Handling Semi-Structured Data

Modern data warehousing has moved past the rigid requirement that all data must be strictly structured before ingestion. The rise of application logs, clickstreams, and document-oriented databases has made semi-structured formats like JSON, Avro, and Parquet primary citizens in data engineering. For Massively Parallel Processing (MPP) architectures, handling these formats requires a shift in modeling strategy. You no longer need to fully normalize complex hierarchies into relational tables immediately. Instead, you can use the native capabilities of platforms like Snowflake, BigQuery, and Redshift to store nested data efficiently while exposing it via standard SQL interfaces.

Native Storage of Semi-Structured Data

Historically, storing JSON in a relational database meant inserting a text string into a VARCHAR or CLOB column. This approach was inefficient because every query required parsing the entire string to extract a single attribute. Modern MPP systems handle this differently. When you load semi-structured data into a specialized data type (such as VARIANT in Snowflake, SUPER in Redshift, or STRUCT/ARRAY in BigQuery), the storage engine does not simply store the raw text.

Instead, the engine performs a process often called "shredding" or "columnarization" on the fly. It identifies the internal schema of the document and stores distinct paths as separate micro-columns within the underlying storage blocks. This allows the execution engine to scan only the necessary sub-columns during a query, significantly reducing I/O.

The logical transformation of a nested JSON document into physical columnar storage. The engine automatically extracts paths (like user.name) into separate storage vectors to optimize retrieval.

This architecture implies that the cost of querying a specific field deep within a JSON object is comparable to querying a standard primitive column, provided the path is consistent across the dataset. However, high variability in the schema can lead to sparse column storage, which degrades compression ratios and scanning performance.

Schema-on-Read Patterns

The primary advantage of retaining semi-structured data in your warehouse is the enabling of Schema-on-Read. In a traditional Schema-on-Write model, any change to the upstream data structure requires a migration of the target table and an update to the ETL pipeline. With Schema-on-Read, new attributes appearing in the source JSON are automatically captured in the variant column without breaking the pipeline.

You define the schema in the query definition rather than the table definition. For example, accessing a nested field uses dot notation or bracket notation depending on the SQL dialect:

-- Extracting data from a VARIANT column named 'raw_payload'
SELECT 
    raw_payload:transaction_id::INTEGER as id,
    raw_payload:items[0].sku::STRING as first_item_sku,
    raw_payload:metadata.device_info.os::STRING as device_os
FROM sales_events
WHERE raw_payload:event_timestamp >= DATEADD('hour', -1, CURRENT_TIMESTAMP());

While convenient, this flexibility introduces a governance challenge. If the upstream application renames device_info to client_info, the query above will return NULL rather than failing explicitly. To mitigate this, production systems often utilize a "Hybrid View" layer. This involves creating a SQL View over the raw table that explicitly casts and aliases the semi-structured paths into typed columns. This view acts as a contract, ensuring that downstream reporting tools interact with a stable schema.

Flattening Arrays and Hierarchies

Data modeling becomes complex when dealing with arrays. A single record in your source system (e.g., an Order) might contain an array of multiple child entities (e.g., Line Items). In a 3NF model, these would be separate tables joined by a foreign key. In semi-structured modeling, they exist within the same row.

To analyze the child entities, you must "flatten" or "unnest" the structure. This operation transforms the relationship from one row per parent to one row per child. In relational algebra, this is effectively a cross-join between the parent row and its own child array.

If we denote the dataset as $R$ and the array attribute within a row $r$ as $r.array$, the flattened result set $R_{flat}$ is defined as:

$R_{flat} = \{ (r_{attributes}, e) \mid r \in R, e \in r.array \}$

Most MPP databases implement this via a LATERAL FLATTEN (Snowflake), UNNEST (BigQuery), or PartiQL extension (Redshift).

SELECT 
    o.raw_data:order_id::INT as order_id,
    i.value:product_id::INT as product_id,
    i.value:quantity::INT as quantity
FROM orders o,
LATERAL FLATTEN(input => o.raw_data:items) i;

This query generates a row for every item in the items array, carrying over the order_id from the parent. This technique is essential for aggregating metrics at the child level, such as calculating the total quantity of specific products sold across all orders.

Performance and Materialization

Querying semi-structured data incurs a computational overhead compared to structured data. The engine must traverse the internal structure and cast types at runtime.

$Cost_{total} = Cost_{scan} + Cost_{parse} + Cost_{cast}$

While the $Cost_{scan}$ is minimized by the columnar storage discussed earlier, the $Cost_{parse}$ and $Cost_{cast}$ can become significant at petabyte scale. If a particular path (e.g., raw_data:user_id) is used in join conditions or clustering keys, relying on the variant path is inefficient.

The recommended pattern is to materialize frequently accessed paths. You can extract these fields into separate, strongly-typed columns within the same table during ingestion. This creates a wide table structure where core business keys are strongly typed columns, and the remainder of the payload resides in a catch-all variant column.

Comparative latency analysis showing the performance penalty of using nested paths in intensive operations like Joins and Filtering. Native columns significantly outperform JSON paths when used as join keys.

As the chart indicates, while simple selection has negligible overhead, using a JSON path as a join key can degrade performance by an order of magnitude. This happens because the database cannot effectively use standard optimizations like bloom filters or merge joins on un-typed data without first decoding it.

Handling Evolution and Drift

Schema drift occurs when the structure of the incoming data changes unexpectedly. In a semi-structured context, this usually manifests as:

1. New Fields: Automatically handled by the variant column.
2. Type Changes: A field sending an Integer suddenly sends a String.
3. Structure Changes: An object becomes an array.

To manage type changes, your queries should use safe casting functions (e.g., TRY_TO_NUMBER or SAFE_CAST). This prevents a single malformed record from failing an entire batch query. For structural changes, advanced modeling requires metadata monitoring. You can run periodic analysis queries on the variant column using functions that infer schemas (such as INFER_SCHEMA in Snowflake) to detect if the dominant structure of the data has shifted, signaling a need to update downstream views or materializations.

By combining the flexibility of variant types with the performance rigor of materialization, you build a data model that is resilient to upstream volatility while maintaining the speed required for high-performance analytics.