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Displaying posts with tag: hadoop (reset)

Using Apache Spark and MySQL for Data Analysis
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What is Spark

Apache Spark is a cluster computing framework, similar to Apache Hadoop. Wikipedia has a great description of it:

Apache Spark is an open source cluster computing framework originally developed in the AMPLab at University of California, Berkeley but was later donated to the Apache Software Foundation where it remains today. In contrast to Hadoop’s two-stage disk-based MapReduce paradigm, Spark’s multi-stage in-memory primitives provides performance up to 100 times faster …

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Log Buffer #443: A Carnival of the Vanities for DBAs
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This Log Buffer Edition finds and publishes blog posts from Oracle, SQL Server and MySQL.


  • SCRIPT execution errors when creating a DBaaS instance with local and cloud backups.
  • Designing Surrogate vs Natural Keys with Oracle SQL Developer.
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Replication in real-time from Oracle and MySQL into data warehouses and analytics
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Analyzing transactional data is becoming increasingly common, especially as the data sizes and complexity increase and transactional stores are no longer to keep pace with the ever-increasing storage. Although there are many techniques available for loading data, getting effective data in real-time into your data warehouse store is a more difficult problem. VMware Continuent provides

What’s the latest with Hadoop
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The Big Data explosion in recent years has created a vast number of new technologies in the area of data processing, storage, and management. One of the biggest names to appear on the scene is Hadoop. In case you need a quick review, Hadoop is a Big Data storage system that takes in large amounts of data from servers and breaks it into smaller, manageable chunks. The technology is complex but at a high level the Hadoop ecosystem essentially takes a “divide and conquer” approach to processing Big Data instead of processing data in tables, as in a relational database like …

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2015: More innovation, but still a year of transition
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First things first: I could use this title for every year, it is an evergreen. In order for this title to make sense, there must be a specific context and in this case the context is Big Data. We have seen new ideas and many announcements in 2014, and in 2015 those ideas will shape up and early versions of innovative products will start flourishing.Like many other people, I prepared some comments and opinions to post back in early January then, soon after the season’s break, I started flying around the world and the daily routine kept me away from the blog for some time. So, as a good last blogger, it may be time for me to post my own predictions, …

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On Hadoop RDBMS. Interview with Monte Zweben.
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“HBase and Hadoop are the only technologies proven to scale to dozens of petabytes on commodity servers, currently being used by companies such as Facebook, Twitter, Adobe and”–Monte Zweben.

Is it possible to turn Hadoop into a RDBMS? On this topic, I have interviewed Monte Zweben, Co-Founder and Chief Executive Officer of Splice Machine.


Q1. What are the main challenges of applications and operational analytics that support real-time, interactive queries on data updated in real-time for Big Data?

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Exorcising the CAP Demon
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Computer science is like an enormous tool box you can rummage through whenever you have a problem to solve. Most of the tools are sturdy and practical, like algorithms for B-trees. Some are also elegant, like consistent hashing in Dynamo. Finally there are some tools that you never quite figure out even after years of reflection. That piece of steel you are looking at could be Excalibur. Or it could be a rusty knife.

The CAP theorem falls into the last category, at least for me.  It was a major topic in the blogosphere a few years ago and Google Trends shows …

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New Continuent Tungsten 3.0 Combines Power of Highly Available Open Source DBMS with Real-Time Analytics
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Business Wire  Oracle Open World 2014, Booth # 430- Continuent, Inc., a leading provider of open source database clustering and replication solutions, today announced Continuent Tungsten 3.0, a powerful solution that combines advanced clustering and replication technologies to meet the transaction processing and analytic needs of the entire business. Continuent Tungsten 3.0 enables constant,

Sneak Peek: Continuent Tungsten 3.0
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Get a preview of the next advance in data management technology!  Continuent Tungsten 3.0 brings the power of advanced clustering and replication to offer data management needs for your entire business including MySQL high availability, disaster recovery, multi-master operation, and real-time data warehouse loading. With Continuent Tungsten you can apply the full power not just of MySQL but all

Replicating from MySQL to Amazon Redshift
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Continuent is delighted to announce an exciting Continuent Tungsten feature addition for MySQL users: replication in real-time from MySQL into Amazon RedShift.  

In this webinar-on-demand we survey Continuent Tungsten capabilities for data warehouse loading, then zero in on practical details of setting up replication from MySQL into RedShift.  We cover:

Introduction to real-time movement

Showing entries 1 to 10 of 150 10 Older Entries

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