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

Data archiving definition:

Data archiving, in the context of modern data pipelines, refers to the process of moving data that is no longer actively used to a separate storage system for long-term retention. Data archiving helps in reducing primary storage costs, enhancing system performance, and ensuring compliance with data retention policies and regulations. The archived data is indexed and has search capabilities, so it can still be easily accessed and retrieved if needed. It's a critical part of a data lifecycle management strategy and is usually one of the last stages in a data pipeline.

Data archiving strategies in data engineering:

Archiving strategies are important to ensure that data is stored efficiently and can be retrieved when needed. Here are some main archiving strategies used in data engineering specific to data pipeline design:

  • Cold storage: This strategy involves storing data that is infrequently accessed, and therefore can be stored on cheaper, slower storage devices. This is often used for archival purposes, where the data needs to be retained for a long period of time, but is not accessed frequently. Examples of cold storage solutions include Amazon Glacier and Google Cloud Storage Nearline.
  • Partitioning: This strategy involves dividing data into smaller, more manageable chunks called partitions, which can be stored separately. This can help to improve query performance by allowing data to be queried in parallel. For example, data can be partitioned by date, region, or any other relevant attribute.
  • Compression: This strategy involves reducing the size of data by compressing it, which can help to reduce storage costs and improve transfer times. There are many compression algorithms available, such as gzip and bzip2, which can be used depending on the type of data being compressed.
  • Incremental backups: This strategy involves backing up only the changes made to data since the last backup, rather than backing up the entire dataset each time. This can help to reduce the amount of data that needs to be backed up, and also reduce backup times.
  • Versioning: This strategy involves keeping multiple versions of data over time, which can be useful for auditing purposes or for keeping track of changes made to the data. Versioning can be achieved through techniques such as snapshotting or using version control systems.
  • Data archiving to the cloud: Cloud providers like AWS, Azure and GCP offer data archival solutions where data can be transferred and stored securely in their cloud infrastructure. This can provide cost savings, flexibility and durability compared to on-premise solutions.

These are some of the main archiving strategies used in data engineering to ensure that data is stored efficiently and can be retrieved when needed. Choosing the appropriate archiving strategy for a particular data pipeline will depend on factors such as data access patterns, data size, cost, and retention requirements.

Other data engineering terms related to
Data Management:


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.


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


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.