Today’s modern data warehouse lets you bring together all your DATA at any scale easily, and to get insights through analytical dashboards, operational reports, or advanced analytics for all your users. Let us help you in the ‘pros and cons’ of the technology design and implications of what works well for you and the new market solutions. At SmallNet we have been designing, deploying, implementing Data Warehousing solutions for over 20 years let us help you on your next step.
If however we start at the beginning a Data Warehouse (DW or EDW), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis, and is considered a core component of business intelligence. In the past DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place that are used for creating analytical reports for workers throughout the enterprise.
The types of data stored in the Data Warehouse is uploaded from the operational systems (such as marketing or sales). The data may pass through an operational data store and may require data cleansing for additional operations to ensure data quality before it is used in the DW for reporting.
Extract, transform, load (ETL) and extract, load, transform (ELT) are the two main approaches used to build a data warehouse system, but many others are available – see our overview of Data Integration.
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In today’s market place though a modern data warehouse lets you bring together all your data at any scale easily, and to get insights through analytical dashboards, operational reports, or advanced analytics for all your users.
Today’s modern Data Architecture address the business demands for speed and agility by enabling organizations to quickly find and unify their data across hybrid data storage technologies. The Modern Data Architecture stores data as is; it does not require pre-modelling. It handles the volume, velocity, and variety of big data.
You will also hear the terms Data lakes and Data warehouses which both are widely used for storing big data, but they are not interchangeable terms. A Data lake is usually a single or multiple stores of data including raw copies of source system data, sensor data, social data etc., spread across multiple hardware platforms or environments .
As we move into next generation warehousing there is a trend and drive towards Data analytics on the cloud and as result is making organisation think closely about on-premises data warehouses. The benefits look towards the convenience of unlimited storage capacity, cloud-scaling and low-cost storage pricing you need for a such technology, along with the control and security.