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

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 involve 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:


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


Add new data or information to an existing dataset to enhance its value. Enhance data with additional information or attributes to enrich analysis and reporting.


Select, organize and annotate data to make it more useful for analysis and modeling.


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


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


Enhance data with additional information from external sources.


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


Create an optimized data structure for fast search and retrieval.


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


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


Combine data from multiple datasets into a single dataset.


Extract useful information, patterns or insights from large volumes of data using statistics and machine learning.


Create a conceptual representation of data objects.


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.


Interpret and convert data from one format to another.


Divide data into smaller subsets for improved performance.


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


Transform raw data before data analysis or machine learning modeling.


Create a copy of data for redundancy or distributed processing.


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.


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


Check data for completeness, accuracy, and consistency.


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