Datasets:

kindred-soul-ltd
/

kindred-ecommerce-merchant-deals-dataset

Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
@context: string
@type: string
name: string
url: string
license: string
creator: struct<@type: string, name: string>
keywords: list<item: string>
distribution: struct<@type: string, encodingFormat: list<item: string>, contentUrl: string>
isAccessibleForFree: bool
conditionsOfAccess: string
must_retain_columns: list<item: string>
tracking_param: string
vs
brand_id: string
name: string
logo: string
domains: list<item: string>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 3335, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2096, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2296, in iter
                  for key, example in iterator:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1856, in __iter__
                  for key, pa_table in self._iter_arrow():
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1878, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 520, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                File "pyarrow/table.pxi", line 4116, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              @context: string
              @type: string
              name: string
              url: string
              license: string
              creator: struct<@type: string, name: string>
              keywords: list<item: string>
              distribution: struct<@type: string, encodingFormat: list<item: string>, contentUrl: string>
              isAccessibleForFree: bool
              conditionsOfAccess: string
              must_retain_columns: list<item: string>
              tracking_param: string
              vs
              brand_id: string
              name: string
              logo: string
              domains: list<item: string>

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Kindred E-commerce Merchant Deals Dataset

AI-ready catalogue of deals and offers for global retail brands.
Structured in CSV and JSONL, validated against JSON Schema.

Train-ready catalogue of promotions, ready for RAG, embeddings, or classic search.

Dataset Overview

File	Rows	Description
`data/csv/brands.csv` or `data/jsonl/brands.jsonl`	~90K	E-Commerce Merchant metadata, Logo URL, and domains
`data/csv/offers.csv` or `data/jsonl/offers.jsonl`	~4M	Offers with redeem_url, detailed summaries, and `sample_q` for RAG training

Kindred E-Commerce Merchant Deals Dataset

A structured, open-access dataset of global E-Commerce merchant deals and offers designed specifically for:

LLM training and fine-tuning
Retrieval Augmented Generation (RAG) systems
Machine learning models for recommendation and search
Natural language processing applications

This dataset includes curated promotional offers from a wide range of online retailers and marketplaces, with structured metadata including offer descriptions, redemption URLs, brand information, and geolocation tags.

Key Features

RAG-optimized: Includes sample_q fields designed for prompt engineering and RAG training
Multi-format: Available in both CSV and JSONL formats with validated JSON Schema
Comprehensive metadata: Brand information, redemption URLs, and country codes
Machine learning ready: Clean, normalized data across multiple retail verticals
No PII: Contains no personally identifiable information

Data Structure

Brands: ~90K unique brands with identifiers, names, logo URLs, and associated domains
Offers: ~4M offers with redemption URLs, detailed descriptions, and sample query patterns

Each offer has a direct relationship with a brand via brand_id, making it easy to build relational models or knowledge graphs for advanced LLM applications.

Keywords

machine-learning, llm-training, rag, retrieval-augmented-generation, dataset, e-commerce, deals, offers, recommendation-system, knowledge-graph, retail-analytics, promotion, redeem-link, public-dataset, kindred, discount, consumer-insights, vector-database

License

Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). Please see LICENSE.md for full details.

Contact

For questions, licensing, or partnership opportunities: [email protected]

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