Data preparation companies, products & suppliers
Cleaning, joining and transforming data before training or analytics. Jobs: profiling and cleaning; joins; pii handling in prep.
What is Data preparation?
Cleaning, joining and transforming data before training or analytics. Jobs: profiling and cleaning; joins; pii handling in prep.
What problems does it solve?
Model work stalls on messy tables, not on algorithms.
Typical business use cases
- Profiling and cleaning
- Joins
- PII handling in prep
Important capabilities
- Pipelines
- Quality rules
- Lineage
What buyers should evaluate
- Where copies live
- PII
- Reproducibility
Risks and governance considerations
A cleaned copy that is the new unsanctioned system of record.
Procurement checklist
- Lineage
- Deletion
- Access
Relevant AI Trustmark assurance
AI Trustmark independent findings appear only when an assessment or certificate exists. Category membership does not imply verification.
Products and providers
Open a product profile for its catalogue details, or use the provider link for the company profile. Claimed listings appear first, then by name. Payment never changes the order.
By Alteryx
Alteryx Designer Cloud is a cloud data preparation product for building and automating data pipelines. It provides a drag-and-drop interface with an interactive results grid, smart
Amazon SageMaker Data Wrangler is a visual data preparation tool in Amazon SageMaker AI. It connects to data sources, profiles data quality, and applies built-in or custom transfor
By Cleanlab
Cleanlab helps teams build safer AI agents by preventing incorrect responses from reaching users. Detect and remediate incorrect responses from any AI agent to ensure safety, compl
Great Expectations is an open-source and commercial data quality framework for validating, documenting, and profiling data. Public marketing also emphasises expectations-as-code da
By Hugging Face
Hugging Face operates a model hub, inference tooling and collaboration products for machine-learning teams. Public pages cover hosting, inference endpoints and open-model distribut
By Lightly
Lightly AG sells data-curation and active-learning software for computer-vision datasets. Public pages describe selecting the right training images, not social captions. LightlyStu
By NVIDIA
NVIDIA NeMo Curator is an open-source platform for scalable, privacy-aware curation of text, image, video and audio data for training large language models and generative AI. Text
By Privitar
Privitar is a London data-privacy company selling software to de-identify and control the use of sensitive data. Public marketing covers privacy engineering for analytics and AI pi
By Scale AI
Scale delivers proven data, evaluations, and outcomes to AI labs, governments, and the Fortune 500. Scale Nucleus is Scale AI's platform, from Scale AI, Inc. Published at nucleus.s
By Snorkel AI
Snorkel AI builds specialized training data, benchmarks, and evaluation environments that help frontier models and agents perform in high-stakes domains. Snorkel Flow is Snorkel AI
Products
Claimed products appear first. Ranking packs and payment do not change this list.
- Alteryx Designer Cloud· Alteryx
- Amazon SageMaker Data Wrangler· Amazon Web Services
- Argilla· Hugging Face
- Cleanlab Studio· Cleanlab
- Great Expectations· Great Expectations
- Hugging Face Datasets· Hugging Face
- LightlyStudio· Lightly
- NVIDIA NeMo Curator· NVIDIA
- NVIDIA RAPIDS· NVIDIA
- Scale Nucleus· Scale AI
- Snorkel Flow· Snorkel AI
Also used in this category
These products have a different primary category so they do not compete for the same ranking queries. They are listed here because buyers still encounter them in this job.
- Privitar· Privitar
Related categories
Relevant procurement and assurance guides
Frequently asked questions
What is Data preparation?
Cleaning, joining and transforming data before training or analytics. Jobs: profiling and cleaning; joins; pii handling in prep.
What should not be listed as Data preparation?
Products whose buyer job is labelling products, feature stores, or analytics copilots. Those belong on their own category page so search queries are not split.
Has AI Trustmark independently assessed every Data preparation supplier?
No. A category listing is descriptive. Independent assessment is shown only on company or product pages that carry Trustmark evidence.
What model or provider changes should a buyer insist on being told about?
Material change usually includes a new model family, new region, new subprocessor, new write-capable tool, or a change that affects logging, privacy or human oversight. Those changes should trigger evidence refresh rather than a silent release.
How should buyers verify where AI customer data is processed?
Ask for the named processing locations, cloud regions and any subprocessors that see prompts, files or outputs. A directory listing is not evidence of residency. Independent assessment records the locations that were in scope on the assessment date.
Does a TrustMark on one product cover the rest of the company?
No. Independent assessment is scoped to the named organisation and, where relevant, the named product. Category pages list suppliers as a topic label. They do not imply that every listed company has been assessed.
What incident-handling evidence is useful for AI suppliers?
Buyers should see how AI-specific failures are detected, contained and notified — including unsafe outputs, data leakage and unauthorised agent actions. An incident policy that never mentions models, prompts or tools is incomplete for this class of product.
What security testing evidence should buyers request for an AI product?
Ask what was tested, against which version, whether prompt-injection, data-exfiltration and tenant isolation were in scope, and where failed prompts were stored. A generic ISO certificate or a vendor scanner screenshot is not by itself an AI TrustMark assessment.