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on AWS

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Javi Moreno



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Agenda: first part

Quick and fast intro

Datalakes with S3

Format transformation and ETL with Glue

Ingestion with Kinesis Streams y Firehose

Preparing raw data with Lambda

Realtime processing with Kinesis Analytics

DynamoDB for processed dataon

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Querying the datalake with Athena

Transforming massive amounts of data with EMR/Hive

Interactive queries with Redshift

Spectrum for integrating Redshift and S3

Data visualization with QuickSight

Realtime dashboards with Kinesis Client Library

Cleanup and conclusion

Agenda: second part

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Quick and fast intro

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Datalakes with S3

Unlimited and affordable object-based storage

99.999999999% data durability

Easy world-wide replication

Data encryption at rest and on the fly

Versioning and life cycle management


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ETL with Glue

Extract, transform and load managed service

Data catalog for partition management

Optional schema discovery with Crawlers

Advanced job schedule

Code generation


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Ingestion with Kinesis

Managed near real-time queue

Capacity measured in Shards (1000 rps or 1MBs)

Unlimited ingestion scalability

Up to seven days retention period

Easy processing

Long-term storage integration with Firehose

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Preparing with Lambda

Serverless star product

Integrated with most services

$0.20 per 1.000.000 invocations

Nodejs, Python, Java, C# and Java

C9 editor integrated with the console

Seamless massive autoscaling

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Kinesis analytics

Codeless near real-time processing

Powered by a SQL-like language

Input transformation available

Results can be processed with Lambda



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Semi-structured database

Very fast and predictable performance

Integrated autoscaling

Global tables

Easy backup procedure

SDK based support for JSON

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Querying with Athena

Query S3 without ETL

Serverless Presto-based infrastructure

Standard SQL variant supported

Pay per scanned amount of data

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Elastic Map Reduce

Managed infrastructure for Yarn

Supports Hadoop MapReduce, Presto, Spark...

Integrated with S3

Master node, core nodes and task nodes

Scale up without problems

Cost effective with spot instances support

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Redshift datawarehouse

Columnar based storage

Interactive query over 1.5PB of data

Postgresql SQL dialect and connectivity

Included snapshot for backup

Change cluster size at any time

Extreme performance for aggregation

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Spectrum tables

Massive managed cluster

Integrated with Redshift nodes

Join with tables stored directly on S3

Pay for amount of scanned data

SQL dialect compatible with Redshift

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Quicksight visualizations

Software as a Service data explorer

Powered by S.P.I.C.E on-memory engine

Flexible visualization library

Easy-to-use and attractive user interface

Integrated with most data sources

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Dashboards with KCL

Powerful java wrapper over Kinesis API

Automatic recovery from failure based on DynamoDB

Supports batch processing

Makes easy to create scalable consumers

Designed to be run on EC2 instances

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S3 buckets

EC2 instances ¡

Glue databases

DynamoDB tables

Athena catalogs

Redshift clusters

Quicksight datasets

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Bigdata on AWS

By Javier Moreno

Bigdata on AWS

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