# Neo4j Features and Practical Usage 🚪 Pick the wrong data-import method up front and every downstream step pays for it. Neo4j's "Import your data" hub helps you choose the right entry point based on scale and frequency. 🏷️ Title: Import method selection guide 🔗 URL: 📘 Overview Neo4j offers several ways to load data, each with different strengths in terms of scale, execution mode (online vs offline), and permission requirements. This page is not a step-by-step tutorial but a decision hub for choosing the right method before you start. ⚙️ How It Works The main options are: ・Data Importer: a browser-based GUI where you drag and drop CSVs and visually map columns to nodes and relationships. No Cypher required; ideal for testing and prototyping. ・`LOAD CSV`: a general-purpose Cypher-based loader. Runs online (database stays up) and is usable by non-admin users. Good up to hundreds of thousands or low millions of rows. ・`neo4j-admin database import`: an offline bulk loader that writes directly to the native store format, making it the fastest path for initial loading of very large datasets (billions of entities). ・Connectors / APOC: continuous sync via Apache Spark, Kafka, and CDC, plus support for diverse formats like JSON, XML, and XLS. 🛠️ Practical Usage Decide the entry point by scale and frequency: ・A few thousand master records, fast → Data Importer (GUI) ・Millions of rows on a schedule / incremental → `LOAD CSV` (made idempotent with `MERGE`) ・Billions of entities, one-shot initial build → `neo4j-admin database import` (offline) ・Always-on continuous sync → Kafka / CDC / Spark connectors Whatever the path, a shared best practice is to create a uniqueness constraint on the key column before importing. ```cypher CREATE CONSTRAINT person_id IF NOT EXISTS FOR (p:Person) REQUIRE IS UNIQUE; ``` 💡 Use Cases Early in a project, split the paths: Data Importer for the PoC, `neo4j-admin import` for the production initial build, and `LOAD CSV` for daily incrementals. This keeps validation light and fast while making the bulk load as fast as possible. ⚠️ Caveats ・`neo4j-admin import` targets an empty database and runs offline, so it cannot be used against a live database. ・`LOAD CSV` tends to hit memory issues as row counts approach hundreds of thousands to millions; split work with `CALL { } IN TRANSACTIONS`. ・Continuous sync (Kafka/CDC) is distinct from initial loading and should be designed alongside it. ・This page is just the entry point; confirm the details of each method in its dedicated docs. #Neo4j# #DataImport#

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240 million more men than women use the internet globally. Men: 75.7% connected. Women: 70.7% connected. The gender gap is widest in South Asia, Africa, and the Middle East - where it can exceed 30 percentage points. Source: DataReportal Digital 2026 / ITU.

📱 Share of social media users who follow influencers 1. 🇬🇭 Ghana: 47% 2. 🇵🇭 Philippines: 43% 3. 🇧🇷 Brazil: 40% 4. 🇿🇦 South Africa: 35% 5. 🇹🇭 Thailand: 33% 6. 🇲🇾 Malaysia: 32% 7. 🇳🇬 Nigeria: 32% 8. 🇮🇩 Indonesia: 32% 9. 🇦🇪 UAE: 30% 10. 🇳🇿 New Zealand: 28% 11. 🇸🇦 Saudi Arabia: 28% 12. 🇰🇪 Kenya: 28% 13. 🇳🇴 Norway: 28% 14. 🇨🇱 Chile: 28% 15. 🇵🇹 Portugal: 28% 16. 🇦🇷 Argentina: 27% 17. 🇮🇪 Ireland: 26% 18. 🇲🇽 Mexico: 26% 19. 🇨🇴 Colombia: 26% 20. 🇦🇺 Australia: 25% 21. 🇪🇬 Egypt: 25% 22. 🇮🇳 India: 24% 23. 🇵🇱 Poland: 24% 24. 🇷🇸 Serbia: 23% 25. 🇪🇸 Spain: 23% 26. 🇸🇬 Singapore: 23% 🌍 World: 23% 27. 🇰🇷 South Korea: 22% 28. 🇭🇷 Croatia: 22% 29. 🇬🇷 Greece: 22% 30. 🇻🇳 Vietnam: 22% 31. 🇺🇸 US: 21% 32. 🇨🇦 Canada: 21% 33. 🇭🇰 Hong Kong: 21% 34. 🇧🇬 Bulgaria: 21% 35. 🇷🇴 Romania: 21% 36. 🇸🇪 Sweden: 20% 37. 🇮🇹 Italy: 19% 38. 🇳🇱 Netherlands: 19% 39. 🇭🇺 Hungary: 19% 40. 🇬🇧 UK: 19% 41. 🇩🇪 Germany: 19% 42. 🇫🇷 France: 18% 43. 🇦🇹 Austria: 18% 44. 🇩🇰 Denmark: 17% 45. 🇮🇱 Israel: 17% 46. 🇨🇭 Switzerland: 17% 47. 🇹🇼 Taiwan: 16% 48. 🇧🇪 Belgium: 16% 49. 🇨🇳 China: 16% 50. 🇨🇿 Czechia: 14% 51. 🇯🇵 Japan: 14% 52. 🇹🇷 Türkiye: 11% 53. 🇷🇺 Russia: 8% Social media users aged 16+. Includes influencers and other experts. Source: GWI, Q2 2025 survey of 53 markets, via DataReportal Digital 2026

🎰 Share of internet users who gambled online in the past month 1. 🇿🇦 South Africa: 32.3% 2. 🇳🇴 Norway: 31.2% 3. 🇬🇷 Greece: 25.8% 4. 🇳🇿 New Zealand: 24.0% 5. 🇬🇭 Ghana: 21.5% 6. 🇭🇺 Hungary: 21.5% 7. 🇬🇧 United Kingdom: 19.6% 8. 🇧🇷 Brazil: 19.0% 9. 🇦🇹 Austria: 18.7% 10. 🇦🇺 Australia: 18.0% 11. 🇳🇬 Nigeria: 18.0% 12. 🇨🇱 Chile: 16.6% 13. 🇰🇪 Kenya: 16.0% 14. 🇵🇹 Portugal: 15.8% 15. 🇭🇷 Croatia: 15.5% 16. 🇹🇭 Thailand: 15.0% 17. 🇭🇰 Hong Kong: 14.9% 18. 🇮🇪 Ireland: 14.8% 19. 🇷🇸 Serbia: 14.4% 20. 🇧🇪 Belgium: 14.3% 21. 🇨🇦 Canada: 14.3% 22. 🇨🇴 Colombia: 13.8% 23. 🇩🇰 Denmark: 13.8% 24. 🇷🇴 Romania: 13.7% 25. 🇲🇽 Mexico: 13.3% 26. 🇩🇪 Germany: 13.2% 27. 🇫🇷 France: 12.7% 28. 🇦🇷 Argentina: 12.5% 29. 🇸🇪 Sweden: 12.5% 30. 🇪🇸 Spain: 12.0% 31. 🇨🇭 Switzerland: 12.0% 32. 🇧🇬 Bulgaria: 11.9% 33. 🇵🇭 Philippines: 11.5% 34. 🇹🇷 Türkiye: 11.1% 35. 🇳🇱 Netherlands: 10.9% 36. 🇸🇬 Singapore: 10.6% 37. 🇵🇱 Poland: 10.4% 38. 🇺🇸 United States: 10.2% 39. 🇮🇹 Italy: 10.1% 40. 🇨🇿 Czechia: 9.9% 41. 🇮🇳 India: 7.6% 42. 🇯🇵 Japan: 7.5% 43. 🇲🇦 Morocco: 7.3% 44. 🇷🇺 Russia: 7.0% 45. 🇮🇱 Israel: 6.8% 46. 🇹🇼 Taiwan: 6.5% 47. 🇻🇳 Vietnam: 5.7% 48. 🇰🇷 South Korea: 2.7% 🌍 Worldwide: 7.8% Internet users aged 16+. Survey from Q2 2025. The worldwide figure is GWI's aggregate across all markets it surveys. Source: GWI, via DataReportal Digital 2026