Misata Studio: verified synthetic data changelog

https://www.misata.studio/llms.txt

− > Misata makes synthetic data for demos and analytics, built from scratch. A person describes a business and the numbers it should show, in plain language, as a drawn schema or as their own tables (SQL DDL, Prisma, dbt and more), and Misata builds every table, connected, with those numbers held exactly month by month; a number it cannot hold is named with the reason, never passed off as met. Every dataset comes with its ground truth: the SQL and value of each stated metric in every month (answer− - [MCP server](https://www.misata.studio/mcp): remote server at https://api.misata.studio/mcp (Streamable HTTP). The assistant designs the tables, Misata generates the rows and returns what it checked: foreign keys, date order, declared totals and rates. No key or account needed for schema-driven datasets; 25,000 rows per dataset anonymous, 100,000 signed in. Tools: generate_dataset, plan_dataset, blueprint_guide, validate_blueprint, get_certificate, check_answers (questions from the answer key,− - [Synthetic Data for SaaS](https://www.misata.studio/synthetic-data/saas): Describe your SaaS and get accounts, subscriptions, MRR movements, invoices, usage, support tickets and a sales pipeline. MRR follows your curve and churn matches your rate, with a certificate.− - [Synthetic Data for CRM](https://www.misata.studio/synthetic-data/crm): Describe your sales team and get accounts, reps, a deal pipeline and activities where every deal moves through the stages in order and rep totals add up.− - [All datasets](https://www.misata.studio/datasets): 12 free multi-table sample datasets for SQL practice, dashboards and machine learning, and 7 premium ones with an answer key. Every free zip holds one CSV per table, a README, INTEGRITY.txt (each foreign and unique key checked in DuckDB, with its result) and ANSWERS.sql (the page's questions with the query that answers each).− - [Platform API, Misata Data Inside Your Own Product](https://www.misata.studio/docs/guides/platform-api.md): Call Misata from your platform: start a dataset with a Misata key, follow it, take its tables, load them into your Postgres or ClickHouse, query it directly, and check your dashboards against its answer key.+ > Misata makes synthetic data for demos and analytics, built from scratch. A person describes a business and the numbers it should show, in plain language, as a drawn schema or as their own tables (SQL DDL, Prisma, dbt and more), and Misata builds every table, connected, with those numbers held exactly month by month; a number it cannot hold is named with the reason, never passed off as met. Every dataset comes with its ground truth: the SQL and value of each stated metric in every month (answer+ - [MCP server](https://www.misata.studio/mcp): remote server at https://api.misata.studio/mcp (Streamable HTTP). The assistant designs the tables, Misata generates the rows and returns what it checked: foreign keys, date order, declared totals and rates. No key or account needed for schema-driven datasets; 25,000 rows per dataset anonymous, 100,000 signed in. Tools: generate_dataset, plan_dataset, blueprint_guide, validate_blueprint, get_certificate, check_answers (questions from the answer key,+ - [Synthetic Data for SaaS](https://www.misata.studio/synthetic-data/saas): Describe your SaaS and get customers and prospects, subscriptions, MRR movements, invoices, usage, support tickets and a sales pipeline whose won deals book the contracts. MRR follows your curve and churn matches your rate, with a certificate.+ - [Synthetic Data for CRM](https://www.misata.studio/synthetic-data/crm): Describe your sales team and get customers and prospects, their contacts, reps, a deal pipeline and activities where every deal moves through the stages in order and rep totals add up. Loads into Salesforce or HubSpot.+ - [All datasets](https://www.misata.studio/datasets): 12 free multi-table sample datasets for SQL practice, dashboards and machine learning, and 9 premium ones with an answer key. Every free zip holds one CSV per table, a README, INTEGRITY.txt (each foreign and unique key checked in DuckDB, with its result) and ANSWERS.sql (the page's questions with the query that answers each).+ - [US Bank and Card Transactions](https://www.misata.studio/datasets/premium/us-bank-transactions): five banks' raw statement lines, with every label behind them. 7,627,186 rows in 14 linked tables, 50 audit checks passed. For: Transaction enrichment; Recurring payment and subscription detection; Cash-flow underwriting and income verification; Card fraud and authorization models; Disputes and chargebacks; Overdraft, fees and credit cards; Building and demoing fintech products; Teaching. $15, com+ - [AML Transaction Monitoring](https://www.misata.studio/datasets/premium/aml-monitoring): a UAE bank's whole trail from KYC to STR, with the laundering nobody caught. 11,711,393 rows in 16 linked tables, 129 audit checks passed. For: Transaction monitoring and model benchmarks; Alert triage and false positives; Case management and STR workflow; Targeted financial sanctions; Drift and robustness; Graph and network detection; KYC, beneficial ownership and screening; A bank that lives in the UAE; + - [Platform API, Misata Data Inside Your Own Product](https://www.misata.studio/docs/guides/platform-api.md): Call Misata from your platform: start a dataset with a Misata key, follow it, take its tables, load them into your Postgres, ClickHouse, HubSpot or Salesforce, query it directly, and check your dashboards against its answer key.+ - [The Misata API for AI Agents](https://www.misata.studio/docs/guides/agents.md): Instructions an AI agent follows to use the Misata API: which route does what, the order of calls, how to wait, what each error asks of it, how to report what held, and the rules that keep its key and its user safe.+ - [Salesforce and HubSpot](https://www.misata.studio/docs/learn/crm.md): Load a Misata dataset into a Salesforce org or a HubSpot portal: the objects and fields it fills, the load order, external ids, picklists and pipelines, and the loader that ships in each export. Bring an org's own shape in as a schema.+ - [One account in the seed: the Stripe bug no test could see, and how it got fixed](https://www.misata.studio/blog/one-account-in-the-seed): Fivetran's dbt package for Stripe reported every account's rolling revenue as the sum of all of them, with every test green. The seed had one account. Reported on 26 July, fixed in dbt_stripe 1.10.0 on 13 August, and the fix found the same flaw one layer down.

https://www.misata.studio/llms.txt

− > Misata Studio is a web app that creates realistic, connected sample and test data from a plain-language description, a drawn schema or imported SQL. It builds the tables, writes believable content for the business and region, applies every number the user states exactly, reviews the result like a domain expert, and returns a certificate of what was checked. No real data is needed, and it can build data for things that do not exist yet. Data can be explored in the browser and exported to CSV, E− - [MCP server](https://www.misata.studio/mcp): remote server at https://api.misata.studio/mcp (Streamable HTTP). The assistant designs the tables, Misata generates the rows and returns what it checked: foreign keys, date order, declared totals and rates. No key or account needed for schema-driven datasets; 25,000 rows per dataset anonymous, 100,000 signed in. Tools: generate_dataset, plan_dataset, blueprint_guide, validate_blueprint, get_certificate, check_answers (questions from the answer key,− ## The two jobs− - [Ground truth for AI analytics evals](https://www.misata.studio/evals): a world where the stated story holds exactly, an answer key (the SQL and true value of every KPI by month), and a checker that says why a dashboard's or an agent's query is wrong.− - [Demo and seed data](https://www.misata.studio/demo-data): connected data for demo accounts and empty states, from your own schema, with the numbers your story states.− ## Free sample datasets (no sign-up; the licence is stated on each page)− - [Retail star schema](https://www.misata.studio/datasets/retail-star-schema)− - [Machine degradation](https://www.misata.studio/datasets/machine-degradation)− - [Ecommerce storefront](https://www.misata.studio/datasets/ecommerce-storefront)− - [B2B SaaS subscription analytics](https://www.misata.studio/datasets/saas-subscription-analytics)− - [Credit risk portfolio](https://www.misata.studio/datasets/credit-risk-portfolio)− - [Network intrusion / netflow](https://www.misata.studio/datasets/network-intrusion-netflow)− - [Contact center queueing (Erlang C)](https://www.misata.studio/datasets/contact-center-erlang)− - [Manufacturing SPC (Cpk / Xbar-R)](https://www.misata.studio/datasets/manufacturing-spc)− - [Synthetic AML transaction dataset with labelled typologies and ground truth](https://www.misata.studio/datasets/aml-transactions-ground-truth)− - [Clinical trial: sites, patients, visits and adverse events](https://www.misata.studio/datasets/clinical-trial-visits)− - [Hospital encounters, diagnoses and claims](https://www.misata.studio/datasets/hospital-encounters-claims)− - [Factory sensors with failure labels](https://www.misata.studio/datasets/factory-sensors-failure-labels)− - [Predictive Maintenance Fleet](https://www.misata.studio/datasets/premium/maintenance-fleet): two years of sensor readings, failures, repairs and costs for 240 machines. 179,038 rows in 6 linked tables, 67 audit checks passed. For: Remaining useful life (RUL) regression; Survival analysis; Anomaly detection and alarm tuning; Failure mode diagnosis; Maintenance strategy and cost; Dashboards and teaching. $15, commercial use allowed; do not resell the dataset itself. Free preview: https://www.mi− - [Healthcare Claims and Denials](https://www.misata.studio/datasets/premium/claims-denials): a year of revenue cycle with the reason behind every denial. 356,609 rows in 8 linked tables, 95 audit checks passed. For: Denial prediction; Denial management and root cause dashboards; Accounts receivable and cash forecasting; Patient responsibility; Payer contract analysis; Teaching and demos. $15, commercial use allowed; do not resell the dataset itself. Free preview: https://www.misata.studio/premi− - [Misata, Proof-Backed Python Synthetic Data Generator](https://www.misata.studio/docs.md): Generate realistic multi-table synthetic data from plain English, YAML, or your own database, with Oracle reports for validation, realism, privacy, and reproducibility.− - [Quick Start, Misata Synthetic Data Generator](https://www.misata.studio/docs/quickstart.md): Get started with Misata in 5 minutes. Install, generate multi-table synthetic datasets from plain English, shape data with narrative growth curves, and export to CSV, Parquet, or your database.− - [Declaration reference: every property Misata can guarantee](https://www.misata.studio/docs/reference/declarations.md): Complete reference for all 29 Misata schema declarations, from exact revenue curves and balancing ledgers to state machines, graph motifs and conditional missingness. Every example runs.− ## Generation− - [Generation Methods, Misata Python Synthetic Data](https://www.misata.studio/docs/generation.md): Six ways to generate synthetic data with Misata, plain English stories, YAML schemas, database introspection, Python dicts, LLM-assisted, and incremental generation.− - [Plain-English Generation, Misata Synthetic Data Generator](https://www.misata.studio/docs/generation/story.md): Generate realistic multi-table synthetic datasets from a single sentence. Misata's StoryParser detects domain, scale, locale, and growth patterns automatically, no config required.− - [YAML Schema](https://www.misata.studio/docs/generation/yaml.md): Define your schema in a file, commit it to git, and reproduce the same dataset anywhere, no LLM required.− - [Database Seeding](https://www.misata.studio/docs/generation/database.md): Point Misata at an existing database schema and fill a dev or staging environment with production-like data, FK ordering handled automatically.− - [Python Dict Schema](https://www.misata.studio/docs/generation/dict.md): Import your own schema structure, useful when you already have a dict representation of your tables (e.g. from an ORM introspection or a JSON schema).− - [LLM-Assisted Generation](https://www.misata.studio/docs/generation/llm.md): Use a language model to produce a richer schema from an open-ended story, useful for complex or unusual domains where the rule-based parser falls short.− - [Incremental Generation](https://www.misata.studio/docs/generation/incremental.md): Grow an existing dataset without regenerating from scratch. IDs auto-offset, FK integrity is maintained across both batches.− ## Overview− - [Localisation, Country-Accurate Synthetic Data in 20 Locales](https://www.misata.studio/docs/localisation.md): Misata generates country-accurate synthetic data for 20 locales including US, UK, Germany, Japan, India, and all six GCC states, with real salary distributions, national ID formats, and currencies.− - [Time Series Synthetic Data, Generate Realistic Temporal Datasets in Python](https://www.misata.studio/docs/timeseries.md): Generate realistic time-series synthetic data with Misata. Add linear/exponential trends, weekly and yearly seasonality, Gaussian noise, and anomaly injection, for metrics, KPIs, and sensor data.− ## Domains− - [18 Supported Domains, Misata Synthetic Data Generator](https://www.misata.studio/docs/domains.md): Misata generates realistic multi-table synthetic data for 18 business domains, SaaS, fintech, ecommerce, healthcare, HR, logistics, social, real estate, pharma, food delivery, edtech, gaming, CRM, crypto, insurance, travel, and streaming.− - [Generate SaaS Synthetic Data in Python | Misata](https://www.misata.studio/docs/domains/saas.md): Generate realistic SaaS synthetic datasets in Python, users, subscriptions, MRR, churn, and invoices with correct FK relationships and narrative growth curves. No API key required.− - [Generate Ecommerce Synthetic Data in Python | Misata](https://www.misata.studio/docs/domains/ecommerce.md): Generate realistic ecommerce synthetic datasets in Python, customers, products, orders, and order items with correct FK relationships, temporal coherence, and Black Friday curves. No training data required.− - [Generate Fintech Synthetic Data in Python | Misata](https://www.misata.studio/docs/domains/fintech.md): Generate realistic fintech synthetic datasets in Python, customers, bank accounts, transactions, fraud flags, and credit scores with locale-aware IBANs and configurable fraud rates. No real data required.− - [Generate Healthcare Synthetic Data in Python | Misata](https://www.misata.studio/docs/domains/healthcare.md): Generate realistic healthcare synthetic datasets in Python: patients, doctors, admissions, diagnoses, and lab results, with real ICD-10 codes, reconciled length-of-stay, comorbidity clusters, severity-driven length of stay, and zero orphaned foreign keys. No real patient data required.+ > Misata makes synthetic data for demos and analytics, built from scratch. A person describes a business and the numbers it should show, in plain language, as a drawn schema or as their own tables (SQL DDL, Prisma, dbt and more), and Misata builds every table, connected, with those numbers held exactly month by month; a number it cannot hold is named with the reason, never passed off as met. Every dataset comes with its ground truth: the SQL and value of each stated metric in every month (answer+ - [Custom datasets for startups](https://www.misata.studio/startups): for a startup with a product and a schema but no customers yet. Send the business, the schema (SQL DDL, Prisma, Django, SQLAlchemy, Rails or dbt) and the numbers the data should show; every table comes back connected, each stated number held and checked on a certificate, delivered as SQL, CSV or Parquet. No real data needed.+ - [MCP server](https://www.misata.studio/mcp): remote server at https://api.misata.studio/mcp (Streamable HTTP). The assistant designs the tables, Misata generates the rows and returns what it checked: foreign keys, date order, declared totals and rates. No key or account needed for schema-driven datasets; 25,000 rows per dataset anonymous, 100,000 signed in. Tools: generate_dataset, plan_dataset, blueprint_guide, validate_blueprint, get_certificate, check_answers (questions from the answer key,+ ## What it is for: demos and analytics+ - [Demo and seed data](https://www.misata.studio/demo-data): connected data for demo accounts, product tours and empty states, from a sentence or your own schema, with the numbers your story states.+ - [Ground truth for analytics and AI analyst evals](https://www.misata.studio/evals): the stated story holds exactly, with the SQL and true value of every KPI by month, and a checker that says why a dashboard's or an agent's query is wrong.+ ## Free sample datasets (CC0, no sign-up)+ - [All datasets](https://www.misata.studio/datasets): 12 free multi-table sample datasets for SQL practice, dashboards and machine learning, and 7 premium ones with an answer key. Every free zip holds one CSV per table, a README, INTEGRITY.txt (each foreign and unique key checked in DuckDB, with its result) and ANSWERS.sql (the page's questions with the query that answers each).+ - [Retail star schema](https://www.misata.studio/datasets/retail-star-schema): A dimensional model you can actually practise joins on: one fact table, four dimensions, 47,051 rows, every join resolving and every total known. 5 linked tables, 47,051 rows, CSV, CC0. For: Dashboards and BI, SQL and joins. Download: https://www.misata.studio/datasets/retail-star-schema.zip+ - [Machine degradation](https://www.misata.studio/datasets/machine-degradation): 100 machines monitored from commissioning to failure, with an exact remaining-useful-life label on every one of 23,118 readings. 3 linked tables, 46,336 rows, CSV, CC0. For: Machine learning. Download: https://www.misata.studio/datasets/machine-degradation.zip+ - [Ecommerce storefront](https://www.misata.studio/datasets/ecommerce-storefront): A year of orders across five joined tables, with best sellers, heavy buyers, a real Q4 peak and totals that reconcile to the cent. 5 linked tables, 49,493 rows, CSV, CC0. For: Dashboards and BI, SQL and joins. Download: https://www.misata.studio/datasets/ecommerce-storefront.zip+ - [B2B SaaS subscription analytics](https://www.misata.studio/datasets/saas-subscription-analytics): Accounts, seats, MRR, churn and support load, where company size actually drives the plan. 5 linked tables, 42,240 rows, CSV, CC0. For: Dashboards and BI, SQL and joins. Download: https://www.misata.studio/datasets/saas-subscription-analytics.zip+ - [Credit risk portfolio](https://www.misata.studio/datasets/credit-risk-portfolio): 3,000 borrowers and 4,200 commercial loans, where a credit rating actually predicts whether the loan defaults, at the rate S&P's own data says it should. 2 linked tables, 7,200 rows, CSV, CC0. For: Machine learning, Security and risk. Download: https://www.misata.studio/datasets/credit-risk-portfolio.zip+ - [Network intrusion / netflow](https://www.misata.studio/datasets/network-intrusion-netflow): 500 hosts and 20,000 labeled flows, where a port scan, a DDoS flood, and a brute-force attempt each carry the real statistical signature their MITRE ATT&CK technique actually leaves in traffic. 2 linked tables, 20,500 rows, CSV, CC0. For: Machine learning, Security and risk. Download: https://www.misata.studio/datasets/network-intrusion-netflow.zip+ - [Contact center queueing (Erlang C)](https://www.misata.studio/datasets/contact-center-erlang): 4 queues, 2,400 half-hour intervals over a full business month, where agents_staffed is what Erlang C (1917) says is actually required to clear each queue's own SLA target, not a plausible-looking number. 2 linked tables, 2,404 rows, CSV, CC0. For: Operations and quality. Download: https://www.misata.studio/datasets/contact-center-erlang.zip+ - [Manufacturing SPC (Cpk / Xbar-R)](https://www.misata.studio/datasets/manufacturing-spc): 5 processes, 450 subgroups, 2,250 measurements, where process capability and out-of-control detection come from a real Xbar-R control chart -- including one process with an engineered tool-wear drift that actually trips Western Electric rules where it happens. 4 linked tables, 2,710 rows, CSV, CC0. For: Operations and quality. Download: https://www.misata.studio/datasets/manufacturing-spc.zip+ - [Synthetic AML transaction dataset with labelled typologies and ground truth](https://www.misata.studio/datasets/aml-transactions-ground-truth): A bank's customers, accounts and transactions, with the laundering typologies labelled, the rule-based alerts raised, and the cases and SARs that followed. 6 linked tables, 44,612 rows, CSV, CC0. For: Machine learning, Security and risk. Download: https://www.misata.studio/datasets/aml-transactions-ground-truth.zip+ - [Clinical trial: sites, patients, visits and adverse events](https://www.misata.studio/datasets/clinical-trial-visits): Two trials across nine sites: enrolment, scheduled visits, dosing, lab biomarkers and graded adverse events, all in the right order. 7 linked tables, 22,496 rows, CSV, CC0. For: Health and pharma. Download: https://www.misata.studio/datasets/clinical-trial-visits.zip+ - [Hospital encounters, diagnoses and claims](https://www.misata.studio/datasets/hospital-encounters-claims): Patients, providers, encounters, ICD-10 diagnoses, prescriptions, labs and insurance claims that reconcile from billed to paid. 7 linked tables, 39,946 rows, CSV, CC0. For: Health and pharma. Download: https://www.misata.studio/datasets/hospital-encounters-claims.zip+ - [Factory sensors with failure labels](https://www.misata.studio/datasets/factory-sensors-failure-labels): A year of hourly sensor readings from six machines, with the degradation stage, remaining useful life, and the failures and work orders they lead to. 5 linked tables, 52,669 rows, CSV, CC0. For: Machine learning. Download: https://www.misata.studio/datasets/factory-sensors-failure-labels.zip+ ## A whole fictional company (synthetic; free preview, full pack paid)+ - [Pepperwind Trading Co.](https://www.misata.studio/pepperwind): a fictional spice and tea trader from Kochi, every row synthetic and made by Misata. Eleven systems (storefront, billing, general ledger in three currencies, warehouse and logistics, support, CRM, HR and payroll, mail, marketing, product analytics) over 36 months, Oct 2023 to Sep 2026: 223 tables and 79 million rows in Parquet, keys intact across systems, with an answer key, 92 KPIs with monthly values, 420 graded questions and 20+ - [Predictive Maintenance Fleet](https://www.misata.studio/datasets/premium/maintenance-fleet): two years of sensor readings, failures, repairs and costs for 240 machines. 179,004 rows in 6 linked tables, 68 audit checks passed. For: Remaining useful life (RUL) regression; Survival analysis; Anomaly detection and alarm tuning; Failure mode diagnosis; Maintenance strategy and cost; Dashboards and teaching. $15, commercial use allowed; do not resell the dataset itself. Free preview: https://www.mi+ - [Healthcare Claims and Denials](https://www.misata.studio/datasets/premium/claims-denials): a year of revenue cycle with the reason behind every denial. 356,609 rows in 8 linked tables, 99 audit checks passed. For: Denial prediction; Denial management and root cause dashboards; Accounts receivable and cash forecasting; Patient responsibility; Payer contract analysis; Teaching and demos. $15, commercial use allowed; do not resell the dataset itself. Free preview: https://www.misata.studio/premi+ - [Health Plan Claims and Reserves](https://www.misata.studio/datasets/premium/health-plan-claims): a payer's year with its incurred truth. 284,051 rows in 7 linked tables, 107 audit checks passed. For: Claims reserving (IBNR); Medical economics; Payment integrity and denials; Claims operations; Benefit design; Teaching and demos. $15, commercial use allowed; do not resell the dataset itself. Free preview: https://www.misata.studio/premium-previews/health-plan-claims-preview.zip+ - [Personal Auto Claims and Reserves](https://www.misata.studio/datasets/premium/pc-auto-claims): an insurer's book with its ultimate truth. 357,438 rows in 6 linked tables, 67 audit checks passed. For: Loss reserving; Ratemaking; Catastrophe analysis; Claims analytics; Teaching and demos. $15, commercial use allowed; do not resell the dataset itself. Free preview: https://www.misata.studio/premium-previews/pc-auto-claims-preview.zip+ The library (MIT, Python 3.10+, version 0.9.8) needs no model, API key or real data. It generates related+ tables from SQL DDL, a YAML or dict schema, a live database or a sentence; refuses before generating when declarations+ contradict (exit 3); writes CSV, JSON Lines, Parquet or DuckDB in bounded memory; and seeds Postgres or SQLite in one+ transaction. `misata attest` re-checks every declared invariant on the written files with DuckDB and writes an in-toto+ Statement (predicate https://www.misata.studio/attestation/certificate/v1) plus a standalone verify.py. `misata solve` moves outcome+ curves until a dbt, SQL or Python pipeline hits per-period targets. `misata evalpack` builds text-to-SQL packs whose+ answers are declared first and verified on rotated databases. `misata publish` uploads a verified folder to Hugging Face.+ Facts to keep straight: `misata.mimic` fits to real rows and is not anonymous or privacy-safe; output is byte-identical+ for the same schema, seed and Misata version, not across versions; no tool makes data indistinguishable from a+ particular production table. Agents writing code against the library: https://github.com/rasinmuhammed/misata/blob/main/AGENTS.md+ - [Misata: Declarative Synthetic Data Generator for Python](https://www.misata.studio/docs.md): Declarative synthetic data for Python. Generate multi-table data from SQL DDL, YAML, a live database or one sentence that holds every fact you declare, with a DuckDB certificate that proves it.+ ## Get Started+ - [Quickstart: Generate Multi-Table Synthetic Data in Python](https://www.misata.studio/docs/quickstart.md): Install Misata, generate a related set of tables from one sentence or a YAML schema, check the plan before generating, attest the result and export to CSV, Parquet, DuckDB or a database.+ ## Get Started: Ways to Describe Data

Showing the first 40 of each.

https://www.misata.studio/llms.txt

− > Misata Studio is a web app that creates realistic, connected sample and test data from a plain-language description, a drawn schema or imported SQL. It builds the tables, writes believable content for the business and region, applies every number the user states exactly, reviews the result like a domain expert, and returns a certificate of what was checked. No real data is needed, and it can build data for things that do not exist yet. Data can be explored in the browser and exported to CSV, E− - [MCP server](https://www.misata.studio/mcp): remote server at https://api.misata.studio/mcp (Streamable HTTP). The assistant designs the tables, Misata generates the rows and returns what it checked: foreign keys, date order, declared totals and rates. No key or account needed for schema-driven datasets; 10,000 rows per dataset anonymous, 100,000 signed in. Tools: generate_dataset, plan_dataset, blueprint_guide, validate_blueprint, get_certificate, query_dataset, export_dataset (25 formats, as− ## Generators by job− - [Dummy data generator](https://www.misata.studio/dummy-data-generator): Misata is a dummy data generator for connected tables. Describe what you need, such as customers, orders and payments for a small online shop, and you get CSV, Excel, SQL or Parquet files where every order belongs to a real customer, every date falls in order and the totals reconcile. Or download one of twelve free sample databases that are ready now.− - [Test data generator](https://www.misata.studio/test-data-generator): Misata generates relational test data from your schema: paste CREATE TABLE statements or describe the tables, and get rows where every foreign key is valid, dates are in order and any rate you declare is exact. The same seed gives identical rows every run, so a fixture never drifts, and the SQL export loads into Postgres, MySQL, SQLite, SQL Server, Oracle, BigQuery or Snowflake.− - [Fake data generator](https://www.misata.studio/fake-data-generator): A fake data generator like a random-value library gives you believable names, emails and addresses one field at a time. Misata generates whole fake datasets: several tables whose keys join, numbers that follow the distributions you set, and declared figures such as a 2% fraud rate or a revenue total that come out exact. Every dataset is checked before you get it.− - [Mock data generator](https://www.misata.studio/mock-data-generator): Misata generates mock data as one connected dataset and exports it in the shapes a front end or API needs: JSON and JSON Lines, a ready mock API bundle, an OpenAPI description, TypeScript types, a JSON Schema, Prisma and SQLAlchemy models. Because the records come from one dataset, a user's orders really are that user's orders on every screen.− - [Realistic data generator](https://www.misata.studio/realistic-data-generator): Realistic data is data whose patterns match how the real process behaves: weekly and seasonal rhythms, a long tail of small customers and a few large ones, prices that cluster at real price points, a machine that wears and is repaired. Misata generates relational data to those patterns and the outcomes you declare, then audits the delivered rows for consistency and realism and tells you what it found.− - [Demo data for SaaS](https://www.misata.studio/demo-data): seed demo and sandbox accounts with connected data from your own schema.− - [Backlot](https://www.misata.studio/backlot): demo data for data products. Seeds synthetic source data into a sandbox schema in your Postgres, measures your pipeline's own output with your SQL, and corrects the source until every month of the demo story is within 2%; shares a read-only demo room with the proof− - [Fraud Lab](https://www.misata.studio/fraud-lab): score a fraud detector in the browser against a synthetic bank with a known answer key+ > Misata Studio is a web app that creates realistic, connected sample and test data from a plain-language description, a drawn schema or imported SQL. It builds the tables, writes believable content for the business and region, applies every number the user states exactly, reviews the result like a domain expert, and returns a certificate of what was checked. No real data is needed, and it can build data for things that do not exist yet. Data can be explored in the browser and exported to CSV, E+ - [MCP server](https://www.misata.studio/mcp): remote server at https://api.misata.studio/mcp (Streamable HTTP). The assistant designs the tables, Misata generates the rows and returns what it checked: foreign keys, date order, declared totals and rates. No key or account needed for schema-driven datasets; 25,000 rows per dataset anonymous, 100,000 signed in. Tools: generate_dataset, plan_dataset, blueprint_guide, validate_blueprint, get_certificate, check_answers (questions from the answer key,+ ## The two jobs+ - [Ground truth for AI analytics evals](https://www.misata.studio/evals): a world where the stated story holds exactly, an answer key (the SQL and true value of every KPI by month), and a checker that says why a dashboard's or an agent's query is wrong.+ - [Demo and seed data](https://www.misata.studio/demo-data): connected data for demo accounts and empty states, from your own schema, with the numbers your story states.

• + check_answers

https://www.misata.studio/llms.txt

− - [Anti-money-laundering transactions with ground truth](https://www.misata.studio/datasets/aml-transactions-ground-truth)− ## Premium datasets ($15 each, free preview, ground truth included; synthetic, commercial use allowed)− - [Retail Data Warehouse](https://www.misata.studio/datasets/premium/retail-warehouse): two fiscal years of a grocery chain, from the shelf to the receipt. 2,780,138 rows in 14 linked tables, 110 audit checks passed. For: Star schema and BI; Demand forecasting; Promotion and price analytics; Inventory and supply chain; Customer analytics; Returns and fraud-style exercises. Free preview: https://www.misata.studio/premium-previews/retail-warehouse-preview.zip− - [SPC for Precision Machining](https://www.misata.studio/datasets/premium/spc-machining): six months of control charts with the ground truth underneath. 504,786 rows in 15 linked tables, 78 audit checks passed. For: Control charts and capability; Run-rule and anomaly detection with labels; Root cause analysis; Tool wear and predictive maintenance; Measurement system analysis; Teaching and dashboards. Free preview: https://www.misata.studio/premium-previews/spc-machining-preview.zip− - [Predictive Maintenance Fleet](https://www.misata.studio/datasets/premium/maintenance-fleet): two years of sensor readings, failures, repairs and costs for 240 machines. 179,029 rows in 6 linked tables, 62 audit checks passed. For: Remaining useful life (RUL) regression; Survival analysis; Anomaly detection and alarm tuning; Failure mode diagnosis; Maintenance strategy and cost; Dashboards and teaching. Free preview: https://www.misata.studio/premium-previews/maintenance-fleet-preview.zip− - [Healthcare Claims and Denials](https://www.misata.studio/datasets/premium/claims-denials): a year of revenue cycle with the reason behind every denial. 356,609 rows in 8 linked tables, 87 audit checks passed. For: Denial prediction; Denial management and root cause dashboards; Accounts receivable and cash forecasting; Patient responsibility; Payer contract analysis; Teaching and demos. Free preview: https://www.misata.studio/premium-previews/claims-denials-preview.zip+ ## Done for you: custom datasets+ - [Request a custom dataset](https://www.misata.studio/custom-dataset): send the schema, volumes, rates and rules; Misata replies (aim: within one business day) with questions, a sample schema, a price and a delivery date, then delivers the files with a certificate of what was checked. Common asks: a demo database shaped like a prospect's business, labelled fraud and AML data with typologies, a dev database from DDL, process mining event logs with a known truth, and course datasets with an answe+ - [Process mining event logs](https://www.misata.studio/use-cases/process-mining-event-logs): Where do I get a realistic event log for process mining, with a known truth?+ - [Teaching datasets with an answer key](https://www.misata.studio/use-cases/teaching-datasets): Where do I find a realistic dataset for a SQL or analytics course, with an answer key?+ - [Synthetic AML transaction dataset with labelled typologies and ground truth](https://www.misata.studio/datasets/aml-transactions-ground-truth)+ ## Premium datasets (free preview, ground truth included; synthetic; price and licence on each line)+ - [Retail Data Warehouse](https://www.misata.studio/datasets/premium/retail-warehouse): two fiscal years of a grocery chain, from the shelf to the receipt. 2,790,852 rows in 14 linked tables, 117 audit checks passed. For: Star schema and BI; Demand forecasting; Promotion and price analytics; Inventory and supply chain; Customer analytics; Returns and fraud-style exercises. $15, commercial use allowed; do not resell the dataset itself. Free preview: https://www.misata.studio/premium-previews/reta+ - [SPC for Precision Machining](https://www.misata.studio/datasets/premium/spc-machining): six months of control charts with the ground truth underneath. 515,442 rows in 15 linked tables, 101 audit checks passed. For: Control charts and capability; Run-rule and anomaly detection with labels; Root cause analysis; Tool wear and predictive maintenance; Measurement system analysis; Teaching and dashboards. $15, commercial use allowed; do not resell the dataset itself. Free preview: https://www.misat+ - [Predictive Maintenance Fleet](https://www.misata.studio/datasets/premium/maintenance-fleet): two years of sensor readings, failures, repairs and costs for 240 machines. 179,038 rows in 6 linked tables, 67 audit checks passed. For: Remaining useful life (RUL) regression; Survival analysis; Anomaly detection and alarm tuning; Failure mode diagnosis; Maintenance strategy and cost; Dashboards and teaching. $15, commercial use allowed; do not resell the dataset itself. Free preview: https://www.mi+ - [Healthcare Claims and Denials](https://www.misata.studio/datasets/premium/claims-denials): a year of revenue cycle with the reason behind every denial. 356,609 rows in 8 linked tables, 95 audit checks passed. For: Denial prediction; Denial management and root cause dashboards; Accounts receivable and cash forecasting; Patient responsibility; Payer contract analysis; Teaching and demos. $15, commercial use allowed; do not resell the dataset itself. Free preview: https://www.misata.studio/premi+ - [Batch Release World](https://www.misata.studio/datasets/premium/batch-release): two years of a pharmaceutical plant's records, from process order to QP release. 661,497 rows in 65 linked tables, 133 audit checks passed. For: Process mining with a known truth; Batch release KPIs and dashboards; Root cause and quality analytics; Data integrity and audit-trail review; SAP PP, QM and MM extracts without client data; Testing AI assistants and text-to-SQL; Teaching. $49, personal licence for one na+ - [How to measure pharma batch release time from SAP-style QM tables, with SQL](https://www.misata.studio/blog/pharma-batch-release-time-from-sap-qm-tables): Batch release lead time, right first time and where the days go, computed from process orders, goods movements, inspection lots and usage decisions. The joins, the five traps that make the numbers wrong, and a free dataset to check them on.