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Backfilling Data in 2023: Best Practices

Data backfilling, the act of populating data retrospectively into a system, has become an indispensable part of a data engineer's arsenal. It ensures a complete and accurate historical record of data for reliable analysis and decision-making. However, navigating the nuances of backfilling in modern data stacks can be challenging. This article aims to share insights on backfilling data, particularly focusing on common pitfalls, best practices, and the potential role of data observability platforms.

May 23, 2023


May 23, 2023
Backfilling Data in 2023: Best Practices

What is Backfilling Data?

Backfilling data, also known as historical data backfilling, is the process of filling in any missing data points in a historical record to ensure completeness and accuracy. Backfilling data can be used for various purposes, including improving forecasting accuracy, optimizing marketing campaigns, and complying with regulatory requirements.

For example, let's say that you're Head of Data at Rainforest, an e-commerce company that sells hydroponic aquariums to high-end restaurants. You have a daily_revenue table used by the VP of Sales to make decisions, and it's missing some data points from the past few months due to an error in your data pipeline. The data observability platform flags this issue and alerts your team, who then backfills the missing data to ensure that the daily_revenue table is complete and accurate for analysis and reporting.

Maintaining a complete and accurate historical record of data is crucial for analysis and reporting. By backfilling data, businesses can ensure that their reporting is based on complete and accurate data, preventing misleading or incorrect conclusions from being drawn.

Challenges with Backfilling Data in 2023

While backfilling data can be beneficial, it is not without its challenges, particularly in modern data stacks. Modern data stacks are complex and require the handling of data from various sources and in different formats. Additionally, the demand for real-time data processing has increased, which can make backfilling data more time-consuming and challenging.

In addition to these challenges, there is also the risk of data quality issues and unintended business consequences due to incomplete or inaccurate historical data. Manual data backfilling can be costly, time-consuming, and introduce errors, particularly when dealing with large volumes of data.

Data quality and latency are critical for successful backfilling, and these challenges are amplified in modern data stacks due to the need for real-time and near real-time processing. This makes the role of data observability platforms even more important in ensuring complete backfills, maintaining data quality, and preventing data integrity issues.

Backfilling Data in Data Warehouses and SaaS Tools

Data warehouses and SaaS tools are common platforms where backfilling occurs. Each poses unique challenges and requires specific considerations for successful backfilling.

In data warehouses, for instance, it’s essential to understand how historical data should be transformed to seamlessly integrate into the newer system without breaking data integrity. Data engineers must also ensure chronological order through timestamp columns and prevent duplicate records using unique keys. Additionally, they need to address potential incompatibility between legacy and new data types to prevent downstream analysis issues.

For SaaS tools, backfilling is typically one of the first steps during integration. It involves gathering relevant data and backfilling it according to the specified format and process. To ensure success, it's vital to double-check the data inserted and confirm that it matches the original data count.

A few key elements to consider while backfilling include:

  • Data Transformation: Understanding how the historical data should be adapted to the new system is vital. This includes cleaning the data, maintaining its integrity, and ensuring compatibility with the new system.
  • Timestamping: Maintaining chronological order and auditing during data backfilling is crucial. This involves deciding on a timestamp per record or per batch of records and attaching timestamps to all records to be backfilled.
  • Unique Keys: Identifying unique keys per table helps prevent duplicate records insertion. Violating these unique constraints should be avoided to maintain data integrity.
  • Datatype Compatibility: Legacy and new datatype incompatibility can be an issue. This requires careful scrutiny of all columns and explicit definition of how each column should be handled in the new system.

Backfilling Data and Data Observability

Data observability platforms can mitigate backfilling challenges by offering real-time monitoring and troubleshooting tools. They help identify data drift and pipeline errors, ensuring complete backfills and preventing data integrity issues. Such platforms can also monitor table updates, track the size of the table with each update, oversee schema for fields and tables, determine outliers in the data, and even trigger alerts based on custom SQL rule-based detection.

Through complete data lineage, data observability platforms can ensure backfilled data complies with data quality standards and regulations, thereby maintaining data quality and preventing data integrity issues. This reduces the time spent on backfilling data, allowing the team to focus more on high-value tasks.

Best Practices for Backfilling Data in 2023

  1. Leverage Automation: Automation reduces the chance of human error and streamlines the process. Automated tools can be used to handle backfilling tasks, making the process more efficient and accurate. These tools can also help with scheduling and monitoring backfill jobs, allowing for better oversight and control.
  2. Data Validation Post-Backfill: It's crucial to confirm that the backfilled data is accurate and complete. This can be done by running checks to confirm that the data matches the original count and meets all quality requirements. Any discrepancies or issues should be promptly addressed to ensure the accuracy and reliability of the data.
  3. Testing in a Staging Environment: To minimize the risk of introducing errors into your production database, always test backfill operations in a staging environment first. This controlled setting lets you identify and resolve issues before they impact your main data set, ensuring a smooth backfill process.
  4. Utilization of Modern ELT Tools: Use modern Extract, Load, Transform (ELT) tools to simplify and accelerate the backfill process. Tools like Fivetran and Airbyte can help automate and streamline data extraction and loading, while modern data warehouses like Snowflake and Redshift offer efficient and scalable data handling.
  5. Adhere to Strong Data Governance Principles: Implementing strong data governance principles is essential. This includes maintaining comprehensive documentation of backfill operations to ensure transparency and accountability. It also involves understanding data lineage to track data from its origin through its transformation journey, and ensuring data security to protect sensitive information.
  6. Parallelizing Backfill Jobs: For large volumes of data, parallelizing backfill tasks can be a game-changer. This involves dividing the data into chunks that can be processed simultaneously, significantly reducing the time taken to backfill data.
  7. Handling Timezone Discrepancies: When dealing with timestamp data from different sources, be mindful of time zone differences. Standardizing all timestamps into a single time zone can help avoid discrepancies and ensure data consistency.
  8. Maintain Open Communication: Communicating with all relevant stakeholders is key during a backfill process. Keeping them informed about the purpose, progress, and impacts of backfilling data can help manage expectations, mitigate concerns, and promote collaboration.
  9. Plan for Rollback: Always have a contingency plan in case something goes wrong during the backfill process. This could involve taking backups of your data before beginning the backfill or having a rollback plan to revert to the previous state if necessary.
  10. Employ Data Observability Platforms: Data observability platforms can be crucial for proactive anomaly detection, resolution, and prevention. By providing real-time monitoring and troubleshooting capabilities, these platforms can help ensure the integrity of your data and significantly reduce the need for data backfilling.
  11. Resource Management: Ensure you have adequate resources in terms of infrastructure and personnel. Monitor system performance during backfill to avoid overloading your data warehouse or system.
  12. Monitor Performance: Regularly monitor the performance of backfill operations. Slow performance can be an indicator of larger issues, and catching these early can prevent further problems down the line.


Maintaining a complete and accurate historical record of data is crucial for business success and decision-making, which is why backfilling data is a critical process. However, backfilling data in modern data stacks can be challenging and time-consuming, particularly with the growing demand for real-time data processing.

Data observability platforms offer real-time monitoring and troubleshooting tools that can help businesses ensure complete backfills, maintain data quality, and prevent data integrity issues. By providing complete data lineage and proactively alerting data teams about data anomalies, data observability platforms can help businesses catch errors in real-time, preventing data quality issues from occurring.

At Metaplane, we provide monitoring and troubleshooting tools to ensure trust in data and prevent data quality issues. Our platform is designed for the modern data stack, enabling data teams to focus on strategic analysis and insights.

Backfilling data is an essential process for businesses today, and data observability platforms are critical for its success. By adopting best practices and leveraging the right tools and technologies, businesses can maintain data accuracy and completeness, improve decision-making, and succeed in their data efforts.

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