Which Zapinner API should I use?
Match the question you need answered to the right endpoint. Know what you need? Call it directly. Not sure? Send your payload to Analyze and let it run the supported operations.
Prepare
Clean and de-duplicate raw data.
- Can you just make this messy data production-ready? → RepairPer record
- Can you turn this data into the shape my app expects? → MapPer record
- Can you stop bad data before it reaches my app? → GuardPer record
- Turn messy data into usable data. → Normalize1 Zap
- Which records are duplicates? → Dedupe1 Zap
- Reshape records to the shape you need. → TransformPer record
- Does this data meet the rules? → ValidatePer record
- Can you make every column type-consistent? → StandardizePer record
- Can you pull typed values out of these fields? → ParsePer record
- What data type is each column? → Type InferencePer record
- Can you map this data to my schema? → Schema MapPer record
- Can you strip PII from these records? → RedactPer record
- Can you mask PII but keep the shape? → MaskPer record
- Can you fill in the missing values sensibly? → ImputePer record
- Can you standardize these phone numbers to E.164? → Normalize PhonePer record
- Can you standardize these postal addresses? → Normalize AddressPer record
- Can you canonicalize these URLs? → Normalize URLPer record
- Can you tidy up this free text? → Text CleanPer record
- Can you group similar records together? → ClusterPer record
- Can you score and rank these records? → RankPer record
- Can you bucket these records by rules? → SegmentPer record
- Can you fix this slightly-broken JSON? → JSON RepairPer record
- Can you flatten these nested objects? → FlattenPer record
- Can you rebuild nested objects from flat keys? → UnflattenPer record
- Can you convert between JSON and CSV? → ConvertPer record
Understand
Compare records and explain results.
- Are these records the same thing? → Match1 Zap
- What changed between these two? → Compare1 Zap
- Why did it decide that? → Explain1 Zap
- What structured data is in this text? → ExtractPer record
- What structured data is on this page? → Web ExtractPer record
- How similar are these two records? → Fuzzy MatchPer record
- What's the schema of this data? → Schema DetectPer record
- Which category does this record belong to? → ClassifyPer record
- What labels apply to this record? → TagPer record
- What is actually in this dataset? → ProfilePer record
- What are the summary statistics for my numeric fields? → StatisticsPer record
- Where are the gaps in my data? → MissingPer record
- How are my values distributed? → DistributionsPer record
- How healthy is this dataset overall? → Quality ScorePer record
- Which numeric fields move together? → CorrelationsPer record
- Is this metric trending up or down over time? → TrendsPer record
- When did this series shift level? → Change PointsPer record
- What formats do the values in this field take? → PatternsPer record
- Is this text positive or negative? → SentimentPer record
- What language is this text in? → LanguagePer record
- What names, emails, and dates are in this text? → EntitiesPer record
- What are the key terms in this text? → KeywordsPer record
- How similar are these two texts? → Text SimilarityPer record
- Can you shorten this text to the key sentences? → SummarizePer record
- What themes run across this set of documents? → TopicsPer record
- Can you pull the readable text out of this HTML? → HTML ExtractPer record
- What comes next in this series? → ForecastPer record
Detect
Surface anomalies and revenue exposure.
- What looks unusual? → Anomaly Detection1 Zap
- Where did the money disappear? → Revenue Leakage1 Zap
- How complete is this dataset? → CompletenessPer record
- Are my columns type-consistent? → ConsistencyPer record
- What's my overall data-quality score? → Data QualityPer record
- Has my data distribution shifted? → DriftPer record
- What changed between these two snapshots? → Change DetectPer record
- How did the shape of my data change between two sources? → Schema DiffPer record
- Do these records conform to my expected schema? → Schema ValidatePer record
- Which of these email addresses are usable? → Validate EmailPer record
- Does this data contain personal information? → PII DetectPer record
Decide
Score records and verify claims.
Recover
Prioritize stale opportunities.
Orchestrate
Run several operations at once.
Reconcile
Match expected against actual.
- Do these numbers match? → Reconciliation1 Zap
- Which records are the same entity? → Entity ResolutionPer record
- Where do records for the same key disagree? → ConflictsPer record
- Can you combine these into one golden record? → MergePer record
- Which value survives per group? → SurvivorshipPer record
- Which records in this set look like near-duplicates? → SimilarityPer record
- Which records in dataset A correspond to records in dataset B? → LinkPer record