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Compare database and Data warehouse.
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Content Database Data Warehouse
Definition Any collection of data organized for storage, accessibility, and retrieval. A type of database that integrates copies of transaction data from different source systems and provides them for analytical use.
Types The term usually applies to an OLTP application database. A data warehouse is an OLAP database. An OLAP database layers on top of OLTPs or other databases to perform analytics.
Similarities OLTP systems store and manage data in the form of tables, columns,indexes, keys , views, and data types.,Both use SQL to query the data. OLAP systems store and manage data in the form of tables, columns,indexes,keys,views,and data types.,Both use SQL to query the data.
How used Typically one application equals one database.,OLTP allows for quick real-time transactional processing.It is built for speed and to quickly record one targeted process. data warehouse equals infinite applications and infinite databases.,OLAP allows for one source of truth for an organization’s data.This source of truth is used to guide analysis and decision-making within an organization.Complex queries are much easier to handle in an OLAP database.
Service Level Agreement (SLA) OLTP databases must typically meet 99.99% uptime.System failure can result in chaos and lawsuits.The database is directly linked to the front end application.Data is available in real time to serve the here-and-now needs of the organization.. With OLAP databases,SLAs are more flexible because occasional downtime for data loads is expected.The OLAP database is separated from front end applications,which allows it to be scalable.Data is refreshed from source systems as needed.It serves historical trend analysis and business decisions.
Optimization Optimized for performing read-write operations of single point transactions. Performing large analytical queries on such a database is a bad practice, because it impacts the performance of the system.An analytical query could take several minutes to run, locking every one out in the meantime. Optimized for efficiently reading/retrieving large data sets and for aggregating data.Because it works with such large data sets,an OLAP database is heavy on CPU and disk bandwidth.A data warehouse is designed to handle large analytical queries.This eliminates the performance strain that analytics would place on a transactional system.
Data Organization An OLTP database structure features very complex tables and joins because the data is normalized (it is structured in such a way that no data is duplicated).Making data relational in this way is what delivers storage and processing efficiencies —and allows those sub-second response times. In an OLAP database structure,data is organized specifically to facilitate reporting and analysis, not for quick-hitting transactional needsThe data is denormalized to enhance analytical query response times and provide ease of use for business users.Fewer tables and a simpler structure result in easier reporting and analysis.
Reporting/Analysis Because of the number of table joins,performing analytical queries is very complex.They usually require the expertise of a developer or database administrator familiar with the application.Reporting is typically limited to more static, siloed needs.You can actually get quite a bit of reporting out of today’s EHRs (which run on an OLTP database),but these reports are static,one-time lists in PDF format. For example, you might generate a monthly report of heart failure read missions or a list of all patients with a central line inserted.These reports are helpful— particularly for real-time reporting for bedside care—but they don’t allow in-depth analysis. With fewer table joins,analytical queries are much easier to perform.This meansthat semi-technical users (anyone who can write a basic SQL query) can fill their own needs.The possibilities for reporting and analysis are endless.When it comes to analyzing data, a static list is insufficient. There’s an intrinsic need for aggregating, summarizing, and drilling down into the data.A data warehouse enables you to perform many types of analysis:,1.Descriptive (what has happened),2.Diagnostic (why it happened),3.Predictive (what will happen),4.Prescriptive (what to do about it),This is the level of analytics required to drive real quality and cost improvement in healthcare.
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