All About The Ecosystem Of Data Science Given how quickly data science is developing, a whole ecosystem of useful tools has emerged. Since data science is so fundamentally interdisciplinary, it can be challenging to classify many of these businesses and tools. At the most fundamental level, however, they can be divided into the three components of a data scientist's workflow. Specifically, gathering, organizing, and evaluating data.
Part 1# – DATA SOURCES The remainder of this ecosystem would only exist with the data needed to operate it. Generally speaking, databases, applications, and third-party data are three very distinct types of data sources.
● Databases Unstructured databases are older than structured databases. The structured database market is estimated to be worth $25 billion, and our ecosystem includes established players like Oracle and a few upstarts like MemSQL. Structured databases, which typically run on SQL and store a set number of data columns, are utilized for business tasks like finance and operations, where accuracy and dependability are crucial considerations. Most structured databases make the fundamental premise that all queries against them must produce flawless, consistent results. Who would be an excellent example of the necessity for a structured database? The bank. Account data, personal identifiers (such as your first and last name), loans that their clients have taken out, etc., are all stored by them. Your account balance, down to the penny, must always be known to the bank. Unstructured databases are an additional option. It's hardly surprising that data scientists invented these because they approach data differently than accountants. Data scientists are more interested in flexibility than they are in exact consistency. As a result, unstructured databases make it easier to store and query large amounts of data in various ways.
● Applications Critical business data stored in the cloud has gone from unfathomable to a regular practice in the past ten years. Perhaps this is the biggest change to the business's IT infrastructure. Why is that important? Data scientists may now leverage powerful data sets from every company's division to perform predictive analysis. Although there is a lot of data, it is now dispersed among several applications. Imagine you wanted to use your SugarCRM app to view a single customer. Are you attempting to determine the number of support tickets they have created? That is most likely in the ZenDesk app. Have they checked to see if their most recent bill has been paid? It is contained in your Xero app. All of that information is spread over several locations, websites, and databases.