Analytic or Transactional Databases for Your Data Strategy?

Your database design depends primarily on your business objectives. Most commercial applications utilize an On-Line Transaction Processing (OLTP) setup for data storage. That’s because it performs fast and intuitive queries that are accessible by multiple users. Examples of OLTP databases are supermarket POS systems and ATMs. While they’re suitable for accurate day-to-day transactions, these systems have limits. An On-Line Analytical Processing (OLAP) database is more suitable for complex queries and aggregations.

The Distinction Between OLTP and OLAP Databases

OLTP databases typically support single applications. They’re preferable for making specific queries, such as customer email addresses. However, businesses need databases that make more complex queries, such as year-over-year profit comparisons. OLAP databases offer multi-dimensional views and integrations of transactional level data.

OLAP systems form data warehouses that operate at a higher level than OLTP databases. Their structure is more suitable for analytics purposes because they can evaluate data from various OLTP databases. A data warehouse, such as Node14 Database Engine, is more comprehensive.

Comparing Databases and Data Warehouses

There are two primary similarities between OLTP and OLAP systems. First, they manage data in columns, tables, keys, indexes, and views. Secondly, both systems query data using SQL. We can categorize their differences in the following ways:

1. Use cases

OLTP databases work well with single applications and targeted processes, such as recording patient details. By comparison, one data warehouse can accommodate infinite applications. OLAP systems can derive more complex analyses, such as the number of customers in a particular age range.

2. Optimization

OLTP databases typically return sub-second responses because they perform simple read-write operations. OLAP databases require large data sets for effective aggregation, meaning they utilize heavier bandwidth and CPU resources. However, the advanced design of data warehouses eliminates the performance strain that would occur if OLTP databases attempted analytical processes.

3. Data organization

A transactional database is structured using complex tables to avoid duplication and deliver fast responses. OLAP databases place more emphasis on analytical responses. To improve speed and ease of use, data warehouses utilize fewer tables and an uncomplicated overall structure.

4. Reporting

This feature is limited to static needs in OLTP databases. Due to the significant number of table joins, analytical queries are complex tasks. They require specialist input by database administrators who understand the application. They’re easier to perform for semi-technical users of analytic databases because they have fewer table joins. An OLAP database also enables various types of analysis, including descriptive, diagnostic, predictive, and prescriptive.

5. Service level agreement (SLA)

An uptime of 99.9% for OLTP databases prevents system failure. They link directly to your front-end application and use real-time data to serve your customers. SLAs in OLAP setups are more flexible because there’s an expectation of downtimes due to the larger data loads. These databases are also scalable because they’re separate from your front-end application. The ability to refresh data from source systems is vital for decision-making and historical trend analysis.

Integrating Transactional and Analytical Databases

The best way to integrate data from multiple OLTP databases is via extract, transform, load (ETL). This process specializes in extracting data from sources that aren’t necessarily suitable for analytics. While the steps might vary from one ETL tool to the next, they all have a similar result.

The ETL definition might imply a three-step process, but there are other phases that it doesn’t capture. They include data transportation, the overlap between stages, and the effect of new technologies on the overall process. The growth of cloud-based analytics solutions has also made the procedure more efficient.

There are various critical components to consider regardless of the ETL sequence you choose. One is the ability to support change data capture (CDC). This feature enables an update of your analytics warehouse without reloading the whole data set. The ETL pipeline also handles detailed auditing and logging to reduce errors. Other crucial ETL components include:

• Manage multiple data sources, including MySQL, MongoDB, the Salesforce API, and your in-house financial applications. The Node14 Database Engine is customizable to your company’s needs with minimal coding.
• Fault tolerance to ensure manageable recovery in case of system problems.
• A dependable notification to alert users about errors.
• Low latency to improve real-time decision-making.
• Scalability to support growing data needs for an expanding organization.
• Accuracy and auditing at every step to minimize or eliminate data corruption.

ETL tools improve efficiency by removing continuous code maintenance caused by changing business needs, data sources, APIs, and regulations. This convenience reduces downtime and losses while allowing your team to focus on other value-adding activities.

Conclusion

Data is an indispensable asset, so the ability to collect and accurately analyze it will give you a competitive advantage. A comprehensive data strategy should seamlessly incorporate transactional and analytic database management. By incorporating ETL tools and cloud computing, database software providers allow you to reap the benefits of emerging technologies, including AI and machine learning. Consult industry experts to ensure higher productivity and long-term growth.