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06 - Analytics

Amazon Athena

Amazon Athena is an interactive query service that utilizes Schema-On-Read, allowing you to run ad-hoc SQL like queries on data from a range of sources
Athena is used to query large dataset (structured, semi-structured, and unstructured) stored in S3 with infrequent access pattern
You are charged for compute time only. You don’t need to maintain separate dataset for Athena, it can directly access S3 bucket

EMR is tool for large-scale parallel processing of big data and other large data workloads
It is based on the Apache Hadoop framework and is delivered as a managed cluster using EC2 instances
It is used for huge-scale log analysis, indexing, machine learning, financial analysis, simulations, bio-informatics and many other large-scale applications
EMR cluster have zero or more core nodes, which are managed by the master node. They run tasks and manage data for HDFS
Data can be input from and output to S3. Intermediate data can be stored using HDFS in the cluster or EMRFS using S3.

Streaming service designed to ingest large amounts of data from hundreds, thousands or even millions of producers
Scalable and Resilient
Consumers can access a rolling window of that data, or it can be stored in persistent storage of database products

A Kinesis data stream can be used to collect, process, and analyze a large amount of incoming data
Storage for all incoming data within a 24 hour default window, which can be increased to seven days for an additional charge
Kinesis Data records (The basis entity written to and read from Kinesis stream, a data record can be up to 1 MB in size) are added by producers and read by consumers

Reliably load streaming data into data lakes, data stores and analytics tools
It can capture, transform, and load streaming data into Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk
It enables near real-time analytics with existing business intelligence tools and dashboards you’re already using today
Kinesis Data Streams can be used as the source(s) to Kinesis Data Firehose
Pay for only the data ingested

Process and analyze real-time, streaming data
Can use standard SQL queries to process Kinesis data streams
A Kinesis Data Analytics application consists of three components:
- Input – the streaming source for your application
- Application code – a series of SQL statements that process input and produce output
- Output – one or more in-application streams to hold intermediate results

Securely ingests and stores video and audio encoded data to consumers such as SageMaker, Rekognition or other services to apply Machine Learning and Video processing