Introduction
Many would say the byword for success in the modern era has to be data. The sheer amount of information stored, processed, and moved around over the past 50 years has seen a staggering increase, without any end in sight. Enterprises are hungry to acquire and process more data to get a leg up on the competition. Scientists especially are looking to use data-intensive methods to advance research in ways that were not possible only a few decades ago.
With this worldwide demand, data-intensive applications have gone through a remarkable transformation since the start of the 21st century. We have seen wide adoption of big data frameworks such as Apache Hadoop, Apache Spark, and Apache Flink. The amazing advances being made in the fields of machine learning (ML) and deep learning (DL) are taking the big data era to new heights for both enterprise and research communities. These fields have further broadened the scope of data-intensive applications, which demand faster and more integrable systems that can operate on both specialized and commodity hardware.
Data-intensive applications deal with storing and extracting information that involves disk access. They can be computing intensive as well, with deep learning and machine learning applications that not only consume massive data but also do a substantial number of computations. Because of memory, storage, and computational requirements, these applications require resources beyond a single computer's ability to provide. ...
Get Foundations of Data Intensive Applications now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.