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Dagster Data Engineering Glossary:

Auto-materialize

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

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A definition of auto-materialization

"auto materialization" refers to the automatic execution of computations and the persistence of their results into a physical storage medium, such as a disk or memory, without the need for manual intervention. This concept is particularly relevant in systems that manage data assets and pipelines, where the goal is to ensure that data is up-to-date and available for downstream consumption.

Dagster, for instance, has the concept of auto materialization policies that can be applied to data assets. These policies determine when and how assets should be automatically materialized based on certain conditions, such as changes in upstream data, freshness requirements, or specific schedules. Auto materialization helps in maintaining the data's relevance and freshness by automating the process of updating data assets when their dependencies have changed, without requiring users to manually trigger these updates.

Auto-materialization policies

"auto-materialization policies" refer to the rules or strategies that determine when and how data assets should be automatically materialized. These policies can be configured to automatically materialize assets based on certain conditions or triggers, such as changes in upstream data, code updates, or according to a schedule.

For example, Dagster supports different types of auto-materialization policies, such as "eager" and "lazy" policies. An eager policy might materialize an asset as soon as any of its dependencies are updated, while a lazy policy might delay materialization until the asset is explicitly requested or needed.

These policies help manage the materialization of assets in a way that balances performance, resource utilization, and the freshness of data. They can be customized to fit the specific needs of a data pipeline, ensuring that data is materialized in the most efficient and effective manner for a given use case.

You can read more about how Dagster handles auto materialization in the Dagster docs.

Other data engineering terms related to

‘Data Management’:

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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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.

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.

Primary Key

A unique identifier for a record in a database table that helps maintain data integrity.

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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.

Schema Inference

Automatically identify the structure of a dataset.

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Schema Mapping

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

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.

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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