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Dagster Data Engineering Glossary:


Data Backup

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

Definition of 'Backup'

In data engineering, both backing up and archiving are processes that involve storing data for long-term retention or backup purposes. However, there are some differences between the two concepts.

Backing up involves creating a copy of data to protect against data loss due to accidental deletion, hardware failure, or other types of disasters. The primary goal of backing up data is to ensure that it can be recovered quickly and easily in the event of a problem. Backups are typically performed on a regular basis (e.g. daily, weekly, or monthly) and are often stored in a different location than the primary data to protect against site-level disasters.

Archiving, on the other hand, involves moving data that is no longer actively used from its original location to a secondary storage location for long-term retention. Archiving is often used for compliance, legal, or historical reasons, or to free up primary storage space for more frequently accessed data. Archived data is typically accessed less frequently than primary data and is often stored on cheaper, slower storage devices.

Backing up vs. archiving: a question of frequency of access

While backing up and archiving can serve similar purposes, the main difference is the frequency of access to the data. Backups are designed for quick and easy recovery of data in the event of a problem, while archives are intended for long-term retention of data that is no longer actively used.

Backing up and archiving data in data engineering typically involves different techniques and processes.

Backing up data usually involves making a copy of the data at a specific point in time and storing it in a separate location, such as a backup server or cloud storage. Backup techniques can include full backups, differential backups, incremental backups, and continuous backups. Backup frequency, retention periods, and restoration processes are also important considerations.

Archiving data, on the other hand, involves moving data that is no longer actively used from its original location to a secondary storage location for long-term retention. Archiving techniques can include compressing and encrypting the data, creating metadata for search and retrieval, and setting retention policies. Archiving may involve different storage options, such as tape or disk storage, and may use different tools or technologies for data movement and management.

While both backing up and archiving data involve storing data for long-term retention, the goals, methods, and techniques for each process are different, and typically require different tools and technologies.

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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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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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.
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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.
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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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