Every single day, billions of data bytes are gathered from heterogeneous systems in both structured and unstructured format. For streaming such big data insights and completely analyzing them for accelerating strategy flow for stimulating business growth and overall efficiency is extremely difficult without a big data analysis solution.
Services in big data not only include the collecting, connect and attuning data in a format. It’s also about generating accurate and intelligence business reports. Service providers empower organizations to have more operational efficiency as well as decrease big data security concerns.
The frenzy on big data continues. It is permeating almost every industry vertical, flooding organizations with more and more information, as well as making software dinosaurs, such as Excel appear more and more inept. Data crunching no longer is only for nerds, and the need for powerful and sophisticated analysis, real-time processing is greater than ever.
THE BEST TOOLS AND LANGUAGES FOR CRUNCHING DATA
1. R. It’s been kicking around since4 1997 as a free option to costly statistical software. But through the years, it’s become the golden child of data science, which is now a household name not just among nerdy statisticians, but on Wall Street traders, Silicon Valley developers and biologists as well. Organizations as diverse as Facebook, Google an others all use R since its commercial utility continues spreading.
R has an obvious yet simple appeal. Through it, one could sift through complex sets of data, manipulate data via sophisticated modeling functions, and build sleek graphics for representing the numbers in only a few code lines. It’s similar to a hyperactive version of Excel.
2. Python. Python is a flexible, easygoing platform. Python is quickly getting a mainstream appeal as a hybrid of R’s sophisticated, fast data mining capability as well as a more practical language for building products. Python is an intuitive and easier to learn platform. The ecosystem has dramatically grown recently, making it more capable of statistical analysis reserved previously for R. Moreover, Python has the advantage of a rich data community, providing huge amounts of features and toolkits. Python is flexible and broad, thus people flock to it.
3. Julia. For big data, such as big data scope in Australia, Julia still is too arcane for widespread industry adoption. However, data hackers get giddy with the potential of ousting R and Python with the tool. Julia is an insanely fast, high-level and expressive language. It is faster than R and has the potential to even be more scalable than Python and fairly easy to learn as well. It’s truly an up and coming tool that big data companies Australia look forward to using. For now, Julia is holding back for now. The Julia data community is in its early stages and more tools are packages are needed before it could compete viably with R or Python. It is young, but is gaining stream and truly promising.
4. Hadoop and Hive. Numerous Java-based tools have popped up to meet the huge demand for data processing. Hadoop exploded as a go-to Java-based framework for batch processing. It’s slower than some other processing tools, but insanely accurate and used widely for backend analysis. It nicely pairs with Hive, which is a query-based framework that runs on top.
5. Java. Data analytics companies in Australia also make use of Java and Java-based frameworks. Java is the foundation language for all data engineering infrastructures. It does not provide the same visualizations quality as R and Python do, and it’s not the best for statistical modeling. However, if moving past prototyping and has to build big systems, then Java is the best bet.
6. Scala. It’s another Java-based language and the same to Java, it’s getting more and more as the tool for anyone doing machine learning at big sales, or creating high-level algorithms, as well as capable of creating robust systems. Scala is similar to working with clay that one could then put into a kiln and then turn into steel.
7. Kafka and Storm. When requiring real-time, fast analytics, then Kafka is one’s best friend. It has been around for half a decade, but only recently became a popular framework for stream processing. Kafka, born inside LinkedIn, is a very fast quer5y messaging system. However, it’s too fast and real-time operation lends itself to error and missing things occasionally. Another framework written in Scala is Storm. It’s gaining huge traction for stream processing in Silicon Valley. It was acquired into Twitter, which without surprise has a big interest in fast event processing.
8. Matlab. It’s been around for eternity, and even with its price tag, is still widely used in specific niches, signal processing, research-extensive machine learning as well as image recognition, to name a few.
9. Octave. It’s much the same to MatLab, except that it is free. Still, it is rarely seen outside academic signal processing circles.
10. Go. It’s another newcomer that is gaining traction. Developed by Google, it loosely derives from C. Furthermore, it’s gaining ground against rivals like Python and Java for creating robust infrastructures.
A big data company in Australia offers comprehensive data analytics services for enabling organizations to accumulate insight in real-time. With this, companies could revamp their strategies, which allow them to drive up their revenue. Developers of big data also have proficiency to provide BI or business intelligence service, which include accurate report generation and fast insight on dashboard, together with data mining, predictive analytics and database turning. Big data is indeed big these days, disrupting organizational and company processes, providing bigger chances of boosting the return on investment.
In this day and age, where technology continues to evolve, a software or web development company, businesses and other organizations, as well as individuals have to stay updated to stay on the competitive edge. Thus, with the big impact of big data and analytics in organization processes, it’s important to be able to find the best possible services to harness the power of big data, for a company or business to make full use of it and stay ahead of the competition.