Big Data Discussions

Big Data Discussions

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Hadoop and Big Data

Doug Cutting, Cloudera’s Chief Architect, helped create Apache Hadoop out of necessity as data from the web exploded, and grew far beyond the ability of traditional systems to handle it. Hadoop was initially inspired by papers published by Google outlining its approach to handling an avalanche of data, and has since become the de facto standard for storing, processing and analyzing hundreds of terabytes, and even petabytes of data.

Apache Hadoop is 100% open source, and pioneered a fundamentally new way of storing and processing data. Instead of relying on expensive, proprietary hardware and different systems to store and process data, Hadoop enables distributed parallel processing of huge amounts of data across inexpensive, industry-standard servers that both store and process the data, and can scale without limits. With Hadoop, no data is too big. And in today’s hyper-connected world where more and more data is being created every day, Hadoop’s breakthrough advantages mean that businesses and organizations can now find value in data that was recently considered useless.


Reveal Insight From All Types of Data,
From All Types of Systems

Hadoop can handle all types of data from disparate systems: structured, unstructured, log files, pictures, audio files, communications records, email – just about anything you can think of, regardless of its native format. Even when different types of data have been stored in unrelated systems, you can dump it all into your Hadoop cluster with no prior need for a schema. In other words, you don’t need to know how you intend to query your data before you store it; Hadoop lets you decide later and over time can reveal questions you never even thought to ask.

By making all of your data useable, not just what’s in your databases, Hadoop lets you see relationships that were hidden before and reveal answers that have always been just out of reach. You can start making more decisions based on hard data instead of hunches and look at complete data sets, not just samples.

Download this whitepaper to learn the history of Apache Hadoop.


Redefine the Economics of Data:
Keep Everything, Forever, Online

In addition, Hadoop’s cost advantages over legacy systems redefine the economics of data. Legacy systems, while fine for certain workloads, simply were not engineered with the needs of Big Data in mind and are far too expensive to be used for general purpose with today’s largest data sets.

One of the cost advantages of Hadoop is that because it relies in an internally redundant data structure and is deployed on industry standard servers rather than expensive specialized data storage systems, you can afford to store data not previously viable. And we all know that once data is on tape, it’s essentially the same as if it had been deleted – accessible only in extreme circumstances.

Enterprises who build their Big Data around Cloudera can afford to store literally all the data in their organization, and keep it all online for real-time interactive querying, business intelligence, analysis and visualization.

Watch Cloudera and CNBC discuss Big Data and Hadoop.


Restructure Your Thinking:
Make Big Data the Lifeblood of Your Enterprise

With data growing so rapidly and the rise of unstructured data accounting for 90% of the data today, the time has come for enterprises to re-evaluate their approach to data storage, management and analytics. Legacy systems will remain necessary for specific high-value, low-volume workloads, and complement the use of Hadoop -optimizing the data management structure in your organization by putting the right Big Data workloads in the right systems. The cost-effectiveness, scalability, and streamlined architectures of Hadoop will make the technology more and more attractive. In fact, the need for Hadoop is no longer a question. The only question now is how to take advantage of it best, and the enterprise-proven answer is Cloudera.


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