What are some best practices for generating and managing surrogate keys in ETL?

Here are some pros and cons of numeric keys (sequences, identity columns) and hash values: Numerics keys + efficient to store + good performance - require lookup operation for referenced keys - lookup has to be done using the natural key - lookup becomes more complex if natural key can contain NULL values - lookup may prevents the parallel execution of ETL processes Hash functions + no lookup required for referenced keys + enable parallel execution of ETL processes - Hash values need more storage than numeric values - The hash values are calculated based on the natural key. If the natural keys contain GDPR data the hash values must be replaced by numeric values.

It’s essential to adopt a consistent approach for generating surrogate keys. In one of my projects integrating customer data from multiple e-commerce platforms, we used UUIDs as the common surrogate key. This ensured that even if customer IDs in the source systems varied in format, the surrogate keys were uniformly applied across the data warehouse. Consistency helped us avoid errors and confusion during queries, where mismatched keys could have led to inaccurate reporting. Consistent documentation of this method ensured that new team members could quickly understand and follow it.

When generating and managing surrogate keys in ETL processes, it's best to use numeric keys, as they are more efficient for storage and indexing. Surrogate keys should be generated using sequential methods, such as database sequences or ETL tool functions, to ensure uniqueness across multiple ETL runs. These keys should remain independent of business logic, so changes in the business don't impact the key structure. Surrogate keys are crucial for handling slowly changing dimensions, allowing you to track historical changes. Additionally, it's important to implement checks to prevent duplicate keys during the ETL process.

When generating and managing surrogate keys in ETL, it's crucial to implement a deterministic and idempotent method. Whether you utilize sequences, UUIDs, or hash functions, ensure that the method is atomic, thread-safe, and scalable to prevent bottlenecks, especially when processing massive data sets in parallel. For distributed systems, leveraging distributed key generators (e.g., Snowflake ID algorithm) can ensure global uniqueness and avoid key collisions across nodes. Additionally, capturing surrogate key lineage in a metadata repository enhances auditability and traceability. Careful optimization of indexing on surrogate key columns further improves query performance, particularly in environments where key lookups are frequent.

Simply put based on my experience, whether you use sequences, identity columns, hash functions, or custom logic, make sure to apply the same approach across all tables and ETL processes. This consistency helps prevent duplication and errors. Also, document your method and explain why you chose it, so other developers and users can understand and follow the same approach.

Natural keys are the key columns in the source system. They are not a good candidate for surrogate key, because they might be recycled. Or if you want to implement SCD type 2 scenarios, then they key column needs to be changing per change and natural key is not able to cover that.

Do not simply follow path of “Always generating surrogate keys”. Maybe consider if a smart int representation is possible that could avoid a further lookup on DIM tables. Take for example dates, why generate a surrogate key when you can generate a smart INTEGER representation of form YYYYMMDD (on the fly transformations). Similarly time of day in the form HHMI. Of course this only works for a few cases but sure is fast. And if you need to handle nulls , that is easily done by allowing for a “unknown” smart surrogate key value of say -1 or similar, now you can have a fact table with no nulls and the date keys are smart allowing you to use them directly without a join to Dim tables.

Avoiding natural keys in ETL is essential due to their susceptibility to volatility, which can compromise data integrity. Natural keys are inherently tied to source system logic, making them prone to changes, duplication, or null values, which leads to referential integrity issues during data integration. Inconsistent formats across diverse systems further exacerbate these risks, introducing complexity in data reconciliation and mapping. Surrogate keys, being decoupled from source data, provide a stable, immutable identifier that remains constant across updates. This ensures reliable join operations and data consistency, especially in slowly changing dimensions (SCD) or large-scale incremental loads where traceability is paramount.

Adding surrogate keys as a meaningless numeric integer is a means for insulating DW systems from troubles we might get into while integrating heterogeneous source systems with varied data patterns of natural key. Therefore, no matter how much attractive is to use combination of natural keys and system date/timestamp, it should be avoided.

I'd avoid using natural keys from the source data, such as customer IDs or product codes. Although natural keys might seem convenient, they can change, vary in format, or be missing, leading to data quality issues like mismatched records or incorrect relationships. Instead, generate surrogate keys that are independent of the source data. This ensures consistency and reliability in your data warehouse. Assign these surrogate keys during the ETL process to maintain data integrity and avoid potential problems associated with natural keys.

Surrogate keys allow us to maintain historical records of changes over time. In a customer loyalty program project, where customers frequently updated their details (such as changing addresses or emails), we used Slowly Changing Dimensions (SCD Type 2). This method assigned new surrogate keys to every version of a customer’s profile while preserving previous data. By using metadata columns like “Effective Start Date” and “End Date,” we captured and maintained a timeline of customer behavior changes, which became invaluable for predictive analytics and marketing campaigns.

Numerical surrogate keys have its own performance benefits. 1. Helps to have much compact fact tables with all numeric foreign keys. 2. Joins between fact and dimension tables perform much better when parent surrogate key of a dimension and foreign key of fact table are numeric.

Preserving verions allow me to track changes and maintain a complete view of data over time. For example, when a customer updates their name or address, surrogate keys help me link both the old and new records, ensuring a comprehensive profile. To manage this effectively, I design our data warehouse and ETL processes to capture and store historical data. I use techniques like scd type 2s and include metadata such as effective dates and version numbers to keep track of record status and validity. We use state oriented fact tables so that helps with the scd 2 for versioning. This approach ensures that we have a reliable and detailed history of our data.

Numeric values generated by sequences or identity columns can be stored efficiently, but require lookup operations if they need to be referenced. Therefore, they may also prevent parallel processing of ETL processes. Hash values, on the other hand, can be calculated without a lookup and enable parallel processing of ETL processes.

Generating and managing surrogate keys can strain performance if not optimized. In a large-scale retail data warehouse where product data was coming from various sources, we used sequence numbers in our database for surrogate keys. To avoid bottlenecks during peak ETL loads, we implemented parallel processing, dividing the ETL tasks into smaller jobs that ran simultaneously. Additionally, caching the latest sequence numbers helped reduce frequent database calls, optimizing the ETL process further. This approach saved both time and resources.

Sequencing as a way of generating surrogate keys can have downsides, you will have to check the last generated value before you can determine the next in the sequence. When there are multiple sources delivering the same natural keys, you will have to query the surrogate key store. When the max value is reached, the sequence has to start again. In case of large historical datasets database limits can be reached in sequence storage of surrogate keys. A more advanced approach can be needed for large historical datasets.

If I use sequences or identity columns, I need to ensure they are synchronized across multiple sources or servers to avoid bottlenecks. When using hash functions, I must make sure they are unique and deterministic, which can add complexity to the ETL process. To address these challenges, I test and optimize surrogate key generation by employing techniques like parallel processing, caching, batching, and indexing. This helps improve the efficiency of the ETL process and ensures smooth performance in the data warehouse.

Based on my experience, monitoring and auditing surrogate keys in ETL is essential to ensure they function correctly and maintain data quality. I regularly check that surrogate keys are unique, consistent, and valid across all tables and ETL processes. It's also important to verify that these keys accurately map and relate the source and target data while preserving historical changes. To keep everything in check, I use tools like data profiling, data lineage, and data quality management to identify and resolve any issues that arise. This helps maintain the integrity of the data and ensures smooth operations in the data warehouse.

Regular monitoring and auditing are critical to ensuring that surrogate keys remain unique, valid, and consistently applied across the data warehouse. In a financial reporting system, we implemented data profiling tools to periodically check the integrity of surrogate keys, ensuring they mapped correctly between source and target systems. We also ran audits to confirm the preservation of historical data. Catching issues early, such as potential key duplication or missing mappings, saved us from data integrity problems down the road.

During a loan application process, a customer’s information may go through several stages (initial submission, verification, and approval). By using surrogate keys in ETL, banks can track each version of the customer’s record accurately while retaining a clear audit trail.