A Data Warehouse process ensures access to and a periodic and automatic transfer of data from a number of operative or even external sources. The first ETL process (Extraction, Transformation, Loading) must ensure data quality. It will not only perform possibility checks on data arriving from very diverse sources but also provides data with structural information allowing a combination of data from different sources.
We follow the efficient way of processing high/large volumes of data is what you call Batch Processing. It is processed, especially where a group of transactions is collected over a period of time. In this process, At first, data is collected, en tered and processed. Afterward, it produces batch results.
Data warehouses in the cloud are built differently. Each warehouse provider offers its own unique structure, distributing workloads and processing data across several physical servers, networks, or software tools while making data easily accessible and more powerful.
On-Premise data warehouses sit on top of a three-tier, nine-layer architecture. The tiers provide the general structure for how data is collected, stored, and used. At the bottom tier, a database server collects data from multiple sources such as financial, sales and marketing, customer, and inventory systems — while an OLAP (online analytical processing) server in the middle tier makes the data usable for analysis. In the top tier, users can then query, access, and manipulate the data through a variety of tools.
SQL (Structured Query Language) is a programming language that is used to manage data in relational databases. A NoSQL database, on the other hand, is self-describing, so does not require a schema.The main differences between SQL and NoSQL are, Language, Scalability, Community, Structure.
Structured data is highly organized information that uploads neatly into a relational database (think traditional row database structures), live in fixed fields, and is easily detectable via search operations or algorithms. Unstructured data may have its own internal structure but does not conform neatly into a spreadsheet or database.
Analysts can easily combine their current likely structured data with unstructured data, such as mapping social media with customer and sales automation data, for example. No matter the complexity and variance, BlueWhale permits users to leverage the data they need early on in order to generate the right outputs for better decision-making.