DataBase vs Data Warehouse

Database

Https://en.wikipedia.org/wiki/Database

A Database is an organized collection of data. ^{[1] A relational database, more restrictively, was A collection of schemas, tables, queries, reports, views, and other Elements. Database designers typically organize the data to model aspects of reality in a-by that supports processes requiring info Rmation, such as (for example) modelling the availability of rooms in hotels in a-the-a-supports finding a hotel with V Acancies.}

Https://searchsqlserver.techtarget.com/definition/database

Definition

Database (DB)

A database is a collection of information that's organized so the it can be easily accessed, managed and updated.

Data is organized into rows, columns and tables, and it's indexed to make it easier to find relevant information. Data gets updated, expanded and deleted as new information is added. Databases process workloads to create and update themselves, querying the data they contain and running applications again St it.

Data Warehouse

Https://en.wikipedia.org/wiki/Data_warehouse

In computing, a Data Warehouse (DW or DWH), also known as an enterprise Data Warehouse (EDW), is a system used for reporting and data analysis, and are considered a core component of business Intellig ence. ^{[1] DWs is central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place[2] that is used for creating analytical reports for worker s throughout the enterprise. [3]}

Difference between the two

The database is modeled for the real world and is used for data service processing.

Data Warehouse, used to summarize data in one place, in order to output analysis report, do data mining and other uses.

Https://www.healthcatalyst.com/database-vs-data-warehouse-a-comparative-review

What I'll refer to as a "database" in this post are one designed to make transactional systems run efficiently. Typically, this type of database was an OLTP (online transaction processing) database.

The important fact is, a transactional database doesn ' t lend itself to analytics. To effectively perform analytics, you need a data warehouse. A data warehouse is a database of a different Kind:an OLAP (Online analytical Processing) database. A data warehouse exists as a layer on top of another database or databases (usually OLTP databases). The Data warehouse takes the data from any these databases and creates a layer optimized for and dedicated to analytics.

The answer to the question I posed above are THIS:A database designed to handle transactions isn ' t designed to ha Ndle Analytics. It isn ' t structured to doing analytics well. A Data Warehouse, on the other hand, are structured to make analytics fast and easy.

TD style= "border:1px solid #000000;" align= "left" valign= "top" >an OLTP database structure features very complex tables an D joins because the data is normalized (it's structured in such a-to-that-no data is duplicated). Making data relational in this delivers storage and processing Efficiencies-and allows those sub-second respon Se times.

	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 disparate source systems and provisions them for Analyt ical use.
Types	there is different types of databases, but the term Usually applies to a OLTP application database, which we ' ll focus on throughout this table. Other types of databases include OLAP (used for data warehouses), XML, CSV files, flat text, and even Excel spreadsheets. We ' ve actually found that many healthcare organizations use Excel spreadsheets to perform analytics (a solution so is no T scalable).	A Data Warehouse is an OLAP database. An OLAP database layers on top of OLTPs or other databases to perform analytics. An important side note on this type of Database:not all olaps is created equal. They differ according to how the data is modeled. Most data warehouses employ either an enterprise or dimensional data model, and at Health Catalyst, we advocate a unique, Adaptive Late-binding™approach. You can learn more on why the Late-binding™approach are so important in healthcare analytics in Late-binding vs. Models : A Comparison of Healthcare Data Warehouse methodologies.
Similarities	Both OLTP and 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 constrained to a single application:one application equals one database. An EHR was a prime example of a healthcare application that runs on an OLTP database. OLTP allows for quick real-time transactional processing. It is built for speed and to quickly record one targeted process (ex:patient admission date and time).	accommodates data storage for any number of Applications:one Data Warehouse equals infinite applications and infinite databases. OLAP allows for one source of truth for a organization ' s data. This source of truth was used to guide analysis and decision-making within a organization (Ex:total patients over age 18 Who has been readmitted, by department and by month). Interestingly enough, complex queries like the one just described is much more difficult to handle in an OLTP 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. In healthcare, this data contributes to clinicians delivering precise, timely bedside care.	with OLAP databases, SLAs is more flexible because occasional downtime for data lo ADS is expected. The OLAP database is separated from frontend applications, which allows it-be scalable. Data is refreshed from source systems as needed (typically this refresh occurs every hours). It serves historical trend analysis and business decisions.
Optimization	Optimized for performing read-write operations of single point transactions. An OLTP database should deliver sub-second response times. Performing large analytical queries on such a database was a bad practice, because it impacts the performance of the system For clinicians trying to use it for their day-to-day work. An analytical query could take several minutes to run and locking all clinicians 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 was heavy on CPU and disk bandwidth. A Data Warehouse is designed to handle large analytical queries. This eliminates the performance strain, the analytics would place on a transactional system.
Data Organization	in a OLAP database structure, data is organized specifically to facilitate reporting and analysis, not for quick-hitting transactional needs. The 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 the reporting out of today's EHRs (which run on an OLTP database), but these reports is S Tatic,one-time lists in PDF format. For example, might generate a monthly report of heart failure readmissions or a list of all patients with a central Li NE inserted. These reports is helpful-particularly for real-time reporting for bedside care-but they don ' t allow in-depth analysis.	With fewer table joins, analytical queries is much easier to perform. This means the Semi-technical users (anyone who can write a basic SQL query) can fill their own needs. The possibilities for reporting and analysis is endless. When it comes-analyzing data, a static list is insufficient. There's a intrinsic need for aggregating, summarizing, and drilling to the data. A Data Warehouse enables perform many types of analysis: Descriptive (what have happened) Diagnostic (why it happened) Predictive (what would happen) Prescriptive (what does about it) This was the level of analytics required to drive real quality and cost improvement in he

Hadoop Eco-Analogy

http://hadoop.apache.org/

• hbase™: A Scalable, distributed database, supports structured data storage for large tables.
• hive™: A Data Warehouse infrastructure that provides data summarization and ad hoc querying.

HIVE

http://hive.apache.org/

The Apache hive™data Warehouse software facilitates reading, writing, and managing large datasets residing in distribute d storage using SQL. Structure can projected onto data already in storage. A command line tool and JDBC driver is provided to connect users to Hive.

HBASE

http://hbase.apache.org/

Apache Hbase™is The Hadoop database, a distributed, scalable, big data store.

Use Apache Hbase™when your need random, realtime Read/write access to your Big Data. This project's goal is the hosting of very large tables--billions of rows X millions of columns--atop clusters of Comm Odity hardware. Apache HBase is a open-source, distributed, versioned, non-relational database modeled after Google ' s bigtable:a distrib uted Storage System for structured Data by Chang et al. Just as Bigtable leverages the distributed data storage provided by the Google File System, Apache HBase provides Bigtable -like capabilities on top of Hadoop and HDFS.

DataBase vs Data Warehouse