ETL is a broad based term usually used for syncing,copying and enriching of data particularly in the context of enterprises. Enterprises keep upgrading and leveraging newer and disruptive technologies all the time. Over the years, this leaves them with whole bunch of disparate systems that need to interact with each other in some form and shape. Be it batch or near real-time or on-demand business driven sharing of data, the ETL has to occur. First came, desktop applications, then web-apps, followed by mobile and now social and a flood of IoT devices. Add to that the regulatory and compliance data retention requirements. HIPAA, PCI, Sarbanes Oxley, Basel III- the list goes on.
The advent of Big Data has created a never before need for ETL-ing of data between the traditional and older systems that are mostly based on RDBMSes or plain file system based storage to Big Data databases. Big Data-storage and processing, both being inherently distributed, throws up new challenges to the enterprise. There are some more things that enterprises have to deal with along side the original challenge of the Big data technology adoption. Here are a few of them:
- How to best leverage the cloud technologies with Big data so that cost of ownership can be optimized to the fullest.
- Identify the most suitable use cases that enterprise should embark on first. It shouldn’t happen that starting on the wrong foot makes it a non starter. This will leave the enterprise vulnerable to the competitors who get the edge that Big Data technology brings to the table.
- The Devops paradigm adoption wherein there are no silos anymore between the developers, server admins and operations workforce. The admins should be fast at provisioning resources while the operations should be monitoring fast enough and most importantly none of these groups should be babysitting on one another.
- Add to that the logistical issue of skill shortage and steep learning curve involved in making the current IT workforce be useful in leveraging these technologies.
All these factors are resulting in need for a layer of abstraction that could shield an enterprise from following pain points:
- ease of getting started in a short period of time.
- without much upfront Big Data expertise availability and investment on “day one”.
- a future-proof layer that insures, evolution of newer Big Data technologies and release of more matured builds of existing Big Data tools, will have little or no impact on the enterprises’ Hadoop journey.
- most importantly, it will enable the enterprise for handling new age data sources that are characterized by the volume, velocity and variety. Enterprise will become more comfortable and prepared to handle data emanating out of social, mobile and IoT apps and factor it in while unearthing valuable insights.
There is a need for an intuitive easy to use self-service ETL bridge that will help overcome this gaping missing piece in the whole Big Data puzzle. The commercial Hadoop vendors like Hortonworks, Cloudera and Map-R are doing their bit to fill these gaps. However the enterprise is much complex animal to tame. We are talking about technologies that are not just disruptive but also that are a completely new platform that will influence future IT road-maps of organizations and how the technology will impact the enterprises’competitiveness. Big data is no more the digital native organizations’ cup of tea. It is fast becoming the holy grail to stay competitive especially when we talk about use cases like analytics, regulatory requirements and operational excellence through actionable insights. Enterprises really need a tool that comes with the in-built agility needed to quickly get them off the ground and see for themselves the value that Big data brings to the table.
What is your Big Data strategy, how far are you in your hadoop journey. Share with us. Contact us for a free assessment at email@example.com or visit www.trehanz.com. Or give us a call at +1.925.400.8475 to discuss the possibilities and opportunities waiting for you on this new IT horizon.