During previous years, there have been several technologies on the rise that 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 or non-structured.
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 a sufficient quantity has been gathered, this data may be used for various purposes. At this point, the data analytics tool walks in to play its role.
Lately, it has been common for every organization to have its own data lake, but this trend is changing now. At this point, corporations must critically analyze their data needs before investing in 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.
Variety Is a Key Driver Of Big Data Investments
Big data is defined by three V’s: volume, velocity, and variety. Currently, all three Vs are registering enormous growth. However, in the near future, variety will be the key driver behind big data investments. This trend will be on the rise as more and more businesses aim to consolidate the data coming from different sources, incorporating more 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.
Big Data Being Shaped By The End User
There is going to be a rise in the self-service data analytics platforms. Keeping this 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 data from a number variety of 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. However, business users want to reduce the time and complexity of preparing data for analysis further, 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 end-user data prep.
Rejection Of Same-Size Frameworks
Currently, many organizations are working toward catering to the need for hybrid data. It is done by utilizing case-specific architecture designs. An organization’s data strategy now depends on a number of factors, like user profiles, the volume of data, questions, timings, 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.
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