From Amazon to Splunk, here's a look at the big data innovators that are now pushing Hadoop, NoSQL and big data analytics to the next level.
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Amazon Covers All Big-Data Bases Amazon is about as big a big data practitioner as you can get. It's also the leading big data services provider. For starters, it introduced Elastic MapReduce (EMR) more than three years ago. Based on Hadoop, EMR isn't just a service for MapReduce sand boxes; it's being used for day-to-day high-scale production data processing by businesses including Ticketmaster and DNA researcher Ion Flux.
Amazon Web Services upped the big data ante in 2012 with two new services: Amazon DynamoDB, a NoSQL database service, and Amazon Redshift, a scalable data warehousing service now in preview and set for release early next year.
DynamoDB, the service, is based on Dynamo, the NoSQL database that Amazon developed and deployed in 2007 to run big parts of its massive consumer website. Needless to say, it's proven at high scale. Redshift has yet to be generally available, but Amazon is promising ten times faster performance than conventional relational databases at one-tenth the cost of on-premises data warehouses. With costs as low as $1,000 per terabyte, per year, there's no doubt Redshift will see adoption.
These three services are cornerstones for exploiting big data, and don't forget Amazon's scalable S3 storage, EC2 compute capacity and myriad integration and connection options for corporate data centers. In short, Amazon has been a big data pioneer, and its services appeal to more than just startups, SMBs and Internet businesses.
6 Tools to Protect Big DataMost IT teams have their conventional databases covered in terms of security and business continuity. But as we enter the era of big data, Hadoop, and NoSQL, protection schemes need to evolve. In fact, big data could drive the next big security strategy shift.
Big Data Brings Big Security ProblemsWhy should big data be more difficult to secure? In a word, variety. But the business wonít wait to use it to predict customer behavior, find correlations across disparate data sources, predict fraud or financial risk, and more.
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