What's your best practice for integrating data from multiple sources into your data warehouse?

Absolutely. A data integration strategy should be grounded on evaluating use cases with value assessment to ensure alignment with business objectives. Identifying sources and transformations becomes more focused when driven by high-value use cases. This approach not only streamlines the integration process but also helps in realizing tangible benefits early in the journey.

In the realm of enterprise long-term strategic goals, it is imperative to establish unequivocal business objectives when it comes to upholding impeccable data quality. By breaking down data silos and enabling real-time access to integrated data, organizations can harness valuable insights, reduce redundancy, and achieve a holistic understanding of their business landscape For sizable corporations or extensive conglomerates with aspirations to embrace the cutting-edge realm of Generative AI, a prevailing trend in the enterprise landscape nowadays, the significance of meticulous data quality planning and sourcing becomes all the more pivotal."

This is a good list for new architecture or management. But in practice you should be re-using business logic already defined. Knowing more the what, why, and acceptance criteria will inform you better the how of integration. As for mapping it out, each individual is different but I find going from simple use cases to ever more technical readouts helps me zero in on the tasks and to not miss anything important. The last step is considering re-usability. It is last because it is more expensive upfront and requires time. Documentation is a must yet how much should be considered. The design that takes the most to fix or adjust is the one that had no helpful documentation. Do yourself a favor and write pithy docs.

Planning a data integration strategy is crucial for effectively managing and utilizing data. Start by defining your objectives and data requirements, understanding what data you need and why. Identify data sources, both internal and external, and assess their quality and compatibility. Select appropriate integration tools and technologies based on your needs, such as ETL (Extract, Transform, Load) tools or data integration platforms. Develop a clear data integration architecture that outlines how data will flow, be transformed, and be stored.

Well! When it comes to data integration from multiple sources it becomes quite a hassle! Below should be more focus - Data type. - Data Mapping - Pushing data to end users - measuring if it is actually useful for data absorbers.

While this article specifically focuses on integrating multiple data sources into a data warehouse, it's worth mentioning that in the practical world, data integration doesn't really need loading. One notable method is data virtualization. This enables real-time data integration by allowing you to access and manipulate data without concerning yourself with the technical specifics, such as data format or physical location. In essence, data virtualization abstracts these complexities, making it an ideal choice for real-time scenarios. Given the fast pace of today's business environment, such agility is invaluable; it allows for quick prototyping before a full-scale implementation, potentially accelerating time-to-market by up to 80%.

Selecting the right data integration approach depends on your specific needs and circumstances. Consider factors such as data volume, velocity, variety, and the complexity of your integration tasks. Options include batch processing for periodic data updates, real-time streaming for immediate data availability, and hybrid approaches for a combination of both. Evaluate whether a point-to-point integration or an ETL (Extract, Transform, Load) process is more suitable. Assess cloud-based integration solutions if scalability and flexibility are essential. Prioritize data security, quality, and compliance throughout the integration process.

These days even some 'low code no code' tools are becoming popular in data integration space. Some such tools include Fivetran, Matillion, Informatica's IICS, Workato, etc. Choosing the best fit would depend on various factors including but not limited to types of sources, complexity of transformations, volume of data, etc.

Performance further underscores the benefits of a multi-method approach to data integration, over a single one. In a multi-method setup, you have a range of options to fall back on. So while data virtualization solutions use caching to boost performance, caching alone may be insufficient for handling large data sets. To optimize performance and scalability, you may need additional control over the data structure, such as creating indexes, distribution keys, and transparent aggregations. This often requires capabilities beyond caching such as data replication. However, performance isn't the sole consideration. Agility, crucial amidst changing business needs, also matters. Balancing both often necessitates diverse data integration methods.

Documentation almost can't be emphasized enough. But many folks detest any type of documentation. Maybe it's just that word... Enter, metadata! This is exactly what metadata and a good data catalog along with lineage as stated above can do for you. Part and parcel of many vendor data platforms now is a data catalog and data lineage to go along with the tools they are implementing, be it a data lakehouse or a data warehouse or other. These data catalogs are so valuable in terms of data lineage, but they can also tell you what jobs and schedules your data is involved in. Now, that is documentation!

Data Lineage Maps are very effective in debugging data quality issues specifically in case of highly complex transformations.

Reviewing and updating the data integration process are critical to business success. A systematic data quality audit, which includes data cleansing and consolidation, ensures data quality. Performance testing evaluates system efficiency and identifies areas for optimization. User feedback provides insights into the usability and effectiveness of the system. Key performance indicators (KPIs) are used to continuously monitor and improve the process. Through these measures, the data integration process remains current and meets the requirements of the changing business environment.

Be sensitive to varied terminology describing the same entity, and be aware that some may believe “their” entity is unrelated to “somebody else’s” when it's not from a data perspective. For example, a “vendor” is somebody who you pay to receive a product/service, but different parts of the organization may have ownership for different types of vendors: onsite contractors, offsite contractors, insurance providers, shippers, and suppliers. They may also have different requirements (which translate into different data elements) for “their” vendors. Rather than trying to convince someone that their perspective is wrong, focus on the differences and adjusting your data model (and corresponding integration approach) accordingly.

Monitoring and Maintenance: Establish regular monitoring of the integrated data to detect and address any issues that may arise over time. This includes monitoring for data quality, performance, and potential updates from source systems. Backup and Recovery: Implement backup and recovery procedures to safeguard the integrated data in case of unexpected failures or data corruption. Performance Optimization: Optimize the performance of the integrated data by considering factors like indexing, partitioning, and materialized views based on the specific analytical needs.

Consider using parameters to switch between full load and incremental load, you would need to switch to full load mechanism in disaster recovery situations. Try to automate packages creation regardless of the technology, using automation make the solution consistent and prevent human errors. Keep data lineage document updated, you will need it to trace back to the source systems and to build trust into your data.