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Displaying posts with tag: big data (reset)

Big Data: Three questions to Aerospike.
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“Many tools now exist to run database software without installing software. From vagrant boxes, to one click cloud install, to a cloud service that doesn’t require any installation, developer ease of use has always been a path to storage platform success.”–Brian Bulkowski.

The fifth interview in the “Big Data: three questions to “ series of interviews, is with Brian Bulkowski, Aerospike co-founder and CTO.

RVZ

Q1. What is your current product offering?

Brian …

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Real-Time Replication from MySQL to Cassandra
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Earlier this month I blogged about our new Hadoop applier, I published the docs for that this week (http://docs.continuent.com/tungsten-replicator-3.0/deployment-hadoop.html) as part of the Tungsten Replicator 3.0 documentation (http://docs.continuent.com/tungsten-replicator-3.0/index.html). It contains some additional interesting nuggets that will appear in future blog posts.

The main part of that functionality that performs the actual …

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MySQL Cluster to Hadoop
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How do you get data from a MySQL Cluster into Hadoop? Easy, replicate from the cluster to a stand alone MySQL instance and from there use the MySQL Hadoop Applier to HDFS.

This question came from a long time MySQL user who has jumped into the Big Data world.


No Hadoop Fun for Me at SCaLE 12X :(
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I blogged a couple of weeks ago about my upcoming MySQL/Hadoop talk at SCaLE 12X. Unfortunately I had to cancel. A few days after writing the article I came down with an eye problem that is fixed but prevents me from flying anywhere for a few weeks. That's a pity as I was definitely looking forward to attending the conference and explaining how Tungsten replicates transactions from MySQL into HDFS.

Meanwhile, we are still moving at full steam with Hadoop-related work at Continuent, which is the basis for the next major …

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Why Aren't All Data Immutable?
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Over the last few years there has been an increasing interest in immutable data management. This is a big change from the traditional update-in-place approach many database systems use today, where new values delete old values, which are then lost. With immutable data you record everything, generally using methods that append data from successive transactions rather than replacing them.  In some DBMS types you can access the older values, while in others the system transparently uses the old values to solve useful problems like implementing eventual consistency.

Baron Schwartz …

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Getting Data into Hadoop in real-time
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Moving data between databases is hard. Without ever intending it, I seem to have spent a lifetime working on solutions for getting data into and out of databases, but more frequently between. In fact, my first job out of university was migrating data from BRS/Text, a free-text database (probably what we would call a NoSQL) into a more structured Oracle.

Today I spend some of my time working in Big Data, more often than not, migrating information from existing data stores into Big Data so that they can be analysed, something I covered in more detail here:

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Fun with MySQL and Hadoop at SCaLE 12X
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It's my pleasure to be presenting at SCaLE 12X on the subject of real-time data loading from MySQL to Hadoop.  This is the first public talk on work at Continuent that enables Tungsten Replicator to move transactions from MySQL to HDFS (Hadoop Distributed File System).  I will explain how replication to Hadoop works, how to set it up, and offer a few words on …

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Amazon’s Big Data Suite – Part 2
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In Part 1 we started our study of Amazon Services and looked at Amazon EC2. In this part, we will look at other Amazon services like EMR, DynamoDB and RDS.

 

  1. 1.      Amazon Elastic Map Reduce

Amazon EMR is a web service which makes cloud computing very easy. Amazon’s EMR cluster comes preconfigured with Hadoop, which as mentioned earlier is a data processing and storage framework. This preconfiguration makes it very easy to start analysing …

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SQL to Hadoop and back again, Part 3: Direct transfer and live data exchange
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The third, and final article in my series on migrating data to and from Hadoop and SQL databases is now available:

Big data is a term that has been used regularly now for almost a decade, and it — along with technologies like NoSQL — are seen as the replacements for the long-successful RDBMS solutions that use SQL. Today, DB2®, Oracle, Microsoft® SQL Server MySQL, and PostgreSQL dominate the SQL space and still make up a considerable proportion of the overall market. In this final article of the series, we will look at more automated solutions for migrating data to and from Hadoop. In the previous …

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Log Buffer #346, A Carnival of the Vanities for DBAs
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Economist says that Physics suggest that storms will get worse as the planet warms. Typhoon Haiyan in Philippines, bush-fires in Australia, floods in China, and extreme unpredictable weather across the planet is a sober reminder. Good news is that technology and awareness is rising, and so is the data. Database technologies are playing their part to intelligently store that data and enabling the stakeholders to analyze and get meaningful results to predict and counter the extreme conditions. This Log Buffer Edition appreciates these efforts.

Big Data:

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10 Newer Entries Showing entries 31 to 40 of 183 10 Older Entries

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