Learn Big Data from scratch

Do you want to enter the world of Big Data and don't know how? Then you are in the right place. The objective of this article is to give an introduction to this concept and explain the professional future of experts in this sector.

Big Data refers to the management of enormous volumes of data. To be able to process this amount of information, it is necessary to use special techniques and technologies that allow operations on data to be carried out quickly, scalably and securely.

Today, data is surely the most important thing companies can have. They contain, for example, customer information that can be used to provide them with products according to their interests and improve the conversion rate.

They can also be used by businesses to optimize their procedures and improve company decision making.

There are a large number of sources from which to learn Big Data technologies. It is advisable to have a base of programming and computer knowledge to accelerate the learning process.

On YouTube there are many videos in both Spanish and English that teach the basics of managing large volumes of data.

Another option is to take some of the courses available on online learning platforms such as Coursera or Udemy. Some of these most popular courses are:

Currently, professions related to the analysis of enormous amounts of data along with programming are the areas where we find the most professional opportunities and with the highest salaries.

Large companies in all sectors are realizing how important information is to draw conclusions that improve the company's results. For this reason, they are making the leap to Big Data technology tools to carry out this process.

Data scientists are professionals with a profile that is highly in demand by companies.

Their job consists of cleaning and processing the data to later create predictive models based on mathematical and statistical analysis techniques.

One of the main missions of data scientists is to create informative dashboards to convey the findings to other departments in the company that do not know about data science. That is, communication is essential to become a good professional.

In order to become a data scientist, it is important to master mathematics, especially that involved in machine learning and artificial intelligence such as linear algebra and many branches of statistics.

In addition, it is also essential to know how to program in Python or R and the libraries used to model and analyze data such as Pandas, Sklearn, Tensorflow or Pytorch.

Another necessary knowledge is to master relational databases such as Mysql or PostgreSQL as well as non-relational databases such as MongoDB.

The Big Data engineer is a specialist in Big Data tools and architecture. Architecture is a term that refers to how we combine the different existing technologies so that together they are capable of providing a solution to a given problem.

A Big Data engineer has to be able to master distributed databases such as HBase or Cassandra, distributed file systems such as Hadoop, data ingestion software such as Kafka, Flume or NiFi.

You also have to master technologies designed for managing large volumes of data in a distributed manner such as Hive, Spark or Pig.

Other recommended knowledge for these professionals is the use of platforms such as Amazon Web Server (AWS) or Microsoft's Azure.

Programming in languages ​​such as Java, Scala, Python or R is essential to be able to use all the tools used until now.

Data analysts are specialists in using statistical analysis to draw conclusions. To do this, these professionals must master programming languages ​​such as Python or R in addition to having a deep knowledge of the SQL language to be able to make queries in relational databases.

The use of tools that allow the creation of dashboards with explanatory graphics created from the data is also common. Some of these software are Tableau, Power BI or QlikView.

This profile is very similar to the previous one. Business intelligence experts use strategies and tools to convert information into knowledge. This allows you to improve decision making and optimize many of a company's processes.

Business intelligence experts make use of dashboard visualization tools such as Power BI or Tableau as well as programs to extract, transform and load data into different sources and databases.

To learn Big Data, it is important to follow a step-by-step learning guide and collect the knowledge you need to be a good professional.

We have prepared a simple roadmap so you know what skills you need to obtain to be able to practice some of these professions.

The first step is to learn to program in one of the most used programming languages ​​​​in the sector of analysis of large amounts of data. These are Python, Java, Scala or R.

It is also important to master the relational database language SQL (Structure Query Language).

Mastering the terminal is essential for anyone working in programming or any technical sector related to computing.

Databases are a fundamental part of any Big Data infrastructure. There are many types of databases, relational and non-relational. There are also distributed databases such as Cassandra or HBase.

Knowing how to extract and save data in all types of databases is vital to having a successful career in the world of data.

Hadoop is the core component of many Big Data tools. This technology allows you to work with data in a distributed way. Mastering Hadoop also requires understanding the Hadoop Distributed File System (HDFS).

MapReduce is a programming model for executing operations on collections of data stored in a distributed manner.

Nowadays, it is barely used. However, it is important to understand how it works since it was the basis of all the technologies that work in a distributed way that we currently have.

Spark is a framework designed for distributed data processing. Some of the modules it includes are Spark SQL, which allows you to work with data through SQL statements, Spark Streaming, which helps process data in real time, or MLlib, a Spark library to perform machine learning operations on data that are stored in a distributed way.

Mastering Spark and its modules is vital for anyone who wants to have a professional future in the Big Data sector.

Apart from Spark, there are many other Hadoop frameworks that are important to understand and master. This ecosystem consists of a family of solutions for the ingestion, storage and analysis of large volumes of data.

Among them we find HBase, Hive, Pig, Sqoop, Pig, Kafka, Storm, Mahout, Flume or Cassandra. To learn more about them you can visit our publication of the best Big Data tools.