SlideShare a Scribd company logo
1 of 14
End-to-end Analytics
          with Apache Cassandra




C*                                #cassandra12
Basics
     • CFIF/CFOF/CFRR/CFRW
     • BulkOutputFormat
     • Input locality (identical to HDFS)
     • Wide row, 2I, composite support
     • Pig, Hive, Mahout, Sqoop, Oozie, DSE
       Analytics

C*                                  #cassandra12
Why Cassandra?

     • Excellent Hadoop capabilities built-in
     • Multi-datacenter support, load isolation
     • Operationally, order of magnitude simpler
     • DSE Analytics is all-in-one, simpler still
     • Review your requirements, do homework
C*                                       #cassandra12
Use Cases

     • Trends, recommendations, reporting, etc.
     • Detect and fix problems in data
     • New realtime (or analytic) query pattern?
      • Backpopulate new CF with historical data

C*                                    #cassandra12
Data Model
     • Consider growth patterns and analytic
       query patterns for your data
      • One-off inquiry or regular processing?
      • Fast growing, want only small slices?
     • Consider active/archive CFs
     • Secondary indexes for small inputs
C*                                      #cassandra12
Miscellaneous Tips


     • Don’t forget about tombstones
     • BOP to enable range slices (Rows with
       keys ‘A*’ to ‘F*’)




C*                                     #cassandra12
Cassandra + Oozie
     • Workflows: cohesive, nestable, scheduled
     • Web UI, CLI, web service
     • Cassandra properties in oozie job
       properties, and workflow.xml
     • Writing out to Cassandra:
       mapreduce.fileoutputcommitter.marksuccessfuljobs to false


     • DSE Analytics works with Oozie 3.2.1+
C*                                                            #cassandra12
Cassandra + Pig
     • Data with validators
      • Pig tuples (address.name, address.value)
     • Data with default validator
      • Bag of key, value pairs (tuples)
      • Unmarshal with Pygmalion
        • Select by regex (eg ‘1369*’, ‘link*’)
C*                                      #cassandra12
Cassandra + Pig
     • Output to Cassandra
      • Can output directly with (key, (name,
         value), (name, value)...) format
      • For tabular data, format output with
         Pygmalion’s ToCassandraBag
      • Use BulkOutputFormat (C* 1.1)
C*                                          #cassandra12
Cassandra + Pig
     • Composite column support (C* 1.0.9+)
     • Counter support (C* 1.0.9+)
     • Secondary Index support for relatively
       small slices (C* 1.1+)
     • Wide row support (C* 1.1+)
     • Composite key support (C* 1.1.3+)
C*                                     #cassandra12
Cassandra + Hadoop X
     • Example: Cassandra + CDH3
      • Start with Cassandra ring
      • Add NN, JT, Oozie server
      • TaskTracker, DataNode on each node
      • Jobs/launching point have Cassandra info
      • Segregate out analytics with virtual DC
C*                                     #cassandra12
Current/Future Work


     • Cassandra core Hive support (outside of
       Brisk) (CASSANDRA-4131)




C*                                     #cassandra12
For More Information


     • Follow @CassandraHadoop on Twitter
     • http://wiki.apache.org/cassandra/
       HadoopSupport




C*                                  #cassandra12
Questions?




C*                #cassandra12

More Related Content

What's hot

Hadoop Hive Talk At IIT-Delhi
Hadoop Hive Talk At IIT-DelhiHadoop Hive Talk At IIT-Delhi
Hadoop Hive Talk At IIT-DelhiJoydeep Sen Sarma
 
The Meta of Hadoop - COMAD 2012
The Meta of Hadoop - COMAD 2012The Meta of Hadoop - COMAD 2012
The Meta of Hadoop - COMAD 2012Joydeep Sen Sarma
 
Cloudera Impala + PostgreSQL
Cloudera Impala + PostgreSQLCloudera Impala + PostgreSQL
Cloudera Impala + PostgreSQLliuknag
 
Amebaサービスのログ解析基盤
Amebaサービスのログ解析基盤Amebaサービスのログ解析基盤
Amebaサービスのログ解析基盤Toshihiro Suzuki
 
How Adobe Does 2 Million Records Per Second Using Apache Spark!
How Adobe Does 2 Million Records Per Second Using Apache Spark!How Adobe Does 2 Million Records Per Second Using Apache Spark!
How Adobe Does 2 Million Records Per Second Using Apache Spark!Databricks
 
Partners in Crime: Cassandra Analytics and ETL with Hadoop
Partners in Crime: Cassandra Analytics and ETL with HadoopPartners in Crime: Cassandra Analytics and ETL with Hadoop
Partners in Crime: Cassandra Analytics and ETL with HadoopStu Hood
 
Migrating structured data between Hadoop and RDBMS
Migrating structured data between Hadoop and RDBMSMigrating structured data between Hadoop and RDBMS
Migrating structured data between Hadoop and RDBMSBouquet
 
Hadoop Operations Powered By ... Hadoop (Hadoop Summit 2014 Amsterdam)
Hadoop Operations Powered By ... Hadoop (Hadoop Summit 2014 Amsterdam)Hadoop Operations Powered By ... Hadoop (Hadoop Summit 2014 Amsterdam)
Hadoop Operations Powered By ... Hadoop (Hadoop Summit 2014 Amsterdam)Adam Kawa
 
Hadoop Pig: MapReduce the easy way!
Hadoop Pig: MapReduce the easy way!Hadoop Pig: MapReduce the easy way!
Hadoop Pig: MapReduce the easy way!Nathan Bijnens
 
Qubole @ AWS Meetup Bangalore - July 2015
Qubole @ AWS Meetup Bangalore - July 2015Qubole @ AWS Meetup Bangalore - July 2015
Qubole @ AWS Meetup Bangalore - July 2015Joydeep Sen Sarma
 
Hd insight essentials quick view
Hd insight essentials quick viewHd insight essentials quick view
Hd insight essentials quick viewRajesh Nadipalli
 
introduction to data processing using Hadoop and Pig
introduction to data processing using Hadoop and Pigintroduction to data processing using Hadoop and Pig
introduction to data processing using Hadoop and PigRicardo Varela
 
Real Time and Big Data – It’s About Time
Real Time and Big Data – It’s About TimeReal Time and Big Data – It’s About Time
Real Time and Big Data – It’s About TimeMapR Technologies
 

What's hot (20)

Nextag talk
Nextag talkNextag talk
Nextag talk
 
Hadoop Hive Talk At IIT-Delhi
Hadoop Hive Talk At IIT-DelhiHadoop Hive Talk At IIT-Delhi
Hadoop Hive Talk At IIT-Delhi
 
מיכאל
מיכאלמיכאל
מיכאל
 
Hadoop sqoop
Hadoop sqoop Hadoop sqoop
Hadoop sqoop
 
Hadoop overview
Hadoop overviewHadoop overview
Hadoop overview
 
The Meta of Hadoop - COMAD 2012
The Meta of Hadoop - COMAD 2012The Meta of Hadoop - COMAD 2012
The Meta of Hadoop - COMAD 2012
 
Cloudera Impala + PostgreSQL
Cloudera Impala + PostgreSQLCloudera Impala + PostgreSQL
Cloudera Impala + PostgreSQL
 
Amebaサービスのログ解析基盤
Amebaサービスのログ解析基盤Amebaサービスのログ解析基盤
Amebaサービスのログ解析基盤
 
Apache drill
Apache drillApache drill
Apache drill
 
How Adobe Does 2 Million Records Per Second Using Apache Spark!
How Adobe Does 2 Million Records Per Second Using Apache Spark!How Adobe Does 2 Million Records Per Second Using Apache Spark!
How Adobe Does 2 Million Records Per Second Using Apache Spark!
 
Partners in Crime: Cassandra Analytics and ETL with Hadoop
Partners in Crime: Cassandra Analytics and ETL with HadoopPartners in Crime: Cassandra Analytics and ETL with Hadoop
Partners in Crime: Cassandra Analytics and ETL with Hadoop
 
Migrating structured data between Hadoop and RDBMS
Migrating structured data between Hadoop and RDBMSMigrating structured data between Hadoop and RDBMS
Migrating structured data between Hadoop and RDBMS
 
Hadoop Operations Powered By ... Hadoop (Hadoop Summit 2014 Amsterdam)
Hadoop Operations Powered By ... Hadoop (Hadoop Summit 2014 Amsterdam)Hadoop Operations Powered By ... Hadoop (Hadoop Summit 2014 Amsterdam)
Hadoop Operations Powered By ... Hadoop (Hadoop Summit 2014 Amsterdam)
 
Hadoop Pig: MapReduce the easy way!
Hadoop Pig: MapReduce the easy way!Hadoop Pig: MapReduce the easy way!
Hadoop Pig: MapReduce the easy way!
 
Qubole @ AWS Meetup Bangalore - July 2015
Qubole @ AWS Meetup Bangalore - July 2015Qubole @ AWS Meetup Bangalore - July 2015
Qubole @ AWS Meetup Bangalore - July 2015
 
Hd insight essentials quick view
Hd insight essentials quick viewHd insight essentials quick view
Hd insight essentials quick view
 
Hadoop and Distributed Computing
Hadoop and Distributed ComputingHadoop and Distributed Computing
Hadoop and Distributed Computing
 
Hadoop
HadoopHadoop
Hadoop
 
introduction to data processing using Hadoop and Pig
introduction to data processing using Hadoop and Pigintroduction to data processing using Hadoop and Pig
introduction to data processing using Hadoop and Pig
 
Real Time and Big Data – It’s About Time
Real Time and Big Data – It’s About TimeReal Time and Big Data – It’s About Time
Real Time and Big Data – It’s About Time
 

Viewers also liked

Plagger はじめよう
Plagger はじめようPlagger はじめよう
Plagger はじめようSeacolor
 
Friends Don't Let Friends Clap on One and Three: a Backbeat Clapping Study
Friends Don't Let Friends Clap on One and Three: a Backbeat Clapping StudyFriends Don't Let Friends Clap on One and Three: a Backbeat Clapping Study
Friends Don't Let Friends Clap on One and Three: a Backbeat Clapping StudyEthan Hein
 
ISUCONの勝ち方 YAPC::Asia Tokyo 2015
ISUCONの勝ち方 YAPC::Asia Tokyo 2015ISUCONの勝ち方 YAPC::Asia Tokyo 2015
ISUCONの勝ち方 YAPC::Asia Tokyo 2015Masahiro Nagano
 
Enduring CSS
Enduring CSSEnduring CSS
Enduring CSSTakazudo
 
120 Awesome Marketing Stats, Charts and Graphs
120 Awesome Marketing Stats, Charts and Graphs120 Awesome Marketing Stats, Charts and Graphs
120 Awesome Marketing Stats, Charts and GraphsHubSpot
 

Viewers also liked (9)

Plagger はじめよう
Plagger はじめようPlagger はじめよう
Plagger はじめよう
 
Friends Don't Let Friends Clap on One and Three: a Backbeat Clapping Study
Friends Don't Let Friends Clap on One and Three: a Backbeat Clapping StudyFriends Don't Let Friends Clap on One and Three: a Backbeat Clapping Study
Friends Don't Let Friends Clap on One and Three: a Backbeat Clapping Study
 
Build Your Brand and Build Your Business
Build Your Brand and Build Your BusinessBuild Your Brand and Build Your Business
Build Your Brand and Build Your Business
 
ISUCONの勝ち方 YAPC::Asia Tokyo 2015
ISUCONの勝ち方 YAPC::Asia Tokyo 2015ISUCONの勝ち方 YAPC::Asia Tokyo 2015
ISUCONの勝ち方 YAPC::Asia Tokyo 2015
 
Design process
Design processDesign process
Design process
 
El Lenguaje Fotografico
El Lenguaje FotograficoEl Lenguaje Fotografico
El Lenguaje Fotografico
 
Enduring CSS
Enduring CSSEnduring CSS
Enduring CSS
 
120 Awesome Marketing Stats, Charts and Graphs
120 Awesome Marketing Stats, Charts and Graphs120 Awesome Marketing Stats, Charts and Graphs
120 Awesome Marketing Stats, Charts and Graphs
 
DISPLAY LUMAscape
DISPLAY LUMAscapeDISPLAY LUMAscape
DISPLAY LUMAscape
 

Similar to End-to-end Analytics with Apache Cassandra

PySpark Cassandra - Amsterdam Spark Meetup
PySpark Cassandra - Amsterdam Spark MeetupPySpark Cassandra - Amsterdam Spark Meetup
PySpark Cassandra - Amsterdam Spark MeetupFrens Jan Rumph
 
Introduction to Real-Time Analytics with Cassandra and Hadoop
Introduction to Real-Time Analytics with Cassandra and HadoopIntroduction to Real-Time Analytics with Cassandra and Hadoop
Introduction to Real-Time Analytics with Cassandra and HadoopPatricia Gorla
 
DataStax - Analytics on Apache Cassandra - Paris Tech Talks meetup
DataStax - Analytics on Apache Cassandra - Paris Tech Talks meetupDataStax - Analytics on Apache Cassandra - Paris Tech Talks meetup
DataStax - Analytics on Apache Cassandra - Paris Tech Talks meetupVictor Coustenoble
 
Deep Dive into Cassandra
Deep Dive into CassandraDeep Dive into Cassandra
Deep Dive into CassandraBrent Theisen
 
NoSQL Intro with cassandra
NoSQL Intro with cassandraNoSQL Intro with cassandra
NoSQL Intro with cassandraBrian Enochson
 
Spark + Cassandra = Real Time Analytics on Operational Data
Spark + Cassandra = Real Time Analytics on Operational DataSpark + Cassandra = Real Time Analytics on Operational Data
Spark + Cassandra = Real Time Analytics on Operational DataVictor Coustenoble
 
Apache Cassandra introduction
Apache Cassandra introductionApache Cassandra introduction
Apache Cassandra introductionfardinjamshidi
 
"Real-time data processing with Spark & Cassandra", jDays 2015 Speaker: "Duy-...
"Real-time data processing with Spark & Cassandra", jDays 2015 Speaker: "Duy-..."Real-time data processing with Spark & Cassandra", jDays 2015 Speaker: "Duy-...
"Real-time data processing with Spark & Cassandra", jDays 2015 Speaker: "Duy-...hamidsamadi
 
Spring one2gx2010 spring-nonrelational_data
Spring one2gx2010 spring-nonrelational_dataSpring one2gx2010 spring-nonrelational_data
Spring one2gx2010 spring-nonrelational_dataRoger Xia
 
Spark cassandra integration, theory and practice
Spark cassandra integration, theory and practiceSpark cassandra integration, theory and practice
Spark cassandra integration, theory and practiceDuyhai Doan
 
Hindsight is 20/20: MySQL to Cassandra
Hindsight is 20/20: MySQL to CassandraHindsight is 20/20: MySQL to Cassandra
Hindsight is 20/20: MySQL to CassandraMichael Kjellman
 
C* Summit 2013 - Hindsight is 20/20. MySQL to Cassandra by Michael Kjellman
C* Summit 2013 - Hindsight is 20/20. MySQL to Cassandra by Michael KjellmanC* Summit 2013 - Hindsight is 20/20. MySQL to Cassandra by Michael Kjellman
C* Summit 2013 - Hindsight is 20/20. MySQL to Cassandra by Michael KjellmanDataStax Academy
 
Munich March 2015 - Cassandra + Spark Overview
Munich March 2015 -  Cassandra + Spark OverviewMunich March 2015 -  Cassandra + Spark Overview
Munich March 2015 - Cassandra + Spark OverviewChristopher Batey
 
Cassandra integrations
Cassandra integrationsCassandra integrations
Cassandra integrationsT Jake Luciani
 
Trivadis TechEvent 2016 Big Data Cassandra, wieso brauche ich das? by Jan Ott
Trivadis TechEvent 2016 Big Data Cassandra, wieso brauche ich das? by Jan OttTrivadis TechEvent 2016 Big Data Cassandra, wieso brauche ich das? by Jan Ott
Trivadis TechEvent 2016 Big Data Cassandra, wieso brauche ich das? by Jan OttTrivadis
 
BI, Reporting and Analytics on Apache Cassandra
BI, Reporting and Analytics on Apache CassandraBI, Reporting and Analytics on Apache Cassandra
BI, Reporting and Analytics on Apache CassandraVictor Coustenoble
 
Developing with Cassandra
Developing with CassandraDeveloping with Cassandra
Developing with CassandraSperasoft
 

Similar to End-to-end Analytics with Apache Cassandra (20)

PySpark Cassandra - Amsterdam Spark Meetup
PySpark Cassandra - Amsterdam Spark MeetupPySpark Cassandra - Amsterdam Spark Meetup
PySpark Cassandra - Amsterdam Spark Meetup
 
Introduction to Real-Time Analytics with Cassandra and Hadoop
Introduction to Real-Time Analytics with Cassandra and HadoopIntroduction to Real-Time Analytics with Cassandra and Hadoop
Introduction to Real-Time Analytics with Cassandra and Hadoop
 
Cassandra & Spark for IoT
Cassandra & Spark for IoTCassandra & Spark for IoT
Cassandra & Spark for IoT
 
DataStax - Analytics on Apache Cassandra - Paris Tech Talks meetup
DataStax - Analytics on Apache Cassandra - Paris Tech Talks meetupDataStax - Analytics on Apache Cassandra - Paris Tech Talks meetup
DataStax - Analytics on Apache Cassandra - Paris Tech Talks meetup
 
Deep Dive into Cassandra
Deep Dive into CassandraDeep Dive into Cassandra
Deep Dive into Cassandra
 
NoSQL Intro with cassandra
NoSQL Intro with cassandraNoSQL Intro with cassandra
NoSQL Intro with cassandra
 
Spark + Cassandra = Real Time Analytics on Operational Data
Spark + Cassandra = Real Time Analytics on Operational DataSpark + Cassandra = Real Time Analytics on Operational Data
Spark + Cassandra = Real Time Analytics on Operational Data
 
Cassandra 2.0 (Introduction)
Cassandra 2.0 (Introduction)Cassandra 2.0 (Introduction)
Cassandra 2.0 (Introduction)
 
Apache Cassandra introduction
Apache Cassandra introductionApache Cassandra introduction
Apache Cassandra introduction
 
"Real-time data processing with Spark & Cassandra", jDays 2015 Speaker: "Duy-...
"Real-time data processing with Spark & Cassandra", jDays 2015 Speaker: "Duy-..."Real-time data processing with Spark & Cassandra", jDays 2015 Speaker: "Duy-...
"Real-time data processing with Spark & Cassandra", jDays 2015 Speaker: "Duy-...
 
Spark Introduction
Spark IntroductionSpark Introduction
Spark Introduction
 
Spring one2gx2010 spring-nonrelational_data
Spring one2gx2010 spring-nonrelational_dataSpring one2gx2010 spring-nonrelational_data
Spring one2gx2010 spring-nonrelational_data
 
Spark cassandra integration, theory and practice
Spark cassandra integration, theory and practiceSpark cassandra integration, theory and practice
Spark cassandra integration, theory and practice
 
Hindsight is 20/20: MySQL to Cassandra
Hindsight is 20/20: MySQL to CassandraHindsight is 20/20: MySQL to Cassandra
Hindsight is 20/20: MySQL to Cassandra
 
C* Summit 2013 - Hindsight is 20/20. MySQL to Cassandra by Michael Kjellman
C* Summit 2013 - Hindsight is 20/20. MySQL to Cassandra by Michael KjellmanC* Summit 2013 - Hindsight is 20/20. MySQL to Cassandra by Michael Kjellman
C* Summit 2013 - Hindsight is 20/20. MySQL to Cassandra by Michael Kjellman
 
Munich March 2015 - Cassandra + Spark Overview
Munich March 2015 -  Cassandra + Spark OverviewMunich March 2015 -  Cassandra + Spark Overview
Munich March 2015 - Cassandra + Spark Overview
 
Cassandra integrations
Cassandra integrationsCassandra integrations
Cassandra integrations
 
Trivadis TechEvent 2016 Big Data Cassandra, wieso brauche ich das? by Jan Ott
Trivadis TechEvent 2016 Big Data Cassandra, wieso brauche ich das? by Jan OttTrivadis TechEvent 2016 Big Data Cassandra, wieso brauche ich das? by Jan Ott
Trivadis TechEvent 2016 Big Data Cassandra, wieso brauche ich das? by Jan Ott
 
BI, Reporting and Analytics on Apache Cassandra
BI, Reporting and Analytics on Apache CassandraBI, Reporting and Analytics on Apache Cassandra
BI, Reporting and Analytics on Apache Cassandra
 
Developing with Cassandra
Developing with CassandraDeveloping with Cassandra
Developing with Cassandra
 

More from Jeremy Hanna

Göteborg Distributed: Eventual Consistency in Apache Cassandra
Göteborg Distributed: Eventual Consistency in Apache CassandraGöteborg Distributed: Eventual Consistency in Apache Cassandra
Göteborg Distributed: Eventual Consistency in Apache CassandraJeremy Hanna
 
Apache Cassandra in the Real World
Apache Cassandra in the Real WorldApache Cassandra in the Real World
Apache Cassandra in the Real WorldJeremy Hanna
 
Apache Cassandra in the Real World
Apache Cassandra in the Real WorldApache Cassandra in the Real World
Apache Cassandra in the Real WorldJeremy Hanna
 
Modern Cassandra for Developers
Modern Cassandra for DevelopersModern Cassandra for Developers
Modern Cassandra for DevelopersJeremy Hanna
 
Troubleshooting Cassandra
Troubleshooting CassandraTroubleshooting Cassandra
Troubleshooting CassandraJeremy Hanna
 
Cassandra + Hadoop: Analisi Batch con Apache Cassandra
Cassandra + Hadoop: Analisi Batch con Apache CassandraCassandra + Hadoop: Analisi Batch con Apache Cassandra
Cassandra + Hadoop: Analisi Batch con Apache CassandraJeremy Hanna
 
Cassandra + Hadoop @ApacheCon
Cassandra + Hadoop @ApacheCon Cassandra + Hadoop @ApacheCon
Cassandra + Hadoop @ApacheCon Jeremy Hanna
 

More from Jeremy Hanna (9)

Göteborg Distributed: Eventual Consistency in Apache Cassandra
Göteborg Distributed: Eventual Consistency in Apache CassandraGöteborg Distributed: Eventual Consistency in Apache Cassandra
Göteborg Distributed: Eventual Consistency in Apache Cassandra
 
Apache Cassandra in the Real World
Apache Cassandra in the Real WorldApache Cassandra in the Real World
Apache Cassandra in the Real World
 
Apache Cassandra in the Real World
Apache Cassandra in the Real WorldApache Cassandra in the Real World
Apache Cassandra in the Real World
 
Modern Cassandra for Developers
Modern Cassandra for DevelopersModern Cassandra for Developers
Modern Cassandra for Developers
 
Troubleshooting Cassandra
Troubleshooting CassandraTroubleshooting Cassandra
Troubleshooting Cassandra
 
Cassandra + Hadoop: Analisi Batch con Apache Cassandra
Cassandra + Hadoop: Analisi Batch con Apache CassandraCassandra + Hadoop: Analisi Batch con Apache Cassandra
Cassandra + Hadoop: Analisi Batch con Apache Cassandra
 
Cassandra eu
Cassandra euCassandra eu
Cassandra eu
 
Cassandra + Hadoop @ApacheCon
Cassandra + Hadoop @ApacheCon Cassandra + Hadoop @ApacheCon
Cassandra + Hadoop @ApacheCon
 
Cassandra+Hadoop
Cassandra+HadoopCassandra+Hadoop
Cassandra+Hadoop
 

Recently uploaded

Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxLoriGlavin3
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Commit University
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteDianaGray10
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsPixlogix Infotech
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxLoriGlavin3
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek SchlawackFwdays
 
A Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersA Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersNicole Novielli
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebUiPathCommunity
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxLoriGlavin3
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningLars Bell
 
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESSALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESmohitsingh558521
 
What is Artificial Intelligence?????????
What is Artificial Intelligence?????????What is Artificial Intelligence?????????
What is Artificial Intelligence?????????blackmambaettijean
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsSergiu Bodiu
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024BookNet Canada
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLScyllaDB
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersRaghuram Pandurangan
 
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxThe Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxLoriGlavin3
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxLoriGlavin3
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxLoriGlavin3
 

Recently uploaded (20)

Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
 
Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!Nell’iperspazio con Rocket: il Framework Web di Rust!
Nell’iperspazio con Rocket: il Framework Web di Rust!
 
Take control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test SuiteTake control of your SAP testing with UiPath Test Suite
Take control of your SAP testing with UiPath Test Suite
 
The Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and ConsThe Ultimate Guide to Choosing WordPress Pros and Cons
The Ultimate Guide to Choosing WordPress Pros and Cons
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
 
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
"Subclassing and Composition – A Pythonic Tour of Trade-Offs", Hynek Schlawack
 
A Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software DevelopersA Journey Into the Emotions of Software Developers
A Journey Into the Emotions of Software Developers
 
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio WebDev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
 
DSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine TuningDSPy a system for AI to Write Prompts and Do Fine Tuning
DSPy a system for AI to Write Prompts and Do Fine Tuning
 
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICESSALESFORCE EDUCATION CLOUD | FEXLE SERVICES
SALESFORCE EDUCATION CLOUD | FEXLE SERVICES
 
What is Artificial Intelligence?????????
What is Artificial Intelligence?????????What is Artificial Intelligence?????????
What is Artificial Intelligence?????????
 
DevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platformsDevEX - reference for building teams, processes, and platforms
DevEX - reference for building teams, processes, and platforms
 
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: BNC CataList - Tech Forum 2024
 
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQLDeveloper Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information Developers
 
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxThe Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
 
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptxMerck Moving Beyond Passwords: FIDO Paris Seminar.pptx
Merck Moving Beyond Passwords: FIDO Paris Seminar.pptx
 

End-to-end Analytics with Apache Cassandra

  • 1. End-to-end Analytics with Apache Cassandra C* #cassandra12
  • 2. Basics • CFIF/CFOF/CFRR/CFRW • BulkOutputFormat • Input locality (identical to HDFS) • Wide row, 2I, composite support • Pig, Hive, Mahout, Sqoop, Oozie, DSE Analytics C* #cassandra12
  • 3. Why Cassandra? • Excellent Hadoop capabilities built-in • Multi-datacenter support, load isolation • Operationally, order of magnitude simpler • DSE Analytics is all-in-one, simpler still • Review your requirements, do homework C* #cassandra12
  • 4. Use Cases • Trends, recommendations, reporting, etc. • Detect and fix problems in data • New realtime (or analytic) query pattern? • Backpopulate new CF with historical data C* #cassandra12
  • 5. Data Model • Consider growth patterns and analytic query patterns for your data • One-off inquiry or regular processing? • Fast growing, want only small slices? • Consider active/archive CFs • Secondary indexes for small inputs C* #cassandra12
  • 6. Miscellaneous Tips • Don’t forget about tombstones • BOP to enable range slices (Rows with keys ‘A*’ to ‘F*’) C* #cassandra12
  • 7. Cassandra + Oozie • Workflows: cohesive, nestable, scheduled • Web UI, CLI, web service • Cassandra properties in oozie job properties, and workflow.xml • Writing out to Cassandra: mapreduce.fileoutputcommitter.marksuccessfuljobs to false • DSE Analytics works with Oozie 3.2.1+ C* #cassandra12
  • 8. Cassandra + Pig • Data with validators • Pig tuples (address.name, address.value) • Data with default validator • Bag of key, value pairs (tuples) • Unmarshal with Pygmalion • Select by regex (eg ‘1369*’, ‘link*’) C* #cassandra12
  • 9. Cassandra + Pig • Output to Cassandra • Can output directly with (key, (name, value), (name, value)...) format • For tabular data, format output with Pygmalion’s ToCassandraBag • Use BulkOutputFormat (C* 1.1) C* #cassandra12
  • 10. Cassandra + Pig • Composite column support (C* 1.0.9+) • Counter support (C* 1.0.9+) • Secondary Index support for relatively small slices (C* 1.1+) • Wide row support (C* 1.1+) • Composite key support (C* 1.1.3+) C* #cassandra12
  • 11. Cassandra + Hadoop X • Example: Cassandra + CDH3 • Start with Cassandra ring • Add NN, JT, Oozie server • TaskTracker, DataNode on each node • Jobs/launching point have Cassandra info • Segregate out analytics with virtual DC C* #cassandra12
  • 12. Current/Future Work • Cassandra core Hive support (outside of Brisk) (CASSANDRA-4131) C* #cassandra12
  • 13. For More Information • Follow @CassandraHadoop on Twitter • http://wiki.apache.org/cassandra/ HadoopSupport C* #cassandra12
  • 14. Questions? C* #cassandra12

Editor's Notes

  1. \n
  2. wide row, 2I and composite key support in 1.1.\ncomposite column support in 1.0.9+\nwide row support is in both pig and hive\n
  3. \n
  4. So I have all this data...\nAll the normal use cases for Hadoop, plus some interesting use cases that are specific to things like Cassandra.\n
  5. If you are going to seriously use Cassandra with Hadoop, your analytic query patterns also have to be considered.\nFor active/archive CFs, denormalize by query - same as with RT query patterns\nYou can have lots of CFs, so no worries there\n
  6. \n
  7. OOZIE-477: hardcoded goodness if don’t want to use Oozie 3.2.1 (tiny patch)\n
  8. \n
  9. \n
  10. \n
  11. Dachis Group: in production with Cassandra + CDH3 going on 18 months.\nLaunching point may be a server from which you test or submit jobs. On that node, just put the Cassandra information in the default Hadoop conf (mapred-site.xml).\nFor tuning, just realize that Cassandra and Hadoop will share each node’s resources, but that you can scale one with the other. Both require memory, CPU, and IO.\n
  12. \n
  13. \n
  14. \n