Top 5 Programming Languages for Taking On Big Data

In today’s computing world, ‘big data’ – data sets that are too large or complex for traditional data-processing application software – are increasingly common and having the ability to work with them is increasingly a to-be-expected requirement of IT professionals. One of the most important decisions these individuals have to make is deciding on a programming languages for big data manipulation and analysis. More is now required than just simply understanding big data and framing the architecture to solve it. Choosing the right language means you’re able to execute effectively, and that’s very valuable.

As a proven reliable Canadian web hosting provider, here at 4GoodHosting we are naturally attuned to developments in the digital world. Although we didn’t know what it would come to be called, we foresaw the rise of big data but we didn’t entirely foresee just how much of a sway of influence it would have for all of us who take up some niche in information technology.

So with big data becoming even more of a buzz term every week, we thought we’d put together a blog about what seems to be the consensus on the top 5 programming languages for working with Big Data.

Best languages for big data

All of these 5 programming languages make the list because they’re both popular and deemed to be effective.

Scala

Scale blends object-oriented and functional programming paradigms very nicely, and is fast and robust. It’s a popular language choice for many IT professionals needing to work with big data. Another testament to its functionality is that both Apache Spark and Apache Kafka have been built on top of Scala.

Scala runs on the JVM, meaning that codes written in Scala can be easily incorporated within a Java-based Big Data ecosystem. A primary factor differentiating Scala from Java is that Scala is a lot less verbose as compared to Java. What would take seemingly forever to write 100s of lines of confusing-looking Java code can be done in 15 or so lines in Scala. One drawback attached to Scala, though, is its steep learning curve. This is especially true compared to languages like Go and Python. In some cases this difficulty puts off beginners looking to use it.

Advantages of Scala for Big Data:

  • Fast and robust
  • Suitable for working with Big Data tools like Apache Sparkfor distributed Big Data processing
  • JVM compliant, can be used in a Java-based ecosystem

Python

Python’s been earmarked as one of the fastest growing programming languages in 2018, and it benefits from the way its general-purpose nature allows it to be used across a broad spectrum of use-cases. Big Data programming is one of the primary ones of them.

Many libraries for data analysis and manipulation which are being used in a Big Data framework to clean and manipulate large chunks of data more frequently. These include pandas, NumPy, SciPy – all of which are Python-based. In addition, most popular machine learning and deep learning frameworks like Scikit-learn, Tensorflow and others are written in Python too, and are being applied within the Big Data ecosystem much more often.

One negative for Python, however, is that its slowness is one reason why it’s not an established Big Data programming language yet. While it is indisputably easy to use, Big Data professionals have found systems built with languages such as Java or Scala to be faster and more robust.

Python makes up for this by going above and beyond with other qualities. It is primarily a scripting language, so interactive coding and development of analytical solutions for Big Data is made easy as a result. Python also has the ability to integrate effortlessly with the existing Big Data frameworks – Apache Hadoop and Apache Spark most notably. This allows you to perform predictive analytics at scale without any problem.

Advantages of Python for big data:

  • General-purpose
  • Rich libraries for data analysis and machine learning
  • Ease of use
  • Supports iterative development
  • Rich integration with Big Data tools
  • Interactive computing through Jupyter notebooks

R

Those of you who put a lot of emphasis on statistics will love R. It’s referred to as the ‘language of statistics’, and is used to build data models which can be implemented for effective and accurate data analysis.

Large repositories of R packages (CRAN, also called as Comprehensive R Archive Network) set you up with pretty much every type of tool you’d need to accomplish any task in Big Data processing. From analysis to data visualization, R makes it all doable. It can be integrated seamlessly with Apache Hadoop, Apache Spark and most other popular frameworks used to process and analyze Big Data.

The easiest flaw to find with R as a Big Data programming language is that it’s not much of a general purpose language with plenty of practicality. Code written in R is not production-deployable and generally has to be translated to some other programming language like Python or Java. For building statistical models for Big Data analytics, however, R is hard to beat overall.

Advantages of R for big data:

  • Ideally designedfor data science
  • Support for Hadoop and Spark
  • Strong statistical modelling and visualization capabilities
  • Support for Jupyter notebooks

Java

Java is the proverbial ‘old reliable’ as a programming language for big data. Much of the traditional Big Data frameworks like Apache Hadoop and the collection of tools within its ecosystem are based in Java, and still used in many enterprises today. This goes along with the fact that Java is the most stable and production-ready language of all the 4 we’ve covered here so far.

Java’s primary advantage is in the way you have an ability to use a large ecosystem of tools and libraries for interoperability, monitoring and much more, and the bulk of them have already been proven trustworthy.

Java’s verbosity is its primary drawback. Having to write hundreds of lines of codes in Java for a task which would require only 15-20 lines of code in Python or Scala is a big minus for many developers. New lambda functions in Java 8 do counter this some. Another consideration is that Java does not support iterative development unlike newer languages like Python. It is expected that future releases of Java will address this, however.

Java’s history and the continued reliance on traditional Big Data tools and frameworks will mean that Java will never be displaced from a list of preferred Big Data languages.

Advantages of Java for big data:

  • Array of traditional Big Data tools and frameworks written in Java
  • Stable and production-ready
  • Large ecosystem of tried & tested tools and libraries

Go

Last but not the least here is Go. one of the programming languages that’s gained a lot of ground recently. Designed by a group of Google engineers who had become frustrated with C++, Go is worthy of consideration simply because of the fact that it powers many tools used in Big Data infrastructure, including Kubernetes, Docker and several others too.

Go is fast, easy to learn, and it is fairly easy to develop applications with this language. Deploying them is also easy. What might be more relevant for it though is as businesses look at building data analysis systems that can operate at scale, Go-based systems are a great fit for integrating machine learning and undertaking parallel processing of data. That other languages can be interfaced with Go-based systems with relative ease is a big plus too.

Advantages of Go for big data:

Fast and easy to use

Many tools used in the Big Data infrastructure are Go-based

Efficient distributed computing

A few other languages will get HMs here too – Julia, SAS and MATLAB being the most notable ones. All of our 5 had better speed, efficiency, ease of use, documentation, or community support, among other things.

Which Language is Best for You?

This really depends on the use-case you will be developing. If your focus is hardcore data analysis involving s a lot of statistical computing, R would likely be your best choice. On the other hand, if your aim is to develop streaming applications, Scala is your guy. If you’ll be using machine learning to leverage Big Data and develop predictive models, Python is probably best. If you’re building Big Data solutions with traditionally-available tools, you shouldn’t stray from the old faithful – Java.

Combining the power of two languages to get a more efficient and powerful solution might be an option too. For example, you can train your machine learning model in Python and then deploy it with Spark in distributed mode. All of this will depend on how efficiently your solution is able to function, and more importantly, how speedy and accurate it’s able to work.

 

The Surprising Ways We Can Learn About Cybersecurity from Public Wi-Fi

A discussion of cybersecurity isn’t exactly a popular topic of conversation for most people, but those same people would likely gush at length if asked about how fond of public wi-fi connections they are! That’s a reflection of our modern world it would seem; we’re all about digital connectivity, but the potential for that connectivity to go sour on us is less of a focus of our attention. That is until it actually does go sour on you, of course, at which point you’ll be wondering why more couldn’t have been done to keep your personal information secure.

Here at 4GoodHosting, cybersecurity is a big priority for us the same way it should be for any of the best Canadian web hosting providers. We wouldn’t have it any other way, and we do work to keep abreast of all the developments in the world of cybersecurity, and in particular these days as it pertains to cloud computing. We recently read a very interesting article about how our preferences for the ways we (meaning the collective whole of society) use public wi-fi can highlight some of the natures and needs related to web security, and we thought it would be helpful to share it and expand on it for you with our blog this week.

Public Wi-Fi and Its Perils

Free, public Wi-Fi is a real blessing for us when mobile data is unavailable, or scarce as if often the case! Few people really know how to articulate exactly what the risks of using public wi-fi are and how we can protect ourselves.

Let’s start with this; when you join a public hotspot without protection and begin to access the internet, the packets of data moving from your device to the router are public and thus open to interception by anyone. Yes, SSL/TLS technology exists but all that’s required for cybercriminal to snoop on your connection is some relatively simple Linux software that he or she can find online without much fuss.

Let’s take a look at some of the attacks that you may be subjected to due to using a public wi-fi network on your mobile device:

Data monitoring

W-fi adapters are usually set to ‘managed’ mode. It then acts as a standalone client connecting to a single router for Internet access. The interface the ignore all data packets with the exception of those that are explicitly addressed to it. However, some adapters can be configured into other modes. ‘Monitor’ mode means an adapter all wireless traffic will be captured in a certain channel, no matter who is the source or intended recipient. In monitor mode the adapter is also able to capture data packets without being connected to a router. It has the ability to sniff and snoop on every piece of data it likes provided it can get its hands on it.

It should be noted that not all commercial wi-fi adapters are capable of this. It’s cheaper for manufacturers to produce models that handle ‘managed’ mode exclusively. Still, should someone get their hands on one and pair it with some simple Linux software, they’ll then able to see which URLs you are loading plus the data you’re providing to any website not using HTTPS – names, addresses, financial accounts etc. That’s obviously going to be a problem for you

Fake Hotspots

Snaring unencrypted data packets out of the air is definitely a risk of public wi-fi, but it’s certainly not the only one. When connecting to an unprotected router, you are then giving your trust to the supplier of that connection. Usually this trust is fine, your local Tim Horton’s probably takes no interest in your private data. However, being careless when connecting to public routers means that cybercriminals can easily set up a fake network designed to lure you in.

Once this illegitimate hotspot has been created, all of the data flowing through it can then be captured, analysed, and manipulated. One of the most common choices here is to redirect your traffic to an imitation of a popular website. This clone site will serve one purpose; to capture your personal information and card details in the same way a phishing scam would.

ARP Spoofing

The reality unfortunately is that cybercriminals don’t even need a fake hotspot to mess with your traffic.
Wi-Fi and Ethernet networks – all of them – have a unique MAC address. This is an identifying code used to ensure data packets make their way to the correct destination. Routers and all other devices discover this information Address Resolution Protocol (ARP).

Take this example; your smartphone sends out a request inquiring which device on the network is associated with a certain IP address. The requested device then provides its MAC address, ensuring the data packets are physically directed to the location determined to be the correct one. The problem is this ARP can be impersonated, or ‘faked’. Your smartphone might send a request for the address of the public wi-fi router, and a different device will answer you with a false address.

Providing the signal of the false device is stronger than the legitimate one, your smartphone will be fooled. Again, this can be done with simple Linux software.

Once the spoofing has taken place, all of your data will be sent to the false router, which can subsequently manipulate the traffic however it likes.

MitM – ‘Man-in-the-Middle’ Attacks

A man-in-the-middle attack (MITM) is a reference to any malicious action where the attacker secretly relays communication between two parties, or alters it for whatever malevolent reason. On an unprotected connection, a cybercriminal can modify key parts of the network traffic, redirect this traffic elsewhere, or fill an existing packet with whatever content they wish.

Examples of this could be displaying a fake login form or website, changing links, text, pictures, or more. Unfortunately, this isn’t difficult to do; an attacker within reception range of an unencrypted wi-fi point is able to insert themselves all too easily much of the time.

Best Practices for Securing your Public Wi-Fi Connection

The ongoing frequency of these attacks definitely serves to highlight the importance of basic cybersecurity best practices. Following these ones to counteract most public wi-fi threats effectively

  1. Have Firewalls in Place

An effective firewall will monitor and block any suspicious traffic flowing between your device and a router. Yes, you should always have a firewall in place and your virus definitions updated as a means of protecting your device from threats you have yet to come across.

While it’s true that properly configured firewalls can effectively block some attacks, they’re not a 100% reliable defender, and you’re definitely not exempt from danger just because of them. They primarily help protect against malicious traffic, not malicious programs, and one of the most frequent instances where they don’t protect you is when you are unaware of the fact you’re running malware. Firewalls should always be paired with other protective measures, and antivirus software being the best of them.

  1. Software updates

Software and system updates are also biggies, and should be installed as soon as you can do so. Staying up to date with the latest security patches is a very proven way to have yourself defended against existing and easily-exploited system vulnerabilities.

  1. Use a VPN

No matter if you’re a regular user of public Wi-Fi or not, A VPN is an essential security tool that you can put to work for you. VPNs serve you here by generating an encrypted tunnel that all of your traffic travels through, ensuring your data is secure regardless of the nature of the network you’re on. If you have reason to be concerned about your security online, a VPN is arguably the best safeguard against the risks posed by open networks.

That said, Free VPNs are not recommended, because many of them have been known to monitor and sell users’ data to third parties. You should choose a service provider with a strong reputation and a strict no-logging policy

  1. Use common sense

You shouldn’t fret too much over hopping onto a public Wi-Fi without a VPN, as the majority of attacks can be avoided by adhering to a few tested-and-true safe computing practices. First, avoid making purchases or visiting sensitive websites like your online banking portal. In addition, it’s best to stay away from any website that doesn’t use HTTPS. The popular browser extender HTTPS everywhere can help you here. Make use of it!

The majority of modern browsers also now have in-built security features that are able to identify threats and notify you if they encounter a malicious website. Heed these warnings.

Go ahead an make good use of public Wi-Fi and all the email checking, web browsing, social media socializing goodness they offer, but just be sure that you’re not putting yourself at risk while doing so.