A dbt model is a modular SQL SELECT statement that transforms raw data into a clean, structured table or view within a data warehouse. In scraping pipelines, it is the critical translation layer that takes messy, nested JSON payloads extracted from the web and normalizes them into typed, deduplicated records ready for downstream analytics or machine learning consumption.
How modern data teams turn raw scraped payloads into reliable, tested datasets without writing brittle Python scripts.
Ask a DataFlirt engineer →A dbt model is simply a .sql file containing a single SELECT statement, enhanced with Jinja templating. Instead of writing complex DDL/DML statements to create tables or manage inserts, you define the business logic, and dbt handles the materialization in your warehouse (Snowflake, BigQuery, Redshift).
A dbt model is a single .sql file containing a SELECT statement. It represents a single logical step in a data transformation pipeline. Unlike traditional stored procedures, you do not write CREATE TABLE or INSERT INTO statements. You simply write the query that produces the desired output, and dbt wraps it in the appropriate DDL/DML based on your configuration.
Models use Jinja templating to reference other models dynamically. By using the ref() function instead of hardcoding table names, dbt automatically infers the dependency graph (DAG) and runs the models in the correct order.
How a model is physically stored in the database is called its materialization. The four primary types are:
Scraping pipelines typically dump raw HTML or JSON into a data lake. The first layer of dbt models (often called the Bronze or Staging layer) is responsible for reading this raw payload, flattening nested arrays, casting strings to dates or floats, and renaming cryptic source fields into standardized business terminology. This isolates the messy reality of web data from the clean analytics tables downstream.
We believe transformation belongs in the warehouse, not the scraper. Our pipelines extract data and load it into your environment in its rawest, highest-fidelity form. To accelerate your time-to-value, we provide pre-written dbt models tailored to the specific schemas of the sites we scrape. You drop these models into your repository, run dbt build, and immediately get clean, deduplicated tables without writing the JSON parsing logic yourself.
A dbt model is only as good as its tests. Because web data changes without warning, models must be paired with schema tests defined in a schema.yml file. If a target site alters their pagination and causes your scraper to ingest duplicate records, a unique test on the primary key will fail the dbt run, alerting your engineering team before the bad data reaches your business intelligence tools.
Running dbt models on raw scraped data can rack up warehouse compute costs if not optimized. We track these metrics to ensure our incremental models run efficiently without burning credits.
A dbt run executing an incremental model that flattens and deduplicates raw e-commerce product data scraped over the last 6 hours.
Scraped data is inherently unstable. When target sites change, the raw data drifts, and dbt models are the first line of defense to catch the anomaly before it poisons downstream dashboards.
not in the scraper.
Historically, scraping pipelines tried to clean, cast, and normalize data in Python before writing to the database. This creates brittle scrapers and hides the raw data. The modern approach is ELT: extract and load the raw JSON exactly as the site served it, then use dbt models to transform it inside the warehouse. If a parsing rule breaks, you don't need to re-scrape the target — you just fix the dbt model and rebuild the table.
Metadata for a standard staging model processing raw scraped JSON.
Anti-bot shifts, scraping infrastructure updates, dataset delivery patterns, and business outcomes from our pipelines. Short, technical, no fluff.
Common questions about using dbt models to process, clean, and test data extracted from web scraping pipelines.
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raw_data:product:price::float) to extract and cast the fields into standard columnar formats.unique and not_null. You can also add accepted_values tests for categorical fields, or use packages like dbt-expectations to ensure scraped prices fall within a realistic numeric range.20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous feed across millions of records — we scope, build, and operate the pipeline.