Schema Mapping | Dagster Glossary

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Schema Mapping

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

Schema mapping definition:

Schema mapping is the process of mapping the schema of one dataset to the schema of another dataset in order to integrate or merge them. It involves mapping the fields or attributes of one dataset to the fields or attributes of another dataset, ensuring that the data types, formats, and other properties match between the two datasets. Schema mapping is a crucial step in data integration, as it enables datasets with different schemas to be combined and analyzed together.

Schema mapping example using Python:

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

In Python, one way to perform schema mapping is by using the PySpark library. Here's an example:

# Importing the required libraries
from pyspark.sql import SparkSession
from pyspark.sql.functions import col

# Creating a SparkSession
spark = SparkSession.builder.appName("Schema Mapping Example").getOrCreate()

# Creating a sample dataframe with multiple columns
data = [("John", 25, "25000.50"), ("Smith", 30, "35000.75"), ("Jane", 28, 32000.25)]
df = spark.createDataFrame(data, ["name", "age", "salary"])

# Defining the mapping schema
mapping_schema = {
    "name": "string",
    "age": "integer",
    "salary": "double"
}

# Creating a new dataframe with the mapped schema
mapped_df = df.select([col(c).cast(mapping_schema[c]).alias(c) for c in mapping_schema.keys()])

# Displaying the original and mapped dataframes
print(f"Original dataframe: {df}")
print(f"Mapped dataframe: {mapped_df}")

# Applying some transformations on the mapped dataframe
filtered_df = mapped_df.filter(mapped_df.age > 25)
grouped_df = filtered_df.groupBy("name").agg({"salary": "sum"})

# Stopping the SparkSession
spark.stop()

This will print out the schema of each dataframe showing the new mapping:

Original dataframe: DataFrame[name: string, age: bigint, salary: string]
Mapped dataframe: DataFrame[name: string, age: int, salary: double]

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 Inference

Automatically identify the structure of a dataset.
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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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