Enterprise data management

In its simplest terms, big data is an accumulation of data sets that are so large or complex that they cannot be effectively used or processed with traditional data processing applications.

Data automation is an essential component of modern business intelligence and big data strategies. It is the process of automating the collection, storage, management, and analysis of data.

A data lake is a storage repository that holds a vast amount of raw data in its native format from various sources, including structured, semi-structured, and unstructured data.

A data lakehouse is a data warehouse that combines the scalability and cost-effectiveness of a data lake with the performance and governance of a data warehouse.

Data modeling is a technique of organizing data in a logical pattern that allows for the efficient storage, retrieval, and manipulation of data within a data warehouse.

Data Ops, or Data Operations, is a modern approach to managing and deploying data pipelines in an agile and efficient manner. This methodology aims to improve the collaboration between data teams and streamline the process of generating insights from raw data.

A data product architecture consists of three layers: the bronze layer, the silver layer, and the gold layer. Each layer represents a different stage in the data processing and refinement process.

A data mart is a subset of a data warehouse that is used to store and analyze specific types of data. It is a virtual repository of data that is used to store and analyze data from a specific source, such as a particular business unit or department.

Data warehousing is a process of collecting, organizing, and managing data from multiple sources into a single, unified system for easy access and analysis.

Metadata management is the process of managing metadata – information such as the structure, definitions, and relationships between different types of data.

Semi-structured data refers to data that does not fit neatly into a traditional structured format like a database table. Examples of semi-structured data include XML files, JSON documents, and social media posts.

Structured data refers to data that is organized into a well-defined format such as a database table or spreadsheet. Examples of structured data include customer information, financial records, and inventory lists.

Unstructured data refers to data that does not have a clearly defined format. Examples of unstructured data include text documents, images, and videos.