In our last article we’ve covered just a few methods for optimizing BigQuery with DBT, but there are many more advanced strategies available. In this article we will discuss one of those advanced strategies, we’ll explore how incremental DBT models can be a game-changer for cost optimization in BigQuery, reducing unnecessary processing as data volumes grow.
A common issue that arises is the increased size of tables in BigQuery due to repeated transformations, which can significantly inflate costs, as each transformation run generates additional queries.
The Challenge: Table Size Growth and Query Costs in BigQuery
In a typical DBT workflow, every transformation is essentially a SQL query that reads data from BigQuery tables and writes the output into new or existing tables.
When models are frequently triggered, the tables being transformed can become larger over time. This table size growth is primarily driven by DBT's default behavior of either creating or replacing tables during each run. Each replacement reprocesses the entire table, increasing query processing costs.
BigQuery operates on a cost-per-query model, meaning that the larger the table being queried and processed, the higher the associated cost. When this process occurs repeatedly, as with DBT models that constantly refresh entire tables, the expenses can accumulate rapidly.
Key Causes of Increased Costs:
Full Table Replacement: Each time a transformation is triggered, DBT’s default behavior of replacing entire tables increases data size unnecessarily.
Frequent Querying: Reprocessing full tables every time leads to multiple full-data queries in BigQuery, which adds to the cost.
Event-Driven Transformations: In event-driven architectures where transformations are triggered by specific events, the table size expands faster than expected, leading to higher storage and query costs.
The Solution: Incremental DBT Models
To mitigate this issue, many teams have turned to incremental DBT models. Incremental models allow DBT to only process and update new or changed data, rather than reprocessing entire tables. This approach brings significant cost savings, especially for large datasets that don’t require full refreshes every time.
How Incremental DBT Models Work:
Partial Table Updates: Instead of creating or replacing entire tables, incremental models append only new or modified data to existing tables. This means DBT runs fewer queries and processes less data overall, reducing costs.
Selective Processing: By only processing a subset of the data—such as records that have been inserted or updated since the last run—incremental DBT models optimize both the query costs and the time required for transformations.
Benefits of Incremental Models:
Cost Savings: Since only new or changed data is processed, the amount of data or rows processed is reduced, leading to significant cost reductions in both query processing and storage.
Performance Gains: Incremental processing is faster than full-table processing, as it deals with smaller data volumes, improving overall efficiency.
Scalability: As your data scales, incremental models allow for more predictable cost management, without the exponential increase in query or storage costs.
Managing costs in BigQuery when using DBT models requires a strategic approach. The default behavior of full table replacements in DBT can significantly inflate query costs over time, particularly with large datasets or frequent event-driven triggers. However, by implementing incremental models, data teams can limit transformations to only new or modified data, thereby reducing costs, speeding up processes, and making their workflows more efficient and scalable.
Adopting incremental DBT models is a best practice for any data engineer working with BigQuery, particularly when the focus is on optimizing costs while maintaining high-quality data pipelines.
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