Ask two people what they mean by big data sources and you’ll get two answers. One means categories: sensor and machine output, transaction records, application logs, public records, web and social content. The other means addresses. Where do I go, right now, to download something real. This page answers the second question, because the first one is already on every vendor glossary and answering it never once got anyone a dataset.
- The 20 big data sources at a glance
- What each big data source had to answer to get on this list
- 1. Google Dataset Search — Best overall starting point for finding data
- 2. Kaggle Datasets — Best for fast prototyping
- 3. Hugging Face Datasets — Best for model training data
- 4. AWS Registry of Open Data — Best for data too big to move
- 5. Data.gov — Best for US federal records
- 6. US Census Bureau — Best for demographics and geography
- 7. World Bank Open Data — Best for cross-country comparison
- 8. FRED — Best for economic time series
- 9. Our World in Data — Best for long-run global indicators
- 10. data.europa.eu — Best for EU public sector data
- 11. Data.gov.uk — Best for UK public sector data
- 12. NOAA NCEI — Best for weather and climate history
- 13. HealthData.gov — Best for US health system data
- 14. NHS England data — Best for UK health and care statistics
- 15. OpenStreetMap — Best for geospatial base layers
- 16. Common Crawl — Best for web-scale text
- 17. DBpedia — Best for knowledge-graph queries
- 18. Google Trends — Best for search demand signals
- 19. Google Books Ngrams — Best for long-run language change
- 20. New York Times developer APIs — Best for news archive metadata
- Big data sources we considered and left out
- If you came back looking for Topsy, Likebutton, Freebase or Google Finance
- How to choose the right big data source for the job
- Frequently asked questions
What follows is 20 free big data sources that are live and maintained as of August 2026. For each one: what the data actually covers, how you get it (bulk download, API, SQL console, git clone), what the licence permits commercially, and the job it genuinely suits. That last pair matters more than the download link. A dataset you can’t licence for commercial use is a dataset your legal team will remove from the product two weeks before launch.
The 20 big data sources at a glance
| # | Source | Best for | What’s in it | Access | Licence position |
|---|---|---|---|---|---|
| 1 | Google Dataset Search | Best overall starting point | Index of datasets published across repositories and portals | Web search, usage-rights filter | No licence of its own; shows the host’s |
| 2 | Kaggle Datasets | Best for fast prototyping | User-uploaded tabular, image and text sets | Web, CLI/API, hosted notebooks | Per-upload, often undeclared |
| 3 | Hugging Face Datasets | Best for model training data | ML-ready corpora, benchmarks, multimodal sets | git-LFS, datasets library, parquet | Per-repo tag; some non-commercial or gated |
| 4 | AWS Registry of Open Data | Best for data too big to move | Satellite imagery, genomics, climate reanalysis | S3, Athena, notebooks | Per-dataset; compute and egress billed |
| 5 | Data.gov | Best for US federal records | Agency datasets across every domain | CKAN catalogue, per-agency APIs | Mostly public domain, check each |
| 6 | US Census Bureau | Best for demographics and geography | ACS, decennial census, economic census, TIGER files | Free API key, bulk FTP-style download | US public domain |
| 7 | World Bank Open Data | Best for cross-country comparison | Development indicators by country and year | REST API, bulk CSV, R/Python clients | Mostly CC BY 4.0 |
| 8 | FRED | Best for economic time series | US and international macro series | API key, Excel add-in, Python wrappers | Free access; some series copyright-restricted |
| 9 | Our World in Data | Best for long-run global indicators | Curated, harmonised series with chart provenance | Per-chart CSV, GitHub, catalogue package | OWID work CC BY; sources keep theirs |
| 10 | data.europa.eu | Best for EU public sector data | EU institutions plus member state portals | Portal, metadata SPARQL endpoint | Varies by publisher |
| 11 | Data.gov.uk | Best for UK public sector data | Spending, transport, crime, land and property | Portal, links to departmental APIs | Mostly Open Government Licence v3 |
| 12 | NOAA NCEI | Best for weather and climate history | Station observations, radar, paleoclimate, reanalysis | Bulk directories, CDO API, AWS mirrors | US public domain, some third-party limits |
| 13 | HealthData.gov | Best for US health system data | CMS, CDC and HHS public use files | Portal, Socrata-style APIs | Per-dataset, mostly open |
| 14 | NHS England data | Best for UK health and care statistics | Activity, workforce, prescribing, waiting times | Publication downloads, some APIs | OGL for statistics; record-level needs DARS |
| 15 | OpenStreetMap | Best for geospatial base layers | Roads, buildings, land use, points of interest | Planet dumps, regional extracts, Overpass | ODbL: attribution and share-alike |
| 16 | Common Crawl | Best for web-scale text | Repeated crawls of the public web | WARC/WET on S3, columnar index | Corpus free; page copyright unchanged |
| 17 | DBpedia | Best for knowledge-graph queries | Structured facts extracted from Wikipedia | SPARQL endpoint, dumps | CC BY-SA, share-alike |
| 18 | Google Trends | Best for search demand signals | Relative search interest since 2004 | UI export, BigQuery top-terms dataset | Terms-limited; not a redistributable dataset |
| 19 | Google Books Ngrams | Best for long-run language change | Word and phrase frequencies by year | Bulk n-gram files, viewer | CC BY for the n-gram data |
| 20 | New York Times APIs | Best for news archive metadata | Article metadata and abstracts from 1851 | Free developer key, rate limited | Restricted; not for commercial reuse |
What each big data source had to answer to get on this list
Four questions, and a source had to answer all of them.
- Is it still being maintained? A portal with a working homepage and no new deposits since 2019 is an archive, not a source. Three entries on the previous version of this list were dead services.
- Can you get the data out in bulk? A chart you can look at is not data. We wanted a download, an API, an S3 prefix or a query endpoint.
- Is the licence position knowable? Not necessarily permissive. Knowable. ODbL share-alike is fine if you know it applies before you build on it.
- Does it do a job something else doesn’t? Two sources that overlap by 80% earn one slot.
What would move an entry down: a licence that quietly changes, an API that starts gating what used to be open, or a catalogue where a growing share of links resolve to nothing. All three have happened to sources that used to sit comfortably in lists like this one.
Basis: SmartDataCollective has covered data and analytics since 2008. This assessment rests on published documentation, licence and terms pages, and stated access methods for each source. We have not run hands-on trials of every dataset here, and nothing below should be read as a benchmark result.
1. Google Dataset Search — Best overall starting point for finding data
Overview
Google indexes datasets that publishers describe with schema.org markup, then lets you search across them. It’s the nearest thing to a big data search engine: repositories, national statistics offices, university archives and government catalogues in one result list.
What it does well
Filters on usage rights, format and update recency. When you know the shape of the data you need but not who publishes it, this is where you start.
Drawbacks
Coverage depends entirely on publishers marking up their pages, so the index skews academic. You can’t preview rows. Duplicate mirrors of the same dataset are common.
Access, cost and licence
Free web search. It hosts nothing, so the licence you must satisfy is always the host’s, and Dataset Search only reports what the host declared.
Best for
Discovery, before you commit to a source.
2. Kaggle Datasets — Best for fast prototyping
Overview
Community uploads across tabular, image, audio and text, sitting next to hosted notebooks and competition data. It’s the fastest route from idea to a working notebook, which is why it displaced most of what a 2014 list would have recommended.
What it does well
Preview pane, schema summary, and thousands of public notebooks showing what other people already tried. Attach a dataset to a notebook and skip the download entirely.
Drawbacks
Provenance is often untraceable. A large share of uploads are re-posts of somebody else’s data with the licence field left as unknown, which makes them unusable in a commercial product no matter how clean they look.
Access, cost and licence
Free account, web download, or the Kaggle CLI with an API token. Licence is per-upload and uploader-declared. Treat undeclared as unusable, not as permissive.
Best for
Prototypes, teaching, and pipeline work you’ll later point at a source you can licence. Pair it with a proper toolchain from our roundup of the best analytics tools grouped by job.
3. Hugging Face Datasets — Best for model training data
Overview
The hub where ML datasets now live. Each dataset is a git repository with large files in LFS, a dataset card, and usually an automatic parquet conversion plus a row viewer.
What it does well
Streaming. The datasets library will iterate a corpus far larger than your disk without a full download, which is the difference between a training run and a procurement request for storage.
Drawbacks
Quality is wildly uneven. Plenty of popular corpora are scraped, undocumented, or duplicated across repos, and CC BY-NC tags are common enough that you have to check every card before training a commercial model. Deduplication and filtering are your problem.
Access, cost and licence
Free to browse and pull; gated repos require accepting terms, sometimes with an approval step. Licence lives in the repo metadata and it is only as reliable as the uploader.
Best for
Anyone training or fine-tuning models. Read it alongside our piece on how data quality and quantity affect AI outcomes, and our list of machine learning companies worth shortlisting if you’d rather buy the capability.
4. AWS Registry of Open Data — Best for data too big to move
Overview
Public datasets hosted in S3, contributed by agencies and research groups. Sentinel and Landsat imagery, 1000 Genomes, climate reanalysis, OSM extracts, and a long tail of domain collections. This is the current home of what used to be called AWS Public Datasets.
What it does well
You compute next to the data instead of shipping terabytes across the internet. Athena, EMR or a notebook in the same region turns an imagery archive into a query.
Drawbacks
Free data, billed compute. Some buckets are requester-pays, and pulling out of region will show up on the invoice. There’s no unified schema across collections, so every dataset is a new integration.
Access, cost and licence
S3 CLI, SDKs, or SQL over the bucket. Licences are set by the contributing organisation and vary from public domain to attribution-required. If you’re already on AWS, see which companies use cloud computing at this scale and how they organise it.
Best for
Geospatial, genomics and climate work at volume.
5. Data.gov — Best for US federal records
Overview
The federal catalogue, plus a good deal of state, county and city data harvested from other portals. Everything from inspection results to transport safety to agricultural output.
What it does well
Breadth no single agency site can match, with a machine-readable catalogue API for anyone building a harvester.
Drawbacks
It’s an index, not a warehouse. Records point at agency systems, and some of those links rot. Metadata quality swings between agencies, and two datasets on the same subject may not share a single common key. Expect real modelling work before they join, and see our comparison of data modeling tools by modeling layer if that’s the stage you’re at.
Access, cost and licence
Free. Works of the US federal government are generally not subject to domestic copyright, but datasets containing third-party or contractor material can carry restrictions, so read the individual entry.
Best for
US regulatory, infrastructure and public administration analysis.
6. US Census Bureau — Best for demographics and geography
Overview
American Community Survey, the decennial census, economic and business surveys, and TIGER/Line geographic files. Population, income, housing, commuting, industry, all cut to small geographies.
What it does well
It’s the backbone of almost every credible US market sizing or site selection model. The API returns variables by geography level, and the shapefiles let you join to anything with an address.
Drawbacks
The variable naming is opaque until you’ve spent time with the documentation. Survey estimates come with margins of error that people routinely drop when they load the data, then present the result as fact.
Access, cost and licence
Free API key, bulk downloads, and mirrored tables in several cloud marketplaces. US public domain, so commercial use is unrestricted.
Best for
Segmentation, catchment analysis, weighting a sample to population.
7. World Bank Open Data — Best for cross-country comparison
Overview
Development indicators for most countries over several decades: GDP measures, trade, energy, education enrolment, health outcomes, population.
What it does well
Harmonised country and indicator codes, which is the whole value. Comparing 200 countries from primary national sources is a months-long job; here it’s an API call.
Drawbacks
Coverage gaps are heaviest exactly where the questions are most interesting, and back-series get revised. Some indicators are modelled estimates rather than measurements, and the API does not shout about which.
Access, cost and licence
Free REST API, bulk CSV per indicator, plus maintained R and Python clients. Most of the catalogue sits under CC BY 4.0 under the Bank’s dataset terms, which permits commercial use with attribution.
Best for
Market entry work, macro context, anything that needs a defensible country baseline.
8. FRED — Best for economic time series
Overview
The St. Louis Fed’s series database. Rates, employment, prices, output, money supply, housing, plus a large set of international and regional series.
What it does well
Vintage data. FRED keeps what a series looked like before revision, which is the only honest way to backtest a model that would have run on the numbers available at the time.
Drawbacks
Not everything in FRED is free to redistribute. Series sourced from commercial providers carry copyright notices and use restrictions, and the fact that you can pull them through a free API does not make them yours to publish.
Access, cost and licence
Free API key, an Excel add-in, and well-maintained Python wrappers. Check the copyright field on every series before it goes into a client-facing product.
Best for
Forecasting, nowcasting, any model with a macro input.
9. Our World in Data — Best for long-run global indicators
Overview
A research group that collects, harmonises and documents global indicators on health, energy, food, emissions, conflict and demography. Every chart carries its sources.
What it does well
Documentation. When a series stitches together three underlying sources across a century, OWID says so, in writing, next to the download. That’s rarer than it should be.
Drawbacks
It’s a curated layer over other people’s data, not a primary collector. Coverage follows research interest, so it’s deep on climate and health and thin elsewhere. For the newest quarterly figure, go upstream.
Access, cost and licence
Per-chart CSV download, a public GitHub repository, and a catalogue package for programmatic access. OWID’s own work is CC BY; upstream sources keep their own terms, which the page names.
Best for
Trend arguments you have to defend in a room full of sceptics.
10. data.europa.eu — Best for EU public sector data
Overview
The official EU portal, combining data published by EU institutions with harvested metadata from member state and regional portals. The old open-data.europa.eu address now redirects here.
What it does well
One search across 30-plus national catalogues, with a SPARQL endpoint over the metadata for anyone building automated discovery.
Drawbacks
Federated catalogues inherit every publisher’s habits. Descriptions arrive in the publisher’s language, formats range from clean CSV to a scanned PDF someone called open data, and dead links persist.
Access, cost and licence
Free. Metadata is broadly open; the datasets themselves carry the publishing body’s licence, which may be CC BY, a national open licence, or something bespoke. Read before commercial use.
Best for
EU regulatory, agricultural, energy and transport analysis.
11. Data.gov.uk — Best for UK public sector data
Overview
The UK catalogue: departmental spending, transport, environment, crime through data.police.uk, and Land Registry price paid records.
What it does well
The Open Government Licence is one of the clearest licence positions in open data. Attribution, commercial use permitted, no negotiation required.
Drawbacks
A good share of entries haven’t been touched in years, and machinery-of-government changes leave datasets orphaned under departments that no longer exist. Often the live version sits on the department’s own site rather than here.
Access, cost and licence
Free portal with links out to departmental downloads and APIs. Mostly OGL v3, though a few datasets sit behind their own terms.
Best for
UK property, policing, local government and public spending work.
12. NOAA NCEI — Best for weather and climate history
Overview
The former National Climatic Data Center, now part of NCEI, holding station observations, hourly global surface data, radar archives, paleoclimate records and reanalysis products.
What it does well
Depth of history at station level. If you need daily temperature and precipitation for a specific location going back a century, this is the archive.
Drawbacks
Formats are institutional rather than friendly. Fixed-width files, station identifier schemes that changed over time, and quality flags you have to read the documentation to interpret. Budget a day just to parse it correctly.
Access, cost and licence
Bulk HTTPS directories, the Climate Data Online API with a free token, and cloud-hosted mirrors including AWS. US government output is generally public domain; a small number of ingested third-party datasets are restricted. Archives this size are also a reminder to think about off-site backup for big data before you build a dependency on a local copy.
Best for
Insurance, agriculture, energy demand modelling, climate risk.
13. HealthData.gov — Best for US health system data
Overview
HHS-run catalogue of health datasets: Medicare and Medicaid utilisation and spending files, provider directories, surveillance and quality reporting.
What it does well
Provider-level detail on cost and utilisation that would be commercially licensed anywhere else. The CMS public use files behind it support real analysis, not just charts.
Drawbacks
Nothing here is patient-level identifiable, and small-cell suppression will punch holes in exactly the subgroups you wanted. Update cadence varies by programme, and reporting definitions shift between years, so multi-year series need care.
Access, cost and licence
Free portal with Socrata-style APIs on many datasets and direct bulk files on others. Mostly open federal data; confirm per dataset. Health data also drags governance in with it, which is where master data governance across multiple clouds stops being theoretical.
Best for
Payer analytics, provider benchmarking, health policy research.
14. NHS England data — Best for UK health and care statistics
Overview
The statistics function that ran as the Health and Social Care Information Centre, then NHS Digital, and moved into NHS England in 2023. Hospital activity, waiting times, workforce, prescribing at practice level, mental health services.
What it does well
Prescribing data is unusually granular for a national health system, and the monthly publication rhythm makes operational tracking possible.
Drawbacks
Two tiers, and only one is free. Published statistics are open; record-level data such as Hospital Episode Statistics runs through the Data Access Request Service, with an application, a contract, an approval wait and usually a fee. Calling that a free source would be misleading. Reorganisations have also scattered older publications across archived addresses.
Access, cost and licence
Free downloads (CSV and Excel) plus some APIs, generally under OGL v3.
Best for
UK healthcare operations, pharma commercial teams, health services research.
15. OpenStreetMap — Best for geospatial base layers
Overview
A crowd-built map of the world: road networks with classifications, buildings, boundaries, land use, waterways, and millions of points of interest.
What it does well
Routing, geocoding and catchment analysis without per-request commercial map fees. Regional extracts and the full planet dump mean you can host it yourself and stop metering API calls.
Drawbacks
Completeness follows contributor density, so a dense European city is excellent and a rural area may be a road and nothing else. Tagging conventions are conventions, not a schema. And the ODbL share-alike obligation is the real catch: publish a derived database and you may be required to release it under the same terms. That’s a product decision, not a data decision.
Access, cost and licence
Free planet files, Geofabrik extracts, the Overpass query API. ODbL 1.0 with attribution.
Best for
Logistics, location intelligence, self-hosted geocoding.
16. Common Crawl — Best for web-scale text
Overview
A nonprofit that crawls the public web repeatedly and publishes the results: raw responses in WARC, extracted metadata, plain text, and a searchable URL index.
What it does well
It’s the only free corpus at this scale, and it underpins a great deal of published language model work. The columnar index lets you pull just the domains you care about rather than processing everything.
Drawbacks
You need distributed compute; a laptop will not touch it. Boilerplate, spam and near-duplicates are everywhere, so extraction and filtering will consume most of your effort. And the crawl being freely available says nothing about the copyright status of the pages inside it, which is now an active legal question for anyone training on it commercially. Our coverage of the data trends shaping analytics in 2026 tracks where that is going.
Access, cost and licence
Free from S3 under Common Crawl‘s terms of use; you pay for the compute.
Best for
Language model pretraining, link graph research, large-scale web measurement.
17. DBpedia — Best for knowledge-graph queries
Overview
Structured facts extracted from Wikipedia infoboxes and text into RDF: people, places, organisations, works, and the relations between them.
What it does well
SPARQL over an encyclopaedia. Ask for every company founded in a given country before 1950 and get a result set instead of a reading list. Good for entity linking and reference data enrichment.
Drawbacks
Extraction artefacts persist. Inconsistent types, duplicate entities, values that were never clean in the source infobox. The public endpoint is best treated as exploratory; production work means loading a dump locally. Wikidata is now the more actively curated option for many of the same questions, and there’s a case for using both.
Access, cost and licence
Free SPARQL endpoint and dumps. CC BY-SA, inherited from Wikipedia, so share-alike applies to derived databases.
Best for
Entity resolution, semantic enrichment, reference data.
18. Google Trends — Best for search demand signals
Overview
Relative search interest for terms and topics since 2004, by geography and time, with related and rising queries.
What it does well
Early demand signal. Search interest often moves before reported sales, which makes it useful as a leading feature where official statistics lag by weeks.
Drawbacks
It isn’t volume. Values are normalised indices from a sample, so two pulls of the same query can differ slightly and cross-term comparisons are only valid inside a single request. Unofficial API clients break whenever the front end changes, and building a business process on one is asking for a Monday morning outage.
Access, cost and licence
Free UI with CSV export per query. Google also publishes a top-terms dataset through the BigQuery public dataset programme, which is the only bulk route we’d build on. The interface is bound by Google’s terms; this is not a redistributable dataset.
Best for
Nowcasting, seasonality checks, keyword-level demand research.
19. Google Books Ngrams — Best for long-run language change
Overview
Frequency counts for words and phrases up to five tokens, by year, across the digitised Google Books corpus in several languages, with part-of-speech variants.
What it does well
Nothing else gives you centuries of usage frequency as flat files you can join to your own timeline. The raw datasets are split by prefix, so you can pull only what you need.
Drawbacks
The corpus is what Google scanned, not a sample of what people wrote, and its composition shifts across decades as library holdings and OCR quality change. Scanning errors distort older centuries badly. Treat sharp inflections as suspect until you’ve checked the underlying volumes.
Access, cost and licence
Free bulk download plus the online viewer. The n-gram datasets are released under a Creative Commons attribution licence, which makes commercial use straightforward.
Best for
Computational linguistics, brand and concept longevity, cultural history.
20. New York Times developer APIs — Best for news archive metadata
Overview
A set of APIs over the paper’s own content: Article Search reaching back to 1851, bestseller lists, movie reviews, most-popular feeds, plus a real-time wire.
What it does well
A consistently edited, deeply indexed news archive with clean metadata across 170-plus years. For event studies and media analysis, the consistency is the point.
Drawbacks
You get metadata, headlines and abstracts. Not full article text. Rate limits are tight enough that large historical pulls need patience and scheduling, and the terms restrict commercial use and redistribution, so this belongs in research work rather than in a product. One publication is also one editorial position, which is a sampling problem if you treat it as “the news”.
Access, cost and licence
Free developer key. Read the API terms before anything ships externally.
Best for
Event dating, media framing analysis, long-run text research.
Big data sources we considered and left out
The candidate list ran to 25. Cutting it to 20 is the part worth explaining.
- Gapminder. Excellent for teaching and for making a point on stage. But it’s a compilation of World Bank, WHO and UN figures, and if you’re building anything you should be pulling from those directly. Our World in Data now does the harmonised-and-documented job with better provenance.
- CIA World Factbook. Still live, still readable, still mostly country narrative rather than bulk-downloadable series. For machine-readable country indicators, World Bank beats it on coverage and licensing clarity.
- Million Song Dataset. A landmark, and frozen since the early 2010s. The audio features reflect a long-dead feature extraction pipeline. Music and audio work now starts on Hugging Face.
- UCI Machine Learning Repository. This one we cut against expectation. Its datasets are famous, small, and so heavily overfitted by the literature that results on them tell you almost nothing. Fine for a lecture, not for a decision.
- Zenodo. A genuinely important repository, but it’s deposit-driven with per-deposit licences and weak discovery. Most people will and should reach it through Google Dataset Search rather than browsing it.
- Facebook Graph API. Not a free big data source any more, whatever old lists say. App review, permissions, business verification, and the removal of most public-content endpoints mean the open querying described in 2014 is gone. Academic access exists through separate programmes with their own approval process.
- Quandl. Frequently recommended in older roundups. It became Nasdaq Data Link, and the interesting series are paid.
One note on infrastructure that gets mistaken for a source: many city and state portals run on Socrata, now part of Tyler Technologies. It’s a platform, not a dataset. Go to the city’s portal, not to the vendor.
If you came back looking for Topsy, Likebutton, Freebase or Google Finance
They were on the previous version of this list. Here’s where they went.
- Topsy was bought by Apple and shut down in December 2015. Nothing free replaced it. Historic X/Twitter search now sits behind that platform’s paid API tiers, and the free tier is not usable for research.
- Likebutton stopped operating and the domain is parked. It depended on Facebook public data that is no longer exposed.
- Freebase was retired by Google in 2016 and its contents migrated to Wikidata, which is where community-curated structured entity data lives now, with a SPARQL endpoint and CC0 licensing.
- Google Finance discontinued its API and is a web page, not a data source. For free market series, FRED covers rates and indices; equity price history means an exchange feed or a commercial vendor.
How to choose the right big data source for the job
Four questions, in this order.
- What does the licence let you ship? Ask before you build, not after. Share-alike terms like ODbL and CC BY-SA can force disclosure of a derived database, and “no licence stated” means no permission, not free rein.
- Do you need row-level detail or an aggregate? Most public health and administrative data is published aggregated with small cells suppressed. If your question needs individual records, you’re heading for an application process, and probably a fee.
- Can you move it, or must you compute where it sits? Anything at Common Crawl or satellite imagery scale changes your architecture. That’s a platform conversation before it’s a data conversation.
- Will it still be there next quarter? A one-off analysis can use anything. A production dependency needs a versioned release, a stable endpoint and a named publisher.
And a question about your own house: who reconciles the number when the dashboard and the export disagree? Free data doesn’t remove that job. It just moves it earlier.
Frequently asked questions
What are 5 sources of data?
Grouped by origin rather than by website: machine and sensor output (telemetry, IoT, GPS); transactional systems (orders, payments, claims); logs and clickstream from applications and networks; public and administrative records (census, registries, regulatory filings); and human-generated content (text, images, reviews, social posts). Most real projects combine internal data from the first three with external big data sources from the last two.
What are examples of big data?
A retailer’s point-of-sale history joined to loyalty records. Hourly readings from thousands of weather stations across a century. A monthly crawl of the public web. Satellite imagery of the same field every five days. Practice-level prescribing records for a national health service. The common thread isn’t the row count. It’s that the volume or update rate makes ordinary tooling break.
What are the four types of data sources?
The usual taxonomy: internal (your own systems), external (public portals, purchased feeds), primary (you collected it for this purpose) and secondary (someone else collected it for theirs). Everything on this page is external and secondary, which is exactly why licence terms and provenance matter more here than in your own warehouse.
Where can I get big data for free?
Government portals for administrative and statistical data: Data.gov, Data.gov.uk, data.europa.eu, the US Census Bureau. Research and ML hubs for training data: Hugging Face, Kaggle. Cloud-hosted collections for anything at terabyte scale: the AWS Registry of Open Data and Common Crawl. Start at Google Dataset Search if you don’t know who publishes what you need. All of these are free to access; free to redistribute is a separate question, answered per dataset.
What is the best free dataset website?
There isn’t one answer, and any page that gives you one is guessing at your job. For discovery, Google Dataset Search. For training models, Hugging Face Datasets. For official statistics you can cite in a board paper, the national portal or statistics agency for the country in question. For scale that won’t fit anywhere local, the AWS Registry of Open Data. If you want a single default for a general analytics project, start with the relevant government portal and treat community uploads as a shortcut, not a source of record.
