Creating a New Data Diff

This powerful algorithm provides full row-, column-, and value-level detail into discrepancies between data tables.

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Creating a new data diff

Setting up a new data diff in Datafold is straightforward. You can configure your data diffs with the following parameters and options:

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Source and Target datasets

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Data connection

Pick your data connection(s).

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Diff type

Choose how you want to compare your data:

• Table: Select this to compare data directly from database tables
• Query: Use this to compare results from specific SQL queries

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Dataset

Choose the dataset you want to compare. This can be a table or a view in your relational database.

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Filter

Insert your filter clause after the WHERE keyword to refine your dataset. For example: created_at > '2000-01-01' will only include data created after January 1, 2000.

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Materialize inputs

Select this option to improve diffing speed when query is heavy on compute, or if filters are applied to non-indexed columns, or if primary keys are transformed using concatenation, coalesce, or another function.

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Column remapping

Designate columns with the same data type and different column names to be compared. Data Diff will surface differences under the column name used in the Source dataset.

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General

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Primary key

The primary key is one or more columns used to uniquely identify a row in the dataset during diffing. The primary key (or keys) does not need to be formally defined in the database or elsewhere as it is used for unique row identification during diffing.

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Egress limit

The egress limit optimizes the diff process by terminating it once a predefined number of rows are downloaded. The limit is set to 1,000,000 by default.

When the egress limit is reached, the diffing process does not produce the same results each time it is run, as it is not deterministic (i.e., the order in which data is processed may vary).

The egress limit prevents redundant analysis in scenarios with minor, repetitive discrepancies, such as formatting differences (e.g., whitespace, rounding differences). For most use cases, it is impractical to continue diffing after it is known that datasets are substantially different.

Since the algorithm aims to detect and return every mismatched row/value, if the datasets have a large percentage of differing rows, the algorithm may be unable to take advantage of checksumming. This can cause a large amount of data to be pulled over the network, which slows down the diffing process, and increases the strain on the database.

Setting an egress limit prevents unwanted runtime and database load by stopping the operation early in cases of substantial dataset discrepancies. It is highly recommended to set an egress limit, taking into account these tradeoffs between cost/speed and rigor.

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Columns

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Columns to compare

Specify which columns to compare between datasets. Note that this has performance implications when comparing a large number of columns, especially in wide tables with 30 or more columns. It is recommended to initially focus on comparisons using only the primary key or to select a limited subset of columns.

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Per-column diff limit

By setting a per-column diff limit, Data Diff will stop identifying differences for any column after a number of differences is found, based on the limit. Data Diff will also stop searching for exclusive and duplicate primary keys after the limit is reached. Setting a per-column diff limit enables your team to find data quality issues that arise during data reconciliation while minimizing compute and time spent searching for differences. Learn more about data reconciliation best practices here.

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Row sampling

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Enable sampling

Use this to compare a subset of your data instead of the entire dataset. This is best for assessing large datasets. Even when sampling is enabled, checksumming for unsampled primary keys still needs to be performed. As a result, if many columns beyond primary keys are involved, the time spent running cross-database differences with sampling may be significantly reduced.

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Sampling tolerance

Sampling tolerance defines the allowable margin of error for our estimate. It sets the acceptable percentage of rows with primary key errors (e.g., nulls, duplicates, or primary keys exclusive to one dataset) before disabling sampling. When sampling is enabled, not every row is examined, which introduces a probability of missing certain discrepancies. This threshold represents the level of difference we are willing to accept before considering the results unreliable and thereby disabling sampling. It essentially sets a limit on how much variance is tolerable in the sample compared to the complete dataset. Default: 0.001%

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Sampling confidence

Sampling confidence reflects our level of certainty that our sample accurately represents the entire dataset. It represents the minimum confidence level that the rate of primary key errors is below the threshold defined in sampling tolerance. To put it simply, a 95% confidence level with a 5% tolerance means we are 95% certain that the true value falls within 5% of our estimate. Default: 99%

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Sampling threshold

Sampling is automatically disabled when the total row count of the largest table in the comparison falls below a specified threshold value. This approach is adopted because, for smaller datasets, a complete dataset comparison is not only more feasible but also quicker and more efficient than sampling. Disabling sampling in these scenarios ensures comprehensive data coverage and provides more accurate insights, as it becomes practical to examine every row in the dataset without significant time or resource constraints.

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Sample size

This provides an estimated count of the total number of rows included in the combined sample from Datasets A and B, used for the diffing process. It’s important to note that this number is an estimate and can vary from the actual sample size due to several factors: The presence of duplicate primary keys in the datasets will likely increase this estimate, as it inflates the perceived uniqueness of rows.

• Applying filters to the datasets tends to reduce the estimate, as it narrows down the data scope.
• The number of rows we sample is not fixed; instead, we use a statistical approach called the Poisson distribution. This involves picking rows randomly from an infinite pool of rows with uniform random sampling. Importantly, we don’t need to perform a full diff (compare every single row) to establish a baseline.

Example: Imagine there are two datasets we want to compare, Source and Target. Since we prefer not to check every row, we use a statistical approach to determine the number of rows to sample from each dataset. To do so, we set the following parameters:

• Sampling tolerance: 5%
• Sampling confidence: 95%

Sampling confidence reflects our level of certainty that our sample accurately represents the entire dataset, while sampling tolerance defines the allowable margin of error for our estimate. Here, with a 95% sampling confidence and a 5% sampling tolerance, we are 95% confident that the true value falls within 5% of our estimate. Datafold will then estimate the sample size needed (e.g., 200 rows) to achieve these parameters.

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Advanced

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Materialize diff results to table

Create a detailed table from your diff results, indicating each row where differences occur. This table will include corresponding values from both datasets and flags showing whether each row matches or mismatches.