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spelling corrected and added header2 issues #843 #847

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Jul 2, 2024
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spelling corrected and added header2 issues #843 #847

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5 changes: 3 additions & 2 deletions docs/deterministicMatching.md
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# Deterministic Matching - *Zingg Enterprise Feature*

## Deterministic Matching - *Zingg Enterprise Feature*

Zingg Enterprise allows the ability to plug rule based deterministic matching along with already Zingg AI's probabilistic matching. If the data contains sure identifiers like emails, SSNs, passport ids etc, we can use these attributes to resolve records. The deterministic matching flow is weaved into Zingg's flow to ensure that each record which has a match finds one, probabilistically, deterministcally or both. If the data has known identifiers, Zingg Enterprise's deterministic matching highly improves both matching accuracy and performance.
Zingg Enterprise allows the ability to plug rule based deterministic matching along with already Zingg AI's probabilistic matching. If the data contains sure identifiers like emails, SSNs, passport ids etc, we can use these attributes to resolve records. The deterministic matching flow is weaved into Zingg's flow to ensure that each record which has a match finds one, probabilistically, deterministically or both. If the data has known identifiers, Zingg Enterprise's deterministic matching highly improves both matching accuracy and performance.

## Example for configuring it in json:
```json
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7 changes: 4 additions & 3 deletions docs/runIncremental.md
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# Run Incremental Loads For New and Updated Records - *Zingg Enterprise Feature*

## Run Incremental Loads For New and Updated Records - *Zingg Enterprise Feature*

Rerunning matching on entire datasets is wasteful, and we lose the lineage of matched records against a persistent identifier. Using Zingg Enterprise, incremental loads can be run to match existing pre resolved entities. The new and updated records are matched to existing clusters, and new persistent ZINGG_IDs generated for records which do not find a match. If a record gets updated and Zingg Enterprise discovers that it is a more suitable match with another cluster, it will be reassigned. Cluster assignment, merge and unmerge happens automatically in the flow. Zingg Entperirse also takes care of human feedback on previously matched data to ensure that it doesnt override the approved records.
Rerunning matching on entire datasets is wasteful, and we lose the lineage of matched records against a persistent identifier. Using Zingg Enterprise, incremental loads can be run to match existing pre resolved entities. The new and updated records are matched to existing clusters, and new persistent ZINGG_IDs generated for records which do not find a match. If a record gets updated and Zingg Enterprise discovers that it is a more suitable match with another cluster, it will be reassigned. Cluster assignment, merge and unmerge happens automatically in the flow. Zingg Enterprise also takes care of human feedback on previously matched data to ensure that it does not override the approved records.

## The incremental phase is run as follows:
`./scripts/zingg.sh --phase runIncremental --conf <location to incrementalConf.json>`
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}
```

## runIncremental can also be triggerred using python by invoking:
## runIncremental can also be triggered using python by invoking:
`./scripts/zingg.sh --run examples/FebrlExample.py`

### Python code example:
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