Understanding the difference between big data, analytics, and data science can be confusing for those who are not familiar with the technology industry. These terms are often used interchangeably, but they have distinct meanings that are important to understand.
In this article, we will explore what big data, analytics, and data science mean and how they are related to each other.
In a nutshell, big data is the large amount of data generated by individuals, companies, and organizations that provides the infrastructure for analytics and Analytics is a branch of mathematics that involves using statistical models, data mining techniques, and machine learning algorithms to identify patterns and trends in data and make decisions.
On the other hand, data science is the interdisciplinary field that combines statistics, computer science, and domain expertise to extract insights from data using analytical and programming skills.
Understanding the differences and interrelationship between big data, analytics, and data science is important for anyone using data to make better decisions or working in the technology industry.
However, you can use big data without analytics, for example, as a repository for logs or media files. You can also use analytics without a big data database by utilising Microsoft Excel, for example.
Analytics
Operations Research is an old term that refers to the use of mathematics to fine-tune industrial and other operations. Prior to the 1990s, mathematicians and statisticians achieved this with the tools available to them at the time. Microsoft Excel and statistical applications such as SPSS for the PC or SaaS for the mainframe are examples. They formerly relied on calculators and slide rules, as well as pencil and paper.
Clinical trials in the pharmaceutical business and research hospitals are another application of applied mathematics. There are statisticians evaluating the efficacy of new medications. They're also trying to figure out if there's a link between things like smoking and lung cancer. Neural networks and other data structures, as well as machine algorithms, are meant to find correlations between variables and perform categorization.
For a variety of reasons, processing medical and industrial data is difficult. First and foremost, a statistician or mathematician (data scientist) is familiar with COBOL, SQL, and other mainframe computer languages and databases. As a result, the data scientist must await the programmer's data loading into the mainframe. The programmer, on the other hand, has little or no experience with applied mathematics. It would be preferable if the data scientist could do the programming themselves.
Second, without machine learning SDKs, programmers would be forced to design their own statistical algorithms. These are, of course, incorporated into SaaS products. COBOL, on the other hand, does not do this unless you tell it to.
Finally, SaaS is a type of proprietary software. IBM SPSS is as well. It is not an open-source project. As a result, researchers and practitioners that work with scikit-learn, TensorFlow, Keras, Spark Machine Language, and other current technologies are unable to contribute their own work to the systems they use. Consider what it would be like if you could only utilize the Microsoft (former), IBM (former), and Oracle (current) monopolies' tools. Their worth would be lowered as a result.
The next issue is that data must be entered into a SQL database in a specified, rigorous row-column format in order to be stored. Unstructured data is not supported.
Finally, unlike distributed databases like Spark, ElasticSearch, and Hadoop, such systems cannot scale practically infinitely.
Big Data
Spark, ElasticSearch, Hadoop, and other tools were created primarily to address Yahoo, Google, and Facebook's massive data demands. Those corporations developed the software with the help of academics at universities such as Stanford, and then released it as open-source software.
These systems can be distributed across a cluster of low-cost commodity PCs. Because you may add as many extra machines to the cluster as you like, they can process more data than even the largest mainframe.
PCs are the machines used in data centres by Amazon EC2, in-house IT shops, and others. Mainframes, Sun Solarix, IBM AIX, and other huge, expensive systems have mostly been replaced by these. As a result, such systems had become more affordable.
Hadoop isn't the same as a database. It's a distributed file system, which means you can save data across numerous machines with it.
This is where the term "big data" originates. Big data is information that will not fit on a single machine's hard drive.
In terms of memory limits, Apache Mesos and comparable systems abstract memory so that it can be larger than a single machine's capacity. You may easily add another computer to increase extra memory. A system is said to scale linearly when adding x machine adds memory by the same amount (x).
Unstructured big data databases like ElasticSearch and MongoDB exist. This implies you can save data in the JSON (JavaScript Object Notation) format, which is quite free. In addition, unlike SQL, you do not need to declare the relationship between entities. To put it another way, there's no need to establish foreign keys or anything like that.
A MongoDB JSON record might look like this:
{ name: "Sean", age: "secret"}
While the following one may differ, for example, by including extra fields:
{ name: "Jean", age: "young", employer: "GRC"}
There are numerous tools available to convert data from a variety of formats to JSON. SQL, on the other hand, is a different storey.
Cassandra is a brand-new computing concept. This database is column-oriented. Missing values may exist in such a database. One employment record, for example, may include a social security number for Americans but not for foreigners. Such a database does not waste space by reserving space for empty values, as Oracle SQL does. It also runs faster since it groups columns with the same value together (rather, alphabetical order).
That concludes a brief overview of the distinction between big data and analytics. You should be aware that programmers can specialize in big data programming by becoming a big data engineer or architect, for example. Data science, on the other hand, can only be done by engineers who have a working knowledge of applied mathematics.
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