OLTP and OLAP database architecture 】 【 : actual comparison

OLTP 和 OLAP：The two terms look similar,But referring to a different type of system.在线事务处理 (OLTP) 实时捕获、Store and process data from transactions.在线分析处理 (OLAP) Use complex queries to analyze from OLTP Aggregated historical data for the system.

什么是 OLTP？

OLTP The system captures and maintains transactional data in a database.Each transaction involves a single database record consisting of multiple fields or columns.Examples include banking and credit card activity or retail checkout scans.

在 OLTP 中,The point is fast processing,因为 OLTP The database is read frequently、写入和更新.如果事务失败,Built-in system logic ensures data integrity.

什么是 OLAP？

OLAP 对从 OLTP Apply complex queries to large amounts of historical data aggregated from databases and other sources,用于数据挖掘、Analytics and business intelligence projects.在 OLAP 中,The point is the response time to these complex queries.Each query involves one or more columns of data aggregated from multiple rows.Examples include year-over-year financial performance or marketing lead generation trends.OLAP Databases and data warehouses enable analysts and decision makers to turn data into information using custom reporting tools.OLAP Query failures in , do not interrupt or delay the client's transaction processing,But it can delay or affect the accuracy of business intelligence insights.

ETL：连接OLTP和OLAP的力量

来自一个或多个 OLTP Database data is extracted through a process called extraction、转换、加载 (ETL) process was ingested OLAP 系统中.使用 ETL 工具,Users can collect data from multiple sources and send it to a destination,例如 OLAP 数据仓库,There it is queried by analytics and business intelligence tools for insights.

OLTP 与 OLAP：并排比较

OLTP 是可操作的,而 OLAP 是信息性的.A glance at the main features of the two treatments illustrates their fundamental difference,以及它们是如何协同工作的.

OLTP Provides an instant record of current business activity,而 OLAP Generate and validate insights from this data over time.This historical perspective enables accurate forecasting,But as with all business intelligence,OLAP The insights generated are only as good as the data pipelines they produce.

Stitch Optimize data pipelines

要从 OLTP Get actionable intelligence from your data,It must be extracted、Transform and load into the data warehouse for analysis.Although this can be done through internal programming resources,但使用 ETL Tools can handle data ingestion more efficiently.ETL Tools are eliminated due to constantly changing data sources API、The need for ongoing maintenance of the code based on reporting requirements and business needs.像 Stitch 这样的 ETL Tools can be optimized OLTP 数据摄取,thereby freeing up time and IT Employees focus on more value-added activities.

Simplify will OLTP The source data is pulled into yours OLAP 仓库的过程.Choose a solution that scales with your data,and give you the support you need,to stay ahead of change and gain insight.

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