What Is Big Data Analytics Tools Available?

what is big data analysis

What Is Big Data Analytics Tools Available?

What is Big Data? It seems like an oxymoron at first. After all, what is big data? Well, it is simply the combination of many small “data sets” into one giant, amorphous entity. Big data is a rapidly growing field which embraces various methods to analyze, extract useful information from, or otherwise cope with exceptionally large and complex data sets.

Large applications in industry, medicine, finance, and other areas all utilize what is known as a supervised or mapreduce framework to best utilize the power and accuracy of large databases and “big data” tools. supervised or mapreduce data analysis centers on the idea that a machine can quickly learn how to extract useful insights from huge amounts of unstructured observational data. Data is said to be “large” when it exceeds the capacity of a human’s mind, is time-consuming to collect, and/or represents something that cannot easily be expressed or processed within a traditional database or web crawler. Humans involved in this process are known as supervised data analysts or mapserers.

One popular tool for what is called MapReduce is the SQL Server hive. A hive is just a compacted version of a traditional relational database. It consists of multiple tables (called “hives”) that each contain one or more values. The value types are typically text, binary, numeric, and text/array. This type of architecture makes it rather easy to “talk” to the hive. All one has to do is use a text language such as SQL statements to connect to the hive.

MapReduce is just one component of what is called the Apache Spark project. This is basically a new project that was started to enable programmers to process large amounts of unstructured data using big data analysis tools. As the project evolved over the years, it became evident that many programmers were having difficulty understanding the underlying concept of what is Big Data. Many of them were rather quick to tell me that there was a need for a simplified vocabulary that would make working with large data analysis tasks a lot easier. Apache Spark was born.

Apache Spark is essentially a framework that allows programmers to efficiently utilize large amounts of unprocessed data in order to perform analytical transformations on it in a way that is easy to understand and execute on the hardware. While the underlying idea behind what is big data analysis and what is spark is very similar, there are actually quite a few differences between them. For example, Apache Spark is not intended to support real-time analytics. Spark’s developers made this very clear from the start as they recognized that this aspect was not necessary for streaming analytics.

What is also clear from the beginning is that the direction that Apache Spark is moving is a step forward in terms of developing truly advanced machine learning tools. Machine learning is one of the most exciting areas of analytics due to the fact that it is open source. Machines are capable of learning from data without being humans, which opens up the door to entirely new ways of making sense of complex business intelligence data sets. Traditionally, machine learning has been relegated to data mining, which is the process of using natural language processing techniques to mine information from massive databases and makes things look “bigger” by making the sets look cleaner and more meaningful. Apache Spark addresses some of the concerns of big data analytics by developing tools that can learn from small sets, allowing business intelligence developers to focus on the pieces of the bigger picture. This is particularly useful when the business is experiencing a rapid growth and it is very difficult for the programmers to squeeze all of the necessary data into the available programming language.

However, despite the advances that Apache Spark is making in terms of functionality, marketers still seem to prefer to use more traditional big data analysis tools. Despite my misgivings about Spark as a commercial tool, I was pleasantly surprised with the insights that it gave me on my path to becoming a data scientist. Apache Spark can make the job of data scientists much easier than having to write the same code as a program like Hadoop. Having to write code for all the steps involved in an analytical procedure is tedious, especially if the developer is not familiar with the analytic language and data structures. However, Apache Spark makes it very easy to quickly evaluate and then implement the insights that they discovered.

In the end, it appears that the biggest challenge that Apache Spark poses for the developers of analytics applications is not so much a problem of speed versus memory consumption or speed versus CPU time but rather it is a problem of developers choosing the right kind of machine learning algorithms. As the field of big data analytics tools grows, it is important for developers to choose a platform that will support what they are trying to achieve. Hadoop seems to be the natural choice for many developers due to its wide range of applications, its ease of use and its cost effectiveness. However, as Spark’s popularity grows, other platforms such as Apache Hadoop Join-Drive and IBM’s Big Data using Juled and Map-Reduce may become viable options in the long run.