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Main Big Data tools
The technology sector has been developing specific solutions for the collection and storage of information. Today, there are numerous Big Data tools , and these are some of the most widely used:
Apache Hadoop – Arguably the industry’s best-seller, used by outlets like The New York Times and even Facebook, it’s a free, open-source framework that makes it easy to process large amounts of data.
Elasticsearch : Professionals point out that you can see the evolution of the data it collects and processes in real time. It indexes different types of content and allows complex searches.
Apache Storm - It also processes large amounts of data in real time and creates big data topologies to transform it. It makes the job much easier.
MongoDB – This is a free, non-relational database that allows you to work with frequently changing data. It is especially used for data from mobile applications and content management systems.
Python : It is said to be a classic of Big Data, simple to use but requiring knowledge for its proper use. It is an interpreted language that runs online and has a remarkable library. One of its drawbacks is speed, because it is somewhat slower than the rest.
Applications for data analysis
There are countless apps and technological developments aimed at data analysis. These must meet several important aspects to be effective, qualities that are present in the Big Data tools mentioned above.
Speed in receiving and acting on incoming information, to be able to offer even real-time monitoring, as seen in some of the data analysis tools mentioned above.
Variety of formats supported, both structured and unstructured.
Selection of data to be processed: it is essential that the application can discover duplicate references, detect anomalies or certain inconsistencies.
A final aspect would be that the acquired resources are of kuwait whatsapp data value, but this must be assessed by the team that analyses the information collected. Hence the importance of having professionals trained in the world of data and information analytics , with postgraduate degrees such as those found at EAE Business Barcelona: the Master in Business Analytics & Data Strategy and the Master in Big Data & Analytics .
Workers around a computer analyzing data
Big Data Programming Language: What are they?
Data collection, analysis and management has required a specific programming language to store and structure the information collected. Thus, there are several programming languages for Big Data that are especially known for being the most used and effective. These are some of them:
Hadoop and MapReduce, considered the pillars of data analytics. Both associated with Java, ideal for huge volumes of resources.
Apache Spark, written in Scala but compatible with Python and Java, stands out for its speed and its highly versatile capabilities, according to Big Data experts .
Python and PySpark: the best of Python and its libraries, with the power of Spark.
Without a doubt, with this mini guide to data analysis tools and applications for Big Data, the reader will be able to understand that there is a real universe destined to generate developments and programs to optimize the use of the information that is collected. The world of Big Data is expanding and is one of the areas that offers the best job prospects and professional possibilities for the future.