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Data Archiving

Store data for long-term retention and access

Data archiving refers to the process of storing data for long-term retention and access, usually for compliance or legal purposes, while keeping it readily accessible and searchable.
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Data Archiving is a critical process in Big Data engineering where historical data that is no longer actively used is systematically moved to a separate storage system for long-term retention. This practice serves multiple purposes in data management strategies. The primary goal of data archiving is to optimize production system performance by transferring rarely accessed data from high-performance (and typically more expensive) storage to more cost-effective storage solutions. This helps maintain speed and efficiency in operational databases while preserving historical information. Data archives differ from backups in their purpose and implementation. While backups are copies created for disaster recovery, archives serve as the authoritative long-term repository for historical data. Archives are organized to enable selective retrieval when needed, often implementing specialized indexing and search capabilities. Effective data archiving requires clear policies regarding what data to archive, when to archive it, how to structure it, and how long to retain it. These policies should align with both business needs and regulatory requirements. In the Big Data ecosystem, archiving solutions often leverage technologies like Hadoop, cloud storage services (AWS Glacier, Google Cloud Storage Coldline), or specialized archive systems. These platforms provide cost advantages through compression, deduplication, and tiered storage approaches. Modern data archiving also incorporates governance features including access controls, audit trails, and retention management to ensure compliance with regulations like GDPR, HIPAA, or industry-specific requirements. When implementing archiving strategies, Big Data engineers must consider factors such as data classification, retrieval requirements, storage costs, and integration with existing data pipelines. The goal is creating a sustainable approach that balances accessibility, compliance, and cost management.

Data Archiving is a critical process in Big Data engineering where historical data that is no longer actively used is systematically moved to a separate storage system for long-term retention. This p…

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Big Data Engineer - Data Archiving Example Questions

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Question 1

What is the role of compression in data archiving?

Correct Answer: Compression reduces the size of archived data, making it easier to store and transport.

Compression helps reduce the size of archived data, making it easier to store and transport. It is a common strategy used when archiving large amounts of data, especially when storage space is at a premium.

Question 2

What is the role of metadata in data archiving?

Correct Answer: Metadata provides additional context and information about archived data, such as the date it was created, the location it was stored, and any relevant keywords or tags.

Metadata provides additional context and information about archived data, making it easier to manage, search, and retrieve. Metadata can include a wide range of information, including the date the data was created, the location it was stored, any relevant keywords or tags, and more.

Question 3

What is the recommended maximum size limit for a single data file in a data archive?

Correct Answer: It depends on the specific archive format and storage device, but generally ranges from a few gigabytes to several terabytes

The maximum size limit for a single data file in a data archive depends on the specific archive format and storage device. While some archive formats may have a limit of a few gigabytes, others may support several terabytes. It is important to consider the limitations of the storage device being used, as well as any compatibility issues with the archive format.

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