Hadoop is a Java-based, open source framework that supports companies in the storage and processing of massive data sets. Currently, many firms still struggle with interpreting Hadoop’s software and are doubtful about whether or not they can depend on it for delivering projects. Even so, it's essential to understand just how much Hadoop enables businesses to do. When it comes to analyzing large amounts of data at low cost, it's hard to do better. Before Hadoop emerged, businesses relied on expensive servers for their data analysis. Now the process has become a lot more organized and a lot more efficient.
Hadoop functions by distributing gigantic data sets across hundreds of inexpensive servers that operate parallel to one another. It is also a cost-effective storage solution for businesses making use of data sets. Hadoop's unique storage method is based on a distributed file system that basically 'maps' data wherever it is located in a cluster. When it comes to handling large data sets in a safe and cost-effective manner, Hadoop has the advantage over relational database management systems, and its value will continue to increase for businesses of all sizes as our world's caches of unstructured data continue to increase all around the world. For this reason, leveraging Hadoop's big data services is of growing importance to more organizations than ever before. This is why the International Institute for Analytics along with SAS has put forward 5 steps for maximizing the value of Hadoop big data services.
Formulating a Strategic Plan
First and foremost, focus on a target audience. The best way to do this is to examine the behavior of customers. The next thing to do is to select a particular data set that is not presently part of any other study in the enterprise data warehouse. The reason for conducting such a study is to obtain insights and feedback from the target audience about the brand and how effective your particular plan/service/commodity will be in the event that your business decides to test it out on the market.
An intelligent way to define and recognize the use cases is by using BAMA(SAS Business Analytic Modernization Assessment). Usually, this service helps in widening the use of analytics in the company and facilitates a smooth communication between the business units and IT.
Weighing the Benefits and Drawbacks of Hadoop
In the past, most companies have been dependent on analytics and business intelligence projects like data warehouses for storing their data. This is because there are times when a data warehouse is still a more reliable tool (though Hadoop is still a much more cost-effective data storage option). Nevertheless, most industry veterans strongly believe that in the years ahead, Hadoop will prove its worth by emerging as a formidable competitor.
Hadoop is not a good option for real-time processing of records that are small in number, but it is perfect for storing things like sensor data. Hadoop can be used to store sensor data as long as the collection of data from sensors is distributed across a large Hadoop cluster of commodity servers – all processing in parallel and ensuring very fast data-processing. For maximum efficiency, store large data types in Hadoop clusters. Then they can be passed on to an enterprise data warehouse whenever a production application is needed.
Augmenting Hadoop for Delivering Value Results
After gaining a better understanding of your software and applying it to attain insights regarding your company's specific needs, the next task is to begin manipulating and managing your data in a manner that continues to be relevant to your goals. While doing so, be sure to select tools that are capable of keeping pace with Hadoop.
Intelligently organizing the overall time to value will further acquaint you with the capabilities of Hadoop. How can this be done? First be sure to have reliable access to the data stored in Hadoop or elsewhere, whenever you need it. You can traverse millions of data rows in seconds and then work with data in Hadoop without the need to move it between different platforms.
Reassess the Need for Governance and Data Integration
The results of a data analysis project obtained here may be used for developing large-scale business strategies. Two major elements are governance and data integration. For these, it is essential to make sure that all the data that is gathered arrives from an authentic, clean source. The organization’s data governance practices must allow it to have the highest standards of confidence in their information sources, and be able to identify faults in the event of manipulations.
Consider Utilizing the Cloud
Instead of trying to figure out how much additional infrastructure you require for analyzing and processing your data, consider utilizing the cloud. Many cloud-based services like AWS(Amazon Web Services) provide subscription services like DynamoDB(a NoSQL Database) or Elastic MapReduce(EMR) for processing big data. AppEngine, the Google’s cloud application hosting service also provides a MapReduce tool.
Provide Self Service
It is critical to offer self-service access for business users. This will provide advantageous insights from data sets as you integrate more information into your business Intelligence framework. Offering built-in drag and drop fields in order to perform iterative and custom analysis is also a very useful way to streamline data analysis tasks and may also help you uncover previously hidden opportunities for creating value. It's also helpful when you are processing and storing data.
Assessing Gaps and Developing a Strategic Plan
Today, big data is only in its initial phase of development. The skills needed to handle a project of any size are only going to continue growing in demand. In order to use Hadoop software productively, expertise is needed in programming languages such as Pig, Sqoop, MapReduce and Hive. Employ people who have these skills or provide sufficient training to your in-house team to become proficient in these programming languages. By following these as well as the other steps specified above, you can maximize the services of Hadoop and achieve best possible results.
Originally published in DATACONOMY