
- AWS Data Pipelines and Data Nodes
- Activity, Pre-Condition, and Schedule
- Demo - Importing Data from S3 into DynamoDB Using Data Pipeline
- Key Takeaways
-
Chapter 4 : Big Data on AWS - Storage
- Learning Objective
- Amazon Glacier and Big Data
- DynamoDB Introduction
- DynamoDB and EMR
- DynamoDB Partitions and Distributions
- DynamoDB GSI LSI
- DynamoDB Stream and Cross-Region Replication
- DynamoDB Performance and Partition Key Selection
- Snowball and AWS Big Data
- AWS DMS
- AWS Aurora in Big Data
- Demo - Amazon Athena Interactive SQL Queries for Data in Amazon S3 Part I
- Demo - Amazon Athena Interactive SQL Queries for Data in Amazon S3 Part II
- Key Takeaways
-
Chapter 5 : Big Data on AWS - Processing
- Learning Objective
- Amazon EMR
- Demo - Analysing Big Data with Amazon EMR
- Apache Hadoop
- EMR Architecture
- EMR Operations - Releases and Cluster
- EMR Operations - Choosing Instance and Monitoring
- Demo - Advanced EMR Setting Options
- Hive on EMR
- HBase with EMR
- Presto with EMR
- Spark with EMR
- EMR File Storage
- Demo - Analysing Large Datasets Using Hive and Spark
- AWS Lambda
- Key Takeaways
-
Chapter 6 : Big Data on AWS - Analysis
- Learning Objective
- Redshift Intro and Use Cases
- Redshift Architecture
- MPP and Redshift in AWS Ecosystem
- Columnar Databases
- Redshift Table Design - Part I
- Redshift Table Design - Part II
- Demo - Generating Random Dataset in EC2 and Loading it in S3
- Demo - Redshift Maintenance and Operations
- Machine Learning Introduction
- Machine Learning Algorithm
- Amazon SageMaker
- Amazon Elasticsearch
- Amazon Elasticsearch Services
- Demo - Loading Datasets into Elasticsearch
- Logstash and RStudio
- Demo - Fetching the File and Analysing it using RStudio
- Athena
- Demo - Running Query on S3 using the Serverless Athena
- Demo - Creating a Redshift Cluster and Loading the Datasets into it from S3 - Part I
- Demo - Creating a Redshift Cluster and Loading the Datasets into it from S3 - Part II
- Key Takeaways
- Chapter 7 : Big Data on AWS - Visualization
- Chapter 8 : Big Data on AWS - Security
Product information
- Title: AWS Certified Big Data - Specialty Certification
- Author(s):
- Release date: August 2020
- Publisher(s): Packt Publishing
- ISBN: 9781800563773
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