Importance of Data Analytics and Big Data

 Big Data and Data Science are areas that have been growing and altering the way business is done and decisions are made. The vast amounts of data available today bring new possibilities that never existed before. Therefore, it is crucial to understand their role, and with a perspective of well-established databases and business intelligence solutions, decide what is the best set of tools for a particular situation.

According to different dictionaries, the data refers to facts and statistics that are collected together for reference or analysis. Data becomes actionable knowledge only when it has been processed by computer algorithms or human analysts. Today, data is mainly stored in digital format and handled using computers. Therefore, with technological advances, the amount of data available has also increased significantly, emphasizing the importance of data management and analysis. Initially, the data was handled with a file system. However, the rapid development of computer systems imposed the need for a more complex, standardized and independent solution.

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For many years, relational database systems (RDBMS) became a de facto standard for data management. Thanks to intuitive data organization within tables and comprehensive database engines, RDBMSs quickly gained more and more users. Once the data is represented with a database schema, the database engine performs a well-defined feat of relational algebra to enable data handling at a high level of abstraction (i.e. using SQL), ensuring the accuracy and consistency of the data. SQL, as a declarative language, removes the burden of users writing procedural code, and instead a database engine provides query execution plans to extract the necessary data. SQL includes both the Data Definition Language for the creation of data structures as well as the Data Manipulation Language that is used for querying, inserting, updating and deleting data. Importantly, RDBMS provide ACID properties:

• Atomicity - each transaction as a single unit must complete or fail completely
• Consistency - each transaction leads to a valid database state that satisfies all related constraints
• Isolation - concurrency control
• Durability - ensuring data persistence and recovery

Not everything is written in tables and not everything has a predefined schema. The massive generation of data from social networks, mobile devices, sensors and other data sources created challenges that motivated the creation of novel tools and techniques. Initially, Big Data was characterized by “3 V`s”: volume, variety and speed. Therefore, huge and rapidly growing volumes of different data challenged RDBMS that do not scale easily due to ACID properties and fixed schema requirements. Since then, many new "Vs" have emerged, such as data variability, truthfulness, value, and so on. This led to the creation of new tools and frameworks that are intended to address one or more of the new challenges. Some examples of these new tools include:

• Hadoop - a distributed storage and processing framework.
• Spark - a cluster computing framework
• Cassandra - a distributed NoSQL database management system
• Zookeeper - a centralized service for cluster management
• Elastic Search - a search engine, etc.

Most of these tools can be assigned to one or more elements of traditional database management systems. For this, it is essential to understand the purpose of each tool / framework and to know how to combine them into a single architecture. In addition, the requirements for ACID properties must be considered according to each tool or framework.

While Big Data focuses on providing tools and techniques to manage and process large and diverse amounts of data, it is not as focused on interpreting the results of data processing to support decision making. This is where Data Science comes in, focusing on the use of advanced statistical techniques to analyze Big Data and interpret the results in a domain-specific context. Therefore, Data Science involves an intersection of several areas including:

• Data Engineering
• Statistics
• Advanced Computing
• Display
• Domain and others

Within this context, tools and frameworks are required to:

• Statistics programming
• Databases
• Data import and cleaning
• Exploratory data analysis
• Machine learning
• Deep learning
• Text mining
• Understanding of Natural Language
• Recommendation Systems, etc.

Overall, Data Science is focused on providing a comprehensive solution to gain valuable information to support decision making in the rapid and heterogeneous context of modern data management and analysis.