How do I get cleaned data instead of raw source data?

To get cleaned data instead of raw source data, you usually need to use a pipeline or view that applies data-quality rules after ingestion.

Common ways to do that

1. Use the cleaned/curated layer

   • In many data systems, raw data is stored in a bronze/raw layer.
   • Cleaned data is in a silver/cleaned or curated layer.
   • Query the cleaned table/view instead of the raw one.

2. Apply a transformation step

   • Remove duplicates
   • Fix data types
   • Standardize dates, categories, and text
   • Handle missing values
   • Filter invalid records

3. Use a prepared dataset or materialized view

   • Some platforms provide a preprocessed dataset.
   • Look for names like:
     • clean_...
     • staging_...
     • curated_...
     • processed_...

4. Run the ETL/ELT job

   • If you only have raw data access, you may need to run the transformation job that produces the cleaned version.

Example

Instead of querying:

SELECT * FROM raw_events;

You might query:

SELECT * FROM cleaned_events;

If you’re building it yourself

A simple cleaning step could look like:

• Drop rows where required fields are null
• Convert timestamps to one format
• Trim whitespace
• Deduplicate by ID

If you tell me what system you’re using — for example SQL, Python/pandas, BigQuery, Snowflake, Databricks, or a specific API — I can show you exactly how to get the cleaned version.