One of the analysts we work with in the big data space says he’s feeling like it’s 1995 all over again. Why? Because Hadoop is so cheap that people are starting to replicate data again. It reminds him of the early days of the data warehousing craze. Remember that? When replicating data for different purposes was the norm?
Then we hit the 2010 time frame and we were talking about having too much data to store. And definitely too much to copy and collect in multiple systems. Now, with Hadoop entering the mainstream, you can just spin up a cluster, grab the data, make a copy and store it for later.
If you don’t think Hadoop is important to your company or your industry, think again. This is incredibly important to you. There is an opportunity to store tons and tons of data in Hadoop at a fraction of the cost compared to what you’re paying with relational database systems.
Loading data into Hadoop
What did you do in the past if you wanted to capture a bunch of data? You had to call IT, ask to allocate a few terabytes of storage, take these data sources and load them up. Next, you had to return to IT to request access to the data. What’s wrong with that? It’s expensive and time consuming, and it increases license fees for storage and data use.
The alternative is a Hadoop data loading system that makes it easy for data scientists to gain access to data and prep it without an IT request for data management support. Data scientists can play an important role here in reducing the workload for IT and gaining self-service access to Hadoop.
Developing an analytic architecture
What other opportunities does Hadoop create? And how can you make Hadoop successful in your environment?
You need to think of Hadoop as more than a simple storage container. Instead, look at Hadoop as a modern analytic architecture where you can:
- Load and store data without limiting it to a tabular format.
- Use visual analytics to explore the data in the location where new data is continually ingested and available for analysis.
- Persist high-performance analytics right inside your Hadoop clusters.
- Conduct analytic procedures inside the clusters using in-memory capabilities.
With these options, you can use Hadoop more strategically – and without learning a new programming language. You don’t just store data there and pull it out when it’s time to analyze. Instead, you can send processing requests down to the Hadoop cluster and use in-memory capabilities to analyze the data that is stored there.
Find out more about cleansing, processing and preparing data in Hadoop.