June 21th, 2019

Python Alternatives for Big Data

A bunch of technologies that can become a substitute for Python in Big Data projects.
So here is Big Data, a term you could come across when surfing through endless realms of the Internet. But unlike other trendy words, this one is harder to explain. But hold on—at a basic level, this technology is pretty simple—it's a common term for all sorts of data sets that are too big for basic software to process.
In our day-to-day lives, Big Data found a niche for analytics: user behavior and prediction are the areas to excel: comparing bazillions of different cases is what makes up the most accurate results. Call it a "statistics never lie" or "based on numbers" approach, but it's hard to find a more accurate way to see the future other than comparing countless cases and see the probability chance for each event.

But how is this done on a technological level? If powerful hardware is the cornerstone, a programming language is what comes next, and you have to be sure that it's a great fit for this type of product.

Python is what surely appears to be the easy answer, but what if there's simply no devs with this skill around? Don't worry, although Big Data is fairly new to this world, there's already a few options to effectively replace Python; luckily Scala and R can serve the same purpose and be effective in the long run.

Although they're not that similar, R stands out as more of a niche solution for Big Data management. All of these languages have their implications when applied to this technology.

Let's see how you could seamlessly swap between the best options for Big Data and which advantages and flaws they bring upon themselves.

This isn't a face-to-face comparison simply due to the fact that three candidates can serve different purposes. Comparing R to Java is like comparing a elephant to a crocodile, but the ways they solve Big Data goals are surely measurable, so here we go:


This programming language is the "universal soldier" of the IT world—it was made in an attempt to create a neat fusion of object-oriented and functional programming. Its name stands for the word "scalable," which allows it be extremely flexible when it comes to solving programming tasks.

Ironically, it was a side product, Apache Spark, that raised interest towards Scala, which was used to create this framework in the first place. This gave Scala the reputation of a handy tool for machine learning, data processing, and streaming analytics. But it wasn't always meant to be a solution for Big Data. The major purpose was to address problems associated with Java usage that required too many lines of code to enable basic software features. Scala, on the other hand, manages to vastly reduce the number of symbols required for delivering software features.

On the flip side, the learning process is somewhat harder when compared to Java, whose syntax is deemed easy-to-learn. This makes developers consider whether it's worth a payoff: both have their strengths and weaknesses.

As a result, we now have a tool that simulates Java in a lot of ways: Scala uses Java Virtual Machine and can work with Java libraries, implement Java interfaces, call Java codes, and dissect Java classes. This can also be done vice versa—these technologies have backward compatibility. Let's view some of the most notable Scala advantages that come in handy while implementing it in Big Data:
It is concise

It's basically a sweet spot between being terse and readable, which makes code easy to understand. With local type inference, Scala is capable of interring them via the compiler. Being able to automatically match patterns allows for easy sorting of types of data via first-match policy. Also, don't forget about using functions as variables reusing utility functions, which may help you drastically reduce the workload.

Class composition

Thanks to being an object-oriented language, Scala is capable of extending the classes with subclasses. Mixin-composition is what can enable code reuse and replace inheritance to avoid its ambiguity. On top of that, modular mixin structure is a fine way to combine the advantages of traits and mixins.

Real-time stream processing

When using the Spark framework, Scala receives such valuable perks as real-time streaming, which makes it a prime option to pick if fast data processing is an important goal.

Great ecosystem

Once again, a Java-based ecosystem has played its role in Scala convenience, which is now backed up by numerous utilities for Big Data: frameworks (Apache Spark, Apache Flink, Apache Samza , Akka, Summingbird, Scrunch), streaming platforms (Apache Kafka), libraries, API (Scalding), and IDEs (IntelliJ and Eclipse) can work seamlessly for both languages.

Special libraries

Despite falling short in this department compared to R or Python, Scala excels in having lots of libraries for Big Data purposes. When it comes to massive data analysis, data visualization, NLP, and machine learning, you can rely on such tools as ScalaNLP , DEEPLEARNING4J, Framian, Apache Spark MLlib , Saddle, and Apache PredictionIO.

It's also worth mentioning that an active community is what makes Scala a good choice for devs—regardless of their experience, they won't be left alone. There are always special segments on Stack Overflow, GitHub, Reddit, and Gitter channels, which made it one of the fastest growing communities in 2016.

Scala sentence:

You can rightfully call Scala a better Java, but it surely has some flaws: a non-steep learning curve is what distracts beginners from it, not to mention its lackluster tool/IDE support when compared to the predecessor. Also, take note that it's necessary to have a fast processor when compiling with Scala or this process can take ages, especially if compared to Java.
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R is a different beast if we compare it to conventional languages; it has a lot to do with enabling data manipulation, simulation, calculation, and displaying it in a graphical way. This serves as a great explanation of why it's so popular among Big Data enthusiasts who strive at statistical analysis.

R is all about being universal and multi-paradigm: it supports imperative, procedural, object-oriented, array, reflective, and functional programming. On top of that, it allows for looping, branching, and modular programming functions thanks to being a scripted and interpreted language.

A neat command line is also designed to facilitate data management by storing numerous analysis steps and vastly simplify its reuse. Data presentation can be easily done via statistical procedures and an extensive set of functions, complemented by a vast number of packages for that matter.

The main distinctive feature is the array nature of this language—there's no need for an explicit loop to apply functions to a vector, which means even the most complex operations can be done via single command. Its structure is like a big scheme where each function, list, or vector has a shaded map where each symbol is associated with certain values.

But let's view in detail what it means to use R in Big Data and what are the key features:

Being much like Python libraries, R has a set of standard packages and those that can be manually added from CRAN, Bioconductor, GitHub, and Omegahat. For example, the CRAN Task View can help you manage medical analysis, genetics, machine learning, finance, etc. But saying that the number of these packages is huge won't display a true story—it's actually humongous; therefore, such tools as Packrat and Checkpoint are must-have tools for managing package dependencies and reproducibility inside R. Or you can simply use the list that RStudio recommends itself.

Data importing

R is susceptible to various kinds of data to consume: statistic software files, text files, flat files, databases, or even web data.

Scraping, however, isn't a strong side for R, which is why it's necessary to use tools like Rvest (pretty much an analogy for Beautiful Soup used in Python) and Rselenium, packages built to allow for easy web data extraction when using this technology.

Such names as PostgreSQL, RMySQL, ROracle, and RODBC may sound familiar or even funny, but that's because they were designed to connect and extract the data from related databases with no R in their names. Xlsx will be great for manage Excel data, while Readr can help you discover flat and tabular text files, plus Haven for all sorts of Stata, SPSS, and SAS files.

In other words, importing isn't plain but very doable when using extra help from packages.

Pre-processing of the data

Given the usual scale of statistical data computing, it's never a short affair, but R is good at facilitating this stage. The whole process isn't done in a single step; instead, it consists of many stages, which include cleansing, centering, scaling, transformation, normalization. That is why preprocessing is often done with the help of numerous packages.

Namely, Tidyr, Editrules, Deducorrect, MatchIt, and many other tools will be the helping hand when you need to restructure, aggregate, tidy-up data, parse ,and apply clearing rules in both manual and automated ways.

Deep analysis

That's the area when R truly shines and allows to perform such specific tasks as regression, principal component analysis, bootstrap, analysis of variance, generalized additive models, and statistical modeling. All of these features are hardly applicable to the trivial needs but serve as a unique way to refine massive data for all kinds of research. Data analysis is arguably the main reason for R's existence—research projects and separate analysis for large servers are the main specialties.

But if we're talking about real business use cases, it still delivers great value when applied to machine learning algorithms. For that matter, there's another set of useful packages that alleviate a lot of hard work and make data refining smooth.

Plyr, Dplyr, and Data.table can become handy for dataset manipulation; Stringr is for easy wrapping; Hmisc serves all kinds of purposes; Igraph will deliver analysis; Rpart will help you segregate classification, regression, and survival trees. A bigger list was provided by KDnuggers, so that you could pick a package for any of the goals.


This is a crucial step that is responsible for making data easy to interpret, and R can deliver when filtering, pruning, transforming, rescaling, merging, and splitting data. Basically, all these actions distill all the "trash" data that was received during the previous stages.

When data transaction begins, Arules serves the best its manipulation, representation, and analyzing. The above-mentioned Caret, Tidyr, and Data.table are a nice fit for post-processing as well as for the "pre" stage.


Being an essential part of post-processing, visualization is one of the strong R sides—it was initially built to display analytical and statistical results. Graphics capabilities are what makes it good and allow for dynamic graphs, graphics devices, graphic displays, and of course visualization. Once again, there a ton of packages that make this process an easy affair rather than a convoluted mess.

After applying Ggplot2 for "grammar of graphics," Ggvis and Plothy can transform data into web-based visualizations; Shiny does the same for making interactive web apps; Rgl does 3D for OpenGL. Rmarkdown is the same as Markdown, but instead, converts derived data into MS Word, HTML, and PDF reports and helps you customize them. GoogleVis is for creating interactive charts based on data frames (Google charts API).

R sentence:

R can be deemed as a "two-faced" best: it's surely not an easy-to-grasp language and is kind of limited in use, but Big Data is just too good a fit to ignore it. It may seem "foreign" for the devs with another set of programming skills and can run slower compared to compiled languages (being an interpreted one has its flaws).

But its "niche drawback" is heavily compensated by a vast versatility inside the very segment—R is accessible on Windows, Linux, MacOS, and UNIX. The number of enthusiasts is also large enough to give it valuable support: a lonely dev will always be able to find info about data management inside his/her project on the Internet.

Rstudio should also be commended for a number of packages that alleviate stress from their users and add great convenience when dealing with massive data clusters. Plus, the documentation is stellar—there's a great learning piece called "Introduction to R," plus a huge set of useful "CRAN Contributed Documentation" materials.

Although R misses a lot of things that the regular languages do, it's well integrated with Java, C, C++, and Python (just add the "r" letter before each word). Also, don't forget about statistical packages, as well as ODBC data source.
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Despite being different to Python at its core, Java is surely the language that has a lot in common with its competitor: the "write once, run anywhere" phrase pretty much describes why it's good—many people know it, the learning curve isn't torture, and it can be applied or ported to other instances.

In general, Java is much like C and C++ that took a wrong turn at some point in life and got different features at a core level. As a result, Java is based on a virtual machine, which is enabled by Java Virtual Machine (JVM) and runs on all sorts of platforms: Linux, Unix, Windows, and Mac.

While being an object-oriented, class-based, concurrent, and general-purpose language, Java is a great choice for data mining purposes, backed by numerous extension sets for that matter. Apache Mahout, Java Data Mining Package, and Weka are the ones that stand out the most among open-source libraries you can find on the Internet.

But here's the best advertisement for Java in Big Data—Apache Hadoop was built on this language, which makes Hadoop MapReduce and HDMS easy to understand for people from the Java world. Also, take note that JVM is a backbone for Storm, Spark, and Kafka, major tools inside the Hadoop ecosystem. Well, even Scala can utilize JVM, although Apache Beam and Google Cloud Dataflow have stepped away from Java just recently.

So while Java platform is great for the "external" tools, it's even better when working with the Java language itself—you get a variety of libraries, APIs, Java Runtime Environment (JRE), JVM, and special plug-ins.

As we can judge from the DZone's survey, such familiar tools as Hadoop, Spark, Hive, and Kafka are popular tools among Java devs as well. At the same time, MongoDB, Cassandra, and Redis are the sheer winners when choosing a database handling, while Elasticsearch is the pick for a handy RESTful search engine.

Java sentence:

So what are the main reasons for Java in Big Data? First, when compared to its rivals in this article, this language stands out in scalability, a crucial factor when we talk about massive things like Big Data. You can't also deny the extra security it brings upon itself—the virtual machine is what enables a thorough verification before the code is executed and vastly reduces the risk factor of outer breaches.

Just being a fairly old and popular language has also played its role— a huge Java community is well presented all over the world, which resulted in tons of useful extensions and documentation becoming available, ready to help anyone who's stuck at a dead end. The whole ecosystem is simply stellar thanks to many of its parts being built on Java, making it very convenient for general usage, a rare case for Big Data as a whole, let alone being portable thanks to the runtime environment.

As mentioned before, Java surely struggles from too many code strings, which doesn't add to the overall execution speed (what has basically spurred the Scala invention). It may also seem unfriendly with the lack of REPL on top of lackluster visualization potential: both input and output stages may just seem messy even for a seasoned Java developer.

Key Takeaways

So, is there point of choosing anything over Python for Big Data? Yes, no, kind of…

As you may notice, many of the candidates' features seem to overlap in terms of practical use. And rightfully so, variety is a big strength for product development—a technological stack is what often defines project's price, duration, and the end value it brings to customers.

As we know, Python is a great choice for multiple purposes: it's platform-independent, which makes it easy to execute programs written in this language, plus it's powered by a consistent syntax. Python is also considered easy-to-learn and the libraries and modules are aplenty, not to mention its multi-paradigm nature. But what makes it great for Big Data is the ability to cope with high-level data structures and managing dynamic type checking that is all enabled on different types of hardware—portability is its another advantage. Don't forget about being able to import code from languages like Java, C, and C++ to Python, and vice versa to embed it back.

As you see, Python is cool and shares a lot of features with the above-mentioned languages as well, and we can take for granted that it's a great candidate to become a go-to pick for your Big Data project. Then why bother making a list?
Well, like anything in this life, it's not ideal, so here are the cases when you should rather pick the competitors against the good old Monty P:

  • If there are no free Python developers around or abroad. Just look around and see who is available for hiring. If there are no Python developers, you can still manage a Big Data project with any of the above-mentioned alternatives; like in a supermarket, there are tons of substitutes that can perform the same types of tasks.
  • If you're aiming for a smartphone app. Python-based mobile apps aren't a big hit, and I wouldn't recommend you to become one of the pioneers in any of the technological fields—it's both expensive and stressful.
  • If you go for a web-based product. Once again, not a strong area for Python, and being outlandish doesn't mean the same as a "unique niche product"—think twice about that.
  • If you value speed. Being an interpreted rather than compiled language puts serious boundaries towards how fast the language runs, so Python won't be a good choice for building a "racecar" type of product.

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