狠狠撸shows by User: JimHatcher

狠狠撸shows by User: JimHatcher / http://www.slideshare.net/images/logo.gif 狠狠撸shows by User: JimHatcher / Sun, 16 Sep 2018 17:26:19 GMT 狠狠撸Share feed for 狠狠撸shows by User: JimHatcher GraphFrames Access Methods in DSE Graph /slideshow/graphframes-access-methods-in-dse-graph/114854744 rseaqtc6qokvc9phqj4e-signature-2e34102920e0083f704761cb8431d8a3db5bfbdbec76cfae9cab985ed287f855-poli-180916172619
GraphFrames is a powerful feature in Spark that allows you to harness Spark's distributed computing framework to operate on your Graph. Tasks like data ingestion, schema migrations, and analytical jobs can all be run against your Graph. In DSE Graph, there are several methods to leverage GraphFrames including Gremlin, Spark SQL, and Motif. This presentation walks through the basics of using GraphFrames with DSE Graph; then shows how these different methods can be used and how you can evaluate which one is the best for your use case.]]>
GraphFrames is a powerful feature in Spark that allows you to harness Spark's distributed computing framework to operate on your Graph. Tasks like data ingestion, schema migrations, and analytical jobs can all be run against your Graph. In DSE Graph, there are several methods to leverage GraphFrames including Gremlin, Spark SQL, and Motif. This presentation walks through the basics of using GraphFrames with DSE Graph; then shows how these different methods can be used and how you can evaluate which one is the best for your use case.]]> Sun, 16 Sep 2018 17:26:19 GMT /slideshow/graphframes-access-methods-in-dse-graph/114854744 JimHatcher@slideshare.net(JimHatcher) GraphFrames Access Methods in DSE Graph JimHatcher GraphFrames is a powerful feature in Spark that allows you to harness Spark's distributed computing framework to operate on your Graph. Tasks like data ingestion, schema migrations, and analytical jobs can all be run against your Graph. In DSE Graph, there are several methods to leverage GraphFrames including Gremlin, Spark SQL, and Motif. This presentation walks through the basics of using GraphFrames with DSE Graph; then shows how these different methods can be used and how you can evaluate which one is the best for your use case. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/rseaqtc6qokvc9phqj4e-signature-2e34102920e0083f704761cb8431d8a3db5bfbdbec76cfae9cab985ed287f855-poli-180916172619-thumbnail.jpg?width=120&height=120&fit=bounds" /> GraphFrames is a powerful feature in Spark that allows you to harness Spark's distributed computing framework to operate on your Graph. Tasks like data ingestion, schema migrations, and analytical jobs can all be run against your Graph. In DSE Graph, there are several methods to leverage GraphFrames including Gremlin, Spark SQL, and Motif. This presentation walks through the basics of using GraphFrames with DSE Graph; then shows how these different methods can be used and how you can evaluate which one is the best for your use case.

GraphFrames Access Methods in DSE Graph from Jim Hatcher

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0 Updates from Cassandra Summit 2016 & SASI Indexes /slideshow/updates-from-cassandra-summit-2016-sasi-indexes/66809946 cassandrameetup-updatesfromcasssummit20161005-161006132601
Updates from Cassandra Summit 2016 including a summary of technical notes from the keynote. Deeper dive on materialized views and SASI indexes.]]>
Updates from Cassandra Summit 2016 including a summary of technical notes from the keynote. Deeper dive on materialized views and SASI indexes.]]> Thu, 06 Oct 2016 13:26:01 GMT /slideshow/updates-from-cassandra-summit-2016-sasi-indexes/66809946 JimHatcher@slideshare.net(JimHatcher) Updates from Cassandra Summit 2016 & SASI Indexes JimHatcher Updates from Cassandra Summit 2016 including a summary of technical notes from the keynote. Deeper dive on materialized views and SASI indexes. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cassandrameetup-updatesfromcasssummit20161005-161006132601-thumbnail.jpg?width=120&height=120&fit=bounds" /> Updates from Cassandra Summit 2016 including a summary of technical notes from the keynote. Deeper dive on materialized views and SASI indexes.

Updates from Cassandra Summit 2016 & SASI Indexes from Jim Hatcher

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0 Using Spark to Load Oracle Data into Cassandra /slideshow/using-spark-to-load-oracle-data-into-cassandra/65992663 csummit-hatcher-usingsparktoloadoracledataintocassandra-160913210737
This presentation describes how you can use Spark as an ETL tool to get data from a relational database into Cassandra. I go through the concept in general and then talk about some specific issues you might run into and how to fix them.]]>
This presentation describes how you can use Spark as an ETL tool to get data from a relational database into Cassandra. I go through the concept in general and then talk about some specific issues you might run into and how to fix them.]]> Tue, 13 Sep 2016 21:07:36 GMT /slideshow/using-spark-to-load-oracle-data-into-cassandra/65992663 JimHatcher@slideshare.net(JimHatcher) Using Spark to Load Oracle Data into Cassandra JimHatcher This presentation describes how you can use Spark as an ETL tool to get data from a relational database into Cassandra. I go through the concept in general and then talk about some specific issues you might run into and how to fix them. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/csummit-hatcher-usingsparktoloadoracledataintocassandra-160913210737-thumbnail.jpg?width=120&height=120&fit=bounds" /> This presentation describes how you can use Spark as an ETL tool to get data from a relational database into Cassandra. I go through the concept in general and then talk about some specific issues you might run into and how to fix them.

Using Spark to Load Oracle Data into Cassandra from Jim Hatcher

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0 Introduction to Data Modeling in Cassandra /slideshow/introduction-to-data-modeling-in-cassandra/63993242 cassandrameetup20160711-160713151841
This is an introduction to data modeling in Cassandra. We cover the concept of denormalization and why and how to embrace that concept using Cassandra. We cover that a CQL table has a primary key that is composed of a partitioning key and clustering columns and why it's so important to get those right! And, we go through some examples.]]>
This is an introduction to data modeling in Cassandra. We cover the concept of denormalization and why and how to embrace that concept using Cassandra. We cover that a CQL table has a primary key that is composed of a partitioning key and clustering columns and why it's so important to get those right! And, we go through some examples.]]> Wed, 13 Jul 2016 15:18:40 GMT /slideshow/introduction-to-data-modeling-in-cassandra/63993242 JimHatcher@slideshare.net(JimHatcher) Introduction to Data Modeling in Cassandra JimHatcher This is an introduction to data modeling in Cassandra. We cover the concept of denormalization and why and how to embrace that concept using Cassandra. We cover that a CQL table has a primary key that is composed of a partitioning key and clustering columns and why it's so important to get those right! And, we go through some examples. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cassandrameetup20160711-160713151841-thumbnail.jpg?width=120&height=120&fit=bounds" /> This is an introduction to data modeling in Cassandra. We cover the concept of denormalization and why and how to embrace that concept using Cassandra. We cover that a CQL table has a primary key that is composed of a partitioning key and clustering columns and why it's so important to get those right! And, we go through some examples.

Introduction to Data Modeling in Cassandra from Jim Hatcher

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0 5 Ways to Use Spark to Enrich your Cassandra Environment /slideshow/5-ways-to-use-spark-to-enrich-your-cassandra-environment/62185475 cassandrameetuppresentation-160519132626
Apache Cassandra is a powerful system for supporting large-scale, low-latency data systems, but it has some tradeoffs. Apache Spark can help fill those gaps, and this presentation will show you how.]]>
Apache Cassandra is a powerful system for supporting large-scale, low-latency data systems, but it has some tradeoffs. Apache Spark can help fill those gaps, and this presentation will show you how.]]> Thu, 19 May 2016 13:26:26 GMT /slideshow/5-ways-to-use-spark-to-enrich-your-cassandra-environment/62185475 JimHatcher@slideshare.net(JimHatcher) 5 Ways to Use Spark to Enrich your Cassandra Environment JimHatcher Apache Cassandra is a powerful system for supporting large-scale, low-latency data systems, but it has some tradeoffs. Apache Spark can help fill those gaps, and this presentation will show you how. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cassandrameetuppresentation-160519132626-thumbnail.jpg?width=120&height=120&fit=bounds" /> Apache Cassandra is a powerful system for supporting large-scale, low-latency data systems, but it has some tradeoffs. Apache Spark can help fill those gaps, and this presentation will show you how.

5 Ways to Use Spark to Enrich your Cassandra Environment from Jim Hatcher

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0 https://public.slidesharecdn.com/v2/images/profile-picture.png https://cdn.slidesharecdn.com/ss_thumbnails/rseaqtc6qokvc9phqj4e-signature-2e34102920e0083f704761cb8431d8a3db5bfbdbec76cfae9cab985ed287f855-poli-180916172619-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/graphframes-access-methods-in-dse-graph/114854744 GraphFrames Access Met... https://cdn.slidesharecdn.com/ss_thumbnails/cassandrameetup-updatesfromcasssummit20161005-161006132601-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/updates-from-cassandra-summit-2016-sasi-indexes/66809946 Updates from Cassandra... https://cdn.slidesharecdn.com/ss_thumbnails/csummit-hatcher-usingsparktoloadoracledataintocassandra-160913210737-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/using-spark-to-load-oracle-data-into-cassandra/65992663 Using Spark to Load Or...