Ideas on how to backfill a SCD Type 2 table when older records have changed

[t33love]

I currently have a situation where i have a large SCD Type 2 table, and records are coming in which have values changed which were previously loaded.

In most cases you'd want to see this as a new records with new valid_from and valid_to dates, however the requirement is to change the older record it matches, and change the given values down the row set.

Is this something that can be handled in Big Query?

i have suggested the following, but was wondering if anyone else has dealt with such a scenario?

Thanks

My idea
CONSUME
VALIDATE
IDENTIFY (rules apply here to, counting how many rows has been marked so it can be validated after insert and any other metrics)
TRIAGE 
Mark records (batch) for editing
Copy to Triage Table
Identify new change record/s
Identify date window 
copy old dates from identified records
merge / upsert data into old record/s
INSERT DATA (into target table)
DELETE MARKED ROWS (as at this point there would be double)

[ms4446]

Your scenario presents an unconventional approach to handling Slowly Changing Dimensions (SCD) Type 2 in data warehousing, particularly in BigQuery. Typically, SCD Type 2 is designed to preserve historical data by creating new record versions upon changes, rather than overwriting existing records. Your method, which involves direct updates to historical records, raises several concerns:

1. Data Integrity and History Loss: Overwriting historical records contradicts the fundamental principle of SCD Type 2, which is to maintain an auditable trail of changes. This approach risks losing valuable historical context and hinders the ability to analyze data evolution over time.

2. Complex Logic and Potential Errors: The process of identifying specific records for updates, managing date windows, transferring old dates, and addressing edge cases can be highly complex. Mistakes in this process could lead to data inconsistencies, compromising the reliability of your data.

3. Performance Overhead: BigQuery is optimized for appending new rows rather than updating existing ones. Consequently, direct updates to large tables may result in reduced performance and efficiency, particularly as the dataset grows.

4. Validation Challenges: The conceptual steps like "IDENTIFY" and "VALIDATE" in your workflow require detailed and robust implementation logic. Ensuring data accuracy and consistency in this context can be challenging and demands meticulous attention.

Given these challenges, I recommend exploring alternative approaches that align more closely with best practices in data warehousing and BigQuery's capabilities:

Alternative Approaches:

1. Hybrid SCD Approach:

   • Insert new records with updated values and a new valid_from date.
   • Update the valid_to date of the previous record to the day before the new valid_from.
   • Include a flag (e.g., is_current) to easily identify the active record for each entity.
   • Advantages: This method preserves historical data while simplifying queries for the current state.

2. Temporary Staging and MERGE:

   • Use a temporary staging table for incoming data.
   • Apply a MERGE statement in BigQuery to handle inserts for new records and updates where changes are detected.
   • Appropriately adjust the valid_to date for previous versions.
   • Advantages: Efficiently manages updates and inserts, maintaining historical context.

If Direct Updates Are Required (though not recommended):

• Implement comprehensive testing and validation to cover various data change patterns and edge cases.
• Consider adding a timestamp or audit column to track updates to historical records, providing some level of traceability.
• Document the rationale and trade-offs of this non-standard approach thoroughly.

[t33love]

That's for reading and understanding the ask.

I completely agree with everything you've said, I also have implemented
your scenarios in other organisations..

I will do some research into what you suggest and raise these concerns with
the program leader.

The concern for me is also cost, and how do we partition the data in such a
way for it to be fast and efficient.. My other concern is they wish to
implement such procs over massive tables in the 100 of mills over multiple
table.

[ms4446]

Hi @t33love ,

You concerns are valid about cost, performance, and scalability when dealing with SCD Type 2 logic on massive tables (100s of millions of rows) in BigQuery. 

Here are some refined strategies to address these challenges:

1. Partitioning:

   • Date or Timestamp Columns: Utilize date-based partitioning, such as using valid_from or a transaction date. This approach allows BigQuery to efficiently scan only relevant partitions, reducing costs and improving query performance.
   • Other Business-Relevant Columns: If queries frequently filter on specific columns (like customer region or product category), consider these for partitioning to further optimize performance.
   • Granularity Balance: Aim for a balance between partition granularity and management overhead. Daily partitions offer more precise optimization but may increase complexity, while monthly partitions are easier to manage but might be less optimal for queries.

2. Clustering (Within Partitions):

   • Frequently Queried Columns: Clustering data within each partition based on commonly used columns in filters or joins can significantly enhance query performance, as BigQuery can quickly locate relevant data blocks.

3. Materialized Views:

   • Common Query Patterns: For repetitive and complex queries, materialized views can pre-calculate and store results, leading to reduced query costs and faster response times.

4. Query and Code Optimization:

   • Efficient SQL Practices: Focus on writing optimized SQL queries, such as avoiding SELECT *, using appropriate joins, and leveraging partition filtering.
   • Optimized MERGE Operations: When using MERGE, batch updates and fine-tune the queries for performance.
   • Minimize Data Movement: Reduce the copying of entire tables and aim for targeted updates where possible.

5. Data Types for valid_from and valid_to:

   • Efficient Use of TIMESTAMP: If your SCD logic primarily revolves around valid_from dates, using a TIMESTAMP column can be more storage and query efficient than handling both valid_from and valid_to as DATE ranges.

6. Batching and Parallelization:

   • Manageable Operations: Break down operations into smaller batches and utilize BigQuery’s ability to parallelize queries, especially when processing multiple tables.

7. Temporary Tables:

   • Intermediate Staging: Use temporary tables for staging intermediate results, which can be more efficient than repeatedly querying or updating the same large target table.

8. Monitor and Adjust:

   • Performance Tracking: Regularly monitor query history and job performance to identify bottlenecks and adjust partitioning, clustering, or query strategies accordingly.

9. Important Considerations:

   • Representative Subset Testing: Test different partitioning schemes, clustering, and query patterns on a smaller dataset before full-scale implementation.
   • Continuous Monitoring: Keep an eye on cost and performance trends over time to identify opportunities for further optimization.

By implementing these strategies, you can effectively manage the complexities of handling large-scale SCD Type 2 scenarios in BigQuery, ensuring both cost-efficiency and high performance.

[Sourabh17]

Thank you for the detailed explanation on how to handle SCD2 in general!

I have a generic question on keeping valid from as partitioned column. Usually, query pattern on SCD2 tables is trying to figure out the latest record or a record on a given day. How keeping valid_from as the partitioned key helps here?

One more question on point 5 "Data Types for valid_from and valid_to", could kindly elaborate how TimeStamp would help?

[ms4446]

Your insights into optimizing query patterns on SCD Type 2 tables in BigQuery are well-founded, particularly regarding the use of valid_from for partitioning and the choice of data types. Let's refine these strategies further:

1. Query Patterns and Partitioning on valid_from:

   • Finding the "Latest Record":

     • Partitioning by valid_from is indeed beneficial for quickly locating the latest records. BigQuery efficiently prunes partitions outside the relevant timeframe, focusing only on recent partitions and thus reducing data scanned.
     • Clustering on the business key within these partitions can further enhance performance, allowing BigQuery to locate relevant blocks swiftly.

   • "Point-in-Time" Queries:

     • For queries targeting a specific date, partitioning by valid_from enables BigQuery to directly access the relevant partition, improving query efficiency.
     • Clustering or indexing on the business key, combined with valid_from and valid_to, within these partitions is crucial for quickly locating specific records.

2. Using TIMESTAMP for valid_from:

   • In scenarios where only the valid_from date is tracked, and valid_to is infrequently updated, opting for a TIMESTAMP column for valid_from can offer storage and query efficiency.
   • While a TIMESTAMP occupies more space than a DATE (8 bytes vs. 4 bytes), using a single TIMESTAMP instead of two DATE columns can lead to overall storage savings.
   • TIMESTAMP may offer advantages in range-based filtering and partitioning operations due to potential internal optimizations in BigQuery's storage and query engine.

3. Important Considerations and Trade-offs:

   • Frequency of valid_to Updates: If valid_to is frequently updated, using separate DATE columns might be more straightforward for query logic.
   • Query Patterns and Workload: Tailor your approach to your specific query patterns. If "latest record" queries are predominant, TIMESTAMP for valid_from and clustering on the business key could be highly effective. For frequent "point-in-time" queries, consider appropriate indexing or clustering strategies.
   • Testing and Evaluation: It's crucial to test different configurations (TIMESTAMP vs. DATE, various clustering/indexing strategies) on your dataset to determine the most effective setup for your workload.

By refining these strategies, you can effectively optimize SCD Type 2 tables in BigQuery for various query patterns, balancing performance and storage considerations. Testing different approaches on your specific data and queries is key to identifying the optimal configuration for your use case.