Many entities gather or produce huge volumes of data in a structured and unstructured form that is quite difficult to process using traditional software and database methods. Such data is referred to as Big Data and some of its sources include government departments, retail businesses, and many others. The processing of Big Data is a problem for many organizations as their systems cannot handle the sheer volume of date. They have to engage Big Data experts like ActiveWizards – spark developers who assist them in managing their data to make decisions from the analysis and improve their entities’ profitability.
The following are some Big data trends you must know about.
Speeding up Hadoop
Hadoop is used in the analysis of Big Data, but at times, it is too slow. Many have been asking: “What can be done to speed up the interactive SQL?” The need for speed has necessitated the adoption of other faster databases like MySQL, and other technologies that facilitate faster processing. There is also the spark that speeds up calculation computing of Hadoop.
Obsolescence of tools meant for Hadoop
We have seen a rise on the number of tools used to analyze Big Data, and most are in relation to Hadoop. As the demand for advanced analysis of data sets in, it is foreseen that tools that are who use data- and source-agnostic may survive, but those that are purposely meant for Hadoop may fail.
Expanded uses of Hadoop
The roles traditionally done by data warehouses are now being handled by Hadoop, including reporting on day-to-day operational issues. It is also serving as a multipurpose engine for unplanned analysis.
Variety, as opposed to velocity and volume, has become the key to Big Data investment
Big Data has been defined earlier in terms of three Vs – large volumes, high velocity and high variety. Variety has become the driver of Big Data investments among the three aspects. The trend is expected to continue expanding as firms seek to include more sources.
Metadata catalogs that are aimed at finding analysis-worthy data
Data users, like companies, discarded data because there were enormous volumes of data to process, until Hadoop emerged and solved the problem of processing Big Data. However, there was another challenge – data was disorganized and so it was quite hard to find what one wanted. Then, the solution to this came in the form of metadata catalogs. These assist users to discover and grasp appropriate data worth analyzing, by making use of self-service tools. We are seeing a greater demand for self-discovery tools as time goes by to enable data users in self-service analytics.
Growing self-service data preparation
Businesses have had issues with using Hadoop data, but the emergence of self-service analytic tools has eased the situation. There is still the need to cut further on the time required to analyze this data and simplify the process. This is particularly crucial when dealing with different types and formats of data.