Differences Between Security Groups and NACLs
This is an introductory course on the differences between security groups and NACLs, or Network Access Control Lists. In this course, we discuss how to secure the networking of your applications in AWS by using these two resources. We also review concepts like stateless and stateful to help you more effectively control traffic flow to and from your application.
AWS CloudTrail Lake Getting Started
In this course, you will learn the benefits and technical concepts of AWS CloudTrail Lake. Using CloudTrail Lake, you can aggregate, immutably store, and query Amazon Web Services (AWS) activities of management and data events recorded by AWS CloudTrail. You can use these events for auditing, security investigations, and operational troubleshooting purposes. CloudTrail Lake simplifies analysis workflows by integrating collection, storage, preparation, and optimization for analysis and querying within the same product. In this course, you will also review the basics of CloudTrail Lake and the business and technical challenges it can solve. Course level: Fundamental Duration: 60 minutes Activities This course includes presentations, demonstrations, and knowledge checks. Course objectives In this course, you will learn to: Learn how CloudTrail Lake works. Recognize the benefits of CloudTrail Lake. Explain the cost structure of CloudTrail Lake. Familiarize yourself with the technical concepts of CloudTrail Lake. List typical use cases for CloudTrail Lake. Specify what it would take to implement CloudTrail Lake in a real-world scenario. Explore how to use CloudTrail Lake on the AWS Management Console and use the AWS Command Line Interface (AWS CLI). Intended audience This course is intended for: AWS customers, Partners, and internal resources that want to understand how CloudTrail Lake can help them operate AWS solutions at scale. Prerequisites We recommend that attendees of this course have: A basic understanding of AWS offerings and the challenges that organizations face when operating Completed the Getting Started with AWS CloudTrail course Course outline Section 1: For Students Lesson 1: How to Use This Course Section 2: Introduction Lesson 2: Introduction to CloudTrail Lake Lesson 3: Architecture and Use Cases Section 3: Using AWS CloudTrail Lesson 4: How Do I Create an Event Data Store for CloudTrail Lake Events? Lesson 5: How Do I Use CloudTrail Lake to Configure Items? Lesson 6: How Do I Create an Event Data Store for Events Outside of AWS? Lesson 7 How Do I Query an Event Data Store? Lesson 8: How Do I Create and Delete an Event Data Store Using AWS CLI? Lesson 9: How Do I Delete Resources? Section 4: Resources Lesson 10: Learn More Lesson 11: Feedback
AWS Glue Getting Started
Course description AWS Glue is a serverless data integration service that you can use to discover, prepare, and combine data for analytics, machine learning, and application development. In this course, you will learn the benefits, typical use cases, and technical concepts of AWS Glue, including AWS Glue Studio and AWS Glue DataBrew. DataBrew is a new visual data preparation tool that helps data analysts and data scientists clean and normalize data to prepare it for analytics and machine learning. You will have an opportunity to try the service through a demonstration using the AWS Management Console. • Course level: Fundamental • Duration: 2 hour¬¬s Activities This course includes presentations, graphics, and a demonstration with the option to follow along. Course objectives In this course, you will learn to: • Understand how AWS Glue works. • Familiarize yourself with the technical concepts of AWS Glue and DataBrew. • List typical use cases for AWS Glue and DataBrew. • Specify what it would take to implement AWS Glue and DataBrew in a real-world scenario. • Recognize the benefits of AWS Glue and DataBrew. • Explain the cost structure of AWS Glue. • Show how to use AWS Glue and DataBrew from the AWS Management Console. Intended audience This course is intended for the following roles: • Developers • Solutions architects • Data engineers • Business analysts Prerequisites AWS Technical Essentials Course outline • AWS Glue Basics o What does AWS Glue do? o What problems does AWS Glue solve? o What are the benefits of AWS Glue? o What is the data integration engine supported by AWS Glue? o How is AWS Glue used to architect a cloud solution? o What are typical use cases for AWS Glue? o What else should I keep in mind when using AWS Glue? • AWS Glue Cost Structure o How much does AWS cost? • Using AWS Glue Catalog and Glue Studio o What are the basic technical concepts I should know about AWS Glue Studio? o How do I crawl, catalog, and perform ETL on my data using AWS Glue? o Glue Studio tutorial video • AWS Glue DataBrew Basics o What are the basic technical concepts I should know about AWS Glue DataBrew? • Using AWS Glue DataBrew Data Profiling and Data Quality Checks o How do I profile my data, detect PII, and transform my data using AWS Glue DataBrew? o AWS Glue DataBrew tutorial video • Learn More o How can I learn more about AWS Glue?
Amazon EMR Getting Started
Course Description Amazon EMR is the industry-leading cloud big data solution for petabyte-scale data processing, interactive analytics, and machine learning using open-source frameworks such as Apache Spark, Apache Hive, and Presto. You can use Amazon EMR to set up, operate, and scale your big data environments and automate time-consuming tasks like provisioning capacity. In this course, you will learn Amazon EMR Serverless which is a new option in Amazon EMR that makes it efficient and cost-effective for data engineers and analysts to run applications built using open-source big data frameworks without having to tune, operate, optimize, secure, or manage clusters. Additionally, you will learn the benefits, typical use cases, and technical concepts of Amazon EMR. You will have an opportunity to try Amazon EMR Serverless and Amazon EMR Cluster through tutorials using the AWS Management Console. • Course level: Fundamental • Duration: 1 Hour Course objectives This course includes presentations, graphics, tutorials, and demonstrations with the option to follow along. Course objectives In this course, you will learn to: • Understand different deployment options available with Amazon EMR. • Understand how Amazon EMR works. • Understand the technical concepts of Amazon EMR Serverless. • List typical use cases for Amazon EMR Serverless. • Understand the technical concepts of Amazon EMR Cluster. • List typical use cases for Amazon EMR Cluster. • Specify what it would take to implement Amazon EMR in a real-world scenario. • Recognize the benefits of Amazon EMR. • Explain the cost structure of Amazon EMR. • Use Amazon EMR Serverless and Amazon EMR Cluster Intended audience This course is intended for: • Developers • Solutions architects • Data engineers • Data architects Prerequisites AWS Technical Essentials Data Analytics Fundamentals Course outline Introduction • Introduction to Amazon EMR • Amazon EMR Serverless Architecture and Use Cases • Amazon EMR Cluster Architecture and Use Cases Using Amazon EMR Serverless • How Do I Run a Spark Job on Amazon EMR Serverless? Using Amazon EMR • How Do I Create an Amazon EMR on EC2 Cluster? • How Do I Create an Amazon EMR Studio? • How Do I Create an Amazon EMR Workspace? • How Do I Run a Spark Job with Amazon EMR Studio Notebook? Resources • Learn More
AWS Networking Basics
This course focuses on an introduction to AWS Networking. As a fundamental level course, it will provide an overview of available network services and common use cases using these services. •Course level: Fundamental •Duration: 2 hours Activities: This course includes written material, information graphics, video, an end of course assessment. The course also includes two hands on activities to apply new learning. Course objectives: In this course, you will learn to: •Identify and understand the basic functions of each AWS networking service. •Recognize the relationship between group AWS networking services by understanding the functions and business goals of each. •Describe how networking concepts and protocols are implemented in AWS. •Recognize ways to balance performance, cost, and availability, for different combinations of AWS networking services. Intended audience: This course is intended for: •Solutions architects •Network engineers •System Operations •Software developers who are new to the cloud with networking responsibilities. Prerequisites: We recommend that attendees of this course have: •A basic understanding of the AWS Cloud, it’s core services and benefits. •Completed the Cloud Practitioner Essentials course but a certification is not required. AWS Services Covered: •Amazon Virtual Private Cloud, AWS Transit Gateway, AWS Privatelink, •AWS App Mesh, Amazon API Gateway, AWS Cloud Map, •Amazon CloudFront, Amazon Route 53, AWS Global Accelerator, •AWS Direct Connect, AWS Site-to-Site VPN, AWS Client VPN, AWS Cloud WAN, •AWS Shield, AWS WAF, AWS Network Firewall, AWS Firewall Manager Course outline: •How To Use This Course •Course Introduction Module 1: AWS Networking Services •Introduction •AWS Networking Services •Services Deep Dive •Networking Concepts •Understanding AWS Implementation •Amazon VPC Basics (Video Networking Conversation, Deep Dive PDF Downloads) •Key Takeaways Module 2: Check your understanding •Course Assessment Module 3: Additional Resources •Resources •Contact Us and Feedback
Amazon Kinesis Video Streams – Getting Started
Course description In this course, you will walk through how to get started with Amazon Kinesis Video Streams. With Kinesis Video Streams, you can securely stream video from connected devices to Amazon Web Services (AWS) for analytics, machine learning (ML), playback, and other processing. • Course level: Fundamental • Duration: 1 hour Activities This course includes demos, sample code, and interactive activities. Course objectives In this course, you will learn to do: • Describe how Kinesis Video Streams works • Familiarize yourself with the technical concepts of Kinesis Video Streams • List typical use cases for Kinesis Video Streams • Specify what it would take to implement Kinesis Video Streams in a real-world scenario • Recognize the benefits of Kinesis Video Streams • Explain the cost structure of Kinesis Video Streams • Explore how to use Kinesis Video Streams on the AWS Management Console and using the AWS Command Line Interface (AWS CLI) Intended audience This course is intended for: • Solutions architects • Connected home enthusiasts • Computer vision developers • Video engineers Prerequisites • Conceptual knowledge of cloud computing • Getting Started with AWS IoT Course outline Section 1: For Students • Lesson 1: How to Use This Course Section 2: Introduction • Lesson 2: Introduction to Kinesis Video Streams • Lesson 3: Architecture and Use Cases Section 3: Using AWS Kinesis Video Streams • Lesson 4: How Do I Create a Kinesis Video Stream in the AWS Management Console? • Lesson 5: How Do I Ingest Video into Kinesis Video Streams Using a Programming Language? • Lesson 6: How Do I Use Amazon Kinesis Video Streams with the AWS Management Console? • Lesson 7: How Do I View Media in Amazon Kinesis Video Streams Using HTTP Live Streaming? • Lesson 8: How Do I Delete a Video Stream for Kinesis Video Streams? Section 4: Resources • Lesson 9: Learn More • Lesson 10: Contact Us
Migrating SAP Workloads to AWS
Course description In this course, you review how a fictitious company migrated their SAP workloads to the AWS Cloud to improve performance, reduce costs, and enable digital transformation. Throughout the course, you learn about the SAP to AWS migration process, relevant AWS services, and available migration tools. • Course level: Fundamental • Duration: 60 minutes Activities This course includes videos, diagrams, and knowledge check questions. Course objectives In this course, you will learn to: • Identify the four phases and related tasks of the SAP to AWS migration process • Recognize the value of migrating your SAP workloads to AWS • Define migration approaches such as homogeneous and heterogeneous • Identify AWS services most relevant for deploying and operating SAP solutions • Identify different migration tools used for migrating SAP workloads to AWS • Identify AWS solutions relevant for optimizing and modernizing SAP solutions Intended audience This course is intended for: • SAP consultants • AWS Solutions Architects • Technical professionals Prerequisites We recommend that attendees of this course have: • Working knowledge of SAP workloads • Basic knowledge of AWS infrastructure services Course outline • Lesson 1: Course Navigation • Lesson 2: Introduction • Lesson 3: Phase 1: Assessment and Discovery • Lesson 4: Phase 2: Mobilization and Planning • Lesson 5: Phase 3: Migration and Cutover • Lesson 6: Phase 4: Optimization and Modernization • Lesson 7: Conclusion
Data Analytics Fundamentals
In this self-paced course, you learn about the process for planning data analysis solutions and the various data analytic processes that are involved. This course takes you through five key factors that indicate the need for specific AWS services in collecting, processing, analyzing, and presenting your data. This includes learning basic architectures, value propositions, and potential use cases. The course introduces you to the AWS services and solutions to help you build and enhance data analysis solutions. Intended Audience: This course is intended for: •Data architects •Data scientists •Data analysts Course Objectives: In this course, you will learn how to: •Identify the characteristics of data analysis solutions and the characteristics that indicate such a solution may be required •Define types of data including structured, semistructured, and unstructured data •Define data storage types such as data lakes, AWS Lake Formation, data warehouses, and the Amazon Simple Storage Service (Amazon S3) •Analyze the characteristics of and differences in batch and stream processing •Define how Amazon Kinesis is used to process streaming data •Analyze the characteristics of different storage systems for source data •Analyze the characteristics of online transaction processing (OLTP) and online analytical processing (OLAP) systems and their impact on the organization of data within these systems •Analyze the differences of row-based and columnar data storage methods •Define how Amazon EMR, AWS Glue, and Amazon Redshift each work to process, cleanse, and transform data within a data analysis solution •Analyze the concept of atomicity, consistency, isolation, and durability (ACID) compliance as well as basic availability, soft state, eventual consistency (BASE) compliance and how an extract, transform, load (ETL) process can help to ensure compliance •Explore the concept of data schemas and understand how they define data and how this information is stored in metastores •Analyze the concept of data versus information •Recognize the ways to analyze data to produce information for reports using tools such as Amazon QuickSight and Amazon Athena •Define how AWS services work together to visualize data Prerequisites: We recommend that attendees of this course have the following prerequisites: •Working knowledge of database concepts •Basic understanding of data storage, processing, and analytics •Experience with enterprise IT systems Delivery Method: This course is delivered through a mix of: •Digital training Duration: •3 Hours 30 Minutes Course Outline: This course covers the following concepts: • Lesson 1: Introduction to data analysis solutions – Data analytics and data analysis concepts – Introduction to the challenges of data analytics • Lesson 2: Volume – data storage – Introduction to Amazon S3 – Introduction to data lakes – Introduction to data storage methods • Lesson 3: Velocity – data processing – Introduction to data processing methods – Introduction to batch data processing – Introduction to stream data processing • Lesson 4: Variety – data structure and types – Introduction to source data storage – Introduction to structured data stores – Introduction to semistructured and unstructured data stores • Lesson 5: Veracity – cleansing and transformation – Understanding data integrity – Understanding database consistency – Introduction to the ETL process • Lesson 6: Value – reporting and business intelligence – Introduction to analyzing data – Introduction to visualizing data • Lesson 7: Key Takeaways – Putting the pieces together – What’s next
Getting Started with Amazon ElastiCache
Amazon ElastiCache is a fully managed, in-memory caching service supporting flexible, real-time use cases. You can use ElastiCache for caching, which helps accelerate application and database performance, or for use cases like session stores, gaming leaderboards, streaming, and analytics. ElastiCache is compatible with Redis and Memcached. In this Getting Started course, you will learn the benefits, typical use cases, and technical concepts of ElastiCache. You will have an opportunity to try the service through a demonstration using the AWS Management Console or programming languages.
Getting Started with Amazon Timestream
Amazon Timestream is a scalable, fully managed, purpose-built serverless time series database that can help you store and analyze time series data. In this course, you will learn the benefits, typical use cases, and technical concepts of Timestream. You can try the service through provided sample code or the interactive tool in the AWS Management Console. • Course level: Fundamental • Duration: 1 hour Activities This course includes demonstrations, graphics, and interactive activities. Course objectives In this course, you will learn to: • Understand how Timestream works. • Familiarize yourself with the technical concepts of Timestream. • List typical use cases for Timestream. • Specify what it would take to implement Timestream in a real-world scenario. • Recognize the benefits of Timestream. • Explain the cost structure of Timestream. • Show how to use Timestream from the AWS Management Console and using the AWS Command Line Interface (AWS CLI). Intended audience This course is intended for: • Database developers • Data architects • Solutions architects • Cloud practitioners • IT operations engineers • IT professionals • IT leaders Prerequisites None Course outline • Introduction to Timestream • Architecture and Use Cases • How Do I Launch a Timestream Database with Tables? • How Do I Query a Timestream Database? • Learn More
Getting Started with Amazon Neptune
Amazon Neptune is a fully managed graph database service that lets you build and run graph applications with highly connected datasets without worrying about hardware provisioning, software patching, setup, configuration, or backups. In this course, you will learn the benefits, typical use cases, and technical concepts of Neptune. You will have an opportunity to try the service through a demonstration using the AWS Management Console, AWS Command Line Interface (AWS CLI), or programming languages. •Course level: Fundamental •Duration: 1 hour Activities: This course includes demonstrations, graphics, and interactive activities. Course objectives: In this course, you will learn to: •Understand how Neptune works. •Familiarize yourself with the technical concepts of Neptune. •List typical use cases for Neptune. •Specify what it would take to implement Neptune in a real-world scenario. •Recognize the benefits of Neptune. •Explain the cost structure of Neptune. •Show how to use Neptune from the AWS Management Console, using the AWS CLI, and with programming languages. Intended audience: This course is intended for: •Developers •Solutions architects •Cloud practitioners •IT operations engineers •IT professionals •IT leaders •Database administrators Prerequisites: No prerequisites needed. Course outline: • Introduction to Neptune • Architecture and Use Cases • How Do I Create a Neptune Cluster in the AWS Management Console? • How Do I Insert and Query Data Using a Neptune Notebook? • How Do I Delete a Neptune Cluster? • Creating Neptune Resources Using the AWS CLI • How Do I Use Neptune with AWS CloudFormation? • How Do I Use Neptune with a Programming Language? • Learn More
Getting Started with Amazon Keyspaces
Amazon Keyspaces (for Apache Cassandra) is a serverless database that can help you manage large, wide-column datastores. It offers single-digit millisecond read/write performance at scale. You can migrate your on-premises Cassandra workloads to Amazon Keyspaces using the same Cassandra Query Language (CQL) and developer tools you use today. In this course, you will learn the benefits, typical use cases, and technical concepts of Amazon Keyspaces. You can try the service through the sample code provided or the interactive tool in the AWS Management Console. • Course level: Fundamental • Duration: 1 hour Activities This course includes presentations, graphics, sample code, and interactive activities. Course objectives In this course, you will learn to: • Understand how Amazon Keyspaces works. • Familiarize yourself with the technical concepts of Amazon Keyspaces. • List typical use cases for Amazon Keyspaces. • Specify what it would take to implement Amazon Keyspaces in a real-world scenario. • Recognize the benefits of Amazon Keyspaces. • Explain the cost structure of Amazon Keyspaces. • Use Amazon Keyspaces from the AWS Management Console and through the AWS Command Line Interface (AWS CLI). Intended audience This course is intended for: • Database developers • Data architects • Solutions architects • Cloud practitioners • IT operations engineers • IT professionals • IT leaders Prerequisites None Course outline • Introduction to Amazon Keyspaces • Architecture and Use Cases • Using Amazon Keyspaces • Learn More
