Data Interview Question | What are the differences between a Data Warehouse and a Data Hub?
In the traditional data warehouse, data is extracted from transactional system and then transformed into the required format and then it is loaded another system for analysis or other business purposes. In an enterprise data hub model, however, data is first loaded into the Hadoop platform, and then analytics and data mining tools are applied to the data where it resides in the hub.
Major points of differences are (from Datavail)
1. Flexibility of adding New Sources
EDW lacks flexibility due to the difficulty in adding new data sources. But this is not the case with the modern EDH, which can connect multiple databases, data warehouses, and other systems. This is achieved on the fly, as quickly as the data can be captured, integrating the diverse data types from multiple sources to reveal relationships.
2. Up-to date data – No Delays
In the world of data warehousing, speed, cost, and value are of paramount importance. With traditional EDW, data can be out of date by days which may affect the day-to-day Business Intelligence (BI) processes. A modern data hub overcomes this problem by presenting fresh data that is readily available for analysis as soon as it is captured. It responds to queries in near real time, and one can immediately start getting analytic value from it.
3. Ability to handle Complex Queries
In traditional EDW, we are typically limited by how our data architect operates in terms of what source of query one can ask and how flexible it is. However, a modern data hub offers a rich set of tools for processing and analyzing data. It can directly query semi-structured data on large volumes of unknown value, including JSON, CSE, XML, and essentially anything you can scan into these modern data sources.
4. Rapid Deployment and Cost Effectiveness
EDH can be deployed in a matter of days or weeks in contrast to the traditional EDW that may take days, weeks, months, and even years to deploy a sole system. Being able to connect directly to the data has made it much simpler to implement and run EDH at a very low cost. It has also made it cost-effective to run data-driven experiments and analysis over unlimited data.
An EDH is an ideal companion to an EDW because, in addition to its storage and processing benefits, it helps alleviate some of the primary challenges that face an EDW. When deployed alongside common EDW infrastructure, an EDH helps streamline data integration processing by offloading data transformations and improving performance, thus reducing costs.
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