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What are some effective strategies for data archiving and purging in ETL workflows?

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Data archiving and purging are essential tasks for maintaining the performance, quality, and security of data in ETL workflows. ETL stands for extract, transform, and load, and it refers to the process of moving data from various sources to a target destination, such as a data warehouse or a data lake. In this article, we will discuss some effective strategies for data archiving and purging in ETL workflows, and how they can benefit your data management and analysis.

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Aleksandrа Tarkowska, MSc Eng

???????????? Data Engineer with a passion for problem-solving and technology
Romeo Jeff Fredson

Data & IT Manager @ Koa Switzerland & Ghana | Data Governance, Data Architecture
JIMMY LEE

Community Top Voice | Tech Enthusiast | Innovative and Strategic | High-impact Performer

1 Why archive and purge data in ETL workflows?

Data archiving and purging are two distinct operations that help optimize the storage and usage of data in ETL workflows. Data archiving involves moving data that is no longer needed for active processing or analysis to a lower-cost or offline storage medium, such as a tape, a cloud service, or a separate database. On the other hand, data purging involves deleting data that is obsolete, redundant, or irrelevant from the source or target systems. Both of these processes can help you improve the performance and efficiency of your ETL workflows by reducing the amount of data that needs to be extracted, transformed, and loaded. Additionally, they can enhance the quality and accuracy of your data by eliminating errors, duplicates, or inconsistencies that may arise from outdated or irrelevant data. Furthermore, they can save on storage costs and resources by freeing up space and bandwidth for more valuable or relevant data. Moreover, they can ensure compliance with data retention policies and regulations by keeping only the data that is required or allowed for a specific period of time. Lastly, they can protect the privacy and security of your data by removing sensitive or confidential information that may pose a risk of breach or misuse.

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Tatiana Branada Suárez

Bilingual - Scrum Master Profesional (SMPC) - Project Manager - Project Management - Postgraduate -Writer - Data Analytics
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Estoy de acuerdo con la estrategia descrita en el texto, solo mencionar que por temas normativos en Chile, kas empresas que guardan información de tipo financiero tienen regulación vinculada con la Cmf organización gubernamental que regula tiempos y contenidos. Las ETLs deben considerar regulaciones a las que “el negocio” se debe suscribir. En Chile en el caso particular de la banca esta observancia es fundamental para el regulador o CMF. Cordiales saludos Tatiana Branada.

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2 How to plan for data archiving and purging in ETL workflows?

Prior to initiating archiving and purging of data in ETL workflows, you must have a comprehensive and consistent plan that outlines what data to archive or purge, when to do it, and where to store it. This plan should be based on factors such as age, frequency, relevance, quality, compliance, and the dependencies and relationships between different data sources and targets. You will also need to decide the schedule and frequency of your archiving and purging activities based on business needs, data usage patterns, and storage capacity. Furthermore, you must select the appropriate storage location and format for the archived data depending on accessibility, durability, and cost requirements. Finally, you must determine how to dispose of purged data by either permanently deleting it, overwriting it, or encrypting it.

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Romeo Jeff Fredson

Data & IT Manager @ Koa Switzerland & Ghana | Data Governance, Data Architecture
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There are various dynamics but key is; 1. Data Versioning: If your ETL workflow involves historical data, consider implementing data versioning. This allows you to keep track of changes over time while maintaining a clean, current dataset. 2. Data Compression and Encryption: Before archiving, consider compressing and encrypting data to reduce storage costs and enhance security. Ensure that you maintain adequate encryption keys and access controls. 3. And finally, Implement audit trails and logs to track when data is archived, purged, or accessed. This is important for compliance and monitoring purposes.

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JIMMY LEE

Community Top Voice | Tech Enthusiast | Innovative and Strategic | High-impact Performer
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Establish a cadence for archiving historical data, develop data retention policies to define what to purge, automate the processes, and create a data governance framework to continue to optimize. You can take it further by using a tiered storage approach and compressing and encrypting the archived data. Partitioning may prove to be useful as well for data removal in ETL workflows. What you choose to do will ultimately depend on what makes sense for your business case.

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3 How to implement data archiving and purging in ETL workflows?

Once you have a plan for data archiving and purging in your ETL workflows, you need to implement it using the tools and techniques that suit your data environment and objectives. To optimize the performance and scalability of your ETL workflows, consider partitioning and indexing your data. This will make it easier to archive or purge data by selecting the relevant partitions or indexes. Additionally, you can reduce the amount of data that needs to be processed and stored by using incremental or delta loading. Compression and deduplication can also help reduce the size and redundancy of data, while backup and recovery can ensure the availability and integrity of your data. All of these methods can help you archive or purge data more efficiently.

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4 How to monitor and evaluate data archiving and purging in ETL workflows?

Data archiving and purging are ongoing and dynamic processes that require regular monitoring and evaluation. You need to track and measure the results and impacts of your data archiving and purging activities, adjusting them as necessary. Factors to consider include data volume and growth, data quality and value, and data compliance and security. Depending on these factors, you may need to increase or decrease your data archiving and purging frequency or scope, revise or refine your criteria or rules, or update or enforce your policies or procedures. This will help ensure that your data meets the legal and ethical standards and expectations while supporting your business goals and decisions.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Aleksandrа Tarkowska, MSc Eng

???????????? Data Engineer with a passion for problem-solving and technology
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Compliance is a crucial aspect of any data management strategy, including data archiving and purging. Many industries, especially those handling sensitive information like healthcare or personal data, are subject to specific data retention and deletion laws and regulations. Examples like GDPR for the EU region, HIPAA for healthcare in the US, not naming a few. It's essential to understand these regulations and how they apply to your data. Ensure everyone involved in ETL workflows understands the regulations and how to ensure compliance. Conduct regular audits, like checking data management practices against the relevant regulations and adjusting as necessary. Failure to comply can result in hefty fines and reputation damage.

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Daryl D.

Data Analyst at Copper State Bolt & Nut Company
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Purging and archiving are tasks that most Data Engineers almost certainly need to manage today. However, I do wonder how much longer this will be the case. Modern ERPs and other systems that are big data sources are integrating data lifecycle management into their systems. They are placing administrative controls over what is exported and what is purged directly into the hands of business decision-makers where it should be. Moving in this direction will free up Engineers to focus more on data quality, efficiency and velocity.

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What are some effective strategies for data archiving and purging in ETL workflows?

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1 Why archive and purge data in ETL workflows?

2 How to plan for data archiving and purging in ETL workflows?

3 How to implement data archiving and purging in ETL workflows?

4 How to monitor and evaluate data archiving and purging in ETL workflows?

5 Here’s what else to consider

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