Big Data needs new tools and technologies that can encompass the complexity of unstructured and continuously expanding data. For this, traditional relational database technologies or RDBMS are not adequate. In addition, advanced analysis and visualization applications are needed in order to extract the full potential of the data and exploit it for our business objectives. Let’s see some of the main tools below:
Hadoop: it is an open source tool that allows us to manage large volumes of data, analyze them and process them. Hadoop implements MapReduce, a programming model that supports parallel computing over large collections of data.
NoSQL: these are systems that do not use SQL as query language, which, despite not being able to guarantee the integrity of the data (ACID principles: atomicity, consistency, integrity and durability), allows them to obtain significant gains in scalability and performance when working with Big Data. One of the most popular NoSQL databases is MongoDB.
Spark: is an open source cluster computing framework that allows you to process data quickly. It allows you to write applications in Java, Scala, Python, R and SQL and works on Hadoop, Apache Mesos, Kubernetes, as well as independently or in the cloud. You can access hundreds of data sources.
Storm: is a free code distributed real-time computing system. Storm allows to process unlimited data flows in real time in a simple way, being able to be used with any programming language.
Hive: is a Data Warehouse infrastructure built on Hadoop. It facilitates the reading, writing and administration of large data sets that reside in distributed storage using SQL.
A: it is one of the programming languages most used in statistical analysis and data mining. It can be integrated with different databases and allows to generate graphics with high quality.
4 key steps to get into Big Data
In order to start enjoying the benefits of Big Data, any organization needs to have four key assets:
First, the data. In an environment where the data is exploding, its availability does not seem to be the problem. What should concern us is rather to be able to maintain their quality, and know how to handle and exploit them correctly.
For this, adequate analytical tools are needed, which also does not represent a barrier for companies today, due to the wide availability in the market of both proprietary and open source tools and platforms.
Which brings us fully to the third fundamental asset, which is the human factor. Having the right professionals in our organization, as data scientists, but also experts in the legal implications of data management and privacy, is emerging as the most important challenge.
However, equipping ourselves with these three assets and putting them to work will not guarantee our success with Big Data either. To be true data driven companies, we will need to carry out a radical transformation of our processes and business culture, to make the data truly stand at the center of our company, and ensure that all departments, from IT to senior management, assume this new focus.
The challenges of Big Data
Nowadays no company can ignore Big Data and the implications it has on its business. However, it is a relatively new and constantly evolving concept, and there are many challenges that organizations face when dealing with big data. Among them:
Technology: Big Data tools like Hadoop are not so easy to administer and require specialized data professionals as well as important resources for maintenance.
Scalability: a Big Data project can grow with great speed, so a company has to take it into account when allocating resources so that the project does not suffer interruptions and the analysis is continuous.
Talent: the necessary profiles for Big Data are scarce and companies are faced with the challenge of finding the right professionals and, at the same time, of training their employees on this new paradigm.
The actionable insights: in front of the amount of data, the challenge for a company is to identify clear business objectives and analyze the appropriate data to achieve them.
Data quality: as we have seen before, it is necessary to keep data clean so that decision making is based on quality data. The costs: the data will continue to grow