Data Warehouse, Data Lake, Data Mart, Data Hub A Definition of Terms
In today’s business environment, most organizations are overwhelmed with data and looking for a way to tame the data overload and make it more manageable to help team members gather and analyze data and make the most of the information contained within the walls of the enterprise. When a business enters the domain of data management, it can often get lost in a morass of terms and concepts and find it nearly impossible to sort through the confusion. Without a clear understanding of the various categories and iterations of data management options, the business may make the wrong choice or become so mired in the review process that it will give up its quest.
This article is the first of two on the topic of Data Management. Here, we will define the various terms so that a business can more easily understand the types of data management solutions and tools. In the second of these two articles entitled, ‘Factors and Considerations Involved in Choosing a Data Management Solution’e discuss the various factors and considerations that a business should include when it is ready to choose a data management solution.
A Data Warehouse (AKA Datawarehouse, DWH, Enterprise Data Warehouse or EDW) solution is designed to centralize and consolidate large bodies of data from disparate, multiple sources and is meant to help users execute queries, perform analytics, provide reporting, and obtain business intelligence. Data Warehouse data is typically comprised of data from applications, log files and historical transactions and integrates and stores data from relational databases and other data sources originating in various business units and operational entities within the enterprise, e.g., sales, marketing, HR, finance.
A Data Warehouse is a structured environment that is comprised of one or more databases and organized in tiers. An interactive, front-end tier provides search results for reporting, analytics and data mining. The search engine accesses and analyzes the data for presentation and the foundational architecture or database server provides the storage and loading repository.
In order to prepare data for analysis, a Data Warehouse environment will typically utilize an Extraction, Transformation and Loading (ETL) process to prepare data for analysis. Team members who access a Data Warehouse may use SQL queries, analytical solutions or BI tools to mine the data, report, visualize, analyze and present the data.
We can think of a Data Mart as a subset of a Data Warehouse but, whereas a Data Warehouse is an enterprise-wide solution that comprises data from across the organization, the Data Mart is a structured environment that is used to store and present data for a specific team or business unit. This approach allows a business team or unit to curate, leverage and manipulate data that is specific to their teams.
