In the last blog on big data, we talked about the Data Integration Tools, the eight layer of the Functional architecture. In this blog I would be listing the Data Languages that forms the ninth layer of the Functional Layer Architecture.
Big data projects are now common to all industries whether big or small all are seeking to take advantage of all the insights the Big Data has to offer. However advanced and GUI based software we develop, Computer programming is at the core of all. I hope that the previous blogs on the types of tools would have helped in the planning of the Big Data Organization for your company. But a layer still remains unfinished, without which you can go a bit ahead in the journey. But later in the journey when the data increases in alarming amounts, it gets complex. And then the only rescue for you would be the Data Languages.
List of Data Languages
1. Java –
Java’s unflinching popularity is evident enough to know that it’s the best programming language for data science. All platforms which are part of JVM ecosystem, like MapReduce, HDFS, Storm, Kafka, Spark, and Apache Beam are compatible with Java. Java gives you access to mongo collections of debugging tools, monitoring tools, libraries and profilers, hence it is the most tested, revised and proven language for data Science.
The biggest benefit Java offers is that it is platform independent and once compiled could be executed across any platform. Hence eliminating the need for compilers specific to the language.
The biggest problem with it is that it’s ridiculously verbose, and there is no REPL for iterative development.
See Also: Best Open Source Data Integration Tools
2. R –
R programming language is among the top 2 programming languages that data scientists and analysts. R programming language is much different from the other languages as it is essentially a dedicated language for statistical computing and graphics. Hence, it is not a substitute for any languages. R has simple and obvious appeal. R can be used to automate huge numbers of these calculations, even when the row and column data is constantly changing or growing.
R was used to create algorithms behind Google, Facebook, Twitter and many other services. It can run on Linux, Windows and MacOS.
3. SQL –
SQL is the acronym for Structured Query Language which has been at the heart of storing and retrieving data for decades. It remains a hugely popular tool among data analysts. Some of the tasks that could be accomplished with SQL are
- It helps you interact with the database
- It is used to filter relevant information from an ocean of data.
- It can reduce the turnaround time for online requests and queries by extracting only relevant part of data and processing it rather than processing entire database tables.
- It is a standardized programming language used for managing relational databases and performing various operations on the data
See Also: Best 19 Free Data Mining Tools
4. Hadoop –
Hadoop is one of the best open source programming languages for data science. It has a Java-based programming framework that supports the processing and storage of extremely large data sets in a distributed computing environment. If you are reading anything about Hadoop then there is no possibility that you would never come across the picture of a little elephant. And if you come across it then you are surely reading about Hadoop.
Hadoop is designed to be robust in your Big Data applications environment, and it would continue its functionality even if individual servers or clusters fail. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Though Hadoop is slower than some other processing tools, but it is proven that the results are very much accurate and which makes it as a best option for backend analysis.
5. JavaScript –
JavaScript is a popular, powerful, dynamic and most widespread scripting and programming languages that is used to crate cool websites and games for the web. We are still confused about the website and web application. It derives much of its syntax from the C language. The most beneficial feature of JavaScript is that it is compatible with all browsers, and is used in over 90% of all web pages.
Though being completely unrelated to Java language, it still does give access to developers to execute client side scripts, interact with the user in real time, control the browser and communicate asynchronously with the server.
6. SAS –
SAS is a short form for Statistical Analysis system is the leader of the best programming languages for data science. It is among the best in commercial analytics space with highest share in private organization. SAS has been used for statistical modelling since the 1960’s and still holds the position after many years of updates and refinements. The main reason behind the popularity is its wide range of statistical functions with a user friendly GUI that could be learned in a very short time. SAS includes a variety of components for accessing databases and flat, un-formatted files, manipulating data, and producing graphical output for publication on web pages and other destinations.
7. SPSS –
SPSS statistics is a software package used for logical batched and non-batched statistical analysis. SPSS is a Windows based program that can be used to perform data entry and analysis and to create tables and graphs. It is capable of handling large amounts of data and can perform all of the analyses covered in text and much more.
See Also: Best Offline Data Cleaning Tools
IBM SPSS has been in the use for decades and since then it is providing powerful tools for statisticians and data scientists. Over the years, the SPSS platform has evolved to support all phases of the data mining process, which also includes the below –
- Model development
- Model deployment
- Model refresh
My list of the best programming languages for data science is not yet complete. The remaining of the list will continue in the next blog. Till then let me know your favorite programming language for data science in the comments below.
