OLTP and OLAP: The two terms look similar but refer to different kinds of systems. Online transaction processing (OLTP) captures, stores, and processes data from transactions in real time. Online analytical processing (OLAP) uses complex queries to analyze aggregated historical data from OLTP systems.
An OLTP system captures and maintains transaction data in a database. Each transaction involves individual database records made up of multiple fields or columns. Examples include banking and credit card activity or retail checkout scanning.
In OLTP, the emphasis is on fast processing, because OLTP databases are read, written, and updated frequently. If a transaction fails, built-in system logic ensures data integrity.
OLAP applies complex queries to large amounts of historical data, aggregated from OLTP databases and other sources, for data mining, analytics, and business intelligence projects. In OLAP, the emphasis is on response time to these complex queries. Each query involves one or more columns of data aggregated from many rows. Examples include year-over-year financial performance or marketing lead generation trends. OLAP databases and data warehouses give analysts and decision-makers the ability to use custom reporting tools to turn data into information. Query failure in OLAP does not interrupt or delay transaction processing for customers, but it can delay or impact the accuracy of business intelligence insights.
The data from one or more OLTP databases is ingested into OLAP systems through a process called extract, transform, load (ETL). With an ETL tool, users can collect data from several sources and send it to a destination, such as an OLAP data warehouse, where it is queried by analytics and business intelligence tools for insights.
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OLTP is operational, while OLAP is informational. A glance at the key features of both kinds of processing illustrates their fundamental differences, and how they work together.
| OLTP | OLAP | |
|---|---|---|
| Characteristics | Handles a large number of small transactions | Handles large volumes of data with complex queries |
| Query types | Simple standardized queries | Complex queries |
| Operations | Based on INSERT, UPDATE, DELETE commands | Based on SELECT commands to aggregate data for reporting |
| Response time | Milliseconds | Seconds, minutes, or hours depending on the amount of data to process |
| Design | Industry-specific, such as retail, manufacturing, or banking | Subject-specific, such as sales, inventory, or marketing |
| Source | Transactions | Aggregated data from transactions |
| Purpose | Control and run essential business operations in real time | Plan, solve problems, support decisions, discover hidden insights |
| Data updates | Short, fast updates initiated by user | Data periodically refreshed with scheduled, long-running batch jobs |
| Space requirements | Generally small if historical data is archived | Generally large due to aggregating large datasets |
| Backup and recovery | Regular backups required to ensure business continuity and meet legal and governance requirements | Lost data can be reloaded from OLTP database as needed in lieu of regular backups |
| Productivity | Increases productivity of end users | Increases productivity of business managers, data analysts, and executives |
| Data view | Lists day-to-day business transactions | Multi-dimensional view of enterprise data |
| User examples | Customer-facing personnel, clerks, online shoppers | Knowledge workers such as data analysts, business analysts, and executives |
| Database design | Normalized databases for efficiency | Denormalized databases for analysis |
OLTP provides an immediate record of current business activity, while OLAP generates and validates insights from that data as it’s compiled over time. That historical perspective empowers accurate forecasting, but as with all business intelligence, the insights generated with OLAP are only as good as the data pipeline from which they emanate.
To get actionable intelligence from OLTP data, it must be extracted, transformed, and loaded into a data warehouse for analysis. While this can be done with in-house programming resources, data ingestion is more effectively handled with an ETL tool. ETL tools remove the need for constant code maintenance due to changing data source APIs, reporting requirements, and business needs. An ETL tool like Stitch optimizes OLTP data ingestion, freeing up time and IT staff to focus on more value-added activities.
Simplify the process of pulling OLTP source data into your warehouse for OLAP. Choose a solution that scales with your data and provides the support you need to stay ahead of changes and on track for insights.
Stitch is designed to make the process of populating your data warehouse simple and easy. Try Stitch today to close the gap between OLTP and OLAP. It's free to try and setup takes minutes.
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