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@leoricklin
Last active August 28, 2022 08:38
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AWS

Architecture

AWS Certified Machine Learning Specialty

Here is a list of recommended links that I've found particularly useful:

  1. AWS Certified Machine Learning Specialty 2020 - Hands On! https://www.udemy.com/course/aws-machine-learning/
  2. AWS Certified Machine Learning Specialty: Full Practice Exam https://www.udemy.com/course/aws-machine-learning-practice-exam/
  3. The Elements of Data Science https://www.aws.training/Details/eLearning?id=26598
  4. Process Model: CRISP-DM on the AWS Stack https://www.aws.training/Details/eLearning?id=27200
  5. Machine Learning Algorithms Explained https://www.aws.training/Details/Video?id=27235
  6. Machine Learning Terminology and Process https://www.aws.training/Details/eLearning?id=20294
  7. Developing Machine Learning Applications https://www.aws.training/Details/Curriculum?id=27243
  8. Exam Readiness: AWS Certified Machine Learning - Specialty https://www.aws.training/Details/eLearning?id=42183

Certification

Resources

Training

O'Reilly

  • Learning Path: AWS Architect Associate Certification Exam Prep—Core AWS Services
  • Learning Path: AWS Architect Associate Certification Exam Prep—Managed AWS Services
  • Learning Path: AWS Architect Associate Certification Exam Prep—Design Fundamentals

Data

Amazon Athena lets you deploy Presto using the AWS Serverless platform, with no servers, virtual machines, or clusters to setup, manage, or tune. Simply point to your data at Amazon S3, define the schema, and start querying using the built-in query editor, or with your existing Business Intelligence (BI) tools. Athena automatically parallelizes your query, and dynamically scales resources for queries to run quickly. You pay only for the queries that you run.

AWS Glue provides both visual and code-based interfaces to make data integration easier. Users can easily find and access data using the AWS Glue Data Catalog. Data engineers and ETL (extract, transform, and load) developers can visually create, run, and monitor ETL workflows with a few clicks in AWS Glue Studio. Data analysts and data scientists can use AWS Glue DataBrew to visually enrich, clean, and normalize data without writing code. With AWS Glue Elastic Views, application developers can use familiar Structured Query Language (SQL) to combine and replicate data across different data stores.

Use cases

  • Build event-driven ETL (extract, transform, and load) pipelines

  • Create a unified catalog to find data across multiple data stores

  • Create, run, and monitor ETL jobs without coding

  • Explore data with self-service visual data preparation

  • Build materialized views to combine and replicate data (in preview)

  • Kinesis Data Streams: Capture, process, and store data streams, (@@kafka) Amazon Kinesis Data Streams is a scalable and durable real-time data streaming service that can continuously capture gigabytes of data per second from hundreds of thousands of sources.

  • Kinesis Data Firehose: Load data streams into AWS data stores, (@@kafka connect) Amazon Kinesis Data Firehose is the easiest way to capture, transform, and load data streams into AWS data stores for near real-time analytics with existing business intelligence tools.

  • Kinesis Data Analytics: Analyze data streams with SQL or Apache Flink, (@@kafka stream/ksql) Amazon Kinesis Data Analytics is the easiest way to process data streams in real time with SQL or Apache Flink without having to learn new programming languages or processing frameworks.

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