Slowly Changing Dimensions Type 1

The Type 1 response to a changing dimension attribute is the simplest and most destructive strategy for data architects. When a dimension attribute changes in the source system, this method overwrites the corresponding attribute in the dimension table with the new value. No history is preserved. The record reflects only the most current state, effectively asserting that the previous value never existed.

This approach is primarily used for correcting data errors, such as spelling mistakes, or for managing attributes where historical accuracy is irrelevant to the business analysis. While it is easy to implement and efficient in terms of storage, it creates specific side effects in analytical reporting that you must understand before deployment.

Mechanics of the Update

When the ETL pipeline detects a change in a source record, the system locates the corresponding row in the dimension table using the natural key. It then updates the specific column(s) that have changed. The surrogate key remains constant, maintaining the referential integrity with the fact tables.

Consider a scenario where a customer, "Acme Corp", is defined in a dimension table. The customer is originally assigned to the "East" sales territory. If the sales organization restructures and moves Acme Corp to the "West" territory, a Type 1 update simply changes the string "East" to "West" in that customer's row.

The following diagram illustrates the state transition of a dimension row during a Type 1 update.

The mechanism of a Type 1 update where the original value is permanently lost and replaced by the new value.

Implementation Logic

From an engineering perspective, Type 1 is often implemented using a MERGE statement or an UPDATE command in SQL. The logic compares the incoming staging data against the existing target dimension.

If we denote the set of attributes for a dimension as $A = \{a_1, a_2, ..., a_n\}$, and the incoming new state as $A'$, the operation ensures that for any row $r$:

$r[a_i] \leftarrow A'[a_i]$

This equality assignment happens regardless of when the row was created. In modern cloud data warehouses like Snowflake or BigQuery, a standard MERGE pattern handles this efficiently:

MERGE INTO dim_customer AS target
USING stg_customer_updates AS source
ON target.customer_id = source.customer_id
WHEN MATCHED AND target.territory != source.territory THEN
  UPDATE SET target.territory = source.territory;

Notice that we only update when the values differ. This prevents unnecessary write operations, which can incur costs in columnar stores that rely on micro-partitioning.

Implications for Historical Reporting

The primary trade-off with SCD Type 1 is the rewriting of history. Because the attribute changes in place, any fact table records linked to that dimension row immediately associate with the new value. This occurs even for transactions that happened years ago.

Using the previous example:

1. In 2022, Acme Corp (East) bought $1,000 worth of goods.
2. A report run in 2022 shows $1,000 revenue for the "East" territory.
3. In 2023, Acme Corp moves to "West". A Type 1 update runs.
4. A report run in 2023 regarding 2022 performance now shows $1,000 revenue for the "West" territory.

This phenomenon violates the principle of report immutability. Aggregations based on the dimension attribute will shift over time. If $R$ is the total revenue for a specific territory $T$, calculated as:

$R_T = \sum_{i=1}^{n} (p_i \times q_i) \text{ where } Territory(d_i) = T$

When $Territory(d_i)$ changes via Type 1, the term $(p_i \times q_i)$ moves from the summation of the old territory to the new one. The total revenue numbers for historical periods will change every time the report is generated.

Appropriate Use Cases

Despite the destruction of history, SCD Type 1 is frequently the correct design choice for specific attributes. You should employ this pattern when:

1. Data Hygiene: The change is a correction of an error (e.g., fixing a misspelled name from "Jonh" to "John"). We want the history to reflect "John" because "Jonh" was never real.
2. Irrelevant History: The business does not require historical tracking for specific columns. For example, if a customer changes their email address, marketing likely only cares about the current valid email for reachability. They do not need to analyze sales performance based on old email domains.
3. Performance Optimization: The dimension is extremely large (millions of rows) and rapidly changing, but the changes are not analytically significant. Maintaining history (Type 2) for every minor update would bloat the table and degrade query performance.

By explicitly choosing Type 1, you prioritize current accuracy and storage efficiency over historical preservation. In the subsequent section, we will explore Type 2, which solves the historical consistency problem by versioning rows rather than overwriting them.