Amazon Athena

Amazon Athena¶

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using
standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the
queries that you run.
Athena is easy to use. Simply point to your data in Amazon S3, define the schema, and start querying
using standard SQL. Most results are delivered within seconds. With Athena, there’s no need for complex
extract, transform, and load (ETL) jobs to prepare your data for analysis. This makes it easy for anyone
with SQL skills to quickly analyze large-scale datasets.
Athena is out-of-the-box integrated with AWS Glue Data Catalog, allowing you to create a unified
metadata repository across various services, crawl data sources to discover schemas and populate your
Catalog with new and modified table and partition definitions, and maintain schema versioning.

AWS analytics

Backlinks¶

AWS overview
- Amazon Athena
  Amazon Elasticsearch Service
  Amazon EMR
  Amazon FinSpace
  Amazon Kinesis
  Amazon Kinesis Data Firehose
  Amazon Kinesis Data Analytics
  Amazon Kinesis Data Streams
  Amazon Kinesis Video Streams
  Amazon Redshift
  Amazon QuickSight
  AWS Data Exchange
  AWS Data Pipeline
  AWS Glue
  AWS Lake Formation
  Amazon Managed Streaming for Apache Kafka
AWS Lake Formation
- AWS Lake Formation is a service that makes it easy to set up a secure data lake in days. A data lake is a centralized, curated, and secured repository that stores all your data, both in its original form and prepared for analysis. A data lake enables you to break down data silos and combine different types of analytics to gain insights and guide better business decisions.
  setting up partitions, turning on encryption and managing keys, defining transformation jobs and monitoring their operation, re-organizing data into a columnar format, configuring access control settings, deduplicating redundant data, matching linked records, granting access to data sets, and auditing access over time.
  Creating a data lake with Lake Formation is as simple as defining where your data resides and what data access and security policies you want to apply. Lake Formation then collects and catalogs data from databases and object storage, moves the data into your new Amazon S3 data lake, cleans and classifies data using machine learning algorithms, and secures access to your sensitive data. Your users can then
  access a centralized catalog of data which describes available data sets and their appropriate usage. Your users then leverage these data sets with their choice of analytics and machine learning services, like Amazon EMR for Apache Spark, Amazon Redshift, Amazon Athena, SageMaker, and Amazon QuickSight.