During previous some years, there have been several technologies on the rise which are aimed at fulfilling the needs of analytics on big data. With the growing complexities, larger corporations are now in need of more than one tool for data analytics and data sourcing. They can get this data from a wide variety of sources like warehouse clouds which may be structured and non-structured.
2. Establishment of manmade data lakes
It is similar to the concept of building a dam on a river and then utilizing the water for different purposes. First, a cluster, a data reservoir is built and data is gathered in it. Once the sufficient quantity has been gathered, this data may be used for various purposes. At this point the data analytics tool walk in to play their role.
Lately, it was common for every organization to have its own data lake but this trend will be changing now. At this point, the corporations more critically analyses their data needs before investing into any infrastructure – be it personnel or data. Companies now demand a repetitive, efficient and effective use of data lakes for better information. It will lead to stronger collaborations between business and IT.
3. Variety is key driver of big data investments
Big data is defined by three V’s, volume, velocity and variety. Currently all three V’s are registering an enormous growth. But in the near future, the variety will be the key driver behind the big data investments. This trend will be on rise as the more and more business will be aiming to consolidate the data coming from different sources, therefore incorporating more variety of high quality data. There are a lot of new data formats like schema-free JSON, nested type databases and non-flat data. In the future, the performance of all the data analytics tools shall be measured by their ability to provide real time connection to data sources.
5. Big data being shaped by the end user
There is going to be a rise in the self-service data analytics platforms. Keeping it in mind, business users are now demanding a reduction in the complexity and time needed to prepare the data analysis as they have to deal with the data from a number of variety and sources. This data is often in a lot of formats. Intelligent, smart and powerful data analytics tools have the capacity to present data as snapshots, which reduces the time and sources needed to interpret the data.
The rise of self-service analytics platforms has improved this journey. But business users want to further reduce the time and complexity of preparing data for analysis, which is especially important when dealing with a variety of data types and formats. Many new companies like Paxata, Alteryx and Trifacta are focusing on the end-user data prep.
6. Rejection of same size frameworks.
Currently, many organizations are working toward catering the hybrid data need. It is being done by utilizing the case specific architecture designs. Organization’s data strategy now depends a number of factors like the user profiles, volume of data, questions, timings and frequency of access, data speed, and the level of consolidation. These are not standardized architectures but are driven by the need. Due to the flexibility of these designs, technology is now reaching new horizons.
