How Apache Spark Helps Rapid Application Development
Apache Spark is a rapid application development tool that is helping developers be more efficient than ever.
When you think you have a great idea but need to test it, you want to test it as quickly and as economically as possible. You don't want to get into a lengthy development and testing cycle and waste a lot of time and money. Apache Spark has been facilitating rapid application development mainly because it allows you to quickly test your ideas with its shell and APIs.
What is Apache Spark?
Technically, Apache Spark is data processing engine which can snake into colossal data chunks and process them in a flash. Its two chief features are data processing speed and in-memory performance. This cluster computing framework is an open-source tool which helps budding developers build their applications in no time.
This advanced data processing framework is mothered by AMP Lab and was published as an open-source tool in 2010 as an integral part of the Apache Project. The whole Spark project is coded using the Scala language and it can run on a Java-based virtual machine (JVM).
Apache Spark — The New Leader in Rapid Application Development
After using Apache Spark, developers across the planet have unanimously branded it as "super-fast." Various performance measurements of Apache Spark show that it’s 100 times faster than its existing rival, namely Hadoop. According to its users, Spark’s in-memory primitives beat the current standard of Hadoop’s disk-based, multi-staged memory structure.
Factually, if the time gap between any ideation and its execution is substantially lengthy, then often these casual approaches have nipped the whole project in the bud. In light of this, what is the most expensive parameter of this ever-evolving tech industry?
Admittedly, it’s time.
There is an old proverb and that says, "No one can stop an idea from getting executed, whose time has come." So, if you dig deep into the very purpose of developing an application, you will find that the purpose is simple and perpetual. You have to solve a general and established problem. Now, if you are not stepping onto the scene, someone else will. So, the need for a tool which can raise the level of "rapid," is the need of the hour.
Apache Spark Features
Apache Spark has many sublime features, and each of them integrates to feed the much-necessary processing power to it. Technically, Spark’s components define its superior ability. Each of Spark's components improves its ability for rapid application development.
Spark’s In-Memory Process
Behind this aptness of Apache Spark’s smart data processing, the major shareholder is its benchmark in-memory technique. So, what is it, actually? Simply, it’s a breakthrough. This in-memory technique gobbles up most of the fetched data in memory first and dumps it on the data storage disks later. That being said, its users can save a portion of the processed data in the memory and the rest of the data on the storage disk. This feature of storing data in-memory makes Apache Spark unparalleled in its niche.
Added to this, it can be deduced that Spark is well equipped with a robust machine learning algorithm as it loads data, requested by user programs, directly into its core or cluster’s memory and queries the same in a regular manner.
It’s the whole foundation of Apache Spark. It mainly deals with the various tasks which are distributed in nature, like I/O executions, scheduling and dispatching. The tech world also knows this as a resilient distributed dataset (RDD), which is an array of partitioned data distributed logically across different connected machines.
Normally, these RDDs can be created through a coarse-grained data transformation process which includes four basic executions: map, filter, reduce and join. Consequently, the entire RDD is launched through an API which is an amalgamation of three different programming languages (Scala, Java, and Python).
This is another component of this framework which instigates a new data abstraction approach, namely SchemaRDD. This new SchemaRDD supports various levels of structured data. It also features an ability to query data with a domain-specific language.
This component is for executing streaming analytics of data with the help of the fast scheduling ability of Spark’s core. It breaks down larger data chunks into multiple small packets or batches and applies RDD transformations on those.
This component is a distributed graph processing network and useful in situations where an expression of complete graph computation is needed.
MLib: The Machine Learning Library
Technically, it is a distributed machine learning framework. Its execution speed is much higher than Hadoop’s disk-based version due to this fact that Spark leverages the distributed memory-based architecture — which is the chief differentiating parameter of Apache Spark — with the other similar framework. MLib basically employs statistical algorithms to solve a wide range of the machine learning riddles like summary statistics, hypothesis testing and data sampling. It also deals with data clustering, collaborative filtering and data regressions.
Spark — A Versatile Tool for Developers
Along with its other features, Spark is also a versatile application development framework for all developers around the world. It can work with different programming languages like Scala, Python, Java, Closure and R.
Spark is the post-Hadoop transformation of big data, as the former possesses a thematic match with the latter. Big data is growing faster with the ever-growing population of the Internet of Things, and the technology world needed something which could keep its pace on par with its growth. Admittedly, Hadoop had its golden days with big data, but it was not the ultimate standard of quick application development in the big data arena. Apache Spark looks to be the face of the next-generation data-intensive application development ecosystem.