How do you manage the change and maintenance of an OLTP data warehouse over time?

1 Assess the requirements

Before you design or modify an OLTP data warehouse, you need to understand the business and technical requirements of your stakeholders. This includes identifying the data sources, data types, data volumes, data frequency, data quality, data security, and data access patterns. You also need to define the scope, objectives, and expectations of the data warehouse project, and align them with the available resources, budget, and timeline. A clear and comprehensive requirements analysis will help you plan and prioritize your data warehouse development and maintenance tasks.

2 Design the data model

The data model of an OLTP data warehouse should reflect the logical and physical structure of the data, as well as the relationships and constraints among the data elements. The data model should also support the business processes and queries that the data warehouse users need. There are different approaches for designing an OLTP data warehouse data model, such as normalized, denormalized, star, snowflake, or hybrid. The choice of the data model depends on factors such as the complexity, diversity, and volatility of the data sources, the performance and scalability requirements, and the trade-offs between storage space and query speed.

3 Implement the ETL process

The ETL (extract, transform, and load) process is the core component of an OLTP data warehouse, as it is responsible for extracting data from various sources, transforming it into a consistent and standardized format, and loading it into the data warehouse. The ETL process should be designed and implemented with care, as it affects the quality, accuracy, and timeliness of the data warehouse data. The ETL process should also be flexible and adaptable, as it may need to accommodate changes in the data sources, data types, data rules, or data warehouse schema.

4 Monitor and tune the performance

The performance of an OLTP data warehouse is measured by how fast and efficiently it can handle the concurrent queries, updates, and inserts from the applications and users. The performance of an OLTP data warehouse depends on various factors, such as the hardware configuration, the network bandwidth, the data model design, the ETL process design, the query optimization, the indexing strategy, the partitioning strategy, and the caching strategy. To ensure optimal performance, you need to monitor and tune these factors regularly, using tools such as performance counters, logs, profilers, or analyzers.

5 Manage the data quality

The data quality of an OLTP data warehouse is determined by how accurate, complete, consistent, and reliable the data is. The data quality of an OLTP data warehouse affects the credibility, usability, and value of the data warehouse for the business decision making. To ensure high data quality, you need to implement and enforce data quality standards, rules, and policies throughout the data warehouse lifecycle. You also need to identify and resolve any data quality issues, such as missing values, duplicates, errors, or anomalies, using tools such as data quality audits, reports, or dashboards.

6 Adapt to the change

The change of an OLTP data warehouse is inevitable, as the business needs and data sources may evolve over time. The change of an OLTP data warehouse can be triggered by factors such as new or modified business requirements, new or modified data sources, new or modified data types, new or modified data rules, or new or modified data warehouse schema. To adapt to the change, you need to follow a structured and systematic change management process, which involves assessing the impact, planning the actions, testing the outcomes, and documenting the results of the change.