Big data refers to an enormous volume of structured, semi-structured and unstructured data that impacts the businesses on a regular basis. A deep cognizance of effectively managing big data with appropriate analysis and insights allows the new age organizations to make strategic business decisions.
A sequence of data processing steps that involves moving the data from source (application) to destination (data warehouse) in an optimized and transformed manner where the output of each step becomes the input to the following step for better business insights is known as Data Pipeline.
In order to efficiently meeting the dynamic business needs of modern age, many organizations are replacing their legacy systems with emerging applications which is contributing towards migration of data from one location to other, one application to other and one format to other, and the whole migration process is termed as data migration.
Data Mesh is a modern distributed data platform that works based on distributed architecture for effective analytical data management. It ensures an optimum advantage of domain oriented self-serve design. Data Mesh supports easy access of data with minimal dependency on data lake and data warehouse.
The technical aspect of managing, maintaining, securing and controlling data base systems in an efficient and effective manner for the greater benefits of enterprises is known as Database Administration.
Algorithms and related programs that are designed to mimic the human intelligence in learning, discernment, and resolving problems are denoted as Artificial intelligence. Advancing levels of AI include reactive machines, limited memory, theory of mind and self-awareness.
Analytics is the methodical computational analysis of data and figures with a goal of identifying, interpreting and communicating the specific patterns in the data. Analytics mainly provides insights to the organizations and help the enterprises to make informed decisions which further enhance the sales volume and optimizes the costs and resources.
The organizations today requires to maintain a track of key data and performance indicators to evaluate their enterprise and processes on a periodic basis in order to make well informed business decisions.
The technical aspects and procedures which augment the cooperation and communication between the data scientists and operations teams are known as MLOps.