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In this webinar, we'll see how to use Spark to process data from various sources in R and Python and how new tools like Spark SQL and data frames make it easy to perform structured data processing.
Data processing with spark in r & python
Data processing with spark in r & python
Maloy Manna, PMP®
HadoopDB in Action: Building Real World Applications
HadoopDB in Action
HadoopDB in Action
Tilani Gunawardena PhD(UNIBAS), BSc(Pera), FHEA(UK), CEng, MIESL
Slides from VLDB presentation of the DocumentDB indexing paper. (Link to the original paper -> http://www.vldb.org/pvldb/vol8/p1668-shukla.pdf)
Schema Agnostic Indexing with Azure DocumentDB
Schema Agnostic Indexing with Azure DocumentDB
Dharma Shukla
ETL Practices for Better or Worse
ETL Practices for Better or Worse
Eric Sun
Graphs are a very popular data structure to store relations like friendship or web pages and their links. Therefore graph databases have become popular recently and some of them even allow sharding, i.e. automatic distribution of the data across multiple machines. On the other hand, very computation-intensive algorithms for graphs are known and used in practice, and they often access very large data sets, which leads to heavy communication loads. Therefore, it is an obvious idea to run such graph algorithms on the database servers, close to the data, making use of the computational power of the storage nodes. Google's Pregel framework allows to implement a lot of graph algorithms in a general system and plays a role similar to the map-reduce skeleton, but for graphs. In this talk I will explain the framework and describe its implementation in the multi-model database ArangoDB.
Processing large-scale graphs with Google Pregel
Processing large-scale graphs with Google Pregel
Max Neunhöffer
Enterprise Data Warehouse and Hadoop
EDW and Hadoop
EDW and Hadoop
Tapio Vaattanen
What to look for when choosing row based or columnar database for a data warehouse system.
Row or Columnar Database
Row or Columnar Database
Biju Nair
data stage basic material
data stage-material
data stage-material
Rajesh Kv
Recomendados
In this webinar, we'll see how to use Spark to process data from various sources in R and Python and how new tools like Spark SQL and data frames make it easy to perform structured data processing.
Data processing with spark in r & python
Data processing with spark in r & python
Maloy Manna, PMP®
HadoopDB in Action: Building Real World Applications
HadoopDB in Action
HadoopDB in Action
Tilani Gunawardena PhD(UNIBAS), BSc(Pera), FHEA(UK), CEng, MIESL
Slides from VLDB presentation of the DocumentDB indexing paper. (Link to the original paper -> http://www.vldb.org/pvldb/vol8/p1668-shukla.pdf)
Schema Agnostic Indexing with Azure DocumentDB
Schema Agnostic Indexing with Azure DocumentDB
Dharma Shukla
ETL Practices for Better or Worse
ETL Practices for Better or Worse
Eric Sun
Graphs are a very popular data structure to store relations like friendship or web pages and their links. Therefore graph databases have become popular recently and some of them even allow sharding, i.e. automatic distribution of the data across multiple machines. On the other hand, very computation-intensive algorithms for graphs are known and used in practice, and they often access very large data sets, which leads to heavy communication loads. Therefore, it is an obvious idea to run such graph algorithms on the database servers, close to the data, making use of the computational power of the storage nodes. Google's Pregel framework allows to implement a lot of graph algorithms in a general system and plays a role similar to the map-reduce skeleton, but for graphs. In this talk I will explain the framework and describe its implementation in the multi-model database ArangoDB.
Processing large-scale graphs with Google Pregel
Processing large-scale graphs with Google Pregel
Max Neunhöffer
Enterprise Data Warehouse and Hadoop
EDW and Hadoop
EDW and Hadoop
Tapio Vaattanen
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Row or Columnar Database
Row or Columnar Database
Biju Nair
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data stage-material
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Rajesh Kv
This presentation explains why NoSQL databases came over SQL databases although SQL databases has been successfully technology for more than twenty years. Moreover, This presentation discuses the characteristics and classifications of NoSQL databases. Finally, These slides cover four NoSQL databases briefly.
NoSQL databases
NoSQL databases
Meshal Albeedhani
Apache Spark is a fast and general engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Spark core
Spark core
Prashant Gupta
This presentation contains the introduction to NOSQL databases, it's types with examples, differentiation with 40 year old relational database management system, it's usage, why and we should use it.
Introduction to NOSQL databases
Introduction to NOSQL databases
Ashwani Kumar
Sql server 2012 dba online training
Sql server 2012 dba online training
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Concepts of Apache Hive in Big Data. contains: what is hive? why hive? how hive works hive Architecture data models in hive pros and cons of hive hiveql pig vs hive
Apache Hive
Apache Hive
tusharsinghal58
Quantopix Analytics System (QAS) is a platform for data analysis and for developing analytics apps. QAS connects to most of Enterprise Class SQL Database Managers and provides instant capabilities to build datasets and data groups from disjointed databases to prepare it for analysis. QAS provides a comprehensive and extensible set of statistical functions to instantly profile your data. It comes with advanced yet easy to invoke charting capabilities for interactively visualizing the data as well as generating static chart images. QAS comes with a built-in PHP and JavaScript App builder to help users extend the system functions and create custom applications for specific business needs. Rapid App Development QAS lets you build analysis Apps within minutes using a powerful set of APIs for data manipulation including time-series and text classifications. QAS includes a comprehensive list of math, statistics, and matrix manipulation functions for numeric analysis. The APIs include Multiple Linear Regression model generation, k-means clustering model generation, and a Predict API for both models.
Quantopix analytics system (qas)
Quantopix analytics system (qas)
Al Sabawi
In this session you will learn: HIVE Overview Working of Hive Hive Tables Hive - Data Types Complex Types Hive Database HiveQL - Select-Joins Different Types of Join Partitions Buckets Strict Mode in Hive Like and Rlike in Hive Hive UDF For more information, visit: https://www.mindsmapped.com/courses/big-data-hadoop/hadoop-developer-training-a-step-by-step-tutorial/
Session 14 - Hive
Session 14 - Hive
AnandMHadoop
An introduction to HBase, its components and brief overview of its architecture.
Introduction To HBase
Introduction To HBase
Anil Gupta
Marcel Kornacker presentation from Strata + Hadoop World NYC 2014
From Raw Data to Analytics with No ETL
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Cloudera, Inc.
Spark auf Hadoop ist hochskalierbar. Cloud Computing ist hochskalierbar. R, die erweiterbare Open Source Data Science Software, eher nicht. Aber was passiert, wenn wir Spark auf Hadoop, Cloud Computing und den Microsoft R Server zu einer skalierbaren Data Science-Plattform zusammenfügen? Stellen Sie sich vor wie es sein könnte, wenn Sie das Erkunden, Transformieren und Modellieren von Daten in jeder beliebigen Größe aus Ihrer Lieblings-R-Umgebung durchführen könnten. Stellen Sie sich nun vor, wie man anschließend die erzeugten Modelle - mit wenigen Klicks - als skalierbare, cloud basierte Web-Services-API bereitstellt. In dieser Session zeigt Sascha Dittmann, wie Sie Ihren R-Code, tausende von Open-Source-R-Pakete sowie die verteilte Implementierungen der beliebtesten Maschine-Learning-Algorithmen nutzen können, um genau dies umzusetzen. Dabei zeigt er wie man ein HDInsight Spark-Cluster inkl. eines Microsoft R Server-Clusters erstellt, sowie das daraus entstandene Model im SQL Server oder als swagger-based API für Anwendungsentwickler bereitstellt.
Microsoft R - Data Science at Scale
Microsoft R - Data Science at Scale
Sascha Dittmann
Hadoop MapReduce and yarn frame work- unit 5 - BCA Couse work
Hadoop mapreduce and yarn frame work- unit5
Hadoop mapreduce and yarn frame work- unit5
RojaT4
Video :- https://youtu.be/RAObZZULjxU
Handling the growth of data
Handling the growth of data
Piyush Katariya
Comparison between RDBMS, Hadoop and Apache based on parameters like Data Variety, Data Storage, Querying, Cost, Schema, Speed, Data Objects, Hardware profile, and Used cases. It also mentions benefits and limitations.
Comparison - RDBMS vs Hadoop vs Apache
Comparison - RDBMS vs Hadoop vs Apache
SandeepTaksande
Presentation to Nugo company about Microsoft Azure, related Big Data technologies and how Azure can change their current environment
Digital Transformation with Microsoft Azure
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Luan Moreno Medeiros Maciel
ArangoDB is a universal open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient sql-like query language or JavaScript/Ruby extensions. The video is also available online: http://2012.nosql-matters.org/bcn/speakers/
Introduction to ArangoDB (nosql matters Barcelona 2012)
Introduction to ArangoDB (nosql matters Barcelona 2012)
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Growth of big datasets Introduction to Apache Hadoop and Spark for developing applications Components of Hadoop, HDFS, MapReduce and HBase Capabilities of Spark and the differences from a typical MapReduce solution Some Spark use cases for data analysis
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Introduction to cassandra database
Appache Cassandra
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nehabsairam
In this talk we present the term polyglot persistence, give a brief introduction to the world of NoSQL database and point out the benefits and costs of polyglot persistence. Thereafter we present the idea of a multi-model database that reduces the costs for polyglot persistence but keeps its benefits. Next up we present ArangoDB as a Multi-Model database
Multi model-databases
Multi model-databases
ArangoDB Database
SQL Server Workshop for Developers - Visual Studio Live! NY 2012
SQL Server Workshop for Developers - Visual Studio Live! NY 2012
Andrew Brust
The presentation contains details on Hive architecture and its job execution mechanisms.
Hive
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Manas Nayak
This presentation is for knowledge sharing.
Emerging database technology multimedia database
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Salama Al Busaidi
Python CodeLabs - Google App Engine - Python http://eueung.github.io/EL6240/gae
Google app engine python
Google app engine python
Eueung Mulyana
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This presentation explains why NoSQL databases came over SQL databases although SQL databases has been successfully technology for more than twenty years. Moreover, This presentation discuses the characteristics and classifications of NoSQL databases. Finally, These slides cover four NoSQL databases briefly.
NoSQL databases
NoSQL databases
Meshal Albeedhani
Apache Spark is a fast and general engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Spark core
Spark core
Prashant Gupta
This presentation contains the introduction to NOSQL databases, it's types with examples, differentiation with 40 year old relational database management system, it's usage, why and we should use it.
Introduction to NOSQL databases
Introduction to NOSQL databases
Ashwani Kumar
Sql server 2012 dba online training
Sql server 2012 dba online training
sqlmasters
Concepts of Apache Hive in Big Data. contains: what is hive? why hive? how hive works hive Architecture data models in hive pros and cons of hive hiveql pig vs hive
Apache Hive
Apache Hive
tusharsinghal58
Quantopix Analytics System (QAS) is a platform for data analysis and for developing analytics apps. QAS connects to most of Enterprise Class SQL Database Managers and provides instant capabilities to build datasets and data groups from disjointed databases to prepare it for analysis. QAS provides a comprehensive and extensible set of statistical functions to instantly profile your data. It comes with advanced yet easy to invoke charting capabilities for interactively visualizing the data as well as generating static chart images. QAS comes with a built-in PHP and JavaScript App builder to help users extend the system functions and create custom applications for specific business needs. Rapid App Development QAS lets you build analysis Apps within minutes using a powerful set of APIs for data manipulation including time-series and text classifications. QAS includes a comprehensive list of math, statistics, and matrix manipulation functions for numeric analysis. The APIs include Multiple Linear Regression model generation, k-means clustering model generation, and a Predict API for both models.
Quantopix analytics system (qas)
Quantopix analytics system (qas)
Al Sabawi
In this session you will learn: HIVE Overview Working of Hive Hive Tables Hive - Data Types Complex Types Hive Database HiveQL - Select-Joins Different Types of Join Partitions Buckets Strict Mode in Hive Like and Rlike in Hive Hive UDF For more information, visit: https://www.mindsmapped.com/courses/big-data-hadoop/hadoop-developer-training-a-step-by-step-tutorial/
Session 14 - Hive
Session 14 - Hive
AnandMHadoop
An introduction to HBase, its components and brief overview of its architecture.
Introduction To HBase
Introduction To HBase
Anil Gupta
Marcel Kornacker presentation from Strata + Hadoop World NYC 2014
From Raw Data to Analytics with No ETL
From Raw Data to Analytics with No ETL
Cloudera, Inc.
Spark auf Hadoop ist hochskalierbar. Cloud Computing ist hochskalierbar. R, die erweiterbare Open Source Data Science Software, eher nicht. Aber was passiert, wenn wir Spark auf Hadoop, Cloud Computing und den Microsoft R Server zu einer skalierbaren Data Science-Plattform zusammenfügen? Stellen Sie sich vor wie es sein könnte, wenn Sie das Erkunden, Transformieren und Modellieren von Daten in jeder beliebigen Größe aus Ihrer Lieblings-R-Umgebung durchführen könnten. Stellen Sie sich nun vor, wie man anschließend die erzeugten Modelle - mit wenigen Klicks - als skalierbare, cloud basierte Web-Services-API bereitstellt. In dieser Session zeigt Sascha Dittmann, wie Sie Ihren R-Code, tausende von Open-Source-R-Pakete sowie die verteilte Implementierungen der beliebtesten Maschine-Learning-Algorithmen nutzen können, um genau dies umzusetzen. Dabei zeigt er wie man ein HDInsight Spark-Cluster inkl. eines Microsoft R Server-Clusters erstellt, sowie das daraus entstandene Model im SQL Server oder als swagger-based API für Anwendungsentwickler bereitstellt.
Microsoft R - Data Science at Scale
Microsoft R - Data Science at Scale
Sascha Dittmann
Hadoop MapReduce and yarn frame work- unit 5 - BCA Couse work
Hadoop mapreduce and yarn frame work- unit5
Hadoop mapreduce and yarn frame work- unit5
RojaT4
Video :- https://youtu.be/RAObZZULjxU
Handling the growth of data
Handling the growth of data
Piyush Katariya
Comparison between RDBMS, Hadoop and Apache based on parameters like Data Variety, Data Storage, Querying, Cost, Schema, Speed, Data Objects, Hardware profile, and Used cases. It also mentions benefits and limitations.
Comparison - RDBMS vs Hadoop vs Apache
Comparison - RDBMS vs Hadoop vs Apache
SandeepTaksande
Presentation to Nugo company about Microsoft Azure, related Big Data technologies and how Azure can change their current environment
Digital Transformation with Microsoft Azure
Digital Transformation with Microsoft Azure
Luan Moreno Medeiros Maciel
ArangoDB is a universal open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient sql-like query language or JavaScript/Ruby extensions. The video is also available online: http://2012.nosql-matters.org/bcn/speakers/
Introduction to ArangoDB (nosql matters Barcelona 2012)
Introduction to ArangoDB (nosql matters Barcelona 2012)
ArangoDB Database
Growth of big datasets Introduction to Apache Hadoop and Spark for developing applications Components of Hadoop, HDFS, MapReduce and HBase Capabilities of Spark and the differences from a typical MapReduce solution Some Spark use cases for data analysis
Apache Hadoop and Spark: Introduction and Use Cases for Data Analysis
Apache Hadoop and Spark: Introduction and Use Cases for Data Analysis
Trieu Nguyen
Introduction to cassandra database
Appache Cassandra
Appache Cassandra
nehabsairam
In this talk we present the term polyglot persistence, give a brief introduction to the world of NoSQL database and point out the benefits and costs of polyglot persistence. Thereafter we present the idea of a multi-model database that reduces the costs for polyglot persistence but keeps its benefits. Next up we present ArangoDB as a Multi-Model database
Multi model-databases
Multi model-databases
ArangoDB Database
SQL Server Workshop for Developers - Visual Studio Live! NY 2012
SQL Server Workshop for Developers - Visual Studio Live! NY 2012
Andrew Brust
The presentation contains details on Hive architecture and its job execution mechanisms.
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Introduction to NOSQL databases
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Quantopix analytics system (qas)
Quantopix analytics system (qas)
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Session 14 - Hive
Introduction To HBase
Introduction To HBase
From Raw Data to Analytics with No ETL
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Microsoft R - Data Science at Scale
Microsoft R - Data Science at Scale
Hadoop mapreduce and yarn frame work- unit5
Hadoop mapreduce and yarn frame work- unit5
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Handling the growth of data
Comparison - RDBMS vs Hadoop vs Apache
Comparison - RDBMS vs Hadoop vs Apache
Digital Transformation with Microsoft Azure
Digital Transformation with Microsoft Azure
Introduction to ArangoDB (nosql matters Barcelona 2012)
Introduction to ArangoDB (nosql matters Barcelona 2012)
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Apache Hadoop and Spark: Introduction and Use Cases for Data Analysis
Appache Cassandra
Appache Cassandra
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SQL Server Workshop for Developers - Visual Studio Live! NY 2012
SQL Server Workshop for Developers - Visual Studio Live! NY 2012
Hive
Hive
Destacado
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Emerging database technology multimedia database
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Salama Al Busaidi
Python CodeLabs - Google App Engine - Python http://eueung.github.io/EL6240/gae
Google app engine python
Google app engine python
Eueung Mulyana
A book that teaches SQL (Structured Query Language) to beginners in record time.
Learn SQL Quickly
Learn SQL Quickly
tutorialbooks
Transparencias usadas para la charla sobre escalabilidad en sistemas con apache y mysql (Semana ESIDE 2008).
Escalabilidad - Apache y MySQL
Escalabilidad - Apache y MySQL
Lorena Fernández
This slide is prepared for Beijing Open Party (a monthly unconference in Beijing China). And it's covered some important points when you are building a scalable web sites. And few page of this slide is in Chinese.
Planning For High Performance Web Application
Planning For High Performance Web Application
Yue Tian
The object-oriented database (OODB) is the combination of object-oriented programming language (OOPL) systems and persistent systems. Object DBMSs add database functionality to object programming languages. They bring much more than persistent storage of programming language objects. A major benefit of this approach is the unification of the application and database development into a seamless data model and language environment. This report presents the comparison between object oriented database and relational database. It gives advantages of OODBMS over RDBMS. It gives applications of OODBMS.
Comparison of Relational Database and Object Oriented Database
Comparison of Relational Database and Object Oriented Database
Editor IJMTER
In the spirit of the book 7 Databases in 7 Weeks, Lara Rubbelke and Karen Lopez cover ~seven databases and datastores in the SQL and NoSQL world, when to use them, and how they are SQL-like. From SQLBitsXV Notice an error? Let me know. I welcome this sort of feedback.
7 Databases in 70 minutes
7 Databases in 70 minutes
Karen Lopez
For more detail visit : https://techforboost.blogspot.com https://youtu.be/OcQZVc7pZZA A multimedia database is a database that include one or more primary media file types such as .txt (documents), .jpg (images), .swf (videos), .mp3 (audio), etc.
Multimedia Database
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Avnish Patel
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Emerging database technology multimedia database
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Google app engine python
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Escalabilidad - Apache y MySQL
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Best Hadoop Institutes : kelly tecnologies is the best Hadoop training Institute in Bangalore.Providing hadoop courses by realtime faculty in Bangalore.
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John Leach Co-Founder and CTO of Splice Machine with 15+ years software development and machine learning experience will discuss how to use HBase co-processors to build an ANSI-99 SQL database with 1) parallelization of SQL execution plans, 2) ACID transactions with snapshot isolation and 3) consistent secondary indexing. Transactions are critical in traditional RDBMSs because they ensure reliable updates across multiple rows and tables. Most operational applications require transactions, but even analytics systems use transactions to reliably update secondary indexes after a record insert or update. In the Hadoop ecosystem, HBase is a key-value store with real-time updates, but it does not have multi-row, multi-table transactions, secondary indexes or a robust query language like SQL. Combining SQL with a full transactional model over HBase opens a whole new set of OLTP and OLAP use cases for Hadoop that was traditionally reserved for RDBMSs like MySQL or Oracle. However, a transactional HBase system has the advantage of scaling out with commodity servers, leading to a 5x-10x cost savings over traditional databases like MySQL or Oracle. HBase co-processors, introduced in release 0.92, provide a flexible and high-performance framework to extend HBase. In this talk, we show how we used HBase co-processors to support a full ANSI SQL RDBMS without modifying the core HBase source. We will discuss how endpoint transactions are used to serialize SQL execution plans over to regions so that computation is local to where the data is stored. Additionally, we will show how observer co-processors simultaneously support both transactions and secondary indexing. The talk will also discuss how Splice Machine extended the work of Google Percolator, Yahoo Labs’ OMID, and the University of Waterloo on distributed snapshot isolation for transactions. Lastly, performance benchmarks will be provided, including full TPC-C and TPC-H results that show how Hadoop/HBase can be a replacement of traditional RDBMS solutions. To view the accompanying slide deck: http://www.slideshare.net/ChicagoHUG/
Using HBase Co-Processors to Build a Distributed, Transactional RDBMS - Splic...
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MongoDB is a cross-platform document-oriented database. Classified as a NoSQL database, MongoDB eschews the traditional table-based relational database structure in favor of JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster.
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The strategic relationship between Hortonworks and SAP enables SAP to resell Hortonworks Data Platform (HDP) and provide enterprise support for their global customer base. This means SAP customers can incorporate enterprise Hadoop as a complement within a data architecture that includes SAP HANA, Sybase and SAP BusinessObjects enabling a broad range of new analytic applications.
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Monte Zweben Co-Founder and CEO of Splice Machine, will discuss how to use HBase co-processors to build an ANSI-99 SQL database with 1) parallelization of SQL execution plans, 2) ACID transactions with snapshot isolation and 3) consistent secondary indexing. Transactions are critical in traditional RDBMSs because they ensure reliable updates across multiple rows and tables. Most operational applications require transactions, but even analytics systems use transactions to reliably update secondary indexes after a record insert or update. In the Hadoop ecosystem, HBase is a key-value store with real-time updates, but it does not have multi-row, multi-table transactions, secondary indexes or a robust query language like SQL. Combining SQL with a full transactional model over HBase opens a whole new set of OLTP and OLAP use cases for Hadoop that was traditionally reserved for RDBMSs like MySQL or Oracle. However, a transactional HBase system has the advantage of scaling out with commodity servers, leading to a 5x-10x cost savings over traditional databases like MySQL or Oracle. HBase co-processors, introduced in release 0.92, provide a flexible and high-performance framework to extend HBase. In this talk, we show how we used HBase co-processors to support a full ANSI SQL RDBMS without modifying the core HBase source. We will discuss how endpoint transactions are used to serialize SQL execution plans over to regions so that computation is local to where the data is stored. Additionally, we will show how observer co-processors simultaneously support both transactions and secondary indexing. The talk will also discuss how Splice Machine extended the work of Google Percolator, Yahoo Labs’ OMID, and the University of Waterloo on distributed snapshot isolation for transactions. Lastly, performance benchmarks will be provided, including full TPC-C and TPC-H results that show how Hadoop/HBase can be a replacement of traditional RDBMS solutions.
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Hoodie - DataEngConf 2017
عصر کلان داده، چرا و چگونه؟
عصر کلان داده، چرا و چگونه؟
Hadoop Technologies
Hadoop Technologies
Hadoop: Distributed Data Processing
Hadoop: Distributed Data Processing
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010
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Hadoop and Voldemort @ LinkedIn
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Más de Miguel Pastor
Slides of my talk at Liferay Berlin Dev Con 2014 about building an analytics platform in Liferay
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My presentation at Liferay NAS 2014 talking about Liferay and Big Data
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Reactive applications and Akka intro used in the Madrid Scala Meetup
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A quick overview about modularity, OSGI and how we are applying it to the Liferay platform
Liferay Module Framework
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Miguel Pastor
A quick overview about Open Source clouds, Liferay architecture on cloud and some "devops" tools
Liferay and Cloud
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Miguel Pastor
Basic slides about some of the news
Jvm fundamentals
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Miguel Pastor
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Miguel Pastor
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Hadoop, Cloud y Spring
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Miguel Pastor
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Scala: un vistazo general
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Miguel Pastor
A brief overview about platform as a service
Platform as a Service overview
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Miguel Pastor
Intro to aspect oriented programming and AspectJ
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Miguel Pastor
Sample measure to check adherence in layered architectures
Software measure-slides
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Miguel Pastor
Una ligera introducción a las arquitecturas software para MMOG más comunes. Aunque le faltan algunos retoques (la actualizaré en breve) creo que está presentable
Arquitecturas MMOG
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Miguel Pastor
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Miguel Pastor
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Software Failures
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HadoopDB
1.
HadoopDB Miguel Angel
Pastor Olivar miguelinlas3 at gmail dot com http://miguelinlas3.blogspot.com http://twitter.com/miguelinlas3
2.
3.
HadoopDB Architecture
4.
Results
5.
Conclusions
6.
Introduction
7.
8.
Data amount is
exploding
9.
Previous problem ->
Shared nothing architectures
10.
11.
Map/Reduce systems
12.
13.
14.
Analytics environments: not
restart querys
15.
Problem at scaling
16.
17.
18.
UDF mechanism
19.
Desirable SQL
and no SQL interfaces
20.
21.
22.
23.
Assumption: failures are
rare
24.
Assumption: dozens of
nodes in clusters
25.
Engineering decisions
26.
Background: Map/Reduce
27.
28.
Works on heterogeneus
environment
29.
30.
31.
SQL not supported
directly ( Hive )
32.
HadoopDB
33.
34.
35.
36.
37.
38.
39.
Job and Task
trackers
40.
Architecture
41.
42.
43.
Execute the SQL
query
44.
45.
46.
47.
Plan to deploy
as separated service
48.
49.
Breaking single data
node in ckunks
50.
51.
52.
53.
Semantic analyzer connects
to catalog
54.
DAG of relational
operators
55.
Optimizer reestructuration
56.
Convert plan to
M/R jobs
57.
DAG in M/R
serialized in xml plan
58.
59.
60.
Traverse DAG (bottom
up). Rule based SQL generator
61.
Benckmarking
62.
63.
64.
2 virtual cores
65.
850 GB storage
66.
64 bits Linux
Fedora 8
67.
68.
1024 MB heap
size
69.
70.
PostgreSQL 8.2.5
71.
No compress data
72.
73.
Used a cloud
edition
74.
75.
Run on EC2
(not cloud edition available)
76.
77.
78.
18 millions ranking
(~1Gigabyte)
79.
Stored as plain
text in HDFS
80.
Loading data
81.
Grep Task
82.
83.
84.
85.
UDF Aggregation Task
86.
87.
DBMS-X 15% overly
optimistic
88.
89.
Fault tolerance and
heterogeneus environments
90.
Benchmarks
91.
92.
Reduce the number
of nodes to achieve the same order of magnitude
93.
Fault tolerance is
important
94.
Conclusions
95.
96.
PostgreSQL is not
a column store
97.
Hadoop and hive
relatively new open source projects
98.
HadoopDB is flexible
and extensible
99.
References
100.
101.
HadoopDB article
102.
HadoopDB project
103.
Vertica
104.
Apache Hive
105.
That´s all!
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