Back to Glossary Index

Dagster Data Engineering Glossary:

Data Deserialization

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

Glossary entry badge for Deserialize

Definition of Deserialization:

In the context of data management, serialization and deserialization are key to storing data persistently (like writing data to disk) and communicating data between different systems (for example, through APIs). It allows the structured, complex data of one system to be understood by another, irrespective of the language or architecture they're built with.

Deserialization is essentially the reverse process of serialization. It is the process of converting the serialized format back into a usable object in the program. This process is used to extract the data or the state of the object from the stored or received serialized format.

Jump to the entry for 'Serialize' for more details and examples.

Note: Deserialization can present potential security risks, especially when dealing with unknown sources. Deserializing data from an untrusted source can lead to what's known as deserialization attacks, where malicious data is loaded into an object, potentially leading to code execution or privilege escalation. Therefore, it's important to ensure that any serialized data is appropriately secured and validated.

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.

An image representing the data engineering concept of 'Append'

Archive

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

An image representing the data engineering concept of 'Archive'

Augment

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

An image representing the data engineering concept of 'Augment'

Auto-materialize

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

An image representing the data engineering concept of 'Auto-materialize'

Backup

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

An image representing the data engineering concept of 'Backup'

Batch Processing

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

An image representing the data engineering concept of 'Batch Processing'

Cache

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

An image representing the data engineering concept of 'Cache'

Categorize

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

An image representing the data engineering concept of 'Categorize'

Checkpointing

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

An image representing the data engineering concept of 'Checkpointing'

Deduplicate

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

An image representing the data engineering concept of 'Deduplicate'

Dimensionality

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

An image representing the data engineering concept of 'Dimensionality'

Encapsulate

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

An image representing the data engineering concept of 'Encapsulate'

Enrich

Enhance data with additional information from external sources.

An image representing the data engineering concept of 'Enrich'

Export

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

An image representing the data engineering concept of 'Export'

Graph Theory

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

An image representing the data engineering concept of 'Graph Theory'

Idempotent

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

An image representing the data engineering concept of 'Idempotent'

Index

Create an optimized data structure for fast search and retrieval.

An image representing the data engineering concept of 'Index'

Integrate

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

An image representing the data engineering concept of 'Integrate'

Lineage

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

An image representing the data engineering concept of 'Lineage'

Linearizability

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

An image representing the data engineering concept of 'Linearizability'

Materialize

Executing a computation and persisting the results into storage.

An image representing the data engineering concept of 'Materialize'

Memoize

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

An image representing the data engineering concept of 'Memoize'

Merge

Combine data from multiple datasets into a single dataset.

An image representing the data engineering concept of 'Merge'

Model

Create a conceptual representation of data objects.

An image representing the data engineering concept of 'Model'

Monitor

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

An image representing the data engineering concept of 'Monitor'

Named Entity Recognition

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

An image representing the data engineering concept of 'Named Entity Recognition'

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.

An image representing the data engineering concept of 'Partition'

Prep

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

An image representing the data engineering concept of 'Prep'

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.

An image representing the data engineering concept of 'Primary Key'

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.

An image representing the data engineering concept of 'Schema Inference'

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.

An image representing the data engineering concept of 'Secondary Index'

Software-defined Asset

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

An image representing the data engineering concept of 'Software-defined Asset'

Synchronize

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

Validate

Check data for completeness, accuracy, and consistency.

An image representing the data engineering concept of 'Validate'

Version

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

An image representing the data engineering concept of 'Version'