Data science is one of the hottest (if not the hottest) jobs of the XXI century. The number of CS students and business science majors who want to know how to analyze insights is growing at a wild rate.
Right now, “Intro to Data Science” is the fastest-growing class at Berkeley. When it comes to Harvard, “Introduction to Statistics” was another hot pick among undergraduates — a change stimulated by the growth of big data and data science.
We all understand that there’s no end in sight when it comes to data production. Since the 2000s, we have been creating terabytes of data contributing to the worldwide data deluge.
In 2021, the need for people who could make sense of all this accessible information is drier than ever. That’s why the demand for data scientists has spiked dramatically.
If you are a computer science enthusiast eager to brand yourself as a big data analyst, you might be confused about what the right starting point is. In this post, I’ll explain why I believe that learning Java is one of the most reasonable decisions a data scientist can make and share some helpful resources to fuel your learning.
Unfortunately, if you come by a tech forum or data science-related Reddit thread, it’s painfully common to hear claims like “Data science will become obsolete in 20 years”. I’d say, there’s no empirical evidence of this happening anytime soon — rather, as BD and data analytics advance, new applications of these technologies emerge.
Here are ten applications for big data that can be an excellent motivation to start learning it even if you work in a field with no direct connection to engineering or computer science.
Targeting customers. Brands and corporations have long discovered the power of BD and aim to make the most out of the information customers share on websites and social media. As for the political world, big data emerged as a killer weapon in reaching out to voters and promoting senate or office candidates.
Optimizing corporate internal processes. The growing number of company and talent managers rely on big data to work productively. They use tracking tools and sensors to get access to employee efficiency insights and rely on ML and BD algorithms to make sense of this information.
Personal life and socialization. The power of big data in online dating has been a hot topic throughout the last decade. Apps like Tinder, OkCupid, and eHarmony proved that it’s possible to break matchmaking down to a series of algorithms and predictable scenarios. In the future, the impact of BD in the dating market will likely be even more widespread, helping love-seekers fulfill desires they never knew they had.
Healthcare and effective treatment. There is a sea of BD applications in the healthcare sector — from leveraging the power of sensors and trackers in wellness to improving the precision of diagnosis and laying the groundwork to facilitate life-or-death decision-making for physicians.
Increasing the relevance of science and the efficiency of academic research. Top research institutions like CERN heavily invest in data centers for a reason — the insights data analysts provide come in handy in making accurate predictions, identifying research areas, relevant to the general public, and broadening a scientist’s perspective.
Improving the performance of athletes. Big data tools have been officially implemented in tennis and soccer to make sure referees don’t make a blind rule on a player’s mistakes. The NFL uses big data as well to help team managers make calculated decisions regarding scouting, running stadiums, or interacting with fans. Team managers and coaches, too, rely on BD and data analytics to plan athlete training and make sure they don’t harm players with excessive or strenuous training.
Optimizing living conditions. Big data is a frontrunner in improving the quality of urban life. City councils rely on BD tools to monitor the flow of traffic and predict road congestion. Electricity and water consumption sensors help communities use resources efficiently and spend less of the taxpayers’ money on maintaining a comfortable living environment in smart cities.
Trading and finance. Big data brought about a revolution in the world of trading. Right now, most equity trading processes rely on ML algorithms — these help track stock market fluctuations, predict the variations of stock prices, and allow investors to make smart, data-backed decisions. Other than that, big data is widely used to discover promising investment and trading opportunities.
Education. The usage of big data at schools and universities is progressively becoming the new normal. Smart progress tracking systems (like the one implemented at the University of Tasmania in Australia) allow students and professors to keep track of classwork, collect behavioral insights to help learners develop an effective study method, and help teachers to fine-tune their performance in class.
Entertainment and media. Netflix and Spotify are leading the way in big data implementation in entertainment. The latter relies on Hadoop (a set of Java-based tools) to collect and process user insights. The ability to analyze user data comes in handy, as it allows creating curated track feeds and promotes higher audience engagement.
There’s no tip-toeing around the fact that Python and R are the standard languages of modern big data. I won’t deny the fact that most BD tools have APIs for Python and R so not knowing Java will rarely be indispensable for a data scientist.
However, there are a ton of Big Data use cases when Java should be one of the languages in your tech stack.
You should learn Java for big data if:
- You want to implement a theoretical model developed in Python. In most teams, Java is a preferred programming language for writing production code that allows you to use and scale BD algorithms.
- You want to integrate your project with enterprise tools. In the world of enterprise tools, Java is huge. There are plenty of tools that use the language — so, if you want to integrate your big data with any of those, learning the basics of Java will spare you a ton of stress.
- You want to scale BD projects. Java helps data scientists process more data, support a higher prediction load, and scale complex ecosystems.
- You want to adapt existing Enterprise-Grade tools to a particular use case.
Java isn’t the newest and hottest language of the market — so it makes sense to wonder why it still has so much impact in Big Data, despite the appearance of newer, more concise technologies.
Personally, I (and many of my peers) am drawn to Java both in application and big data development for the following reasons:
- Broad user base. Simply put, Java is popular among my clients so knowing how to leverage its tools lands me jobs I’d otherwise get “passed” on.
- A lot of learning tools. There are a lot of books, video tutorials, and learning platforms for learning Java. Compared to newer languages, I feel like Java learners have a clearer sense of direction and can create an effective study method relatively easily. Thus, learning Java is worth it even if you will not be using it as a primary language in day-to-day BD tasks.
- Java is the base for the majority of big data tools — Hadoop, Spark, Storm, Mahout, and more. Since the Hadoop ecosystem is so widely used in BD, some developers go as far as to say that “Java IS Big Data”.
- Scala is a relative of Java. The backbone of Apache Spark — is essentially a language designed using JVM. That’s why learning Java helps developers smoothen the transition to Scala (for most it’s still rough, however) and become confident Spark users.
- Java is flexible, allowing developers to build a practically limitless tech stack on top of it. I also believe that Java gets bonus points thanks to its support of scalability and multithreading.
Hadoop is a framework that helps data scientists process large datasets. Companies use the tool to aggregate all external data in one system, group, and categorize it.
These are the tool’s main features:
- Failover support: ensures safe data transfer between slave machines in case one of them shuts down.
- Scalability: each new machine can easily become part of the Hadoop ecosystem.
- Low intensity on hardware: compared to other large-scale BD solutions, Hadoop can run on lower-tier machines allowing company managers and data scientists to cut hardware costs.
- Local data processing: saves bandwidth and increases the speed of information processing.
Is there a flipside? Plenty: Hadoop is hard to learn and to implement so a growing number of data scientists prefer to move on to other tools (according to statistics, 11% of Gartner survey respondents said that they plan to invest in Hadoop).
Having said that, the demand for Hadoop is still outmatching the supply. At the time of writing, there are nearly 2,500 Hadoop developer job openings on Indeed. The salaries of Hadoop engineers are worth considering as well — according to ZipRecruiter, the national average is at $125,000.
Spark is a multi-purpose tool data scientists use for just about everything: stream processing, machine learning analytics, and many other processes. By flexibility, speed, and the smoothness of the learning curve, the framework is a huge cut above Hadoop.
It’s worth noting that Spark is built in Scala, not Java (there’s a Java API you can integrate to be fully comfortable). Even if you set your sights on learning Scala, the good news is, there are plenty of similarities between Java and Scala — I outlined the main ones below.
- Both languages are based on JVM.
- Commonly used Java IDEs (e.g. Eclipse, IntelliJ) support Scala.
- Both are OOP languages (with Scala going a step further and extending its tools to functional programming as well).
- Developers can reuse Java libraries in Scala and vice versa.
Storm is another handy tool used to process real-time data streams. The framework approaches streaming similarly to the way Hadoop handles batch processing.
Storm has a wide range of applications in big data: ETL, continuous computation, machine learning, and many more.
Main features of the framework:
- Ease of setup. To understand the range of Storm adoption, it’s enough to take a look at some of its adopters: Twitter, Spotify, Alibaba, and many more.
“Spotify serves streaming music to over 10 million subscribers and 40 million active users. Storm powers a wide range of real-time features at Spotify, including music recommendation, monitoring, analytics, and ad targeting. Together with Kafka, memcached, Cassandra, and netty-zmtp based messaging, Storm enables us to build low-latency fault-tolerant distributed systems with ease.”
- Spotify team on using Storm
If you can’t wait to start learning Java to improve your versatility as a data scientist, it’s helpful to have a resource deck for reference.
While I am not a huge fan of using multiple learning tools at once, I put together a deck of useful books, courses, video tutorials, and forum threads for those eager to learn Java and use it in BD.
- Introduction to Java Programming and Data Structures — gives a concise overview of algorithms, data structures, networking, and almost every other Java concept. It’s one of the fullest and useful programming resources I have ever read.
- Spring in Action — although Spring isn’t Java, developers deal with it in most daily tasks. Reading this guide will help you get a clear and up-to-day understanding of Spring programming and save developers a ton of workplace stress.
- Head First Java — often used as a textbook at programming classes, it’s a top choice for students since the book mirrors most university curriculums.
- Effective Java.
- Clean Code: A Handbook For Agile Programming — it’s not a Java textbook per se, but it’s beneficial for getting to know best coding practices.