{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Naser Tamimi",
  "title_listed": "Backfilling Mastery: Elevating Data Engineering Expertise",
  "keywords": "",
  "domain": "数据处理",
  "content_type": "深度技术解析",
  "is_paid_flag": "否",
  "url": "https://towardsdatascience.com/backfilling-mastery-elevating-data-engineering-expertise-a6873fb7ed2e/",
  "publish_date": "2023-11-17",
  "corpus_stage": "",
  "notes": "",
  "id": "d44f52cedf16",
  "collected_at": "2026-10-02T05:11:42",
  "title": "Backfilling Mastery: Elevating Data Engineering Expertise | Towards Data Science",
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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# Backfilling Mastery: Elevating Data Engineering Expertise\nA go-to guide for data engineers wading through the backfilling maze\n[Naser Tamimi](https://towardsdatascience.com/author/tamimi-naser/)\nNovember 17, 20237 min read\n![](../images/000.jpg)\n### DATA ENGINEERING\n## What is Backfilling?\nImagine starting a new data pipeline and getting data from a source you've never parsed before (e.g. pulling info from an API or an existing hive table). Now, you're on a mission to make it seem like you collected this data ages ago. That's one example of what we call data backfilling in data engineering.\nBut it's not just about starting a new data pipeline or table. You could have a table that's been gathering data for a while, and suddenly, you need to change the data (for example due to a new metric definition), or toss in more data from a new data source. Or maybe there's an awkward gap in your data, and you just want to patch it up. All these situations are examples of data backfilling. The common thread is turning \"back\" in time and \"filling\" up your table with some historical data.\nThe following figure (Figure 1) shows a straightforward backfilling scenario. In this instance, a daily job retrieves data from two upstream sources (one for platform A and another for platform B). The dataset is structured with the first partition being 'ds,' and the second partition (or sub-partitions) representing the platforms. Unfortunately, data for the period from 2023–10–03 to 2023–10–05 is absent due to certain issues. To address this gap, a backfilling operation was initiated (the backfilling job started on 2023–10–08).\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist), [Computer Science](https://roadmap.sh/computer-science) and [Data Engineer](https://roadmap.sh/data-engineer) roadmaps.\n## Backfilling and Restating\nA brief heads-up before proceeding further: within the domain of data engineering, we normally encounter two scenarios: \"backfilling\" a table or \"restating\" a table. These processes, while sharing some similarities, have some subtle differences. Backfilling, as a practice, is about populating missing or incomplete data in a dataset. Its application is commonly directed towards updating historical data or rectifying gaps. Conversely, restating a table involves effecting substantial alterations to the content or structure of the table. This transformation is typically undertaken to rectify errors, update data models, or implement significant modifications to the dataset. For the sake of simplicity, acknowledging the subtle differences of these terms, both processes will be referred to as \"backfilling\" in this article.\nIt's worth mentioning that there could be variations in how different engineers refer to these processes.\n## Let's Talk About Design\nLet's dive a bit deeper. You are designing your new table schema and planning out your ETL processes. Definitely give some serious thought to backfilling. So, take a moment to ask yourself: Is my design good to go for both the regular (mostly daily) tasks and handling future backfilling tasks? Can I easily fill in missing data when I need to, or is it going to be a headache with manual steps every time I want to patch things up?\nSometimes you need to find a sweet spot in your design. You want things to run smoothly, not just for your everyday data flow but also when you're trying to fix up any gaps in your dataset. So, a little extra attention to your design now can save you from headaches later on!\nFor example, you might think that if you do a backfill for this table, you need to overwrite the previous data and not append to it. In this case, if you are using Hive tables, maybe Hive partitions can help you achieve this goal more easily. In this case, you can define a partition based on the date (mostly called 'ds') and change your ETL to overwrite the partition instead of appending to it. With this schema design, every time you write on your table, the previous partitions (in this case, previous 'ds' dates) will be removed and replaced with new data. However, you might foresee that every backfill might only need to affect not the entire 'ds,' but part of that. For example, look at Figure 1 and ds=2023–10–05. In this case, part of the data is loaded successfully and part of that failed. In such cases, you can define a secondary partition (or even tertiary if needed) to support partial backfilling for a specific date in the future. At the same time, you want to keep your number of partitions as low as possible to avoid small partitions (which might cause less efficient data processing).\nAnother effective strategy involves writing the newly backfilled data to a distinct partition location rather than overwriting the existing partition location on the disk. This can be achieved by, for instance, introducing a unique runtime_id directory within your partition location path. Subsequently, update the partition location in the Hive metastore from the old path to the new path. This approach enables the retention of previous data (as a precaution) while still facilitating the update of the entire partition. The following folder structure is an example of how we can runtime_id to keep different versions of backfilling under the same table location. Remember, this technique requires that your ETL update partition locations in the Hive metastore database.\ntext\n\n```\ntable_location_path\n       └── runtime_id_1651023692 (*new backfill)\n          ├── ds=2021-01-01      (*active/newest partition)\n             ├── data_file\n          ├── ds=2021-01-02      (*active/newest partition)\n             └── data_file\n          └── ds=2021-01-03      (*active/newest partition)\n             └── data_file\n       └── runtime_id_1609562892 (*daily job run)\n          └── ds=2021-01-01      (*inactive/archived partition)\n             └── data_file\n```\n\nAlso, in addition to your regular (for example, daily) workflow, you might need to develop a ready-to-go backfilling workflow for cases in which you need to backfill the data. For instance, if you are working on Apache Airflow, you can have a daily DAG for your regular scheduled data loading tasks and a separate DAG for backfilling. In this case, if something happens that requires backfilling data quickly, you have a DAG ready to go.\n## Start Backfilling\nYou carefully crafted your table schema and ETL pipelines, anticipating the need for future backfilling. Now, after days, weeks, or even months, the long-awaited moment for backfilling has arrived. But what steps should you take? Is it as straightforward as running your backfill workflow/DAG for a specified date range, or does it demand more careful consideration before hitting the backfill button?\nUnfortunately, the design isn't the sole challenge to overcome. The backfilling process itself can be quite intricate. Initially, you must ensure that backfilling is feasible. For instance, certain APIs may not support historical data beyond a specific lookback period, while some source tables might not retain data for an extended duration due to privacy constraints. It's essential to confirm whether backfilling for your specific timeframe is viable.\nNow, let's assume luck is on your side, and data availability poses no issues. The next consideration revolves around your table users. Adding or modifying data, especially historical data, can impact downstream users. A responsible data engineer takes into account the consequences of such actions on product users and demonstrates respect for their experience. In some scenarios, notifying downstream users in advance about the backfilling operation may be necessary for them to obtain the most up-to-date data. Additionally, you may need to assess how changes to a column affect other tables. In an open coding ecosystem, engineers can collaborate to check other projects and data pipelines for verification. If a downstream user relies on a column slated for modification, it's crucial to evaluate the impact and explore alternatives, such as expanding the table instead of altering column logic. Can you assist them in updating their ETL logic, or should you redirect them to an alternative data source?\nIn essence, displaying responsibility for what you and your team own is what distinguishes a good engineer from a less effective one.\n## Validation\nNow that your backfilling is complete, the next crucial step is validation. Employing some fundamental techniques can expedite the validation process.\nfirst, it is crucial to verify the completion of the backfilling process. Occasionally, even when it appears that the backfilling job has been executed and completed successfully, an incorrect parameter may result in no actual updates or data being written to the table. Therefore, prior to validating the backfilled data, it is imperative to confirm the successful execution of the backfilling process. Introducing a specific column in the table that captures the processing timestamp can aid in validating whether the data has been recently updated. Alternatively, checking the `runtime_id` associated with new partition locations is another method. As outlined in the design section, the inclusion of an optional `runtime_id` in the partition path allows for direct examination of the table path. This enables the straightforward identification of whether new `runtime_id` folders have been created after the backfilling operation.\nIf your backfilling addressed gaps using a source table, the most straightforward approach is to aggregate metrics from the source table and compare them with your target table. Crafting a query that replicates your ETL logic allows for a comprehensive comparison, albeit it may require a bit of time and effort. However, this query serves a dual purpose, not only aiding in immediate validation but also serving as a valuable tool for future checks. It is advisable to document and store such queries in your ETL documentation for ongoing use.\nIn instances where the backfilling focused solely on certain columns without introducing new rows to the table, an additional validation method involves scrutinizing the unchanged columns in comparison to their previous states. For instance, evaluating the sum of these columns before and after backfilling ensures that no unintended alterations have occurred in the unaffected areas.\n## Summary\nTo sum up, some best backfilling practices in data engineering are introduced in this article. We broke down the differences between backfilling and restating a table. Also the vital role of smart design in handling both regular tasks and backfilling adventures are stressed. We tackled the hurdles of the backfilling process, dealing with issues like data availability and keeping our downstream pals in the loop. 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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# Backfilling Mastery: Elevating Data Engineering Expertise\nA go-to guide for data engineers wading through the backfilling maze\n[Naser Tamimi](https://towardsdatascience.com/author/tamimi-naser/)\nNovember 17, 20237 min read\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/11/0MMasAxcohTChwk2x-scaled.jpg)\n### DATA ENGINEERING\n## What is Backfilling?\nImagine starting a new data pipeline and getting data from a source you've never parsed before (e.g. pulling info from an API or an existing hive table). Now, you're on a mission to make it seem like you collected this data ages ago. That's one example of what we call data backfilling in data engineering.\nBut it's not just about starting a new data pipeline or table. You could have a table that's been gathering data for a while, and suddenly, you need to change the data (for example due to a new metric definition), or toss in more data from a new data source. Or maybe there's an awkward gap in your data, and you just want to patch it up. All these situations are examples of data backfilling. The common thread is turning \"back\" in time and \"filling\" up your table with some historical data.\nThe following figure (Figure 1) shows a straightforward backfilling scenario. In this instance, a daily job retrieves data from two upstream sources (one for platform A and another for platform B). The dataset is structured with the first partition being 'ds,' and the second partition (or sub-partitions) representing the platforms. Unfortunately, data for the period from 2023–10–03 to 2023–10–05 is absent due to certain issues. To address this gap, a backfilling operation was initiated (the backfilling job started on 2023–10–08).\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist), [Computer Science](https://roadmap.sh/computer-science) and [Data Engineer](https://roadmap.sh/data-engineer) roadmaps.\n## Backfilling and Restating\nA brief heads-up before proceeding further: within the domain of data engineering, we normally encounter two scenarios: \"backfilling\" a table or \"restating\" a table. These processes, while sharing some similarities, have some subtle differences. Backfilling, as a practice, is about populating missing or incomplete data in a dataset. Its application is commonly directed towards updating historical data or rectifying gaps. Conversely, restating a table involves effecting substantial alterations to the content or structure of the table. This transformation is typically undertaken to rectify errors, update data models, or implement significant modifications to the dataset. For the sake of simplicity, acknowledging the subtle differences of these terms, both processes will be referred to as \"backfilling\" in this article.\nIt's worth mentioning that there could be variations in how different engineers refer to these processes.\n## Let's Talk About Design\nLet's dive a bit deeper. You are designing your new table schema and planning out your ETL processes. Definitely give some serious thought to backfilling. So, take a moment to ask yourself: Is my design good to go for both the regular (mostly daily) tasks and handling future backfilling tasks? Can I easily fill in missing data when I need to, or is it going to be a headache with manual steps every time I want to patch things up?\nSometimes you need to find a sweet spot in your design. You want things to run smoothly, not just for your everyday data flow but also when you're trying to fix up any gaps in your dataset. So, a little extra attention to your design now can save you from headaches later on!\nFor example, you might think that if you do a backfill for this table, you need to overwrite the previous data and not append to it. In this case, if you are using Hive tables, maybe Hive partitions can help you achieve this goal more easily. In this case, you can define a partition based on the date (mostly called 'ds') and change your ETL to overwrite the partition instead of appending to it. With this schema design, every time you write on your table, the previous partitions (in this case, previous 'ds' dates) will be removed and replaced with new data. However, you might foresee that every backfill might only need to affect not the entire 'ds,' but part of that. For example, look at Figure 1 and ds=2023–10–05. In this case, part of the data is loaded successfully and part of that failed. In such cases, you can define a secondary partition (or even tertiary if needed) to support partial backfilling for a specific date in the future. At the same time, you want to keep your number of partitions as low as possible to avoid small partitions (which might cause less efficient data processing).\nAnother effective strategy involves writing the newly backfilled data to a distinct partition location rather than overwriting the existing partition location on the disk. This can be achieved by, for instance, introducing a unique runtime_id directory within your partition location path. Subsequently, update the partition location in the Hive metastore from the old path to the new path. This approach enables the retention of previous data (as a precaution) while still facilitating the update of the entire partition. The following folder structure is an example of how we can runtime_id to keep different versions of backfilling under the same table location. Remember, this technique requires that your ETL update partition locations in the Hive metastore database.\ntext\n\n```\ntable_location_path\n       └── runtime_id_1651023692 (*new backfill)\n          ├── ds=2021-01-01      (*active/newest partition)\n             ├── data_file\n          ├── ds=2021-01-02      (*active/newest partition)\n             └── data_file\n          └── ds=2021-01-03      (*active/newest partition)\n             └── data_file\n       └── runtime_id_1609562892 (*daily job run)\n          └── ds=2021-01-01      (*inactive/archived partition)\n             └── data_file\n```\n\nAlso, in addition to your regular (for example, daily) workflow, you might need to develop a ready-to-go backfilling workflow for cases in which you need to backfill the data. For instance, if you are working on Apache Airflow, you can have a daily DAG for your regular scheduled data loading tasks and a separate DAG for backfilling. In this case, if something happens that requires backfilling data quickly, you have a DAG ready to go.\n## Start Backfilling\nYou carefully crafted your table schema and ETL pipelines, anticipating the need for future backfilling. Now, after days, weeks, or even months, the long-awaited moment for backfilling has arrived. But what steps should you take? Is it as straightforward as running your backfill workflow/DAG for a specified date range, or does it demand more careful consideration before hitting the backfill button?\nUnfortunately, the design isn't the sole challenge to overcome. The backfilling process itself can be quite intricate. Initially, you must ensure that backfilling is feasible. For instance, certain APIs may not support historical data beyond a specific lookback period, while some source tables might not retain data for an extended duration due to privacy constraints. It's essential to confirm whether backfilling for your specific timeframe is viable.\nNow, let's assume luck is on your side, and data availability poses no issues. The next consideration revolves around your table users. Adding or modifying data, especially historical data, can impact downstream users. A responsible data engineer takes into account the consequences of such actions on product users and demonstrates respect for their experience. In some scenarios, notifying downstream users in advance about the backfilling operation may be necessary for them to obtain the most up-to-date data. Additionally, you may need to assess how changes to a column affect other tables. In an open coding ecosystem, engineers can collaborate to check other projects and data pipelines for verification. If a downstream user relies on a column slated for modification, it's crucial to evaluate the impact and explore alternatives, such as expanding the table instead of altering column logic. Can you assist them in updating their ETL logic, or should you redirect them to an alternative data source?\nIn essence, displaying responsibility for what you and your team own is what distinguishes a good engineer from a less effective one.\n## Validation\nNow that your backfilling is complete, the next crucial step is validation. Employing some fundamental techniques can expedite the validation process.\nfirst, it is crucial to verify the completion of the backfilling process. Occasionally, even when it appears that the backfilling job has been executed and completed successfully, an incorrect parameter may result in no actual updates or data being written to the table. Therefore, prior to validating the backfilled data, it is imperative to confirm the successful execution of the backfilling process. Introducing a specific column in the table that captures the processing timestamp can aid in validating whether the data has been recently updated. Alternatively, checking the `runtime_id` associated with new partition locations is another method. As outlined in the design section, the inclusion of an optional `runtime_id` in the partition path allows for direct examination of the table path. This enables the straightforward identification of whether new `runtime_id` folders have been created after the backfilling operation.\nIf your backfilling addressed gaps using a source table, the most straightforward approach is to aggregate metrics from the source table and compare them with your target table. Crafting a query that replicates your ETL logic allows for a comprehensive comparison, albeit it may require a bit of time and effort. However, this query serves a dual purpose, not only aiding in immediate validation but also serving as a valuable tool for future checks. It is advisable to document and store such queries in your ETL documentation for ongoing use.\nIn instances where the backfilling focused solely on certain columns without introducing new rows to the table, an additional validation method involves scrutinizing the unchanged columns in comparison to their previous states. For instance, evaluating the sum of these columns before and after backfilling ensures that no unintended alterations have occurred in the unaffected areas.\n## Summary\nTo sum up, some best backfilling practices in data engineering are introduced in this article. We broke down the differences between backfilling and restating a table. Also the vital role of smart design in handling both regular tasks and backfilling adventures are stressed. We tackled the hurdles of the backfilling process, dealing with issues like data availability and keeping our downstream pals in the loop. 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