Definition of Hadoop
What does is it mean and how is it used
Why use Hadoop?
Hadoop saves time by giving you a single platform for analytics as you can use it to get valuable data analytics from any source. It also has a variety of functions such as data warehousing, fraud detection, and market campaign analysis. It’s relatively cheap to set up and it’s easy to make multiple copies of your data.
On the downside, real-time analytics can be slow – especially if your data is growing as all those copies will start to slow down your systems. And with GDPR you might want to look at security – although Java is a common language now, if you put all your sensitive data in this language it is easy for cyber criminals to hack.
Did you know?
Doug Cutting, one of the two creators of Hadoop, named it after his son’s stuffed toy elephant.
Hadoop is based on a research paper Google released in late 2004 describing the MapReduce model they were using internally at the time for their search engine.
Latest Hadoop Insights
Hadoop is able to store massive amounts of data on cost-effective, commodity hardware. While it excels at storing massive amounts of data it has trouble with real-time data analysis. Read on to learn how the limitations of Hadoop can be amended to make it fitting to a growing organization’s analytics needs.
Spark is suitable to complement Hadoop as well as some components of the Hadoop concepts can be partially replaced by it. Find out how Exasol can maximize Apache Spark’s performance to provide a superior solution for your business needs.
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