Enviar búsqueda
Cargar
HBaseCon 2013: Integration of Apache Hive and HBase
•
38 recomendaciones
•
9,884 vistas
Cloudera, Inc.
Seguir
Presented by: Enis Söztutar (Hortonworks) and Ashtutosh Chahaun (Hortonworks)
Leer menos
Leer más
Tecnología
Diseño
Denunciar
Compartir
Denunciar
Compartir
1 de 30
Recomendados
Mar 2012 HUG: Hive with HBase
Mar 2012 HUG: Hive with HBase
Yahoo Developer Network
Integration of HIve and HBase
Integration of HIve and HBase
Hortonworks
HBaseCon 2015: Analyzing HBase Data with Apache Hive
HBaseCon 2015: Analyzing HBase Data with Apache Hive
HBaseCon
A Survey of HBase Application Archetypes
A Survey of HBase Application Archetypes
HBaseCon
Dancing with the elephant h base1_final
Dancing with the elephant h base1_final
asterix_smartplatf
Harmonizing Multi-tenant HBase Clusters for Managing Workload Diversity
Harmonizing Multi-tenant HBase Clusters for Managing Workload Diversity
HBaseCon
Data Evolution in HBase
Data Evolution in HBase
HBaseCon
HBase Read High Availability Using Timeline-Consistent Region Replicas
HBase Read High Availability Using Timeline-Consistent Region Replicas
HBaseCon
Recomendados
Mar 2012 HUG: Hive with HBase
Mar 2012 HUG: Hive with HBase
Yahoo Developer Network
Integration of HIve and HBase
Integration of HIve and HBase
Hortonworks
HBaseCon 2015: Analyzing HBase Data with Apache Hive
HBaseCon 2015: Analyzing HBase Data with Apache Hive
HBaseCon
A Survey of HBase Application Archetypes
A Survey of HBase Application Archetypes
HBaseCon
Dancing with the elephant h base1_final
Dancing with the elephant h base1_final
asterix_smartplatf
Harmonizing Multi-tenant HBase Clusters for Managing Workload Diversity
Harmonizing Multi-tenant HBase Clusters for Managing Workload Diversity
HBaseCon
Data Evolution in HBase
Data Evolution in HBase
HBaseCon
HBase Read High Availability Using Timeline-Consistent Region Replicas
HBase Read High Availability Using Timeline-Consistent Region Replicas
HBaseCon
HBaseCon 2012 | You’ve got HBase! How AOL Mail Handles Big Data
HBaseCon 2012 | You’ve got HBase! How AOL Mail Handles Big Data
Cloudera, Inc.
HBaseCon 2015: HBase and Spark
HBaseCon 2015: HBase and Spark
HBaseCon
HBase at Bloomberg: High Availability Needs for the Financial Industry
HBase at Bloomberg: High Availability Needs for the Financial Industry
HBaseCon
Meet hbase 2.0
Meet hbase 2.0
enissoz
HBaseCon 2013: Honeycomb - MySQL Backed by Apache HBase
HBaseCon 2013: Honeycomb - MySQL Backed by Apache HBase
Cloudera, Inc.
Large-scale Web Apps @ Pinterest
Large-scale Web Apps @ Pinterest
HBaseCon
HBaseCon 2013: Project Valta - A Resource Management Layer over Apache HBase
HBaseCon 2013: Project Valta - A Resource Management Layer over Apache HBase
Cloudera, Inc.
HBaseCon 2015: Apache Phoenix - The Evolution of a Relational Database Layer ...
HBaseCon 2015: Apache Phoenix - The Evolution of a Relational Database Layer ...
HBaseCon
Hadoop Demystified + MapReduce (Java and C#), Pig, and Hive Demos
Hadoop Demystified + MapReduce (Java and C#), Pig, and Hive Demos
Lester Martin
Apache Spark on Apache HBase: Current and Future
Apache Spark on Apache HBase: Current and Future
HBaseCon
HBaseCon 2015: Trafodion - Integrating Operational SQL into HBase
HBaseCon 2015: Trafodion - Integrating Operational SQL into HBase
HBaseCon
HBase Backups
HBase Backups
HBaseCon
HBaseCon 2015: Just the Basics
HBaseCon 2015: Just the Basics
HBaseCon
HBase: Just the Basics
HBase: Just the Basics
HBaseCon
Apache HBase™
Apache HBase™
Prashant Gupta
Batch is Back: Critical for Agile Application Adoption
Batch is Back: Critical for Agile Application Adoption
DataWorks Summit/Hadoop Summit
Introduction to HBase
Introduction to HBase
Byeongweon Moon
Realtime Analytics with Hadoop and HBase
Realtime Analytics with Hadoop and HBase
larsgeorge
HBaseCon 2013: Using Coprocessors to Index Columns in an Elasticsearch Cluster
HBaseCon 2013: Using Coprocessors to Index Columns in an Elasticsearch Cluster
Cloudera, Inc.
Real-Time Video Analytics Using Hadoop and HBase (HBaseCon 2013)
Real-Time Video Analytics Using Hadoop and HBase (HBaseCon 2013)
Suman Srinivasan
HBaseCon 2013: Apache HBase Table Snapshots
HBaseCon 2013: Apache HBase Table Snapshots
Cloudera, Inc.
Apache HBase Low Latency
Apache HBase Low Latency
Nick Dimiduk
Más contenido relacionado
La actualidad más candente
HBaseCon 2012 | You’ve got HBase! How AOL Mail Handles Big Data
HBaseCon 2012 | You’ve got HBase! How AOL Mail Handles Big Data
Cloudera, Inc.
HBaseCon 2015: HBase and Spark
HBaseCon 2015: HBase and Spark
HBaseCon
HBase at Bloomberg: High Availability Needs for the Financial Industry
HBase at Bloomberg: High Availability Needs for the Financial Industry
HBaseCon
Meet hbase 2.0
Meet hbase 2.0
enissoz
HBaseCon 2013: Honeycomb - MySQL Backed by Apache HBase
HBaseCon 2013: Honeycomb - MySQL Backed by Apache HBase
Cloudera, Inc.
Large-scale Web Apps @ Pinterest
Large-scale Web Apps @ Pinterest
HBaseCon
HBaseCon 2013: Project Valta - A Resource Management Layer over Apache HBase
HBaseCon 2013: Project Valta - A Resource Management Layer over Apache HBase
Cloudera, Inc.
HBaseCon 2015: Apache Phoenix - The Evolution of a Relational Database Layer ...
HBaseCon 2015: Apache Phoenix - The Evolution of a Relational Database Layer ...
HBaseCon
Hadoop Demystified + MapReduce (Java and C#), Pig, and Hive Demos
Hadoop Demystified + MapReduce (Java and C#), Pig, and Hive Demos
Lester Martin
Apache Spark on Apache HBase: Current and Future
Apache Spark on Apache HBase: Current and Future
HBaseCon
HBaseCon 2015: Trafodion - Integrating Operational SQL into HBase
HBaseCon 2015: Trafodion - Integrating Operational SQL into HBase
HBaseCon
HBase Backups
HBase Backups
HBaseCon
HBaseCon 2015: Just the Basics
HBaseCon 2015: Just the Basics
HBaseCon
HBase: Just the Basics
HBase: Just the Basics
HBaseCon
Apache HBase™
Apache HBase™
Prashant Gupta
Batch is Back: Critical for Agile Application Adoption
Batch is Back: Critical for Agile Application Adoption
DataWorks Summit/Hadoop Summit
Introduction to HBase
Introduction to HBase
Byeongweon Moon
Realtime Analytics with Hadoop and HBase
Realtime Analytics with Hadoop and HBase
larsgeorge
HBaseCon 2013: Using Coprocessors to Index Columns in an Elasticsearch Cluster
HBaseCon 2013: Using Coprocessors to Index Columns in an Elasticsearch Cluster
Cloudera, Inc.
Real-Time Video Analytics Using Hadoop and HBase (HBaseCon 2013)
Real-Time Video Analytics Using Hadoop and HBase (HBaseCon 2013)
Suman Srinivasan
La actualidad más candente
(20)
HBaseCon 2012 | You’ve got HBase! How AOL Mail Handles Big Data
HBaseCon 2012 | You’ve got HBase! How AOL Mail Handles Big Data
HBaseCon 2015: HBase and Spark
HBaseCon 2015: HBase and Spark
HBase at Bloomberg: High Availability Needs for the Financial Industry
HBase at Bloomberg: High Availability Needs for the Financial Industry
Meet hbase 2.0
Meet hbase 2.0
HBaseCon 2013: Honeycomb - MySQL Backed by Apache HBase
HBaseCon 2013: Honeycomb - MySQL Backed by Apache HBase
Large-scale Web Apps @ Pinterest
Large-scale Web Apps @ Pinterest
HBaseCon 2013: Project Valta - A Resource Management Layer over Apache HBase
HBaseCon 2013: Project Valta - A Resource Management Layer over Apache HBase
HBaseCon 2015: Apache Phoenix - The Evolution of a Relational Database Layer ...
HBaseCon 2015: Apache Phoenix - The Evolution of a Relational Database Layer ...
Hadoop Demystified + MapReduce (Java and C#), Pig, and Hive Demos
Hadoop Demystified + MapReduce (Java and C#), Pig, and Hive Demos
Apache Spark on Apache HBase: Current and Future
Apache Spark on Apache HBase: Current and Future
HBaseCon 2015: Trafodion - Integrating Operational SQL into HBase
HBaseCon 2015: Trafodion - Integrating Operational SQL into HBase
HBase Backups
HBase Backups
HBaseCon 2015: Just the Basics
HBaseCon 2015: Just the Basics
HBase: Just the Basics
HBase: Just the Basics
Apache HBase™
Apache HBase™
Batch is Back: Critical for Agile Application Adoption
Batch is Back: Critical for Agile Application Adoption
Introduction to HBase
Introduction to HBase
Realtime Analytics with Hadoop and HBase
Realtime Analytics with Hadoop and HBase
HBaseCon 2013: Using Coprocessors to Index Columns in an Elasticsearch Cluster
HBaseCon 2013: Using Coprocessors to Index Columns in an Elasticsearch Cluster
Real-Time Video Analytics Using Hadoop and HBase (HBaseCon 2013)
Real-Time Video Analytics Using Hadoop and HBase (HBaseCon 2013)
Destacado
HBaseCon 2013: Apache HBase Table Snapshots
HBaseCon 2013: Apache HBase Table Snapshots
Cloudera, Inc.
Apache HBase Low Latency
Apache HBase Low Latency
Nick Dimiduk
Integration of Hive and HBase
Integration of Hive and HBase
Hortonworks
HBase and HDFS: Understanding FileSystem Usage in HBase
HBase and HDFS: Understanding FileSystem Usage in HBase
enissoz
Hadoop Summit 2012 | Improving HBase Availability and Repair
Hadoop Summit 2012 | Improving HBase Availability and Repair
Cloudera, Inc.
Agile project management with green hopper 6 blueprints
Agile project management with green hopper 6 blueprints
Jaibeer Malik
Mapreduce over snapshots
Mapreduce over snapshots
enissoz
Sentiment Analysis Using Solr
Sentiment Analysis Using Solr
Pradeep Pujari
NoSQL HBase schema design and SQL with Apache Drill
NoSQL HBase schema design and SQL with Apache Drill
Carol McDonald
Introduction to sqoop
Introduction to sqoop
Uday Vakalapudi
hive HBase Metastore - Improving Hive with a Big Data Metadata Storage
hive HBase Metastore - Improving Hive with a Big Data Metadata Storage
DataWorks Summit/Hadoop Summit
Apache Sqoop: A Data Transfer Tool for Hadoop
Apache Sqoop: A Data Transfer Tool for Hadoop
Cloudera, Inc.
Introduction to Apache Sqoop
Introduction to Apache Sqoop
Avkash Chauhan
Introduction to Apache HBase, MapR Tables and Security
Introduction to Apache HBase, MapR Tables and Security
MapR Technologies
Apache Kylin – Cubes on Hadoop
Apache Kylin – Cubes on Hadoop
DataWorks Summit
How To Analyze Geolocation Data with Hive and Hadoop
How To Analyze Geolocation Data with Hive and Hadoop
Hortonworks
Using Cassandra with your Web Application
Using Cassandra with your Web Application
supertom
Optimizing Hive Queries
Optimizing Hive Queries
Owen O'Malley
Hadoop, Hbase and Hive- Bay area Hadoop User Group
Hadoop, Hbase and Hive- Bay area Hadoop User Group
Hadoop User Group
What Comes After The Star Schema? Dimensional Modeling For Enterprise Data Hubs
What Comes After The Star Schema? Dimensional Modeling For Enterprise Data Hubs
Cloudera, Inc.
Destacado
(20)
HBaseCon 2013: Apache HBase Table Snapshots
HBaseCon 2013: Apache HBase Table Snapshots
Apache HBase Low Latency
Apache HBase Low Latency
Integration of Hive and HBase
Integration of Hive and HBase
HBase and HDFS: Understanding FileSystem Usage in HBase
HBase and HDFS: Understanding FileSystem Usage in HBase
Hadoop Summit 2012 | Improving HBase Availability and Repair
Hadoop Summit 2012 | Improving HBase Availability and Repair
Agile project management with green hopper 6 blueprints
Agile project management with green hopper 6 blueprints
Mapreduce over snapshots
Mapreduce over snapshots
Sentiment Analysis Using Solr
Sentiment Analysis Using Solr
NoSQL HBase schema design and SQL with Apache Drill
NoSQL HBase schema design and SQL with Apache Drill
Introduction to sqoop
Introduction to sqoop
hive HBase Metastore - Improving Hive with a Big Data Metadata Storage
hive HBase Metastore - Improving Hive with a Big Data Metadata Storage
Apache Sqoop: A Data Transfer Tool for Hadoop
Apache Sqoop: A Data Transfer Tool for Hadoop
Introduction to Apache Sqoop
Introduction to Apache Sqoop
Introduction to Apache HBase, MapR Tables and Security
Introduction to Apache HBase, MapR Tables and Security
Apache Kylin – Cubes on Hadoop
Apache Kylin – Cubes on Hadoop
How To Analyze Geolocation Data with Hive and Hadoop
How To Analyze Geolocation Data with Hive and Hadoop
Using Cassandra with your Web Application
Using Cassandra with your Web Application
Optimizing Hive Queries
Optimizing Hive Queries
Hadoop, Hbase and Hive- Bay area Hadoop User Group
Hadoop, Hbase and Hive- Bay area Hadoop User Group
What Comes After The Star Schema? Dimensional Modeling For Enterprise Data Hubs
What Comes After The Star Schema? Dimensional Modeling For Enterprise Data Hubs
Similar a HBaseCon 2013: Integration of Apache Hive and HBase
HBase for Architects
HBase for Architects
Nick Dimiduk
Hive and Hbase inegration
Hive and Hbase inegration
Humoyun Ahmedov
Apache HBase + Spark: Leveraging your Non-Relational Datastore in Batch and S...
Apache HBase + Spark: Leveraging your Non-Relational Datastore in Batch and S...
DataWorks Summit/Hadoop Summit
Yahoo! Hack Europe Workshop
Yahoo! Hack Europe Workshop
Hortonworks
Hortonworks Technical Workshop: HBase and Apache Phoenix
Hortonworks Technical Workshop: HBase and Apache Phoenix
Hortonworks
Apache Phoenix + Apache HBase
Apache Phoenix + Apache HBase
DataWorks Summit/Hadoop Summit
Apache Phoenix and Apache HBase: An Enterprise Grade Data Warehouse
Apache Phoenix and Apache HBase: An Enterprise Grade Data Warehouse
Josh Elser
Hbase mhug 2015
Hbase mhug 2015
Joseph Niemiec
Whats newinhive090hadoopsummit2012bof
Whats newinhive090hadoopsummit2012bof
Gopi Krishna
Meet HBase 2.0 and Phoenix 5.0
Meet HBase 2.0 and Phoenix 5.0
DataWorks Summit
Hive 3 a new horizon
Hive 3 a new horizon
Artem Ervits
Meet HBase 2.0 and Phoenix 5.0
Meet HBase 2.0 and Phoenix 5.0
Ankit Singhal
Techincal Talk Hbase-Ditributed,no-sql database
Techincal Talk Hbase-Ditributed,no-sql database
Rishabh Dugar
La big datacamp2014_vikram_dixit
La big datacamp2014_vikram_dixit
Data Con LA
Apache HBase Internals you hoped you Never Needed to Understand
Apache HBase Internals you hoped you Never Needed to Understand
Josh Elser
Hive 3.0 - HDPの最新バージョンで実現する新機能とパフォーマンス改善
Hive 3.0 - HDPの最新バージョンで実現する新機能とパフォーマンス改善
HortonworksJapan
Hive Quick Start Tutorial
Hive Quick Start Tutorial
Carl Steinbach
Stinger Initiative - Deep Dive
Stinger Initiative - Deep Dive
Hortonworks
Hive 3 - a new horizon
Hive 3 - a new horizon
Thejas Nair
מיכאל
מיכאל
sqlserver.co.il
Similar a HBaseCon 2013: Integration of Apache Hive and HBase
(20)
HBase for Architects
HBase for Architects
Hive and Hbase inegration
Hive and Hbase inegration
Apache HBase + Spark: Leveraging your Non-Relational Datastore in Batch and S...
Apache HBase + Spark: Leveraging your Non-Relational Datastore in Batch and S...
Yahoo! Hack Europe Workshop
Yahoo! Hack Europe Workshop
Hortonworks Technical Workshop: HBase and Apache Phoenix
Hortonworks Technical Workshop: HBase and Apache Phoenix
Apache Phoenix + Apache HBase
Apache Phoenix + Apache HBase
Apache Phoenix and Apache HBase: An Enterprise Grade Data Warehouse
Apache Phoenix and Apache HBase: An Enterprise Grade Data Warehouse
Hbase mhug 2015
Hbase mhug 2015
Whats newinhive090hadoopsummit2012bof
Whats newinhive090hadoopsummit2012bof
Meet HBase 2.0 and Phoenix 5.0
Meet HBase 2.0 and Phoenix 5.0
Hive 3 a new horizon
Hive 3 a new horizon
Meet HBase 2.0 and Phoenix 5.0
Meet HBase 2.0 and Phoenix 5.0
Techincal Talk Hbase-Ditributed,no-sql database
Techincal Talk Hbase-Ditributed,no-sql database
La big datacamp2014_vikram_dixit
La big datacamp2014_vikram_dixit
Apache HBase Internals you hoped you Never Needed to Understand
Apache HBase Internals you hoped you Never Needed to Understand
Hive 3.0 - HDPの最新バージョンで実現する新機能とパフォーマンス改善
Hive 3.0 - HDPの最新バージョンで実現する新機能とパフォーマンス改善
Hive Quick Start Tutorial
Hive Quick Start Tutorial
Stinger Initiative - Deep Dive
Stinger Initiative - Deep Dive
Hive 3 - a new horizon
Hive 3 - a new horizon
מיכאל
מיכאל
Más de Cloudera, Inc.
Partner Briefing_January 25 (FINAL).pptx
Partner Briefing_January 25 (FINAL).pptx
Cloudera, Inc.
Cloudera Data Impact Awards 2021 - Finalists
Cloudera Data Impact Awards 2021 - Finalists
Cloudera, Inc.
2020 Cloudera Data Impact Awards Finalists
2020 Cloudera Data Impact Awards Finalists
Cloudera, Inc.
Edc event vienna presentation 1 oct 2019
Edc event vienna presentation 1 oct 2019
Cloudera, Inc.
Machine Learning with Limited Labeled Data 4/3/19
Machine Learning with Limited Labeled Data 4/3/19
Cloudera, Inc.
Data Driven With the Cloudera Modern Data Warehouse 3.19.19
Data Driven With the Cloudera Modern Data Warehouse 3.19.19
Cloudera, Inc.
Introducing Cloudera DataFlow (CDF) 2.13.19
Introducing Cloudera DataFlow (CDF) 2.13.19
Cloudera, Inc.
Introducing Cloudera Data Science Workbench for HDP 2.12.19
Introducing Cloudera Data Science Workbench for HDP 2.12.19
Cloudera, Inc.
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19
Cloudera, Inc.
Leveraging the cloud for analytics and machine learning 1.29.19
Leveraging the cloud for analytics and machine learning 1.29.19
Cloudera, Inc.
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19
Cloudera, Inc.
Leveraging the Cloud for Big Data Analytics 12.11.18
Leveraging the Cloud for Big Data Analytics 12.11.18
Cloudera, Inc.
Modern Data Warehouse Fundamentals Part 3
Modern Data Warehouse Fundamentals Part 3
Cloudera, Inc.
Modern Data Warehouse Fundamentals Part 2
Modern Data Warehouse Fundamentals Part 2
Cloudera, Inc.
Modern Data Warehouse Fundamentals Part 1
Modern Data Warehouse Fundamentals Part 1
Cloudera, Inc.
Extending Cloudera SDX beyond the Platform
Extending Cloudera SDX beyond the Platform
Cloudera, Inc.
Federated Learning: ML with Privacy on the Edge 11.15.18
Federated Learning: ML with Privacy on the Edge 11.15.18
Cloudera, Inc.
Analyst Webinar: Doing a 180 on Customer 360
Analyst Webinar: Doing a 180 on Customer 360
Cloudera, Inc.
Build a modern platform for anti-money laundering 9.19.18
Build a modern platform for anti-money laundering 9.19.18
Cloudera, Inc.
Introducing the data science sandbox as a service 8.30.18
Introducing the data science sandbox as a service 8.30.18
Cloudera, Inc.
Más de Cloudera, Inc.
(20)
Partner Briefing_January 25 (FINAL).pptx
Partner Briefing_January 25 (FINAL).pptx
Cloudera Data Impact Awards 2021 - Finalists
Cloudera Data Impact Awards 2021 - Finalists
2020 Cloudera Data Impact Awards Finalists
2020 Cloudera Data Impact Awards Finalists
Edc event vienna presentation 1 oct 2019
Edc event vienna presentation 1 oct 2019
Machine Learning with Limited Labeled Data 4/3/19
Machine Learning with Limited Labeled Data 4/3/19
Data Driven With the Cloudera Modern Data Warehouse 3.19.19
Data Driven With the Cloudera Modern Data Warehouse 3.19.19
Introducing Cloudera DataFlow (CDF) 2.13.19
Introducing Cloudera DataFlow (CDF) 2.13.19
Introducing Cloudera Data Science Workbench for HDP 2.12.19
Introducing Cloudera Data Science Workbench for HDP 2.12.19
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19
Leveraging the cloud for analytics and machine learning 1.29.19
Leveraging the cloud for analytics and machine learning 1.29.19
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19
Leveraging the Cloud for Big Data Analytics 12.11.18
Leveraging the Cloud for Big Data Analytics 12.11.18
Modern Data Warehouse Fundamentals Part 3
Modern Data Warehouse Fundamentals Part 3
Modern Data Warehouse Fundamentals Part 2
Modern Data Warehouse Fundamentals Part 2
Modern Data Warehouse Fundamentals Part 1
Modern Data Warehouse Fundamentals Part 1
Extending Cloudera SDX beyond the Platform
Extending Cloudera SDX beyond the Platform
Federated Learning: ML with Privacy on the Edge 11.15.18
Federated Learning: ML with Privacy on the Edge 11.15.18
Analyst Webinar: Doing a 180 on Customer 360
Analyst Webinar: Doing a 180 on Customer 360
Build a modern platform for anti-money laundering 9.19.18
Build a modern platform for anti-money laundering 9.19.18
Introducing the data science sandbox as a service 8.30.18
Introducing the data science sandbox as a service 8.30.18
Último
Vector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector Databases
Zilliz
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
Fwdays
My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024
The Digital Insurer
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
Fwdays
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial Buildings
Memoori
The Future of Software Development - Devin AI Innovative Approach.pdf
The Future of Software Development - Devin AI Innovative Approach.pdf
SeasiaInfotech2
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!
Manik S Magar
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Safe Software
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project Setup
Florian Wilhelm
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024
Enterprise Knowledge
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
ScyllaDB
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):
comworks
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
gvaughan
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
Fwdays
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
null - The Open Security Community
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024
Lorenzo Miniero
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck Presentation
Slibray Presentation
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
carlostorres15106
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
UiPathCommunity
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Patryk Bandurski
Último
(20)
Vector Databases 101 - An introduction to the world of Vector Databases
Vector Databases 101 - An introduction to the world of Vector Databases
"ML in Production",Oleksandr Bagan
"ML in Production",Oleksandr Bagan
My INSURER PTE LTD - Insurtech Innovation Award 2024
My INSURER PTE LTD - Insurtech Innovation Award 2024
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
"Federated learning: out of reach no matter how close",Oleksandr Lapshyn
AI as an Interface for Commercial Buildings
AI as an Interface for Commercial Buildings
The Future of Software Development - Devin AI Innovative Approach.pdf
The Future of Software Development - Devin AI Innovative Approach.pdf
Anypoint Exchange: It’s Not Just a Repo!
Anypoint Exchange: It’s Not Just a Repo!
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Beyond Boundaries: Leveraging No-Code Solutions for Industry Innovation
Streamlining Python Development: A Guide to a Modern Project Setup
Streamlining Python Development: A Guide to a Modern Project Setup
Designing IA for AI - Information Architecture Conference 2024
Designing IA for AI - Information Architecture Conference 2024
Developer Data Modeling Mistakes: From Postgres to NoSQL
Developer Data Modeling Mistakes: From Postgres to NoSQL
CloudStudio User manual (basic edition):
CloudStudio User manual (basic edition):
WordPress Websites for Engineers: Elevate Your Brand
WordPress Websites for Engineers: Elevate Your Brand
"Debugging python applications inside k8s environment", Andrii Soldatenko
"Debugging python applications inside k8s environment", Andrii Soldatenko
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
E-Vehicle_Hacking_by_Parul Sharma_null_owasp.pptx
SIP trunking in Janus @ Kamailio World 2024
SIP trunking in Janus @ Kamailio World 2024
Connect Wave/ connectwave Pitch Deck Presentation
Connect Wave/ connectwave Pitch Deck Presentation
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Kotlin Multiplatform & Compose Multiplatform - Starter kit for pragmatics
Dev Dives: Streamline document processing with UiPath Studio Web
Dev Dives: Streamline document processing with UiPath Studio Web
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
Integration and Automation in Practice: CI/CD in Mule Integration and Automat...
HBaseCon 2013: Integration of Apache Hive and HBase
1.
© Hortonworks Inc.
2011 Integration of Apache Hive and HBase Enis Soztutar enis [at] apache [dot] org Page 1 Ashutosh Chauhan hashutosh [at] apache [dot] org
2.
© Hortonworks Inc.
2011 About Us Page 2 Architecting the Future of Big Data Enis Soztutar • In the Hadoop space since 2007 • Committer and PMC Member in Apache HBase and Hadoop • Twitter: @enissoz Ashutosh Chauhan • In the Hadoop space since 2009 • Committer and PMC Member in Apache Hive and Pig
3.
© Hortonworks Inc.
2011 Agenda Page 3 Architecting the Future of Big Data • Overview of Hive • Hive + HBase Features and Improvements • Future of Hive and HBase • Q&A
4.
© Hortonworks Inc.
2011 Apache Hive Overview • Apache Hive is a data warehouse system for Hadoop • SQL-like query language called HiveQL • Built for PB scale data • Main purpose is analysis and ad hoc querying • Database / table / partition / bucket – DDL Operations • SQL Types + Complex Types (ARRAY, MAP, etc) • Very extensible • Not for : small data sets, OLTP Page 4 Architecting the Future of Big Data
5.
© Hortonworks Inc.
2011 Apache Hive Architecture Page 5 Architecting the Future of Big Data Metastore RDBMS Hive Thrift Server Driver CLI JDBC/ODBC Hive Web Interface HDFS MapReduce Execution Parser Planner Optimizer M S C l i e n t
6.
© Hortonworks Inc.
2011 Hive + HBase Features and Improvements Architecting the Future of Big Data Page 6
7.
© Hortonworks Inc.
2011 Hive + HBase Motivation • Hive over HDFS and HBase has different characteristics – Batch Online – Structured vs Unstructured – Analysts Programmers • Hive datawarehouses on HDFS are – Long ETL times – Access to real time data • Analyzing HBase data with MapReduce requires custom coding • Hive and SQL are already known by many analysts Page 7 Architecting the Future of Big Data
8.
© Hortonworks Inc.
2011 Use Case 1: HBase as ETL Data Sink Page 8 Architecting the Future of Big Data From HUG - Hive/HBase Integration or, MaybeSQL? April 2010 John Sichi Facebook http://www.slideshare.net/hadoopusergroup/hive-h-basehadoopapr2010 HDFS Tables INSERT … SELECT …! FROM … ! HBase Online Queries
9.
© Hortonworks Inc.
2011 Use Case 2: HBase as Data Source Page 9 Architecting the Future of Big Data From HUG - Hive/HBase Integration or, MaybeSQL? April 2010 John Sichi Facebook http://www.slideshare.net/hadoopusergroup/hive-h-basehadoopapr2010 HDFS Tables SELECT … JOIN …! GROUP BY … ! HBase Query Result
10.
© Hortonworks Inc.
2011 Use Case 3: Low Latency Warehouse Page 10 Architecting the Future of Big Data From HUG - Hive/HBase Integration or, MaybeSQL? April 2010 John Sichi Facebook http://www.slideshare.net/hadoopusergroup/hive-h-basehadoopapr2010 HDFS Tables HBase Continuous Updates HIVE QUERIES! Periodic Dump
11.
© Hortonworks Inc.
2011 Hive + HBase Example (HBase table) hbase(main):001:0> create 'short_urls', {NAME => 'u'}, {NAME=>'s'} hbase(main):014:0> scan 'short_urls' ROW COLUMN+CELL bit.ly/aaaa column=s:hits, value=100 bit.ly/aaaa column=u:url, value=hbase.apache.org/ bit.ly/abcd column=s:hits, value=123 bit.ly/abcd column=u:url, value=example.com/foo Page 11 Architecting the Future of Big Data
12.
© Hortonworks Inc.
2011 Hive + HBase Example (Hive table) CREATE TABLE short_urls( short_url string, url string, hit_count int ) STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler' WITH SERDEPROPERTIES ("hbase.columns.mapping" = ":key, u:url, s:hits") TBLPROPERTIES ("hbase.table.name" = ”short_urls"); Page 12 Architecting the Future of Big Data
13.
© Hortonworks Inc.
2011 Storage Handler • Hive defines HiveStorageHandler class for different storage backends: HBase/ Cassandra / MongoDB/ etc • Storage Handler has hooks for – Getting input / output formats – Meta data operations hook: CREATE TABLE, DROP TABLE, etc • Storage Handler is a table level concept – Does not support Hive partitions, and buckets Page 13 Architecting the Future of Big Data
14.
© Hortonworks Inc.
2011 Apache Hive + HBase Architecture Page 14 Architecting the Future of Big Data Metastore RDBMS Hive Thrift Server Driver CLI Hive Web Interface HDFS MapReduce Execution Parser Planner Optimizer M S C l i e n t HBase StorageHandler
15.
© Hortonworks Inc.
2011 Hive + HBase Integration • For Input/OutputFormat, getSplits(), etc underlying HBase classes are used • Column selection and certain filters can be pushed down • HBase tables can be used with other(Hadoop native) tables and SQL constructs • Hive DDL operations are converted to HBase DDL operations via the client hook. – All operations are performed by the client – No two phase commit Page 15 Architecting the Future of Big Data
16.
© Hortonworks Inc.
2011 Schema / Type Mapping Architecting the Future of Big Data Page 16
17.
© Hortonworks Inc.
2011 Schema Mapping • Hive table + columns + column types <=> HBase table + column families (+ column qualifiers) • Every field in Hive table is mapped to either – The table key (using :key as selector) – A column family (cf:) -> MAP fields in Hive – A column (cf:cq) • Hive table does not need to include all columns in Hbase Page 17 Architecting the Future of Big Data
18.
© Hortonworks Inc.
2011 Schema Mapping - Example Page 18 Architecting the Future of Big Data CREATE TABLE short_urls( short_url string, url string, hit_count int, props, map<string,string> ) WITH SERDEPROPERTIES ("hbase.columns.mapping" = ":key, u:url, s:hits, p:")
19.
© Hortonworks Inc.
2011 Schema Mapping - Example Page 19 Architecting the Future of Big Data CREATE TABLE short_urls( short_url string, url string, hit_count int, props map<string,string> ) WITH SERDEPROPERTIES ("hbase.columns.mapping" = ":key, u:url, s:hits, p:")
20.
© Hortonworks Inc.
2011 Type Mapping • Added in Hive (0.9.0) • Previously all types were being converted to strings in HBase • Hive has: – Primitive types: INT, STRING, BINARY, DOUBLE etc – ARRAY<Type> – MAP<PrimitiveType, Type> – STRUCT<a:INT, b:STRING, c:STRING> • HBase does not have types – Bytes.toBytes() Page 20 Architecting the Future of Big Data
21.
© Hortonworks Inc.
2011 Type Mapping • Table level property "hbase.table.default.storage.type” = “binary” • Type mapping can be given per column after # – Any prefix of “binary” , eg u:url#b – Any prefix of “string” , eg u:url#s – The dash char “-” , eg u:url#- Page 21
22.
© Hortonworks Inc.
2011 Type Mapping - Example Page 22 Architecting the Future of Big Data CREATE TABLE short_urls( short_url string, url string, hit_count int, props, map<string,string> ) WITH SERDEPROPERTIES ("hbase.columns.mapping" = ":key#b,u:url#b,s:hits#b,p:#s")
23.
© Hortonworks Inc.
2011 Type Mapping • If the type is not a primitive or Map, it is converted to a JSON string and serialized • Still a few rough edges for schema and type mapping: – No support for DECIMAL, BINARY Hive types – No mapping of HBase timestamp (can only provide put timestamp) – No arbitrary mapping of Structs / Arrays into HBase schema Page 23 Architecting the Future of Big Data
24.
© Hortonworks Inc.
2011 Bulk Load • Steps to bulk load: – Sample source data for range partitioning – Save sampling results to a file – Run CLUSTER BY query using HiveHFileOutputFormat and TotalOrderPartitioner – Import Hfiles into HBase table • Ideal setup should be SET hive.hbase.bulk=true INSERT OVERWRITE TABLE web_table SELECT …. Page 24 Architecting the Future of Big Data
25.
© Hortonworks Inc.
2011 Filter Pushdown Architecting the Future of Big Data Page 25
26.
© Hortonworks Inc.
2011 Filter Pushdown • Idea is to pass down filter expressions to the storage layer to minimize scanned data • To access indexes at hdfs or hbase • Example: CREATE EXTERNAL TABLE users (userid LONG, email STRING, … ) STORED BY 'org.apache.hadoop.hive.hbase.HBaseStorageHandler’ WITH SERDEPROPERTIES ("hbase.columns.mapping" = ":key,…") SELECT ... FROM users WHERE userid > 1000000 and email LIKE ‘%@gmail.com’; -> scan.setStartRow(Bytes.toBytes(1000000)) Page 26 Architecting the Future of Big Data
27.
© Hortonworks Inc.
2011 Filter Decomposition • Optimizer pushes down the predicates to the query plan • Storage handlers can negotiate with the Hive optimizer to decompose the filter x > 3 AND upper(y) = 'XYZ’ • Handle x > 3, send upper(y) = ’XYZ’ as residual for Hive • Works with: key = 3, key > 3, etc key > 3 AND key < 100 • Only works against constant expressions Page 27 Architecting the Future of Big Data
28.
© Hortonworks Inc.
2011 Future of Hive + HBase • Improve on schema / type mapping • Fully secure Hive deployment options • HBase bulk import improvements • Filter pushdown: non key column filters • Sortable signed numeric types in HBase • Use HBase’s new typing API’s (upcoming in HBase) • Integration with Phoenix / extract common modules, hbase- sql ? Page 28 Architecting the Future of Big Data
29.
© Hortonworks Inc.
2011 References • Type mapping / Filter Pushdown – https://issues.apache.org/jira/browse/HIVE-1634 – https://issues.apache.org/jira/browse/HIVE-1226 – https://issues.apache.org/jira/browse/HIVE-1643 – https://issues.apache.org/jira/browse/HIVE-2815 Page 29 Architecting the Future of Big Data
30.
© Hortonworks Inc.
2011 Thanks Questions? Architecting the Future of Big Data Page 30