Term Big Data Technology is a relatively new concept in the technology industry. Big Data is data in such a volume that traditional method of analysis cannot be utilized to extract information. Special methods that use predictive analysis to extract all important and relevant information are used on big data. Why is it important you ask? Big data is an amalgamation of data from multiple platforms, whether it is from a website, social media channel or data collected through an app on the phone.

One of the biggest uses of big data is predicting customer behavior, customer sentiments to wards a product and trends in the market. Of course, these are just commercial uses but there are also uses for big data in the government and military. Startups are quickly catching on the big data trend. Initially, it was too expensive to get the tools to analyze big data. But recently, with a lot of developments in the tools surrounding big data, it has become accessible to small companies as well.

Cloud Computing
Big data was initially supposed to run on physical computing machines. The cost that goes into establishing a physical space is extremely high. Cloud computing is a smart solution that lets companies store data as well as perform analysis within a cloud server. This also increases accessibility and agility when it comes to looking at data. For startups, the cloud computing part of big data makes it a more viable option, especially when they don’t have the funds for physical equipment.

Popularity of Hadoop
The java based programming system Hadoop is fast gaining popularity with developers because of the ease of using big data in that environment. Analytics programs are expanding to make Hadoop an integral part of their system. Uniformity across all platforms allows users to integrate multiple programs based on their requirements. Hadoop is fast becoming the choice of companies when it comes to not only running analysis but also storing large amounts of data.

Apache Spark
Apache Spark is faster when it comes to performing analytics and producing results. The advantage of quicker processing speed, by as much as twenty times has made Apache Spark so popular. While databases are still stored in Hadoop, Apache spark is the preferred choice for performing different functions on the data. Companies as big as Goldman Sachs are integrating Apache Spark into their analytics, and with the increasing focus on the program, new developments will ensure that it surpasses Hadoop in no time.

In-Memory Databases
The most useful function of in-memory databases is when data needs to be constantly worked on to produce reports and results. When this is the requirement, it makes sense for a company to store data and work on it in the same environment. In-memory databases store data and perform functions on it. In spite of this evolution of technology, companies that do not require frequent analytics would be wasting money and resources while investing in such a database.

Data Lakes
Big data is not just big in name; it is also big in size. Which is why storage of this data is a problem. As we discussed before, cloud computing is of course the preferred option over physical equipment. Data lakes are attractive to companies because they store data in its original format. It is economically viable and stores data until real time analysis. Since it continues to attract companies, data lakes will continue to evolve to meet the customer’s requirements.

Integration of Different Technologies
Combining a java based programming language with an analytics platform in a cloud server is only possible with constant research and innovation in existing technologies. As these technologies have converged to get big data to where it is right now, constant innovation will also ensure that this integration continues. Data will be analyzed in real time in the future, which means that as soon as it is collected or even while it is in the process of being collected form various devices and platforms, reports will be generated according to the client’s requirements.