�ݺ�ߣshows by User: NoSQLmatters

�ݺ�ߣshows by User: NoSQLmatters / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: NoSQLmatters / Tue, 16 Jun 2015 07:01:05 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: NoSQLmatters Nathan Ford- Divination of the Defects (Graph-Based Defect Prediction through Change Metrics) - NoSQL matters Dublin 2015 /slideshow/bug-prediction-presentation/49440892 bugpredictionpresentation-150616070105-lva1-app6892
While metrics generated by static code analysis are well established as predictors of possible future defects, there is another untapped source of useful information, namely your source code revision history. This presentation will discuss converting this revision information into a graph representation, various defect prediction models and how to generate their related change metrics through graph traversal, as well as the potential applications and benefits of these graph enabled prediction models.]]>
While metrics generated by static code analysis are well established as predictors of possible future defects, there is another untapped source of useful information, namely your source code revision history. This presentation will discuss converting this revision information into a graph representation, various defect prediction models and how to generate their related change metrics through graph traversal, as well as the potential applications and benefits of these graph enabled prediction models.]]> Tue, 16 Jun 2015 07:01:05 GMT /slideshow/bug-prediction-presentation/49440892 NoSQLmatters@slideshare.net(NoSQLmatters) Nathan Ford- Divination of the Defects (Graph-Based Defect Prediction through Change Metrics) - NoSQL matters Dublin 2015 NoSQLmatters While metrics generated by static code analysis are well established as predictors of possible future defects, there is another untapped source of useful information, namely your source code revision history. This presentation will discuss converting this revision information into a graph representation, various defect prediction models and how to generate their related change metrics through graph traversal, as well as the potential applications and benefits of these graph enabled prediction models. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bugpredictionpresentation-150616070105-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> While metrics generated by static code analysis are well established as predictors of possible future defects, there is another untapped source of useful information, namely your source code revision history. This presentation will discuss converting this revision information into a graph representation, various defect prediction models and how to generate their related change metrics through graph traversal, as well as the potential applications and benefits of these graph enabled prediction models.

Nathan Ford- Divination of the Defects (Graph-Based Defect Prediction through Change Metrics) - NoSQL matters Dublin 2015 from NoSQLmatters

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0 Stefan Hochdörfer - The NoSQL Store everyone ignores: PostgreSQL - NoSQL matters Dublin 2015 /slideshow/stefan-hochdrfer-the-nosql-store-everyone-ignores-postgresql-nosql-matters-dublin-2015/49391641 nosql15postgresnosql-150615063612-lva1-app6891
PostgreSQL is well known being an object-relational database management system. In it`s core PostgreSQL is schema-aware dealing with fixed database tables and column types. However, recent versions of PostgreSQL made it possible to deal with schema-free data. Learn which new features PostgreSQL supports and how to use those features in your application.]]>
PostgreSQL is well known being an object-relational database management system. In it`s core PostgreSQL is schema-aware dealing with fixed database tables and column types. However, recent versions of PostgreSQL made it possible to deal with schema-free data. Learn which new features PostgreSQL supports and how to use those features in your application.]]> Mon, 15 Jun 2015 06:36:12 GMT /slideshow/stefan-hochdrfer-the-nosql-store-everyone-ignores-postgresql-nosql-matters-dublin-2015/49391641 NoSQLmatters@slideshare.net(NoSQLmatters) Stefan Hochdörfer - The NoSQL Store everyone ignores: PostgreSQL - NoSQL matters Dublin 2015 NoSQLmatters PostgreSQL is well known being an object-relational database management system. In it`s core PostgreSQL is schema-aware dealing with fixed database tables and column types. However, recent versions of PostgreSQL made it possible to deal with schema-free data. Learn which new features PostgreSQL supports and how to use those features in your application. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nosql15postgresnosql-150615063612-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> PostgreSQL is well known being an object-relational database management system. In it`s core PostgreSQL is schema-aware dealing with fixed database tables and column types. However, recent versions of PostgreSQL made it possible to deal with schema-free data. Learn which new features PostgreSQL supports and how to use those features in your application.

Stefan Hochd旦rfer - The NoSQL Store everyone ignores: PostgreSQL - NoSQL matters Dublin 2015 from NoSQLmatters

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0 Adrian Colyer - Keynote: NoSQL matters - NoSQL matters Dublin 2015 /slideshow/adrian-colyer-keynote-nosql-matters-nosql-matters-dublin-2015/49211520 nosqlmatterskeynote-150610090811-lva1-app6891
NoSQL matters, on that much I'm sure we can all agree. But if we take a closer look, what really matters when it comes to choosing a data store and/or a data processing platform? What really matters when it comes to getting the most out of that platform? And what is really going to matter as we take things to the next level?]]>
NoSQL matters, on that much I'm sure we can all agree. But if we take a closer look, what really matters when it comes to choosing a data store and/or a data processing platform? What really matters when it comes to getting the most out of that platform? And what is really going to matter as we take things to the next level?]]> Wed, 10 Jun 2015 09:08:10 GMT /slideshow/adrian-colyer-keynote-nosql-matters-nosql-matters-dublin-2015/49211520 NoSQLmatters@slideshare.net(NoSQLmatters) Adrian Colyer - Keynote: NoSQL matters - NoSQL matters Dublin 2015 NoSQLmatters NoSQL matters, on that much I'm sure we can all agree. But if we take a closer look, what really matters when it comes to choosing a data store and/or a data processing platform? What really matters when it comes to getting the most out of that platform? And what is really going to matter as we take things to the next level? <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nosqlmatterskeynote-150610090811-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> NoSQL matters, on that much I'm sure we can all agree. But if we take a closer look, what really matters when it comes to choosing a data store and/or a data processing platform? What really matters when it comes to getting the most out of that platform? And what is really going to matter as we take things to the next level?

Adrian Colyer - Keynote: NoSQL matters - NoSQL matters Dublin 2015 from NoSQLmatters

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0 Peter Bakas - Zero to Insights - Real time analytics with Kafka, C*, and Spark - NoSQL matters Dublin 2015 /NoSQLmatters/from-zero-to-insights fromzerotoinsights-150610063826-lva1-app6892
In this talk, Peter will cover his experience using Spark, Cassandra & Kafka to build a real time analytics platform that processed billions events a day. He will cover the challenges in how to turn all those raw events into actionable insights. He will also cover scaling the platform across multiple regions, as well as across multiple cloud environments.]]>
In this talk, Peter will cover his experience using Spark, Cassandra & Kafka to build a real time analytics platform that processed billions events a day. He will cover the challenges in how to turn all those raw events into actionable insights. He will also cover scaling the platform across multiple regions, as well as across multiple cloud environments.]]> Wed, 10 Jun 2015 06:38:26 GMT /NoSQLmatters/from-zero-to-insights NoSQLmatters@slideshare.net(NoSQLmatters) Peter Bakas - Zero to Insights - Real time analytics with Kafka, C*, and Spark - NoSQL matters Dublin 2015 NoSQLmatters In this talk, Peter will cover his experience using Spark, Cassandra & Kafka to build a real time analytics platform that processed billions events a day. He will cover the challenges in how to turn all those raw events into actionable insights. He will also cover scaling the platform across multiple regions, as well as across multiple cloud environments. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/fromzerotoinsights-150610063826-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this talk, Peter will cover his experience using Spark, Cassandra & Kafka to build a real time analytics platform that processed billions events a day. He will cover the challenges in how to turn all those raw events into actionable insights. He will also cover scaling the platform across multiple regions, as well as across multiple cloud environments.

Peter Bakas - Zero to Insights - Real time analytics with Kafka, C*, and Spark - NoSQL matters Dublin 2015 from NoSQLmatters

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0 Dan Sullivan - Data Analytics and Text Mining with MongoDB - NoSQL matters Dublin 2015 /slideshow/no-sql-mattersdublin2015sullivan/49155796 nosqlmattersdublin2015-sullivan-150609060748-lva1-app6891
Data analysis is an exploratory process that requires a variety of tools and a flexible data store. Data analysis projects are easy to start but quickly become difficult to manage and error prone when depending on file-based data storage. Relational databases are poorly equipped to accommodate the dynamic demands complex analysis. This talk describes best practices for using MongoDB for analytics projects. Examples will be drawn from a large scale text mining project (approximately 25 million documents) that applies machine learning (neural networks and support vector machines) and statistical analysis. Tools discussed include R, Spark, Python scientific stack, and custom pre-processing scripts but the focus is on using these with the document database.]]>
Data analysis is an exploratory process that requires a variety of tools and a flexible data store. Data analysis projects are easy to start but quickly become difficult to manage and error prone when depending on file-based data storage. Relational databases are poorly equipped to accommodate the dynamic demands complex analysis. This talk describes best practices for using MongoDB for analytics projects. Examples will be drawn from a large scale text mining project (approximately 25 million documents) that applies machine learning (neural networks and support vector machines) and statistical analysis. Tools discussed include R, Spark, Python scientific stack, and custom pre-processing scripts but the focus is on using these with the document database.]]> Tue, 09 Jun 2015 06:07:48 GMT /slideshow/no-sql-mattersdublin2015sullivan/49155796 NoSQLmatters@slideshare.net(NoSQLmatters) Dan Sullivan - Data Analytics and Text Mining with MongoDB - NoSQL matters Dublin 2015 NoSQLmatters Data analysis is an exploratory process that requires a variety of tools and a flexible data store. Data analysis projects are easy to start but quickly become difficult to manage and error prone when depending on file-based data storage. Relational databases are poorly equipped to accommodate the dynamic demands complex analysis. This talk describes best practices for using MongoDB for analytics projects. Examples will be drawn from a large scale text mining project (approximately 25 million documents) that applies machine learning (neural networks and support vector machines) and statistical analysis. Tools discussed include R, Spark, Python scientific stack, and custom pre-processing scripts but the focus is on using these with the document database. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nosqlmattersdublin2015-sullivan-150609060748-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Data analysis is an exploratory process that requires a variety of tools and a flexible data store. Data analysis projects are easy to start but quickly become difficult to manage and error prone when depending on file-based data storage. Relational databases are poorly equipped to accommodate the dynamic demands complex analysis. This talk describes best practices for using MongoDB for analytics projects. Examples will be drawn from a large scale text mining project (approximately 25 million documents) that applies machine learning (neural networks and support vector machines) and statistical analysis. Tools discussed include R, Spark, Python scientific stack, and custom pre-processing scripts but the focus is on using these with the document database.

Dan Sullivan - Data Analytics and Text Mining with MongoDB - NoSQL matters Dublin 2015 from NoSQLmatters

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0 Mark Harwood - Building Entity Centric Indexes - NoSQL matters Dublin 2015 /slideshow/entity-centric-indexing-no-sql-dublin/49110432 entitycentricindexingnosqldublin-150608080035-lva1-app6892
Sometimes we need to step back and take a look at the bigger picture - not just counting huge piles of individual log records, but reasoning about the behaviors of the people who are ultimately generating this firehose of data. While your DevOps folks care deeply about log records from a machine utlization perspective, marketing wants to know what these records tell us about the customers' needs. Elasticsearch Aggregations are a great feature but are not a panacea. We can happily use them to summarise complex things like the number of web requests per day broken down by geography and browser type on a busy website, but we would quickly run out of memory if we tried to calculate something as simple as a single number for the average duration of visitor web sessions when using the very same dataset. Why does this occur? A web session duration is an example of a behavioural attribute not held on any one log record; it has to be derived by finding the first and last records for each session in our weblogs, requiring some complex query expressions and a lot of memory to connect all the data points. We can maintain a more useful joined-up-picture if we run an ongoing background process to fuse related events from one index into ?entity-centric? summaries in another index e.g: • Web log events summarised into ?web session? entities • Road-worthiness test results summarised into ?car? entities • Reviews in a marketplace summarised into a ?reviewer? entity Using real data, this session will demonstrate how to incrementally build entity-centric indexes alongside event-centric indexes by using simple scripts to uncover interesting behaviours that accumulate over time. We'll explore: • Which cars are driven long distances after failing roadworthiness tests? • Which website visitors look to be behaving like ?bots?? • Which seller in my marketplace has employed an army of ?shills? to boost his feedback rating? Attendees will leave this session with all the tools required to begin building entity-centric indexes and using that data to derive richer business insights across every department in their organization.]]>
Sometimes we need to step back and take a look at the bigger picture - not just counting huge piles of individual log records, but reasoning about the behaviors of the people who are ultimately generating this firehose of data. While your DevOps folks care deeply about log records from a machine utlization perspective, marketing wants to know what these records tell us about the customers' needs. Elasticsearch Aggregations are a great feature but are not a panacea. We can happily use them to summarise complex things like the number of web requests per day broken down by geography and browser type on a busy website, but we would quickly run out of memory if we tried to calculate something as simple as a single number for the average duration of visitor web sessions when using the very same dataset. Why does this occur? A web session duration is an example of a behavioural attribute not held on any one log record; it has to be derived by finding the first and last records for each session in our weblogs, requiring some complex query expressions and a lot of memory to connect all the data points. We can maintain a more useful joined-up-picture if we run an ongoing background process to fuse related events from one index into ?entity-centric? summaries in another index e.g: • Web log events summarised into ?web session? entities • Road-worthiness test results summarised into ?car? entities • Reviews in a marketplace summarised into a ?reviewer? entity Using real data, this session will demonstrate how to incrementally build entity-centric indexes alongside event-centric indexes by using simple scripts to uncover interesting behaviours that accumulate over time. We'll explore: • Which cars are driven long distances after failing roadworthiness tests? • Which website visitors look to be behaving like ?bots?? • Which seller in my marketplace has employed an army of ?shills? to boost his feedback rating? Attendees will leave this session with all the tools required to begin building entity-centric indexes and using that data to derive richer business insights across every department in their organization.]]> Mon, 08 Jun 2015 08:00:35 GMT /slideshow/entity-centric-indexing-no-sql-dublin/49110432 NoSQLmatters@slideshare.net(NoSQLmatters) Mark Harwood - Building Entity Centric Indexes - NoSQL matters Dublin 2015 NoSQLmatters Sometimes we need to step back and take a look at the bigger picture - not just counting huge piles of individual log records, but reasoning about the behaviors of the people who are ultimately generating this firehose of data. While your DevOps folks care deeply about log records from a machine utlization perspective, marketing wants to know what these records tell us about the customers' needs. Elasticsearch Aggregations are a great feature but are not a panacea. We can happily use them to summarise complex things like the number of web requests per day broken down by geography and browser type on a busy website, but we would quickly run out of memory if we tried to calculate something as simple as a single number for the average duration of visitor web sessions when using the very same dataset. Why does this occur? A web session duration is an example of a behavioural attribute not held on any one log record; it has to be derived by finding the first and last records for each session in our weblogs, requiring some complex query expressions and a lot of memory to connect all the data points. We can maintain a more useful joined-up-picture if we run an ongoing background process to fuse related events from one index into ?entity-centric? summaries in another index e.g: • Web log events summarised into ?web session? entities • Road-worthiness test results summarised into ?car? entities • Reviews in a marketplace summarised into a ?reviewer? entity Using real data, this session will demonstrate how to incrementally build entity-centric indexes alongside event-centric indexes by using simple scripts to uncover interesting behaviours that accumulate over time. We'll explore: • Which cars are driven long distances after failing roadworthiness tests? • Which website visitors look to be behaving like ?bots?? • Which seller in my marketplace has employed an army of ?shills? to boost his feedback rating? Attendees will leave this session with all the tools required to begin building entity-centric indexes and using that data to derive richer business insights across every department in their organization. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/entitycentricindexingnosqldublin-150608080035-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> Sometimes we need to step back and take a look at the bigger picture - not just counting huge piles of individual log records, but reasoning about the behaviors of the people who are ultimately generating this firehose of data. While your DevOps folks care deeply about log records from a machine utlization perspective, marketing wants to know what these records tell us about the customers' needs. Elasticsearch Aggregations are a great feature but are not a panacea. We can happily use them to summarise complex things like the number of web requests per day broken down by geography and browser type on a busy website, but we would quickly run out of memory if we tried to calculate something as simple as a single number for the average duration of visitor web sessions when using the very same dataset. Why does this occur? A web session duration is an example of a behavioural attribute not held on any one log record; it has to be derived by finding the first and last records for each session in our weblogs, requiring some complex query expressions and a lot of memory to connect all the data points. We can maintain a more useful joined-up-picture if we run an ongoing background process to fuse related events from one index into ?entity-centric? summaries in another index e.g: • Web log events summarised into ?web session? entities • Road-worthiness test results summarised into ?car? entities • Reviews in a marketplace summarised into a ?reviewer? entity Using real data, this session will demonstrate how to incrementally build entity-centric indexes alongside event-centric indexes by using simple scripts to uncover interesting behaviours that accumulate over time. We'll explore: • Which cars are driven long distances after failing roadworthiness tests? • Which website visitors look to be behaving like ?bots?? • Which seller in my marketplace has employed an army of ?shills? to boost his feedback rating? Attendees will leave this session with all the tools required to begin building entity-centric indexes and using that data to derive richer business insights across every department in their organization.

Mark Harwood - Building Entity Centric Indexes - NoSQL matters Dublin 2015 from NoSQLmatters

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0 Prassnitha Sampath - Real Time Big Data Analytics with Kafka, Storm & HBase - NoSQL matters Dublin 2015 /slideshow/prassnitha-sampath-real-time-big-data-analytics-with-kafka-storm-hbase-nosql-matters-dublin-2015/49109991 relevanceanddealpersonalizationatgroupon-prassnithasampath-150608074221-lva1-app6891
Relevance and Personalization is crucial to building personalized local commerce experience at Groupon. Talk covers overview of the real time analytics infrastructure that handles over 3 million events/ second and stores and scales to billions of data points. Solution covers how our Kafka -> Storm -> Redis/ HBase pipeline is used to generate real time analytics for hundreds of millions of users of Groupon. Solution includes various architecture design choices and tradeoffs including some interesting algorithmic choices such as Bloom Filters & Hyper Log Log. Attendees can take away learnings from our real-life experience that can help them understand various tuning methods, their tradeoffs and apply them in their solutions]]>
Relevance and Personalization is crucial to building personalized local commerce experience at Groupon. Talk covers overview of the real time analytics infrastructure that handles over 3 million events/ second and stores and scales to billions of data points. Solution covers how our Kafka -> Storm -> Redis/ HBase pipeline is used to generate real time analytics for hundreds of millions of users of Groupon. Solution includes various architecture design choices and tradeoffs including some interesting algorithmic choices such as Bloom Filters & Hyper Log Log. Attendees can take away learnings from our real-life experience that can help them understand various tuning methods, their tradeoffs and apply them in their solutions]]> Mon, 08 Jun 2015 07:42:21 GMT /slideshow/prassnitha-sampath-real-time-big-data-analytics-with-kafka-storm-hbase-nosql-matters-dublin-2015/49109991 NoSQLmatters@slideshare.net(NoSQLmatters) Prassnitha Sampath - Real Time Big Data Analytics with Kafka, Storm & HBase - NoSQL matters Dublin 2015 NoSQLmatters Relevance and Personalization is crucial to building personalized local commerce experience at Groupon. Talk covers overview of the real time analytics infrastructure that handles over 3 million events/ second and stores and scales to billions of data points. Solution covers how our Kafka -> Storm -> Redis/ HBase pipeline is used to generate real time analytics for hundreds of millions of users of Groupon. Solution includes various architecture design choices and tradeoffs including some interesting algorithmic choices such as Bloom Filters & Hyper Log Log. Attendees can take away learnings from our real-life experience that can help them understand various tuning methods, their tradeoffs and apply them in their solutions <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/relevanceanddealpersonalizationatgroupon-prassnithasampath-150608074221-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Relevance and Personalization is crucial to building personalized local commerce experience at Groupon. Talk covers overview of the real time analytics infrastructure that handles over 3 million events/ second and stores and scales to billions of data points. Solution covers how our Kafka -> Storm -> Redis/ HBase pipeline is used to generate real time analytics for hundreds of millions of users of Groupon. Solution includes various architecture design choices and tradeoffs including some interesting algorithmic choices such as Bloom Filters & Hyper Log Log. Attendees can take away learnings from our real-life experience that can help them understand various tuning methods, their tradeoffs and apply them in their solutions

Prassnitha Sampath - Real Time Big Data Analytics with Kafka, Storm & HBase - NoSQL matters Dublin 2015 from NoSQLmatters

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0 Akmal Chaudhri - How to Build Streaming Data Applications: Evaluating the Top Contenders - NoSQL matters Dublin 2015 /NoSQLmatters/akmal-chaudhri-how-to-build-streaming-data-applications-evaluating-the-top-contenders-nosql-matters-dublin-2015 streamingapplications-150608072211-lva1-app6891
Building applications on streaming data has its challenges. If you are trying to use programs such as Apache Spark or Storm to build applications, this presentation will explain the advantages and disadvantages of each solution and how to choose the right tool for your next streaming data project. Building streaming data applications that can manage the massive quantities of data generated from mobile devices, M2M, sensors and other IoT devices, is a big challenge that many organizations face today. Traditional tools, such as conventional database systems, do not have the capacity to ingest data, analyze it in real-time, and make decisions. New technologies such as Apache Spark and Storm are now coming to the forefront as possible solutions to handing fast data streams. Typical technology choices fall into one of three categories: OLAP, OLTP, and stream-processing systems. Each of these solutions has its benefits, but some choices support streaming data and application development much better than others. Employing a solution that handles streaming data, provides state, ensures durability, and supports transactions and real-time decisions is key to benefitting from fast data. During this presentation you will learn: - The difference between fast OLAP, stream-processing, and OLTP database solutions. - The importance of state, real-time analytics and real-time decisions when building applications on streaming data. - How streaming applications deliver more value when built on a super-fast in-memory, SQL database.]]>
Building applications on streaming data has its challenges. If you are trying to use programs such as Apache Spark or Storm to build applications, this presentation will explain the advantages and disadvantages of each solution and how to choose the right tool for your next streaming data project. Building streaming data applications that can manage the massive quantities of data generated from mobile devices, M2M, sensors and other IoT devices, is a big challenge that many organizations face today. Traditional tools, such as conventional database systems, do not have the capacity to ingest data, analyze it in real-time, and make decisions. New technologies such as Apache Spark and Storm are now coming to the forefront as possible solutions to handing fast data streams. Typical technology choices fall into one of three categories: OLAP, OLTP, and stream-processing systems. Each of these solutions has its benefits, but some choices support streaming data and application development much better than others. Employing a solution that handles streaming data, provides state, ensures durability, and supports transactions and real-time decisions is key to benefitting from fast data. During this presentation you will learn: - The difference between fast OLAP, stream-processing, and OLTP database solutions. - The importance of state, real-time analytics and real-time decisions when building applications on streaming data. - How streaming applications deliver more value when built on a super-fast in-memory, SQL database.]]> Mon, 08 Jun 2015 07:22:11 GMT /NoSQLmatters/akmal-chaudhri-how-to-build-streaming-data-applications-evaluating-the-top-contenders-nosql-matters-dublin-2015 NoSQLmatters@slideshare.net(NoSQLmatters) Akmal Chaudhri - How to Build Streaming Data Applications: Evaluating the Top Contenders - NoSQL matters Dublin 2015 NoSQLmatters Building applications on streaming data has its challenges. If you are trying to use programs such as Apache Spark or Storm to build applications, this presentation will explain the advantages and disadvantages of each solution and how to choose the right tool for your next streaming data project. Building streaming data applications that can manage the massive quantities of data generated from mobile devices, M2M, sensors and other IoT devices, is a big challenge that many organizations face today. Traditional tools, such as conventional database systems, do not have the capacity to ingest data, analyze it in real-time, and make decisions. New technologies such as Apache Spark and Storm are now coming to the forefront as possible solutions to handing fast data streams. Typical technology choices fall into one of three categories: OLAP, OLTP, and stream-processing systems. Each of these solutions has its benefits, but some choices support streaming data and application development much better than others. Employing a solution that handles streaming data, provides state, ensures durability, and supports transactions and real-time decisions is key to benefitting from fast data. During this presentation you will learn: - The difference between fast OLAP, stream-processing, and OLTP database solutions. - The importance of state, real-time analytics and real-time decisions when building applications on streaming data. - How streaming applications deliver more value when built on a super-fast in-memory, SQL database. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/streamingapplications-150608072211-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Building applications on streaming data has its challenges. If you are trying to use programs such as Apache Spark or Storm to build applications, this presentation will explain the advantages and disadvantages of each solution and how to choose the right tool for your next streaming data project. Building streaming data applications that can manage the massive quantities of data generated from mobile devices, M2M, sensors and other IoT devices, is a big challenge that many organizations face today. Traditional tools, such as conventional database systems, do not have the capacity to ingest data, analyze it in real-time, and make decisions. New technologies such as Apache Spark and Storm are now coming to the forefront as possible solutions to handing fast data streams. Typical technology choices fall into one of three categories: OLAP, OLTP, and stream-processing systems. Each of these solutions has its benefits, but some choices support streaming data and application development much better than others. Employing a solution that handles streaming data, provides state, ensures durability, and supports transactions and real-time decisions is key to benefitting from fast data. During this presentation you will learn: - The difference between fast OLAP, stream-processing, and OLTP database solutions. - The importance of state, real-time analytics and real-time decisions when building applications on streaming data. - How streaming applications deliver more value when built on a super-fast in-memory, SQL database.

Akmal Chaudhri - How to Build Streaming Data Applications: Evaluating the Top Contenders - NoSQL matters Dublin 2015 from NoSQLmatters

]]> 1205 1 https://cdn.slidesharecdn.com/ss_thumbnails/streamingapplications-150608072211-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Michael Hackstein - NoSQL meets Microservices - NoSQL matters Dublin 2015 /NoSQLmatters/michael-hackstein-nosql-meets-microservices-nosql-matters-dublin-2015 nosqlmeetsmicroservices-150608071345-lva1-app6891
Just a few years ago all software systems were designed to be monoliths running on a single big and powerful machine. But nowadays most companies desire to scale out instead of scaling up, because it is much easier to buy or rent a large cluster of commodity hardware then to get a single machine that is powerful enough. In the database area scaling out is realized by utilizing a combination of polyglot persistence and sharding of data. On the application level scaling out is realized by microservices. In this talk I will briefly introduce the concepts and ideas of microservices and discuss their benefits and drawbacks. Afterwards I will focus on the point of intersection of a microservice based application talking to one or many NoSQL databases. We will try and find answers to these questions: Are the differences to a monolithic application? How to scale the whole system properly? What about polyglot persistence? Is there a data-centric way to split microservices?]]>
Just a few years ago all software systems were designed to be monoliths running on a single big and powerful machine. But nowadays most companies desire to scale out instead of scaling up, because it is much easier to buy or rent a large cluster of commodity hardware then to get a single machine that is powerful enough. In the database area scaling out is realized by utilizing a combination of polyglot persistence and sharding of data. On the application level scaling out is realized by microservices. In this talk I will briefly introduce the concepts and ideas of microservices and discuss their benefits and drawbacks. Afterwards I will focus on the point of intersection of a microservice based application talking to one or many NoSQL databases. We will try and find answers to these questions: Are the differences to a monolithic application? How to scale the whole system properly? What about polyglot persistence? Is there a data-centric way to split microservices?]]> Mon, 08 Jun 2015 07:13:45 GMT /NoSQLmatters/michael-hackstein-nosql-meets-microservices-nosql-matters-dublin-2015 NoSQLmatters@slideshare.net(NoSQLmatters) Michael Hackstein - NoSQL meets Microservices - NoSQL matters Dublin 2015 NoSQLmatters Just a few years ago all software systems were designed to be monoliths running on a single big and powerful machine. But nowadays most companies desire to scale out instead of scaling up, because it is much easier to buy or rent a large cluster of commodity hardware then to get a single machine that is powerful enough. In the database area scaling out is realized by utilizing a combination of polyglot persistence and sharding of data. On the application level scaling out is realized by microservices. In this talk I will briefly introduce the concepts and ideas of microservices and discuss their benefits and drawbacks. Afterwards I will focus on the point of intersection of a microservice based application talking to one or many NoSQL databases. We will try and find answers to these questions: Are the differences to a monolithic application? How to scale the whole system properly? What about polyglot persistence? Is there a data-centric way to split microservices? <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nosqlmeetsmicroservices-150608071345-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Just a few years ago all software systems were designed to be monoliths running on a single big and powerful machine. But nowadays most companies desire to scale out instead of scaling up, because it is much easier to buy or rent a large cluster of commodity hardware then to get a single machine that is powerful enough. In the database area scaling out is realized by utilizing a combination of polyglot persistence and sharding of data. On the application level scaling out is realized by microservices. In this talk I will briefly introduce the concepts and ideas of microservices and discuss their benefits and drawbacks. Afterwards I will focus on the point of intersection of a microservice based application talking to one or many NoSQL databases. We will try and find answers to these questions: Are the differences to a monolithic application? How to scale the whole system properly? What about polyglot persistence? Is there a data-centric way to split microservices?

Michael Hackstein - NoSQL meets Microservices - NoSQL matters Dublin 2015 from NoSQLmatters

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0 Chris Ward - Understanding databases for distributed docker applications - NoSQL matters Dublin 2015 /slideshow/chris-ward-understanding-databases-for-distributed-docker-applications-nosql-matters-dublin-2015/49108852 understandingdatabasesfordistributeddockerapplications-150608070018-lva1-app6891
In this talk we'll focus on the use of Crate alongside Weave in Docker containers, the technical challenges, best practices learned, and getting a big data application running alongside it. You'll learn about the reasons why Crate.IO is building "yet another NoSQL database" and why it's unique and important when running web scale containerized applications. We'll show why the shared-nothing architecture is so important when deploying large clusters in containers and how it addresses the issues and fears of a Docker-based persistence layer. You will learn how to deploy a Crate cluster in the cloud within minutes using Docker, some of the challenges you'll encounter, and how to overcome them in order to scale your backends efficiently. We focused on super simple integration with any cloud provider, striving it to be as turnkey as possible with minimal up-front configuration required to establish a cluster. Once established, we'll show how to scale the cluster horizontally by simply adding more nodes. The session will also give you examples when you should use Crate compared to other similar technologies such as MongoDB, Hadoop, Cassandra or FoundationDB. We'll talk about this approach's strengths and what types of applications are well-suited for this type of data store, as well what is not. Finally we'll outline how to architect an application that is easy to scale using Crate and Docker.]]>
In this talk we'll focus on the use of Crate alongside Weave in Docker containers, the technical challenges, best practices learned, and getting a big data application running alongside it. You'll learn about the reasons why Crate.IO is building "yet another NoSQL database" and why it's unique and important when running web scale containerized applications. We'll show why the shared-nothing architecture is so important when deploying large clusters in containers and how it addresses the issues and fears of a Docker-based persistence layer. You will learn how to deploy a Crate cluster in the cloud within minutes using Docker, some of the challenges you'll encounter, and how to overcome them in order to scale your backends efficiently. We focused on super simple integration with any cloud provider, striving it to be as turnkey as possible with minimal up-front configuration required to establish a cluster. Once established, we'll show how to scale the cluster horizontally by simply adding more nodes. The session will also give you examples when you should use Crate compared to other similar technologies such as MongoDB, Hadoop, Cassandra or FoundationDB. We'll talk about this approach's strengths and what types of applications are well-suited for this type of data store, as well what is not. Finally we'll outline how to architect an application that is easy to scale using Crate and Docker.]]> Mon, 08 Jun 2015 07:00:18 GMT /slideshow/chris-ward-understanding-databases-for-distributed-docker-applications-nosql-matters-dublin-2015/49108852 NoSQLmatters@slideshare.net(NoSQLmatters) Chris Ward - Understanding databases for distributed docker applications - NoSQL matters Dublin 2015 NoSQLmatters In this talk we'll focus on the use of Crate alongside Weave in Docker containers, the technical challenges, best practices learned, and getting a big data application running alongside it. You'll learn about the reasons why Crate.IO is building "yet another NoSQL database" and why it's unique and important when running web scale containerized applications. We'll show why the shared-nothing architecture is so important when deploying large clusters in containers and how it addresses the issues and fears of a Docker-based persistence layer. You will learn how to deploy a Crate cluster in the cloud within minutes using Docker, some of the challenges you'll encounter, and how to overcome them in order to scale your backends efficiently. We focused on super simple integration with any cloud provider, striving it to be as turnkey as possible with minimal up-front configuration required to establish a cluster. Once established, we'll show how to scale the cluster horizontally by simply adding more nodes. The session will also give you examples when you should use Crate compared to other similar technologies such as MongoDB, Hadoop, Cassandra or FoundationDB. We'll talk about this approach's strengths and what types of applications are well-suited for this type of data store, as well what is not. Finally we'll outline how to architect an application that is easy to scale using Crate and Docker. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/understandingdatabasesfordistributeddockerapplications-150608070018-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this talk we'll focus on the use of Crate alongside Weave in Docker containers, the technical challenges, best practices learned, and getting a big data application running alongside it. You'll learn about the reasons why Crate.IO is building "yet another NoSQL database" and why it's unique and important when running web scale containerized applications. We'll show why the shared-nothing architecture is so important when deploying large clusters in containers and how it addresses the issues and fears of a Docker-based persistence layer. You will learn how to deploy a Crate cluster in the cloud within minutes using Docker, some of the challenges you'll encounter, and how to overcome them in order to scale your backends efficiently. We focused on super simple integration with any cloud provider, striving it to be as turnkey as possible with minimal up-front configuration required to establish a cluster. Once established, we'll show how to scale the cluster horizontally by simply adding more nodes. The session will also give you examples when you should use Crate compared to other similar technologies such as MongoDB, Hadoop, Cassandra or FoundationDB. We'll talk about this approach's strengths and what types of applications are well-suited for this type of data store, as well what is not. Finally we'll outline how to architect an application that is easy to scale using Crate and Docker.

Chris Ward - Understanding databases for distributed docker applications - NoSQL matters Dublin 2015 from NoSQLmatters

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0 Philipp Krenn - Host your database in the cloud, they said... - NoSQL matters Dublin 2015 /NoSQLmatters/host-yourdatabaseinthecloud hostyourdatabaseinthecloud-150608062706-lva1-app6892
More than two years ago we faced the decision whether to run our MongoDB database on Amazon's EC2 ourselves or to rely on a Database as a Service provider. Common wisdom told us that a well known provider, focusing all its knowledge and energy on running MongoDB, would be a better choice than us trying it on the side. Well, this talk describes what can go wrong, since we have seen a lot of interesting minor and major hiccups — including stopped instances, broken backups, a major security incident, and more broken backups. Additionally, we discuss some reasons why a hosted solution is not always the better choice and which new challenges arise from it.]]>
More than two years ago we faced the decision whether to run our MongoDB database on Amazon's EC2 ourselves or to rely on a Database as a Service provider. Common wisdom told us that a well known provider, focusing all its knowledge and energy on running MongoDB, would be a better choice than us trying it on the side. Well, this talk describes what can go wrong, since we have seen a lot of interesting minor and major hiccups — including stopped instances, broken backups, a major security incident, and more broken backups. Additionally, we discuss some reasons why a hosted solution is not always the better choice and which new challenges arise from it.]]> Mon, 08 Jun 2015 06:27:06 GMT /NoSQLmatters/host-yourdatabaseinthecloud NoSQLmatters@slideshare.net(NoSQLmatters) Philipp Krenn - Host your database in the cloud, they said... - NoSQL matters Dublin 2015 NoSQLmatters More than two years ago we faced the decision whether to run our MongoDB database on Amazon's EC2 ourselves or to rely on a Database as a Service provider. Common wisdom told us that a well known provider, focusing all its knowledge and energy on running MongoDB, would be a better choice than us trying it on the side. Well, this talk describes what can go wrong, since we have seen a lot of interesting minor and major hiccups — including stopped instances, broken backups, a major security incident, and more broken backups. Additionally, we discuss some reasons why a hosted solution is not always the better choice and which new challenges arise from it. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/hostyourdatabaseinthecloud-150608062706-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds" /> More than two years ago we faced the decision whether to run our MongoDB database on Amazon's EC2 ourselves or to rely on a Database as a Service provider. Common wisdom told us that a well known provider, focusing all its knowledge and energy on running MongoDB, would be a better choice than us trying it on the side. Well, this talk describes what can go wrong, since we have seen a lot of interesting minor and major hiccups — including stopped instances, broken backups, a major security incident, and more broken backups. Additionally, we discuss some reasons why a hosted solution is not always the better choice and which new challenges arise from it.

Philipp Krenn - Host your database in the cloud, they said... - NoSQL matters Dublin 2015 from NoSQLmatters

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0 Lucian Precup - Back to the Future: SQL 92 for Elasticsearch? - NoSQL matters Paris 2015 /slideshow/lucian-precup-nosql-matters-paris-2015/46568336 lucianprecupbacktothefuturesql92forelasticsearchnosqlmattersparis-150402032018-conversion-gate01
What if we would try to make Elasticsearch SQL 92 compliant (http://www.contrib.andrew.cmu.edu/~shadow/sql/sql1992.txt)? This wouldn't serve that much nowadays, you would say. Well, we actually tried to do the exercise and we have some interesting conclusions. While we take Elasticsearch as an example for this "side by side", the issues we are addressing also apply to nosql in general. With this unusual exercise, we take the occasion to compare relational databases / sql with Elasticsearch / nosql on all the levels : functionality, semantics, performance and user experience.]]>
What if we would try to make Elasticsearch SQL 92 compliant (http://www.contrib.andrew.cmu.edu/~shadow/sql/sql1992.txt)? This wouldn't serve that much nowadays, you would say. Well, we actually tried to do the exercise and we have some interesting conclusions. While we take Elasticsearch as an example for this "side by side", the issues we are addressing also apply to nosql in general. With this unusual exercise, we take the occasion to compare relational databases / sql with Elasticsearch / nosql on all the levels : functionality, semantics, performance and user experience.]]> Thu, 02 Apr 2015 03:20:18 GMT /slideshow/lucian-precup-nosql-matters-paris-2015/46568336 NoSQLmatters@slideshare.net(NoSQLmatters) Lucian Precup - Back to the Future: SQL 92 for Elasticsearch? - NoSQL matters Paris 2015 NoSQLmatters What if we would try to make Elasticsearch SQL 92 compliant (http://www.contrib.andrew.cmu.edu/~shadow/sql/sql1992.txt)? This wouldn't serve that much nowadays, you would say. Well, we actually tried to do the exercise and we have some interesting conclusions. While we take Elasticsearch as an example for this "side by side", the issues we are addressing also apply to nosql in general. With this unusual exercise, we take the occasion to compare relational databases / sql with Elasticsearch / nosql on all the levels : functionality, semantics, performance and user experience. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/lucianprecupbacktothefuturesql92forelasticsearchnosqlmattersparis-150402032018-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> What if we would try to make Elasticsearch SQL 92 compliant (http://www.contrib.andrew.cmu.edu/~shadow/sql/sql1992.txt)? This wouldn't serve that much nowadays, you would say. Well, we actually tried to do the exercise and we have some interesting conclusions. While we take Elasticsearch as an example for this "side by side", the issues we are addressing also apply to nosql in general. With this unusual exercise, we take the occasion to compare relational databases / sql with Elasticsearch / nosql on all the levels : functionality, semantics, performance and user experience.

Lucian Precup - Back to the Future: SQL 92 for Elasticsearch? - NoSQL matters Paris 2015 from NoSQLmatters

]]> 1412 1 https://cdn.slidesharecdn.com/ss_thumbnails/lucianprecupbacktothefuturesql92forelasticsearchnosqlmattersparis-150402032018-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Bruno Guedes - Hadoop real time for dummies - NoSQL matters Paris 2015 /slideshow/no-sql-matterspres2015zenika/46489896 nosqlmatterspres2015zenika-150331071705-conversion-gate01
There are many frameworks that can offer real time on top of Hadoop. This talk will show you the usage of Pivotal HAWQ and how it is easy to use SQL for querying your Hadoop data. Come and see the power and easy of use that can help you on using the Hadoop ecosystem.]]>
There are many frameworks that can offer real time on top of Hadoop. This talk will show you the usage of Pivotal HAWQ and how it is easy to use SQL for querying your Hadoop data. Come and see the power and easy of use that can help you on using the Hadoop ecosystem.]]> Tue, 31 Mar 2015 07:17:05 GMT /slideshow/no-sql-matterspres2015zenika/46489896 NoSQLmatters@slideshare.net(NoSQLmatters) Bruno Guedes - Hadoop real time for dummies - NoSQL matters Paris 2015 NoSQLmatters There are many frameworks that can offer real time on top of Hadoop. This talk will show you the usage of Pivotal HAWQ and how it is easy to use SQL for querying your Hadoop data. Come and see the power and easy of use that can help you on using the Hadoop ecosystem. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nosqlmatterspres2015zenika-150331071705-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> There are many frameworks that can offer real time on top of Hadoop. This talk will show you the usage of Pivotal HAWQ and how it is easy to use SQL for querying your Hadoop data. Come and see the power and easy of use that can help you on using the Hadoop ecosystem.

Bruno Guedes - Hadoop real time for dummies - NoSQL matters Paris 2015 from NoSQLmatters

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0 DuyHai DOAN - Real time analytics with Cassandra and Spark - NoSQL matters Paris 2015 /slideshow/duyhai-doan-real-time-analytics-with-cassandra-and-spark-nosql-matters-paris-2015/46483962 real-timedataprocessingwithsparkcassandranosqlmatters2015paris-150331042026-conversion-gate01
Apache Spark is a general data processing framework which allows you perform map-reduce tasks (but not only) in memory. Apache Cassandra is a highly available and massively scalable NoSQL data-store. By combining Spark flexible API and Cassandra performance, we get an interesting alternative to the Hadoop eco-system for both real-time and batch processing. During this talk we will highlight the tight integration between Spark & Cassandra and demonstrate some usages with live code demo.]]>
Apache Spark is a general data processing framework which allows you perform map-reduce tasks (but not only) in memory. Apache Cassandra is a highly available and massively scalable NoSQL data-store. By combining Spark flexible API and Cassandra performance, we get an interesting alternative to the Hadoop eco-system for both real-time and batch processing. During this talk we will highlight the tight integration between Spark & Cassandra and demonstrate some usages with live code demo.]]> Tue, 31 Mar 2015 04:20:26 GMT /slideshow/duyhai-doan-real-time-analytics-with-cassandra-and-spark-nosql-matters-paris-2015/46483962 NoSQLmatters@slideshare.net(NoSQLmatters) DuyHai DOAN - Real time analytics with Cassandra and Spark - NoSQL matters Paris 2015 NoSQLmatters Apache Spark is a general data processing framework which allows you perform map-reduce tasks (but not only) in memory. Apache Cassandra is a highly available and massively scalable NoSQL data-store. By combining Spark flexible API and Cassandra performance, we get an interesting alternative to the Hadoop eco-system for both real-time and batch processing. During this talk we will highlight the tight integration between Spark & Cassandra and demonstrate some usages with live code demo. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/real-timedataprocessingwithsparkcassandranosqlmatters2015paris-150331042026-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Apache Spark is a general data processing framework which allows you perform map-reduce tasks (but not only) in memory. Apache Cassandra is a highly available and massively scalable NoSQL data-store. By combining Spark flexible API and Cassandra performance, we get an interesting alternative to the Hadoop eco-system for both real-time and batch processing. During this talk we will highlight the tight integration between Spark & Cassandra and demonstrate some usages with live code demo.

DuyHai DOAN - Real time analytics with Cassandra and Spark - NoSQL matters Paris 2015 from NoSQLmatters

]]> 1723 1 https://cdn.slidesharecdn.com/ss_thumbnails/real-timedataprocessingwithsparkcassandranosqlmatters2015paris-150331042026-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Benjamin Guinebertière - Microsoft Azure: Document DB and other noSQL databases - NoSQL matters Paris 2015 /slideshow/2015-0327azure-nosql/46364761 2015-03-27-azurenosql-150327093216-conversion-gate01
When deploying your service to Microsoft Azure, you have a number of options in terms of noSQL: you can install databases on Linux or Windows virtual machines by yourself, or via the marketplace, or you can use open source databases available as a service like HBase or proprietary and managed databases like Document DB. After showing these options, we'll show Document DB in more details. This is a noSQL database as a service that stores JSON.]]>
When deploying your service to Microsoft Azure, you have a number of options in terms of noSQL: you can install databases on Linux or Windows virtual machines by yourself, or via the marketplace, or you can use open source databases available as a service like HBase or proprietary and managed databases like Document DB. After showing these options, we'll show Document DB in more details. This is a noSQL database as a service that stores JSON.]]> Fri, 27 Mar 2015 09:32:14 GMT /slideshow/2015-0327azure-nosql/46364761 NoSQLmatters@slideshare.net(NoSQLmatters) Benjamin Guinebertière - Microsoft Azure: Document DB and other noSQL databases - NoSQL matters Paris 2015 NoSQLmatters When deploying your service to Microsoft Azure, you have a number of options in terms of noSQL: you can install databases on Linux or Windows virtual machines by yourself, or via the marketplace, or you can use open source databases available as a service like HBase or proprietary and managed databases like Document DB. After showing these options, we'll show Document DB in more details. This is a noSQL database as a service that stores JSON. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/2015-03-27-azurenosql-150327093216-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> When deploying your service to Microsoft Azure, you have a number of options in terms of noSQL: you can install databases on Linux or Windows virtual machines by yourself, or via the marketplace, or you can use open source databases available as a service like HBase or proprietary and managed databases like Document DB. After showing these options, we'll show Document DB in more details. This is a noSQL database as a service that stores JSON.

Benjamin Guineberti竪re - Microsoft Azure: Document DB and other noSQL databases - NoSQL matters Paris 2015 from NoSQLmatters

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0 David Pilato - Advance search for your legacy application - NoSQL matters Paris 2015 /slideshow/0327-no-sqlmatters/46362492 0327-nosqlmatters-150327083558-conversion-gate01
How do you mix SQL and NoSQL worlds without starting a messy revolution?This live coding talk will show you how to add Elasticsearch to your legacy application without changing all your current development habits. Your application will have suddenly have advanced search features, all without the need to write complex SQL code!David will start from a Spring, Hibernate and Postgresql based application and will add a complete integration of Elasticsearch, all live from the stage during his presentation.]]>
How do you mix SQL and NoSQL worlds without starting a messy revolution?This live coding talk will show you how to add Elasticsearch to your legacy application without changing all your current development habits. Your application will have suddenly have advanced search features, all without the need to write complex SQL code!David will start from a Spring, Hibernate and Postgresql based application and will add a complete integration of Elasticsearch, all live from the stage during his presentation.]]> Fri, 27 Mar 2015 08:35:57 GMT /slideshow/0327-no-sqlmatters/46362492 NoSQLmatters@slideshare.net(NoSQLmatters) David Pilato - Advance search for your legacy application - NoSQL matters Paris 2015 NoSQLmatters How do you mix SQL and NoSQL worlds without starting a messy revolution?This live coding talk will show you how to add Elasticsearch to your legacy application without changing all your current development habits. Your application will have suddenly have advanced search features, all without the need to write complex SQL code!David will start from a Spring, Hibernate and Postgresql based application and will add a complete integration of Elasticsearch, all live from the stage during his presentation. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/0327-nosqlmatters-150327083558-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> How do you mix SQL and NoSQL worlds without starting a messy revolution?This live coding talk will show you how to add Elasticsearch to your legacy application without changing all your current development habits. Your application will have suddenly have advanced search features, all without the need to write complex SQL code!David will start from a Spring, Hibernate and Postgresql based application and will add a complete integration of Elasticsearch, all live from the stage during his presentation.

David Pilato - Advance search for your legacy application - NoSQL matters Paris 2015 from NoSQLmatters

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0 Tugdual Grall - From SQL to NoSQL in less than 40 min - NoSQL matters Paris 2015 /slideshow/tugdual-grall/46362231 tgrall-nosql-matters-paris-150327082744-conversion-gate01
During this live-coding session, Tugdual will move an old fashion full SQL application (JavaEE) to the new NoSQL world.Using MongoDB, and REST, he will show the benefits of this new architecture: * Easyness * Flexibility * High availability * Scalability; During this presentation, you will learn more about: * Document Oriented Model * JSON * REST * Iterative development; This demonstration is also a good opportunity to see how you can migrate data from a relational database, and the various schema options.]]>
During this live-coding session, Tugdual will move an old fashion full SQL application (JavaEE) to the new NoSQL world.Using MongoDB, and REST, he will show the benefits of this new architecture: * Easyness * Flexibility * High availability * Scalability; During this presentation, you will learn more about: * Document Oriented Model * JSON * REST * Iterative development; This demonstration is also a good opportunity to see how you can migrate data from a relational database, and the various schema options.]]> Fri, 27 Mar 2015 08:27:44 GMT /slideshow/tugdual-grall/46362231 NoSQLmatters@slideshare.net(NoSQLmatters) Tugdual Grall - From SQL to NoSQL in less than 40 min - NoSQL matters Paris 2015 NoSQLmatters During this live-coding session, Tugdual will move an old fashion full SQL application (JavaEE) to the new NoSQL world.Using MongoDB, and REST, he will show the benefits of this new architecture: * Easyness * Flexibility * High availability * Scalability; During this presentation, you will learn more about: * Document Oriented Model * JSON * REST * Iterative development; This demonstration is also a good opportunity to see how you can migrate data from a relational database, and the various schema options. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/tgrall-nosql-matters-paris-150327082744-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> During this live-coding session, Tugdual will move an old fashion full SQL application (JavaEE) to the new NoSQL world.Using MongoDB, and REST, he will show the benefits of this new architecture: * Easyness * Flexibility * High availability * Scalability; During this presentation, you will learn more about: * Document Oriented Model * JSON * REST * Iterative development; This demonstration is also a good opportunity to see how you can migrate data from a relational database, and the various schema options.

Tugdual Grall - From SQL to NoSQL in less than 40 min - NoSQL matters Paris 2015 from NoSQLmatters

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0 Gregorry Letribot - Druid at Criteo - NoSQL matters 2015 /NoSQLmatters/druid-elixir-for-analytics druidelixirforanalytics-150327081926-conversion-gate01
How do you monitor performance for one of your clients on a specific user segmentation when dealing with billions of events a day ? With over 2 billion ads served and 230Tb of data processed a day, we at Criteo have a comprehensive need for an interactive analytics stack. And by interactive, we mean a querying system with dynamic filtering to drill down over multiple dimensions, answering within sub-second latency. This session will take you on our journey with Druid, ""an open-source data store designed for real-time exploratory analytics on large data sets"". We will explore Druid's architecture and noticeable concepts, how relevant they are for some use cases and how it really performs.]]>
How do you monitor performance for one of your clients on a specific user segmentation when dealing with billions of events a day ? With over 2 billion ads served and 230Tb of data processed a day, we at Criteo have a comprehensive need for an interactive analytics stack. And by interactive, we mean a querying system with dynamic filtering to drill down over multiple dimensions, answering within sub-second latency. This session will take you on our journey with Druid, ""an open-source data store designed for real-time exploratory analytics on large data sets"". We will explore Druid's architecture and noticeable concepts, how relevant they are for some use cases and how it really performs.]]> Fri, 27 Mar 2015 08:19:26 GMT /NoSQLmatters/druid-elixir-for-analytics NoSQLmatters@slideshare.net(NoSQLmatters) Gregorry Letribot - Druid at Criteo - NoSQL matters 2015 NoSQLmatters How do you monitor performance for one of your clients on a specific user segmentation when dealing with billions of events a day ? With over 2 billion ads served and 230Tb of data processed a day, we at Criteo have a comprehensive need for an interactive analytics stack. And by interactive, we mean a querying system with dynamic filtering to drill down over multiple dimensions, answering within sub-second latency. This session will take you on our journey with Druid, ""an open-source data store designed for real-time exploratory analytics on large data sets"". We will explore Druid's architecture and noticeable concepts, how relevant they are for some use cases and how it really performs. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/druidelixirforanalytics-150327081926-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> How do you monitor performance for one of your clients on a specific user segmentation when dealing with billions of events a day ? With over 2 billion ads served and 230Tb of data processed a day, we at Criteo have a comprehensive need for an interactive analytics stack. And by interactive, we mean a querying system with dynamic filtering to drill down over multiple dimensions, answering within sub-second latency. This session will take you on our journey with Druid, ""an open-source data store designed for real-time exploratory analytics on large data sets"". We will explore Druid's architecture and noticeable concepts, how relevant they are for some use cases and how it really performs.

Gregorry Letribot - Druid at Criteo - NoSQL matters 2015 from NoSQLmatters

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0 Michael Hackstein - Polyglot Persistence & Multi-Model NoSQL Databases - NoSQL matters Paris 2015 /slideshow/multi-modeldatabases-46361166/46361166 multi-model-databases-150327075822-conversion-gate01
In many modern applications the database side is realized using polyglot persistence – store each data format (graphs, documents, etc.) in an appropriate separate database. This approach yields several benefits, databases are optimized for their specific duty, however there are also drawbacks: * keep all databases in sync * queries might require data from several databases * experts needed for all used systems A multi-model database is not restricted to one data format, but can cope with several of them. In this talk i will present how a multi-model database can be used in a polyglot persistence setup and how it will reduce the effort drastically.]]>
In many modern applications the database side is realized using polyglot persistence – store each data format (graphs, documents, etc.) in an appropriate separate database. This approach yields several benefits, databases are optimized for their specific duty, however there are also drawbacks: * keep all databases in sync * queries might require data from several databases * experts needed for all used systems A multi-model database is not restricted to one data format, but can cope with several of them. In this talk i will present how a multi-model database can be used in a polyglot persistence setup and how it will reduce the effort drastically.]]> Fri, 27 Mar 2015 07:58:22 GMT /slideshow/multi-modeldatabases-46361166/46361166 NoSQLmatters@slideshare.net(NoSQLmatters) Michael Hackstein - Polyglot Persistence & Multi-Model NoSQL Databases - NoSQL matters Paris 2015 NoSQLmatters In many modern applications the database side is realized using polyglot persistence – store each data format (graphs, documents, etc.) in an appropriate separate database. This approach yields several benefits, databases are optimized for their specific duty, however there are also drawbacks: * keep all databases in sync * queries might require data from several databases * experts needed for all used systems A multi-model database is not restricted to one data format, but can cope with several of them. In this talk i will present how a multi-model database can be used in a polyglot persistence setup and how it will reduce the effort drastically. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/multi-model-databases-150327075822-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> In many modern applications the database side is realized using polyglot persistence – store each data format (graphs, documents, etc.) in an appropriate separate database. This approach yields several benefits, databases are optimized for their specific duty, however there are also drawbacks: * keep all databases in sync * queries might require data from several databases * experts needed for all used systems A multi-model database is not restricted to one data format, but can cope with several of them. In this talk i will present how a multi-model database can be used in a polyglot persistence setup and how it will reduce the effort drastically.

Michael Hackstein - Polyglot Persistence & Multi-Model NoSQL Databases - NoSQL matters Paris 2015 from NoSQLmatters

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0 Rob Harrop- Key Note The God, the Bad and the Ugly - NoSQL matters Paris 2015 /slideshow/rob-harrop-key-note/46360854 nosqlmatters-2015-150327075052-conversion-gate01
The impact that NoSQL has had on the technology community cannot be overstated. The proliferation of new and exciting data systems has led to a slew of interesting solutions to problems that were once solved the relational way. In this session we explore all that is great and good about NoSQL: the innovative software, the clever storage paradigms and the reigniting of developer interest in data access. It is unfortunate that NoSQL is not only a force for good in our community. We'll explore some of the darker corners of NoSQL: the disregard for years of proven technology, the overbearing hype, the overblown marketing and the ever present arguments over which technology is best. We close the session by exploring what can be done to extract even more value from the NoSQL movement, where we can improve how the community interacts with the larger technology community and what the future holds for data access technologies.]]>
The impact that NoSQL has had on the technology community cannot be overstated. The proliferation of new and exciting data systems has led to a slew of interesting solutions to problems that were once solved the relational way. In this session we explore all that is great and good about NoSQL: the innovative software, the clever storage paradigms and the reigniting of developer interest in data access. It is unfortunate that NoSQL is not only a force for good in our community. We'll explore some of the darker corners of NoSQL: the disregard for years of proven technology, the overbearing hype, the overblown marketing and the ever present arguments over which technology is best. We close the session by exploring what can be done to extract even more value from the NoSQL movement, where we can improve how the community interacts with the larger technology community and what the future holds for data access technologies.]]> Fri, 27 Mar 2015 07:50:52 GMT /slideshow/rob-harrop-key-note/46360854 NoSQLmatters@slideshare.net(NoSQLmatters) Rob Harrop- Key Note The God, the Bad and the Ugly - NoSQL matters Paris 2015 NoSQLmatters The impact that NoSQL has had on the technology community cannot be overstated. The proliferation of new and exciting data systems has led to a slew of interesting solutions to problems that were once solved the relational way. In this session we explore all that is great and good about NoSQL: the innovative software, the clever storage paradigms and the reigniting of developer interest in data access. It is unfortunate that NoSQL is not only a force for good in our community. We'll explore some of the darker corners of NoSQL: the disregard for years of proven technology, the overbearing hype, the overblown marketing and the ever present arguments over which technology is best. We close the session by exploring what can be done to extract even more value from the NoSQL movement, where we can improve how the community interacts with the larger technology community and what the future holds for data access technologies. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/nosqlmatters-2015-150327075052-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> The impact that NoSQL has had on the technology community cannot be overstated. The proliferation of new and exciting data systems has led to a slew of interesting solutions to problems that were once solved the relational way. In this session we explore all that is great and good about NoSQL: the innovative software, the clever storage paradigms and the reigniting of developer interest in data access. It is unfortunate that NoSQL is not only a force for good in our community. We'll explore some of the darker corners of NoSQL: the disregard for years of proven technology, the overbearing hype, the overblown marketing and the ever present arguments over which technology is best. We close the session by exploring what can be done to extract even more value from the NoSQL movement, where we can improve how the community interacts with the larger technology community and what the future holds for data access technologies.

Rob Harrop- Key Note The God, the Bad and the Ugly - NoSQL matters Paris 2015 from NoSQLmatters

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0 https://cdn.slidesharecdn.com/profile-photo-NoSQLmatters-48x48.jpg?cb=1523344230 www.nosql-matters.org https://cdn.slidesharecdn.com/ss_thumbnails/bugpredictionpresentation-150616070105-lva1-app6892-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/bug-prediction-presentation/49440892 Nathan Ford- Divinatio... https://cdn.slidesharecdn.com/ss_thumbnails/nosql15postgresnosql-150615063612-lva1-app6891-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/stefan-hochdrfer-the-nosql-store-everyone-ignores-postgresql-nosql-matters-dublin-2015/49391641 Stefan Hochdörfer - Th... https://cdn.slidesharecdn.com/ss_thumbnails/nosqlmatterskeynote-150610090811-lva1-app6891-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/adrian-colyer-keynote-nosql-matters-nosql-matters-dublin-2015/49211520 Adrian Colyer - Keynot...