In 2015, we are standing in the prime era of big data. Analytics platforms are evolving day by day, just like the way they had continuously evolved in past years, but now it’s time for implementation.
For the very long time, IT departments have been burdened with data collection in service of optimizing and automating all types of business processes. This phenomenon in turning inward as high number of IT teams are accumulating petabytes of sensor, raw machine and log data in hopes of visualizing and after that optimizing their very own operations.
Getting meaningful outcome from such massive data quantity is quite challenging. An ecosystem concerning IT operations management solutions is functioning around the use of Hadoop-based, open source data lakes. As the technology gradually matures, companies are moving from batch analytics and storage to real-time data processing and streaming built on modular, flexible platforms. Because early adopters have visibly won significant competitive advantage with the help of their data initiatives, analytics are becoming mainstream, making way for the new wave of impressive solutions.
Data lakes are faster and easier solution to park and process ginormous amounts of data (unstructured) from multiple sources; the most impressive salient feature of Hadoop is you do not requires schema-on-write necessarily. That way data lake is a timely solution for corporates knowing they have quite large amount of valuable data but aren’t sure of what to do with that data yet. Data scientists will also hugely benefit from conducting experiments in such an evolving and open framework.
However, depending on the data case, data type, or desired outcome, the lack of ideal structure can prove to be a major drawback. The addition of information to a data lake does not carry any metadata, and without a modicum of governance and curating, it is challenging to determine the provenance and quality of the data.
On other side, data warehouses organize and sanitize data upon entry, enabling predictable and consistent analysis across structures that are pre-categorized. The capacity to replicate standard reports and queries over the time covering uniform datasets is critical to many enterprises. In another words, data warehouses leads to value creating that can’t be replaced by data lakes, irrespective of the fact that how flexible and critical they are.
Be it data lacks or data warehouses, at the end corporates must get their basics right. Analyzing and measuring the right things, involving efficient stakeholders and asking the right questions at the right time always leads to success.
In business, after a meaty problem has been identified and then assessed, the task of picking ideal tools gets easier that can provide with optimum business solution. Sometimes it requires data analysis in rigid silos; while in some instances it requires sample drawing from a fluid pool filled with nothing but data.
As next-generation analytics are evolving continuously, without any doubt, human being will invent new approaches that can perfectly blend these models that to get deeper level of knowledge and beyond.