Large scale data processing is one of the most important aspects of growing any business in a strategic manner. It is necessary to collect and analyze all the data collected from customers,social media, websites and other sources in order to identify the business’ shortcomings and its progress. The large volumes of data collected from different sources are called Big Data. A very powerful analytical tool is required to analyze all the data collected. Apache Hadoop and Apache Spark are some of the most popular analytical tools used in the industry.
What is Apache Spark?
It is a powerful opensource processing engine which was built on the fundamental principles of speed, ease of use and sophisticated analytics. It is almost 100 times faster than Apache Hadoop. Spark gives a unified framework which is comprehensive and manages big data requirements with the aid of many diverse data sets like text data, image data, graph data, as well as the source of data. It also lets you write applications in Java, Scala or Python. Apache Hadoop has Map and Reduce modules for data processing. In addition to these modules, Spark can support SQL queries, graph data processing, streaming data and machine learning.
Developers can use these functions individually or use them in combination operations in a single data pipeline. A higher level API is also provided to improve developer productivity. Spark doesn’t write intermediate results on the disk, it stores the results in the memory which is a huge bonus when you have to work on the same data multiple times. It is an execution-engine that works both in-memory and on-disk. It will try to retain as much data in the memory before storing it to the disk. It is used by popular companies like Yahoo, Baidu and Tencent.
What is Scala?
Scala is a programming language and is an acronym for “Scalable language.” It is the precise integration of functional language and object-oriented concepts. This implies that Scala can be used to write one-line expressions or for large scale critical systems. The syntax to write the code is succinct. It also contains REPL and IDE worksheets for quick feedback. Scala is also the preferred language for many mission critical server systems. The quality of the code is on par with Java, but, since the typing is precise, most errors are detected during the compile time and not during run time.
Why is Scala Considered the Best Choice for Apache Spark?
Before I answer this question, the choice of language used in a Spark project depends on the requirements of the team, their skill set and ultimately, personal taste. An Apache Spark project can be written in Java, Scala, Python and R language (version 1.4). Amongst these, Scala is considered as the best choice because:
More lines of code are required in Java than Scala to achieve the same goal. Unlike Scala, REPL(Read-Evaluate-Print Loop) interactive shell is not supported by Java. This is a deal breaker for many developers because, with REPL, developers can access their data set and prototype their application without going through the entire development cycle.
Scala is faster than Python, so, the time required to execute a code is less and improves the performance of the application considerably. If you are proficient in Scala, you can check the source code if something doesn’t work the way you want, considering that Apache Spark is built on Scala. It is because of this most of the latest features are first accessible by Scala and then later ported to Python and Java.