Video description
In this course, you will learn streaming massive data with AWS Kinesis; queuing messages with Simple Queue Service (SQS); wrangling the explosion data from the Internet of Things (IOT); transitioning from small to big data with the AWS Database Migration Service (DMS); storing massive data lakes with the Simple Storage Service (S3); optimizing transactional queries with DynamoDB; tying your big data systems together with AWS Lambda; making unstructured data query-able with AWS Glue, Glue ETL, Glue DataBrew, Glue Studio, and Lake Formation; processing data at an unlimited scale with Elastic MapReduce; applying neural networks at massive scale with deep learning, MXNet, and TensorFlow; applying advanced machine learning algorithms at scale with Amazon SageMaker; analyzing streaming data in real time with Kinesis Analytics; searching and analyzing petabyte-scale data with Amazon OpenSearch (formerly Elasticsearch) Service; querying S3 data lakes with Amazon Athena; hosting massive-scale data warehouses with Redshift and Redshift Spectrum; integrating smaller data with your big data using the Relational Database Service (RDS) and Aurora; visualizing your data interactively with QuickSight; and finally, keeping your data secure with encryption, KMS, HSM, IAM, Cognito, STS, and more.
By the end of this course, you will be well-versed in the essential concepts and major domains necessary to pass the AWS DAS-C01 exam.
What You Will Learn
- Store big data with S3 and DynamoDB in a scalable, secure manner
- Move and transform massive data streams with Amazon Kinesis
- Use the Hadoop ecosystem with AWS using Elastic MapReduce
- Discover various methods to analyze big data
- Visualize big data in the cloud using AWS QuickSight
- Keep your data secure with encryption, KMS, HSM, IAM, Cognito, and STS
Audience
This course is for experienced technologists seeking certification in big data technologies through Amazon Web Services. If you are looking to achieve this certification, it is recommended to have associate-level certification first.
About The Authors
Frank Kane: Frank Kane has spent nine years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers all the time. He holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology and teaches others about big data analysis.
Stéphane Maarek: Stéphane Maarek is a solutions architect, consultant, and software developer who has a particular interest in all things related to big data and analytics. He is also a bestseller instructor on Udemy for his courses on Apache Kafka, Apache NiFi, and AWS Lambda. He loves Apache Kafka and regularly contributes to the Apache Kafka project.
Stéphane has also written a guest blog post that was featured on the Confluent website, the company behind Apache Kafka. He is also an AWS Certified Solutions Architect and has many years of experience with technologies such as Apache Kafka, Apache NiFi, Apache Spark, Hadoop, PostgreSQL, Tableau, Spotfire, Docker, Ansible, and more.
Table of contents
- Chapter 1 : Introduction
-
Chapter 2 : Domain 1: Collection
- Collection Section Introduction
- Kinesis Data Streams Overview
- Kinesis Producers
- Kinesis Consumers
- Kinesis Data Streams - Hands On
- Kinesis Enhanced Fan Out
- Kinesis Scaling
- Kinesis - Handling Duplicate Records
- Kinesis Security
- Kinesis Data Firehose
- CloudWatch Subscription Filters with Kinesis
- (Exercise) Kinesis Firehose, Part 1
- (Exercise) Kinesis Firehose, Part 2
- (Exercise) Kinesis Firehose, Part 3
- (Exercise) Kinesis Data Streams
- SQS Overview
- Kinesis Data Streams Versus SQS
- Database Migration Service (DMS)
- Direct Connect
- Snow Family
- MSK: Managed Streaming for Apache Kafka
- MSK Connect
- MSK Serverless
- Kinesis vs MSK
-
Chapter 3 : Domain 2: Storage
- S3 Overview
- S3 Hands-On
- S3 Security: Bucket Policy
- S3 Security: Bucket Policy Hands-On
- S3 Versioning
- S3 Versioning - Hands On
- S3 Replication
- S3 Replication Notes
- S3 Replication – Hands-On
- S3 Storage Classes Overview
- S3 Storage Classes Hands-On
- S3 Lifecycle Rules (with S3 Analytics)
- S3 Lifecycle Rules – Hands-On
- S3 Event Notifications
- S3 Event Notifications – Hands-On
- S3 Performance
- S3 Select and Glacier Select
- S3 Encryption
- S3 Encryption – Hands-On
- S3 Default Encryption
- S3 Access Points
- S3 Object Lambda
- DynamoDB Overview
- DynamoDB Basics - Hands-On
- DynamoDB in Big Data
- DynamoDB RCU and WCU - Throughput
- DynamoDB RCU and WCU – Hands-On
- DynamoDB Basic APIs
- DynamoDB Basic APIs – Hands-On
- DynamoDB Indexes (GSI + LSI)
- DynamoDB Indexes (GSI + LSI) – Hands-On
- DynamoDB PartiQL
- DynamoDB DAX
- DynamoDB DAX - Hands-On
- DynamoDB Streams
- DynamoDB Streams – Hands-On
- DynamoDB TTL
- DynamoDB Patterns with S3
- DynamoDB Security
- (Exercise) DynamoDB
- ElastiCache Overview
-
Chapter 4 : Domain 3: Processing
- Section Introduction: Processing
- What Is AWS Lambda?
- Lambda Integration - Part 1
- Lambda Integration - Part 2
- Lambda Costs, Promises, and Anti-Patterns
- (Exercise) AWS Lambda
- What Is Glue? + Partitioning Your Data Lake
- Glue, Hive, and ETL
- Modifying the Glue Data Catalog from ETL Scripts
- Glue ETL: Developer Endpoints, Running ETL Jobs with Bookmarks
- Glue Costs and Anti-Patterns
- AWS Glue Studio
- AWS Glue Data Quality
- AWS Glue DataBrew
- AWS Lake Formation
- AWS Lake Security
- Elastic MapReduce (EMR) Architecture and Usage
- EMR, AWS integration, and Storage
- EMR Promises; Introduction to Hadoop
- EMR Serverless, EMR, and EKS
- Introduction to Apache Spark
- Spark Integration with Kinesis and Redshift
- Spark integration with Athena
- Hive on EMR
- Pig on EMR
- HBase on EMR
- Presto on EMR
- Zeppelin and EMR Notebooks
- Hue, Splunk, and Flume
- S3DistCP and Other Services
- EMR Security and Instance Types
- (Exercise) Elastic MapReduce, Part 1
- (Exercise) Elastic MapReduce, Part 2
- AWS Data Pipeline
- AWS Step Functions
-
Chapter 5 : Domain 4: Analysis
- Section Introduction: Analysis
- Introduction to Kinesis Analytics
- Kinesis Analytics Costs; RANDOM_CUT_FOREST
- (Exercise) Kinesis Analytics, Part 1
- (Exercise) Kinesis Analytics, Part 2
- (Exercise) Kinesis Analytics, Part 3
- (Exercise) Kinesis Analytics, Part 4
- Introduction to OpenSearch (formerly Elasticsearch)
- Amazon OpenSearch Service
- OpenSearch Index Management and Designing for Stability
- Amazon OpenSearch Service Performance
- Amazon OpenSearch Serverless
- (Exercise) Amazon OpenSearch Service
- Introduction to Athena
- Athena and Glue, Costs, and Security
- Athena Performance
- Athena ACID Transactions
- (Exercise) AWS Glue and Athena
- Redshift Introduction and Architecture
- Redshift Spectrum and Performance Tuning
- Redshift Durability and Scaling
- Redshift Distribution Styles
- Redshift Sort Keys
- Redshift Data Flows and the COPY command
- Redshift Integration / WLM / Vacuum / Anti-Patterns
- Redshift Resizing (Elastic Versus Classic) and New Redshift Features in 2020
- Newer Redshift Features, AQUA
- Redshift Security Concerns
- Redshift Serverless
- (Exercise) Redshift Spectrum, Part 1
- (Exercise) Redshift Spectrum, Part 2
- Amazon Relational Database Service (RDS) and Aurora
- Chapter 6 : Domain 5: Visualization
- Chapter 7 : Domain 6: Security
- Chapter 8 : Everything Else
- Chapter 9 : Preparing for the Exam
- Chapter 10 : Appendix - Machine Learning Topics for the Legacy AWS Certified Big Data Exam
- Chapter 11 : Wrapping Up
Product information
- Title: AWS Certified Data Analytics Specialty (2023) Hands-on
- Author(s):
- Release date: December 2020
- Publisher(s): Packt Publishing
- ISBN: 9781838983383
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