The term Big Data has created a lot of hype already in the business world. Hadoop and Spark are both Big Data frameworks – they provide some of the most popular tools used to carry out common Big Data-related tasks. In this blog, we will cover what is the difference between Spark and Hadoop MapReduce.
GigaSpaces recently released a unified analytics service as part of its in-memory computing platform. The service, AnalyticsXtreme, accelerates access to data lakes and data warehouses to enable faster and smarter analytics. The new service is designed to simplify the development of analytics applications and enables them to leverage both streaming (or real-time data) and historic data. "It used to be quite complicated for customers to develop these types of applications. We've really unified it into a single interface and we've actually been able to accelerate the access to the historical data, which is known to be too slow for real-time [analytics]," said Karen Krivaa, vice president of marketing at GigaSpaces.
Let us start with what is Hadoop and what are Hadoop features that make it so popular. Hadoop is an open-source software framework for distributed storage and distributed processing of extremely large data sets. Important features of Hadoop are: Hadoop is an open source project. It means its code can be modified to business requirements. In Hadoop, data is highly available and accessible despite hardware failure due to multiple copies of data.
Are people in your data analytics organization contemplating the impending data avalanche from the internet of things and thus asking this question: "Spark or Hadoop?" The internet of things (IOT) will generate massive quantities of data. In most cases, these will be streaming data from ubiquitous sensors and devices. Often, we will need to make real-time (or near real-time) decisions based off of this tsunami of data inputs. How will we efficiently manage all of this, make effective use of it, and become lord over it before it becomes lord over us?
We live in exponential times. Especially if we are talking about data. The world is moving fast and more data is generated every day. With all that data coming your way, you need the right tools to deal with the growing amounts of data. If you want to get any insights from all that data, you need tools that can process massive amounts of data quickly and efficiently.