Data Import Tool

Ingest, map, validate, and transform data from multiple sources into a unified schema for analytics and warehousing.

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About This Tool

The Data Import Tool enables teams to bring in data from CSV, JSON, or Excel files and convert it into a consistent, query-ready dataset. It supports field-level mappings, schema inference, type coercion, date parsing, and numeric normalization to align source data with a target model. The tool enforces validation rules, derives new fields when needed, and can deduplicate records based on composite keys. It produces several outputs: a normalized dataset, a mapped schema, and an error report that identifies problematic rows without failing the entire import. The process is deterministic and reproducible, given the same inputs and mappings, making it suitable for migrations, data lake preparation, and routine data ingestion. Advanced users can define transformation expressions, configure locale settings, and enable data lineage logging for auditability. The tool is valuable for data engineers, analysts, and product teams seeking higher data quality and faster time-to-insight. Unique value comes from tight schema inference, robust error isolation, and the ability to export both the transformed dataset and its metadata for downstream systems. Core features include multi-format ingestion, flexible encoding, explicit field mappings, validation rules, transformations, deduplication, and detailed reporting. Use cases include migrating legacy data to a warehouse, harmonizing customer data across apps, or preparing feeds for BI dashboards. The tool emphasizes reproducibility, traceability, and integration readiness, reducing manual rework and accelerating data readiness for analysis and decision-making.

How to Use

  1. Provide inputs: upload input_file, set input_format, encoding, and delimiter if CSV is chosen.
  2. Define mappings: supply field_mappings or attach a target_schema to guide mapping.
  3. Run import: execute ingestion, validation, and transformation to produce a normalized dataset.
  4. Review outputs: inspect the normalized dataset, mapped schema, and error_report for any anomalies.
  5. Export: save the dataset to the destination system or as a file (CSV/JSON) and capture the transformation_log for auditing.
How to use data import tool

Frequently Asked Questions

Find Quick Answers

What formats are supported?
The tool supports CSV, JSON, and XLSX (Excel) sources. Delimiters for CSV can be configured, and encoding can be declared to handle non-ASCII data.
Can I map fields across different source schemas?
Yes. You can provide explicit field_mappings or supply a target_schema. The tool infers or validates types, and applies transformations accordingly.
How are errors reported?
Errors are reported per-row with codes and messages in an error_report. Rows with issues are isolated while valid rows are processed and exported.
Is data lineage and auditing supported?
Yes. Transformation logs and a schema map are produced to support auditing, reproducibility, and compliance with data governance policies.

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