Transactional databases serve as the traditional choice for OLTP (Online Transaction Processing). OLTP systems manage and support operational transactions and day-to-day business processes. They handle real-time, high-volume, and short-duration transactions involving data creation, modification, or deletion. OLTP systems prioritise quick response times, aiming for sub-second or low-latency transaction processing to support real-time business operations.

OLTP systems primarily serve operational staff, such as salespeople, customer service representatives, or employees directly involved in day-to-day business activities. Examples: eCommerce systems, banking transaction systems, inventory management systems, point-of-sale systems.

In comparison, data warehouses form the basis for OLAP (Online Analytical Processing). OLAP systems are designed for complex analysis, reporting, and decision-making. They focus on aggregating and summarising data from multiple sources to support business intelligence and strategic analysis.

OLAP systems prioritise query performance and allow for longer response times to handle complex analytical queries involving large datasets. They serve business analysts, data scientists, and decision-makers who require in-depth analysis, trend identification, and strategic insights from the data. Examples: data warehouses, business intelligence platforms, executive dashboards, data mining applications.

While OLTP and OLAP systems serve different purposes and have distinct characteristics, they often complement each other in an organisation’s data ecosystem. Connecting a transactional database to a data warehouse serves several important needs and provides significant benefits for organisations.

However, when connecting a transactional database to a data warehouse, data engineers may encounter several challenges.

The most important obstacles that data engineers need to overcome are:

1. Data schema and structure: Transactional databases and data warehouses often have different schemas and structures. Transforming the data from the source database to match the schema of the data warehouse can be complex, especially when dealing with changes in data types, relationships, or aggregations.

2. Data consistency and integrity: Ensuring data consistency and integrity between the transactional database and the data warehouse is crucial. Synchronising data in real-time or near real-time can introduce challenges such as handling conflicts, maintaining referential integrity, and dealing with transactional anomalies.

3. Latency and real-time requirements: Some use cases demand real-time or near real-time data availability in the data warehouse. Achieving low-latency data integration can be challenging, especially when dealing with high-volume updates and ensuring the timely propagation of changes.

4. Performance impact on the source database: Extraction processes, whether batch or real-time, can impact the performance of the transactional database. Heavy extraction queries, resource contention, or additional overhead from CDC or replication processes can lead to increased load and potential performance issues on the source database.

Other challenges include:

1. Data volume and velocity: Transactional databases often contain large volumes of data, and they can generate high-velocity updates. Handling and processing such large volumes of data in near real-time or batch mode can strain the resources of both the source database and the data warehouse.

2. Data governance and compliance: Moving data from a transactional database to a data warehouse involves considering data governance and compliance requirements. This includes managing access controls, data privacy, masking or anonymisation, and ensuring proper security measures are in place.

3. Tooling and compatibility: Different databases and technologies have varying levels of support for the integration methods mentioned earlier. Ensuring compatibility between the source database and the data warehouse, as well as the availability of appropriate tooling or connectors, can be a challenge.

4. Error handling and monitoring: Monitoring and handling errors or failures that occur during the data integration process is crucial. This includes managing connectivity issues, data inconsistencies, data loss, and implementing effective error handling mechanisms and monitoring tools.

5. Scalability and resilience: As data volumes and workloads grow, ensuring scalability and resilience of the integration architecture becomes essential. The ability to handle increasing data volumes, adapt to changes in the source database, and handle failures or outages requires careful design and infrastructure considerations.

Addressing these challenges often requires a combination of technical expertise, careful planning, and the selection of appropriate tools and technologies. It is important to understand the specific requirements of the integration, assess the capabilities and limitations of the source database and data warehouse, and design robust and scalable architectures to overcome these challenges effectively.