We all want fast, accurate, and cost-effective data insights. But when it comes to updating your large tables of historical data, the way you choose to add new information can drastically impact your bottom line and reporting speed.
Imagine your data warehouse is a detailed customer ledger that tracks every single sale, transaction, and interaction. Currently, your data process probably uses something called a TRUNCATE and INSERT strategy. This sounds technical, but it’s actually the most brute-force, least efficient way to manage updates.
The core benefit of dbt’s Incremental Materialization is a fundamental shift in how your data warehouse updates its tables. Rather than relying on the inefficient method of rebuilding the entire dataset, dbt uses the is_incremental() macro to intelligently detect just the new or changed records—known as the “delta”—since the last run. This precise, targeted approach ensures processing power is focused only on the necessary subset of data.
Incremental Load Techniques
To keep up with ever-growing datasets and the demand for fresh insights, efficient data flow is crucial in data warehousing. Instead of full reloads, incremental loading strategies allow you to update only new or changed records. Picking the right strategy is essential for boosting performance, cutting costs, and maintaining data accuracy.
Let’s explore the most common types:
1. Append Only (Insert Only)
What it is: Fire and forget. This is the rawest form of data ingestion. The pipeline grabs the newest records and stacks them on top of the existing dataset without looking back. It assumes every incoming row is unique and necessary.
When to use: Perfect for event logs or transaction histories (e.g., website clicks, sensor readings, or financial transactions). Each new record is a unique event that never changes. Use this when you’re okay with potential duplicates (maybe you’ll clean them up later) or if your source data guarantees no duplicates.
Pros: Immutable event streams. Think server logs, user click history, or IoT telemetry—data that records a moment in time and never needs to be updated.
Cons: It is the cheapest and fastest method available. However, it has zero “protection.” If a glitch runs the job twice, you pay the price in duplicate records.
2. Merge/Upsert (Update + Insert)
What it is: Think of this as updating your phone’s contact list. This strategy compares new incoming data with existing records using a unique identifier (like a phone number). If a record matches an existing one, it updates the old record with the new information. If there’s no match, it’s a new record, so it gets inserted.
When to use: Ideal for data that changes frequently (e.g., customer profiles, order statuses, product prices). You want to always see the latest version of a record without creating duplicates.
Pros: Handles both new data and changes to existing data, keeping your table accurate and duplicate-free.
Cons: More complex to set up than Append Only. It can also use more computing power because it has to scan and compare both the new data and the existing data.
3. Delete + Insert
What it is: This is a two-step approach. For any incoming records that match existing ones, you first delete the old versions from your table, and then you insert all the new and updated records.
When to use: Often used when your database doesn’t have a direct MERGE command, or when you prefer a complete “refresh” for a specific set of records rather than an in-place update. It’s also suitable for Slowly Changing Dimensions (SCD Type 1) where you only care about the very latest attribute values.
Pros: Ensures a clean replacement of records, so you always have the most current data.
Cons: Can be less efficient than Merge for tables with only a few changes, as it involves both a DELETE and an INSERT operation.
4. Insert Overwrite (Partition-Based)
What it is: This strategy is specifically for tables that are divided into partitions (like organizing your library books by genre or year). Instead of updating individual rows, it replaces entire sections (aka partitions) of your table with new data. You tell it which partitions to overwrite (e.g., all data for “January 2024”).
When to use: Perfect for very large tables where data frequently changes within specific timeframes or categories (e.g., daily sales aggregates, reprocessing data for a specific day or region).
Pros: Super efficient for large, partitioned tables because it avoids scanning the whole table. It only rewrites the affected sections. Great for handling late-arriving data in specific partitions.
Cons: Only works with partitioned tables. If not managed carefully, you could accidentally delete or overwrite data you didn’t intend to.
5. Microbatching
What it is: The parallel processor. When you reach enterprise scale, processing 24 hours of data in one go can cause memory failures. Microbatching slices that massive load into smaller, digestible windows (e.g., 30-minute increments) and processes them concurrently.
Pros: Resilience at scale. Essential for teams managing massive time-series data who cannot afford pipeline failures due to memory overloads.
Cons: Microbatching introduces overhead. If you are processing small data (e.g., 100k rows), setting up microbatching will actually make your pipeline slower because the warehouse spends more time starting and stopping the batches than actually moving data.
Smart Data, Faster Insights
In today’s fast-paced world, decisions are only as good as the data they are built upon. But when getting that data takes too long, or it costs too much, what happens?
Ultimately, the power of this method lies in its efficiency and precision. By preserving all existing, unchanged data and only focusing on the seamless integration of fresh information, this strategic optimization delivers immense value
It’s time to transform your data. At Atrium, we help businesses build truly smarter data systems. Our core philosophy revolves around efficiency and reliability, ensuring you’re always working with the most current information. This transforms your data from a static liability into a dynamic asset that truly drives growth and innovation
Reach out to discuss how we help implement dbt incremental load strategies by designing and setting up the smart data rules that tell your system what is new and what is old.