![]() ![]() ![]() This approach is also known as vertical scaling, scaling up the capacity of a single system.īut when data is growing day by day in a rapid phase, it is hard to scale up vertically. The storage capacity of the machines was scaled up. ![]() Isn’t it? This was the approach that was taken to solve this storage problem. If you are running out of storage capacity on your desktop or laptop, what would you do? Simply, you attach external storage to your machine. Therefore, the world needed a better solution. This much data cannot be stored in a normal computer, because it doesn’t have enough resources to do so. With the growth of data, storing them also became a problem. The development of technology, especially the internet, causes data to grow exponentially. Here, we are talking about data files with millions of records in them and hundreds and thousands of such data files. So what is big data? As its name suggests, the term “big data” refers to huge amounts of data. Everyone is more alert about the term than ever before. Everyone is searching for the term big data and its related topics. Nowadays, big data is a very popular term. => Visit Official Spark Website History of Big Data Big data Unlike MapReduce, Spark can process data in real-time and in batches as well. Apache Spark is ten to a hundred times faster than MapReduce. You can use SQL, machine learning, R, graph computations in the Spark environment using these packages and libraries.Īpache Spark is a better alternative for Hadoop’s MapReduce, which is also a framework for processing large amounts of data. You can choose the language according to your preference. It also provides packages and libraries to enhance its functionality. You can simply work on it as you are using a single machine. Spark is easy to use, you need not worry about the cluster of computers you are working on. If you want to process data in such an environment, you can get the help of Apache Spark. Nowadays, a large amount of data or big data is stored in clusters of computers. By writing an application using Apache Spark, you can complete that task quickly. Assume you have a large amount of data to process. Apache Spark is a data processing engine for distributed environments. ![]()
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