For the past years since its launch, the Hadoop application has been widely regarded as the number one ally of organizations for managing and processing Big Data. Information Week says that Fortune 500 companies are steadily adopting it, and some of its biggest customers include legendary international tech brands like Google, Facebook, LinkedIn, and Yahoo. ITProPortal further forecasts its revenues to climb up to $84 billion in five years’ time.
The attraction of Hadoop lies in the way it categorizes, organizes, retrieves, and interlinks millions of bits of seemingly unrelated information. The secret to its efficiency lies in its methodology of processing all these data simultaneously in parallel across different computing nodes. The creators of Hadoop rightly deduced that the IT business world’s unstoppable growth of data will soon become too large to be taken care of by one database or one computer. They designed Hadoop to divide all this massive stockpile of information, now known as Big Data, into a more manageable scale and store them in various structures.
This delegation of data management under one processing platform is nothing short of revolutionary. For example, calling up specific files from the database became faster because several computing systems look for them. The files are easier to locate because different nodes classify them according to data, or other categories chosen by the IT administrator. Traditional computing that searches for these files in one database is like looking for one folder in the standard office filing cabinet that has only one partition. You must comb through each folder to find that one document you need. Finally, Hadoop could find the connections between these seeming disparate bits of data to come up with a statistical analysis.
Hadoop processing can become a powerful tool in data containment and security, and in the sourcing of business intelligence needed by decision-makers. In comparison, the old database model of the IT infrastructure of the 1990s looks like a mere storage compartment.
The tools, layers, job scheduling functions, applications, and other devices that empower Hadoop to connect and manage Big Data have actually made Hadoop transcend its mere search-and-store functionalities to become a fully-functional ecosystem. Place this entire operating data management and analysis process under a Workload Automation solution and it becomes more efficient, productive, and cost-effective.
First, automating the entire Hadoop workflow can accelerate its operations and ensure the delivery of service-level agreements. Certain job tasks such as the classification of data, file retrieval on specific occasions, and more extensive data correlation for research projects can be scheduled and monitored. Batch processing management will make sure that each task is performed according to the time frame, and none is neglected. Data can be moved between the Hadoop clusters without the added effort of creating new scripts.
Second, Workload Automation can use Hadoop as a platform to assist organizations in complying with industry standards. One example is the financial sector’s fulfilling the requirements of the Fundamental Review of Trading Book. IT administrators and organization heads can automate the generation of necessary reports and documents in Hadoop that will guide them in their compliance efforts. Hadoop can perform this task efficiently because it can import and export huge amounts of diverse data from various sources in the IT structure without putting it under a lot of strain.
Finally, managing the Hadoop process through a Workload Automation solution provides IT administrators and their organization heads more maneuvering ground in conserving their resources. Automating tasks can free human personnel to do more creative work. It will also make unnecessary the yearly upgrading of database equipment and other solutions helping the organization save more money.
Integrating Hadoop into your IT system equals creating a functioning artificial ecosystem that will make the non-stop flow of data into your organization a valuable tool in your arsenal. Employing Workload Automation to manage Hadoop will make that ecosystem an even more powerful organic partner in your organization’s strides toward business success.