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Deserialize

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

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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’:

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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Deduplicate

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

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Dimensionality

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

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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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Idempotent

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

Index

Create an optimized data structure for fast search and retrieval.

Integrate

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

Lineage

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

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Materialize

Executing a computation and persisting the results into storage.

Memoize

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

Merge

Combine data from multiple datasets into a single dataset.

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Model

Create a conceptual representation of data objects.

Monitor

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

Named Entity Recognition

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

Parse

Interpret and convert data from one format to another.

Partition

Divide data into smaller subsets for improved performance.

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Prep

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

Preprocess

Transform raw data before data analysis or machine learning modeling.

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 Mapping

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

Synchronize

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

Validate

Check data for completeness, accuracy, and consistency.

Version

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

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