What languages and tools do you need to know and learn how to use to perform data mining, machine learning, big data or stream processing tasks?
We have talked long about various programming tools, and languages focused on the analysis, extraction and manipulation of large volumes of data (data mining or data crunching). The fever for Big Data continues, the result of a dependence on the large companies to monetize the information generated by their services, campaigns, social networks, etc. Data management is no longer an exclusive stop for a select group of geeks, and there are more and more options for the average user to access sophisticated methods of real-time analysis and processing. Without further delay here we summarize the main resources that you will have to manage if you want to delve into a sector increasingly demanded by the industry.
It wouldn’t make sense to start with a language other than R, the first free alternative to industry giants, such as MatLab or SAS. In recent years R has broken down the barriers of the most academic circles to enter the world of finance, hand in hand with Wall Street traders, biologists, developers. Companies such as Google, Facebook and the New York Times have used this language to manage complex datasets, perform modelling functions, and create graphs to represent results in a more accessible way.
One of the greatest virtues of R is its ecosystem of users and developers. The R community is estimated to be above two million users and, according to a survey of KDnuggets.com, remains the most popular language when it comes to working with data science, being used by 49% of the total respondents. However, Python follows closely after experiencing a user increase of about 51% in 2016.
R is increasing its use for the creation of financial models, but above all, as a visualization tool. It’s easy to structure presentations with a more striking visual result with just a few lines of code to spend the night bearing with Excel. The problem with R comes with the systematization of the management of large volumes of data on a recurring basis. For many, R works better when designing than implementing solutions. The prototype may have been programmed to R, but the end result is likely to opt for other, more solvent solutions in record management, such as Java or Python.
If, for example, we had to make a timely analysis of the correct use of the randomness algorithm of Casino roulette, without a doubt R would be the most immediate option to handle with solvency, simplicity and power – although perhaps not in the most efficient way – the volumes necessary to obtain a reliable result within stringent variance parameters. If we had to do it on a regular basis, we would surely not opt for this solution.
Where R strictly sins, Python’s flexibility appears. Python is gaining support thanks to the combination of R’s sophisticated capabilities for data mining and simplicity. When developing complete products, not to mention that its expansion at the educational level has generated a spectacular volume of material. Leading to a park user is largely responsible that their possibilities have expanded to cover territories statistical analysis previously reserved for R .
It has been threatening to become the industry standard for years. In data processing, there is always a negotiation between dimension and sophistication, and Python seems to have found a point of agreement. Bank of America uses Python to develop new products and communication spaces between the client and the bank’s infrastructure, but also to manage the analysis of its vast databases. Of course, we are not facing the most efficient language, and we will only find it as a large-scale infrastructure core engine in particular situations.
Java, Hadoop and Rapid Miner appear in the ins and outs of the largest Silicon Valley companies. A look at the depths of Twitter, LinkedIn, or Facebook will reveal to Java as the functional language of all its structures related to data engineering. Java may not provide the same quality in the visualizations that R or Python can offer us, and it may not be the best in terms of statistical modeling. But once the prototype phase is over, when designing large-scale systems, it is often common for us to meet this veteran.
Java-based tools have also flourished thanks to the demand for data processing. Hadoop is the reference framework for working with batches. It may be slower than other processing tools, but it is extraordinarily accurate and commonly used in backend analysis. And, besides, it gets along with Rapid Miner, a platform for the creation of analytical workflows.
RapidMiner dramatically facilitates the work, from the preprocessing of data to machine learning and the verification of models, since it gathers all its functions in a single environment, starting with a visual designer of workflows for teams, through the exchange and development of Predictive models, to the execution of flows directly in Hadoop.
Ok, we still need to work with high volumes and, also, time is a crucial factor: Kafka and Storm appear here.
If the priority is to have fast analytics, in real-time, ask for Kafka. It has been in circulation for more than six years, but it has become one of the most popular frameworks for real-time flow processing , a consequence of the new event management needs. Kafka, born as a project in LinkedIn offices, is an ultra-fast message queue system. Against it, you can only say that sometimes it is too fast, which leads to errors. The need to work in real-time is usually conducive to these types of situations.
As a solution to this type of inconvenience, and facing the impossibility of giving up immediacy, large companies often use a compromise solution: Kafka or Storm in real-time processing and Hadoop for batch processing systems in which the Accuracy is crucial, not so much time.
Storm is another framework written in Scala (Java-based language, which is also gaining support among those in need of large-scale machine learning services or the use of high-level algorithms ) that is gaining strength in flow processing projects. It was acquired by Twitter, which gives an idea of its ability to quickly process events.
We left many along the way, heavyweights like Matlab or Octave and “newcomers” like Go, but we wanted to keep the most representative tools and languages, as a quick guide for the newcomer or the curious.
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Data mining, Machine Learning, Big data, Stream processing … all of them are new markets for a whole new generation of programmers who can find in the data analysis that work exit that they had been looking for a long time.