Integrating Data: Key Points to Ponder Over
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In the realm of data integration, focusing on the source system, the data process, and the target system is crucial. Here are some key considerations to ensure a smooth and effective data integration project.
Diverse Data Origins and Consolidation
When embarking on a data integration project, it is essential to consider various data origins such as ERP systems, customer platforms, IoT devices, third-party applications, legacy batch systems, real-time streaming data, and external databases. These diverse sources need to be consolidated and harmonized using platforms like SAP BW/4HANA, Databricks, or cloud-based tools to create a unified, clean, and governed data foundation for analysis and decision-making.
Understanding the Business Behind the Source System
Comprehending the business processes behind the source system is crucial. This understanding helps in understanding data errors, designing data transformations, and optimizing the source system. Knowing who or what generates the data in the source system is equally important.
Data Structures and Types
Data structures may differ between the source and target systems, and this should be accounted for in the ETL/ELT process. For instance, Oracle databases work with 0 and 1 or 'y' and 'n', while other technologies use Booleans. In such cases, conversion of data types may be necessary to facilitate easier data processing later, such as via BI tools. If the source system is a classic database, it may be necessary to denormalize data in state-of-the-art data warehouses services like Google's Big Query or Amazon's Redshift.
Load Times and Optimization
Load times of data from the source system should be considered. For large data loads, techniques like Change Data Capture (CDC) or database replication services may be used. Google's guidance on "Avoid repeated joins and subqueries" (2021) could be relevant for optimizing data processing.
Monitoring and Retry Mechanisms
Monitoring of the source system and implementing re-try mechanisms are essential to handle unavailability issues. O'REILLY's "Oracle PL/SQL Programming" (2021) might offer useful information for data integration projects involving Oracle databases.
Source System Load and Availability
The source system should not receive unnecessary load from a data transfer, especially during peak times. The data engineers and stakeholders should consider the availability of the source system when integrating data to system B. Monitoring and re-try mechanisms should be implemented to handle unavailability issues.
Best Practices for Stable Data Processing
Christian Lauer's "Five Best Practices for Stable Data Processing" (2021) may provide useful insights for data integration projects. As a project manager or product owner, understanding the business process of the system that maps this process is essential.
In the new world of Big Data, the use of column-based non-relational databases and data warehouses is on the rise. These advancements, combined with the best practices outlined above, can help ensure a successful data integration project.
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