Learn the fundamentals of Pixeltable tables, types, and how to build in Pixeltable
Learn more about Pixeltable tables and the data operations with our in-depth guide.
Tables are the fundamental data storage units in Pixeltable. They function similarly to SQL database tables but with enhanced capabilities designed specifically for AI and ML workflows. Each table consists of columns with defined data types and can store both structured data and unstructured media assets.
In Pixeltable, tables:
Creating a table requires defining a name and schema that describes its structure:
Pixeltable allows you to explicitly cast column values to ensure they conform to the expected type. This is particularly useful when working with computed columns or transforming data from external sources.
Column casting helps maintain data consistency and prevents type errors when processing your data.
Filter and retrieve data:
Manipulate text data:
Add new data:
Create tables or insert data directly from external sources:
Pixeltable supports importing from various data sources:
.csv
).xls
, .xlsx
).parquet
, .pq
, .parq
).json
)Modify existing data:
Remove data with conditions:
Manage table structure:
Manage table versions:
Extract data for analysis:
Combine data from multiple tables using different join types.
Returns only matching records from both tables.
Returns all records from the left table and matching records from the right table.
Returns all records from the right table and matching records from the left table.
Returns all possible combinations of records from both tables.
get_table()
to fetch existing tablesDevelopment Workflow
table.py
for structureapp.py
for usageProduction Setup
Remember that Pixeltable automatically handles versioning and lineage tracking. Every operation is recorded and can be reverted if needed.