Glue Data Tools

A lightweight tool for data engineers and analysts to fuse datasets, map fields, and validate matches across sources without code.

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1,164+

Total Calculations Run

< 15ms

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100%

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4.9 / 5.0

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About This Tool

Glue Data Tools provides a structured, low-code approach to data fusion. Users define two or more data sources, select join keys, specify field mappings, and set validation rules. The system then generates a merged schema and produces a unified dataset.
Conceptually, it treats data fusion as a controlled transformation: infer schemas, align types, and apply join semantics. It handles missing keys, conflicting values, and deduplication according to user rules, while preserving provenance for auditability.
Who benefits: data engineers, analytics teams, and product BI groups; Unique differentiators include strong schema inference, transparent lineage, delta processing, and flexible export options. Use cases cover customer 360, marketing attribution, product analytics, and warehouse refresh.
Core features span source definitions, join configuration, field mapping, normalization, validation, deduplication, batch/stream readiness, and multiple export formats.

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How to Use

Provide inputs: define data sources A and B, formats, and sample rows.
Choose join keys and join type, then map fields as needed.
Configure data normalization and validation rules.
Run the merge to generate the unified dataset.
Review the join report and export in CSV, JSON, or Parquet.

Frequently Asked Questions

Find Quick Answers

What data formats are supported?

The tool supports CSV, JSON, Parquet, and database-like payloads; it can ingest in-memory datasets as well as file-based sources, enabling flexible fusion workflows for analytics teams.

Can I schedule automated joins?

Yes. The tool supports scheduled runs and delta updates to progressively merge new data without reprocessing entire datasets, aiding ongoing analytics pipelines.

How is data quality handled?

Validation rules enforce required fields and type consistency, with optional deduplication and a quality report included in exports to highlight uncertainties or conflicts.

Is transformation limited to field mappings?

Primarily yes: you can rename fields and derive simple computed fields; it is not designed for deep ML-based matching but supports configurable rules for robust joins.

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