Big data is utilizing the services of Apache Hadoop successfully for quite some time but the incoming data is also getting bigger, which affects the performance.
So, Apache has provided a new framework that utilizes in-memory capabilities to deliver fast processing with the name called, Spark, which is being increasingly being used now.
Apache Spark is a fast engine for data processing which is suitable for analysis applications based on big data. The main thing is that Spark can be used with a Hadoop environment, standalone or in the cloud. Also, it is a very cost-effective product.
Also read: Impact of Hadoop Technology on Core Business Functions

Spark’s Importance Over Hadoop:

Developers are finding it easy to handle as it offers developers with an application framework that works around a centered data structure. Spark can process massive amounts of data in a very short period.
It has about 100 times faster processing than Hadoop’s MapReduce for the same amount of data. Moreover, it uses fewer resources and can work with other resource managers like YARN also.
Spark has application program interface (API) for several languages such as Scala, Java, Python, and Spark SQL. An API allows two software programs to communicate with each other. It becomes easy to write user-defined functions. It can also work as an interactive mode for running commands. Hadoop has tools to assist in the process, but, it is very difficult to program in Java.
Apache Spark has some unique features that make it a better proposition to its competitors in data processing, for example:

In-Memory Technology:

Spark loads all the data into the internal memory of the system and then unloads it on the disk later. Therefore, a user can save a part of the processed data on the internal memory and leave the remaining on the disk. This makes it very fast.

Spark’s Core:

Spark’s core can set tasks and interactions as well as can produce input/output operations. It is called resilient distributed dataset. It is a collection of objects. Each dataset is divided into logical partitions, which may be computed on different nodes of the cluster. Basically, this data is spread across several machines via the network. It is created by mapping, sorting, reducing and joining the data. This release of the RDD is done with support from an API. This API is a combination of Scala, Java and Python languages.

Spark’s SQL:

Apache Spark’s SQL arranges the data into many levels and can also query data via a specific language.

Easy Graph Analysis:

Spark can process graphs and graphical information. This enables the easy analysis with great precision.

Streaming:

This procedure makes smaller packets of large pieces of data with help from the core and transforms to accelerate the creation of the RDD.

Machine Learning Library:

Spark has a machine learning library that implements faster than Hadoop. It can solve several problems like statistical reading, data sampling and premise testing.

Spark Needs Time to Establish:

Spark has provided a comparatively new platform and is yet to be tested, so, it will take some time to make its mark.

• Hadoop offers a larger set of tools.
• Hadoop has several practices that are recognized in the industry.
• Hadoop’s MapReduce is easier to configure and has set industry standards in running full-fledged operations.
• Spark has not been operated with complete reliability. The organizations need to fine tune it, so as to make it ready for their set of requirements.

Practical Implementations:

Apache Spark is being employed by numerous companies that suit their data processing requirements. Some of them are Shopify, Pinterest and TripAdvisor. They can identify developing trends and then uses it to understand the behavior of users.

Conclusion:

Apache Spark’s has the processing power, speed, and compatibility that sets the tone for several things to come. However, it needs to improve to realize its full potential. Apache Spark is giving Hadoop a tough fight and is considered the future platform for data processing requirements.