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Displaying posts with tag: Impala (reset)

Using InfiniDB MySQL server with Hadoop cluster for data analytics
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In my previous post about Hadoop and Impala I benchmarked performance of analytical queries in Impala.

This time I’ve tried InfiniDB for Hadoop (open-source version) on the modern hardware with an 8-node Hadoop cluster. One of the main advantages (at least for me) of InifiniDB for Hadoop is that it stores the data inside the Hadoop cluster but uses the MySQL server to execute queries. This allows for an easy “migration” of existing analytical tools. The results are quite interesting and promising.

Quick How-To

The InfiniDB documentation is not very clear on step-by-step instructions so I’ve created this

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Using Apache Hadoop and Impala together with MySQL for data analysis
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Apache Hadoop is commonly used for data analysis. It is fast for data loads and scalable. In a previous post I showed how to integrate MySQL with Hadoop. In this post I will show how to export a table from  MySQL to Hadoop, load the data to Cloudera Impala (columnar format) and run a reporting on top of that. For the examples below I will use the “ontime flight performance” data from my previous post (Increasing MySQL performance with parallel query execution). I’ve used the

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MySQL webinar: ‘Introduction to open source column stores’
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Join me Wednesday, September 18 at 10 a.m. PDT for an hour-long webinar where I will introduce the basic concepts behind column store technology. The webinar’s title is: “Introduction to open source column stores.”

What will be discussed?

This webinar will talk about Infobright, LucidDB, MonetDB, Hadoop (Impala) and other column stores

  • I will compare features between major column stores (both open and closed source).
  • Some benchmarks will be used to demonstrate the basic
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Deploying Cloudera Impala on EC2 with Example Live Demo
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A little while ago I blogged about (and open sourced) an Impala-powered soccer visualization demo, designed to demonstrate just how responsive Impala queries can be. Since not everyone has the time or resources to run the project themselves, we’ve decided to host it ourselves on an EC2 instance. You can try the visualization; we’ve also opened up the Impala web interface, where you can see query profiles and performance numbers, and Hue (username and password are both ‘test’), where you can run your own queries on the dataset.

Deploying  [Read more...]

The Data Day, Two days: January 15/16 2013
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Funding for Ayasdi and Zettaset. NuoDB launches cloud database. And more

For 451 Research clients: NuoDB launches distributed ‘cloud data management system’ bit.ly/UO3ssM

— Matt Aslett (@maslett) January 15, 2013

For 451 clients: Armed with $20m series C, Lattice Engines looks to bring sales intelligence inside bit.ly/11z4VdF By Krishna Roy

— Matt Aslett (@maslett) January 16, 2013

Ayasdi Launches with $10 Million from Khosla Ventures and FLOODGATE. bit.ly/X7oemJ

— Matt Aslett (@maslett)

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Typical “Big” Data Architecture
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Here is the typical “Big” data architecture, that covers most components involved in the data pipeline. More or less, we have the same architecture in production in number of places[...]
Showing entries 1 to 6

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