Database
https://en.wikipedia.org/wiki/Database
A database is an organized collection of data.[1] A relational database, more restrictively, is a collection of schemas, tables, queries, reports, views, and other elements. Database designers typically organize the data to model aspects of reality in a way that supports processes requiring information, such as (for example) modelling the availability of rooms in hotels in a way that supports finding a hotel with vacancies.
https://searchsqlserver.techtarget.com/definition/database
Definition
database (DB)
A database is a collection of information that is organized so that it can be easily accessed, managed and updated.
Data is organized into rows, columns and tables, and it is 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 against 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 is considered a core component of business intelligence.[1] DWs are central repositories of integrated data from one or more disparate sources. They store current and historical data in one single place[2] that are used for creating analytical reports for workers throughout the enterprise.[3]
两者区别
数据库面向现实世界的建模， 用于数据业务处理。
数据仓库，用于归纳数据于一处， 以便输出分析报告， 做数据挖掘等使用。
https://www.healthcatalyst.com/database-vs-data-warehouse-a-comparative-review
What I will refer to as a “database” in this post is one designed to make transactional systems run efficiently. Typically, this type of database is an OLTP (online transaction processing) database.
The important fact is that 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 all these databases and creates a layer optimized for and dedicated to analytics.
So the short answer to the question I posed above is this: A database designed to handle transactions isn’t designed to handle analytics. It isn’t structured to do analytics well. A data warehouse, on the other hand, is structured to make analytics fast and easy.
| 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 analytical use. |
| Types | There are different types of databases, but the term usually applies to an 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 that is not 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 about this type of database: Not all OLAPs are created equal. They differ according to how the data is modeled. Most data warehouses employ either an enterprise or dimensional data model, but at Health Catalyst, we advocate a unique, adaptive Late- Binding™ approach. You can learn more about why the Late-Binding™ approach is 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 is 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 an organization’s data. This source of truth is used to guide analysis and decision-making within an organization (ex: total patients over age 18 who have been readmitted, by department and by month). Interestingly enough, complex queries like the one just described are 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 are more flexible because occasional downtime for data loads is expected. The OLAP database is separated from frontend applications, which allows it to be scalable.Data is refreshed from source systems as needed (typically this refresh occurs every 24 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 is 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, 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 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 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 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 readmissions 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 means that 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:
This is the level of analytics required to drive real quality and cost improvement in he |
Hadoop生态类比
http://hadoop.apache.org/
- HBase™: A scalable, distributed database that 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 distributed storage using SQL. Structure can be projected onto data already in storage. A command line tool and JDBC driver are 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 you 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 commodity hardware. Apache HBase is an open-source, distributed, versioned, non-relational database modeled after Google's Bigtable: A Distributed 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.