Data quality is your moat, this is your guide.
During ongoing data replication
Data replication is similarly prone to error as data migrations, but the primary challenges look a little different.
Common challenges of data replication
1. People aren’t testing their replicated data enough, or at all
The elephant in the room, when it comes to data quality testing, is that it’s just often not happening. This makes data reconciliation during data replication a nice-to-have rather than a non-negotiable priority. Consequently, discrepancies between source and target data often go undetected, leading to inaccuracies and inconsistencies found downstream by a stakeholder.
Why is this so important? Data replication between backend and analytical databases serves as your source of truth for your business. Replication between different regions of databases is vital for data reliability and accessibility. The data replicated between databases is often some of the most mission-critical data to your business, and yet there’s often little to no data quality checks on it—until it’s too late.
Without robust testing and reconciliation processes in place, organizations operate in the dark, undermining the effectiveness of downstream analytics.
2. Custom solutions break due to sheer volume
As data volume increases, custom-built replication pipelines struggle to maintain performance and reliability, leading to breakdowns or failures in the replication process. We spend more time fixing breakdowns instead of verifying the quality of replicated data.
3. Data movement providers are great–when there aren’t outages or bugs
ETL and data movement providers are often easier to maintain than in-house/custom solutions for data replication, but they’re not immune to service disruptions (SaaS software is just like us humans—imperfect!). A dependence on ETL vendors (like almost any other tool out there!) introduces the risk of downtime, system failures and bugs, or interruptions. Any of these can disrupt data replication processes and compromise data quality.
4. Replication tools move data, but don’t validate it
A major source of confusion arises from assuming that tools that move data also make sure what you’re moving is indeed consistent across systems–but they’re often two completely different functions. While data replication/movement vendors efficiently transfer data from source to target systems, they often lack built-in mechanisms for robust data validation and ensuring parity between systems. This oversight can lead to a false sense of security, as organizations may assume that data integrity is always maintained simply by replicating it.
How they are typically solved
There are three ways that practitioners have typically approached data quality testing during replication, ranging from least to most acceptable.
| Approach | Purpose | How it measures up |
|---|---|---|
| Row count tests | Verifies data integrity by checking the number of rows transferred during replication through simple tests like SQL assert statements. | Rudimentary: It’s a basic validation check that offers minimal assurance of data quality. |
| Alerts from replication tools | Relies on alerts from replication tools like Fivetran or Airbyte to detect issues such as connector downtime. | Better, but insufficient: Waiting for alerts is a reactive approach that results in delayed detection of data replication failures. |
| Datafold’s data diffs to ensure parity, and on a scheduled basis | Involves comparing source and target datasets, at the value-level, to identify discrepancies, and at predefined intervals for preventative data validation. | Best practice: The gold standard for data validation is always a value-level data diff. Scheduled monitoring is also a preventative strategy for ensuring data parity. |