Schema Inference | Dagster Glossary

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Schema Inference

Automatically identify the structure of a dataset.

Schema inference definition:

Schema inference is a process where the structure of a dataset (such as the data types and column names) is automatically identified, often when working with semi-structured or unstructured data.

An example of schema inference in Python using Pandas and PyArrow

In Python, schema inference can be demonstrated using libraries such as Pandas for data manipulation and PyArrow for more advanced schema inference, especially useful with large datasets or in the context of distributed computing.

Please note that you need to have the necessary Python libraries installed in your Python environment to run this code.

Say we have a simple source file as follows:

id,name,age,date_of_birth,role,is_related
1,Thom Yorke,55,1968-10-07,vocals,False
2,Jonny Greenwood,52,1971-10-05,guitar,True
3,Ed OBrien,55,1968-04-15,guitar,False
4,Philip Selway,56,1967-05-23,drums,False
5,Colin Greenwood,54,1969-06-26,bass,True
  1. Read .csv File: Use Pandas to read a .csv file. Pandas infers the data types for each column.
  2. Advanced Schema Inference with PyArrow: After reading the data with Pandas, we'll convert the Pandas DataFrame into a PyArrow table for more advanced schema inference, which is particularly useful in big data scenarios.

Assuming we have the .csv file saved locally as our_data.csv we can run the following simple script:

import pandas as pd
import pyarrow as pa

def infer_schema_with_pandas(file_path):
    """
    Infer schema using Pandas
    """
    df = pd.read_csv(file_path)
    return df.dtypes

def convert_to_pyarrow_table(df):
    """
    Convert Pandas DataFrame to PyArrow Table
    """
    return pa.Table.from_pandas(df)

def infer_schema_with_pyarrow(table):
    """
    Infer schema using PyArrow
    """
    return table.schema

def print_full_schema(schema):
    """
    Print out the schema fields
    """
    for field in schema:
        print(f"Field name: {field.name}, Type: {field.type}, Nullable: {field.nullable}, Metadata: {field.metadata}")

# Path to your CSV file
file_path = 'our_data.csv'

# Infer schema with Pandas
pandas_schema = infer_schema_with_pandas(file_path)
print("Schema inferred by Pandas:")
print(pandas_schema)

# Advanced Schema Inference with PyArrow
df = pd.read_csv(file_path)
arrow_table = convert_to_pyarrow_table(df)
arrow_schema = infer_schema_with_pyarrow(arrow_table)
print("\nSchema inferred by PyArrow:")
print_full_schema(arrow_schema)

In this very basic example:

  • infer_schema_with_pandas function reads the .csv file using Pandas and prints the inferred data types.
  • convert_to_pyarrow_table converts the Pandas DataFrame to a PyArrow Table.
  • infer_schema_with_pyarrow uses PyArrow to print a more detailed schema, which can include more nuanced data types and is more suitable for big data applications.

The output will be as follows:

Schema inferred by Pandas:
id                int64
name             object
age               int64
date_of_birth    object
role             object
is_related         bool
dtype: object

Schema inferred by PyArrow:
Field name: id, Type: int64, Nullable: True, Metadata: None
Field name: name, Type: string, Nullable: True, Metadata: None
Field name: age, Type: int64, Nullable: True, Metadata: None
Field name: date_of_birth, Type: string, Nullable: True, Metadata: None
Field name: role, Type: string, Nullable: True, Metadata: None
Field name: is_related, Type: bool, Nullable: True, Metadata: None

Other data engineering terms related to
Data Management:
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Append

Adding or attaching new records or data items to the end of an existing dataset, database table, file, or list.
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Archive

Move rarely accessed data to a low-cost, long-term storage solution to reduce costs. Store data for long-term retention and compliance.
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Augment

Add new data or information to an existing dataset to enhance its value.
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Auto-materialize

The automatic execution of computations and the persistence of their results.
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Backup

Create a copy of data to protect against loss or corruption.
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Batch Processing

Process large volumes of data all at once in a single operation or batch.
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Cache

Store expensive computation results so they can be reused, not recomputed.
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Categorize

Organizing and classifying data into different categories, groups, or segments.
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Checkpointing

Saving the state of a process at certain points so that it can be restarted from that point in case of failure.
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Deduplicate

Identify and remove duplicate records or entries to improve data quality.
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Deserialize

Deserialization is essentially the reverse process of serialization. See: 'Serialize'.
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Dimensionality

Analyzing the number of features or attributes in the data to improve performance.
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Encapsulate

The bundling of data with the methods that operate on that data.
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Enrich

Enhance data with additional information from external sources.
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Export

Extract data from a system for use in another system or application.
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Graph Theory

A powerful tool to model and understand intricate relationships within our data systems.
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Idempotent

An operation that produces the same result each time it is performed.
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Index

Create an optimized data structure for fast search and retrieval.
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Integrate

Combine data from different sources to create a unified view for analysis or reporting.
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Lineage

Understand how data moves through a pipeline, including its origin, transformations, dependencies, and ultimate consumption.
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Linearizability

Ensure that each individual operation on a distributed system appear to occur instantaneously.
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Materialize

Executing a computation and persisting the results into storage.
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Memoize

Store the results of expensive function calls and reusing them when the same inputs occur again.
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Merge

Combine data from multiple datasets into a single dataset.
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Model

Create a conceptual representation of data objects.
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Monitor

Track data processing metrics and system health to ensure high availability and performance.
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Named Entity Recognition

Locate and classify named entities in text into pre-defined categories.
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Parse

Interpret and convert data from one format to another.
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Partition

Data partitioning is a technique that data engineers and ML engineers use to divide data into smaller subsets for improved performance.
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Prep

Transform your data so it is fit-for-purpose.
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Preprocess

Transform raw data before data analysis or machine learning modeling.
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Replicate

Create a copy of data for redundancy or distributed processing.

Scaling

Increasing the capacity or performance of a system to handle more data or traffic.
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Schema Mapping

Translate data from one schema or structure to another to facilitate data integration.
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Secondary Index

Improve the efficiency of data retrieval in a database or storage system.
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Software-defined Asset

A declarative design pattern that represents a data asset through code.
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Synchronize

Ensure that data in different systems or databases are in sync and up-to-date.
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Validate

Check data for completeness, accuracy, and consistency.
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Version

Maintain a history of changes to data for auditing and tracking purposes.
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