Most miles between talks
Bruce Momjian covered 149,109 miles between 42 in-person events. Great-circle distance from each in-person event a speaker spoke at to the next, in date order, 2019 onward.
Top 20 by total miles
Sum of the distances between consecutive in-person events. Hover a bar for the longest single trip.
Tightest long-haul turnarounds
Hops of 1,000+ miles ranked by miles per day between the end of one event and the start of the next. Each speaker's tightest hop only; speakers who made the same hop are grouped.
Top 100 of 5,077 speakers with 3+ located in-person events.
| # | Speaker | Miles | Events | Per trip | Countries | Quick turns |
|---|---|---|---|---|---|---|
| 1 | Bruce Momjian EDB | 149,109 | 42 | 3,637 | 17 | 6 |
| 2 | Joe Reis Reis Megacorp; Nerd Herd | 129,550 | 30 | 4,467 | 7 | 8 |
| 3 | Bob Ward Microsoft | 127,717 | 40 | 3,275 | 6 | 2 |
| 4 | Cheuk Ting Ho JetBrains; TerminusDB | 126,664 | 43 | 3,016 | 17 | 4 |
| 5 | Jack Gidding STAC | 115,650 | 23 | 5,257 | 6 | 1 |
| 6 | Grant Fritchey Redgate | 106,507 | 35 | 3,133 | 10 | 1 |
| 7 | Russell Keith-Magee Anaconda; BeeWare Project | 102,860 | 15 | 7,347 | 6 | 1 |
| 8 | Jim Mlodgenski Amazon Web Services | 97,695 | 18 | 5,747 | 3 | — |
| 9 | Seth Friedman Liquid-Markets | 96,394 | 22 | 4,590 | 4 | — |
| 10 | Pavlo Golub CYBERTEC | 96,219 | 39 | 2,532 | 17 | 2 |
| 11 | James Corcoran STAC | 87,546 | 19 | 4,864 | 6 | 2 |
| 12 | Ryan Booz Redgate; pganalyze | 83,149 | 28 | 3,080 | 11 | 2 |
| 13 | Matt Gordon Centric Consulting; Lean TECHniques | 79,903 | 32 | 2,578 | 5 | 1 |
| 14 | Peter Zaitsev Percona; Percona, Coroot, FerretDB | 78,672 | 24 | 3,421 | 7 | — |
| 15 | Claire Giordano Microsoft | 76,460 | 25 | 3,186 | 13 | — |
| 16 | Franck Pachot MongoDB; Yugabyte | 74,824 | 26 | 2,993 | 10 | 1 |
| 17 | Magnus Hagander Redpill Linpro | 71,781 | 28 | 2,659 | 14 | — |
| 18 | Frank Geisler GDS Business Intelligence GmbH | 67,803 | 21 | 3,390 | 5 | — |
| 19 | Thomas Munro EDB; Microsoft | 66,849 | 11 | 6,685 | 5 | — |
| 20 | Pablo Galindo Salgado Bloomberg; Python Software Foundation | 66,507 | 16 | 4,434 | 7 | — |
| 21 | Aaron Foo FMAD Engineering; FMADIO | 64,814 | 12 | 5,892 | 5 | 1 |
| 22 | Robert Haas EDB; Databricks | 63,101 | 19 | 3,506 | 9 | — |
| 23 | Alastair Richardson Xilinx; AMD | 62,873 | 11 | 6,287 | 6 | 1 |
| 24 | Hannes Mühleisen DuckDB Labs; CWI | 62,616 | 26 | 2,505 | 7 | 1 |
| 25 | Patrick LeBlanc Microsoft | 61,602 | 19 | 3,422 | 5 | — |
| 26 | David Snowdon Arista; Timebeat | 59,935 | 17 | 3,746 | 5 | — |
| 27 | Pat Wright Red Gate Software; Redgate | 59,027 | 17 | 3,689 | 6 | 1 |
| 28 | Stefanie Molin Bloomberg | 58,828 | 15 | 4,202 | 10 | — |
| 29 | Robert Treat Amazon Web Services; pgtreats.info | 56,186 | 21 | 2,809 | 12 | 3 |
| 30 | Alexander Kukushkin Zalando; Microsoft | 56,016 | 23 | 2,546 | 15 | — |
| 31 | Christian Kleinerman Snowflake | 55,189 | 14 | 4,245 | 3 | 1 |
| 32 | Andreas Scherbaum Adjust GmbH; Andreas Scherbaum IT-Services | 54,431 | 19 | 3,024 | 12 | 2 |
| 33 | Leonard Lobel Sleek Technologies, Inc | 54,280 | 31 | 1,809 | 2 | 1 |
| 34 | Łukasz Langa Python Software Foundation | 53,558 | 13 | 4,463 | 7 | — |
| 35 | Laurent de Barry Exegy; Enyx | 53,486 | 20 | 2,815 | 3 | — |
| 36 | James Powell Don't Use This Code | 53,302 | 19 | 2,961 | 7 | 2 |
| 37 | David Meyer Databricks | 52,786 | 11 | 5,279 | 6 | — |
| 38 | Andrey Borodin Yandex Cloud; Amazon Web Services | 52,159 | 15 | 3,726 | 6 | — |
| 39 | Cliff Maddox NovaSparks; Exegy | 51,899 | 21 | 2,595 | 3 | — |
| 40 | Reuven M. Lerner Lerner Consulting; Reuven Lerner — Python training | 51,884 | 13 | 4,324 | 6 | — |
| 41 | Karen (Karrie) Jex Crunchy Data; EDB | 51,690 | 28 | 1,914 | 15 | 3 |
| 42 | Avinash Vallarapu Percona; MigOps | 51,566 | 13 | 4,297 | 5 | 2 |
| 43 | Evan Kohilas nohumanerrors | 51,266 | 7 | 8,544 | 4 | — |
| 44 | Devrim Gündüz EDB; Gündüz Danışmanlık | 51,229 | 18 | 3,013 | 11 | 1 |
| 45 | Lætitia AVROT EDB; Docker | 51,169 | 21 | 2,558 | 12 | 1 |
| 46 | Andrea Suardi Xelera | 50,268 | 8 | 7,181 | 5 | — |
| 47 | Tomas Vondra Microsoft; EDB | 50,000 | 29 | 1,786 | 11 | 1 |
| 48 | Jay Kreps Confluent | 49,621 | 10 | 5,513 | 3 | — |
| 49 | Emil Eifrem Neo4j | 49,468 | 13 | 4,122 | 5 | — |
| 50 | Holden Karau Fight Health Insurance; Google | 49,353 | 15 | 3,525 | 4 | 2 |
| 51 | Juan Sequeda data.world; Capsenta | 48,551 | 15 | 3,468 | 4 | — |
| 52 | Matei Zaharia Stanford and Databricks; Databricks | 47,540 | 15 | 3,396 | 4 | — |
| 53 | Deb Nicholson Python Software Foundation; Open Source Initiative | 46,066 | 10 | 5,118 | 8 | — |
| 54 | Ines Montani Explosion | 45,870 | 24 | 1,994 | 13 | 1 |
| 55 | Dilip Kumar Amazon Web Services; Google | 45,698 | 10 | 5,078 | 3 | — |
| 56 | Matt Topol Columnar; Voltron Data | 45,308 | 14 | 3,485 | 4 | 1 |
| 57 | Vahan Sardaryan LDA Technologies | 45,300 | 17 | 2,831 | 4 | — |
| 58 | Alvaro Hernandez OnGres; reserved | 45,197 | 14 | 3,477 | 5 | — |
| 59 | Christophe Pettus PostgreSQL Experts, Inc; PGX, Inc. | 45,081 | 18 | 2,652 | 9 | — |
| 60 | Andres Freund Microsoft; EDB | 44,958 | 13 | 3,747 | 8 | — |
| 61 | Dustin Ingram Python Software Foundation; Google | 44,796 | 10 | 4,977 | 7 | — |
| 62 | Jim Dowling Hopsworks; Logical Clocks | 44,568 | 14 | 3,428 | 6 | 2 |
| 63 | Hans-Jürgen Schönig CYBERTEC | 44,516 | 21 | 2,226 | 12 | 1 |
| 64 | Dave Weber Lenovo | 44,477 | 10 | 4,942 | 4 | — |
| 65 | Peter Eisentraut EDB; 2ndQuadrant | 44,422 | 16 | 2,961 | 8 | 1 |
| 66 | Zoe Steinkamp InfluxDB; InfluxData | 44,302 | 10 | 4,922 | 5 | 1 |
| 67 | Luigi Nardi DBtune | 44,235 | 19 | 2,458 | 11 | — |
| 68 | Alex Merced Dremio | 44,107 | 20 | 2,321 | 6 | 2 |
| 69 | Henrietta Dombrovskaya Enterprise DB; DRW | 43,617 | 19 | 2,423 | 11 | — |
| 70 | Sahir Azam MongoDB | 43,381 | 13 | 3,615 | 5 | — |
| 71 | Kellyn Gorman Redgate; Microsoft | 43,127 | 25 | 1,797 | 4 | 1 |
| 72 | Danica Fine Confluent; Snowflake | 42,828 | 14 | 3,294 | 6 | — |
| 73 | Neeraj Pandey Ashoka University | 42,560 | 8 | 6,080 | 3 | — |
| 74 | Adam Hill Amazon Web Services; ComplyAdvantage | 42,437 | 12 | 3,858 | 3 | — |
| 75 | Adi Polak Treeverse; Confluent | 42,016 | 13 | 3,501 | 4 | 3 |
| 76 | Ginger Grant Desert Isle Group | 41,946 | 20 | 2,208 | 3 | 1 |
| 77 | Jimmy Angelakos SolarWinds MSP; 2ndQuadrant | 41,845 | 21 | 2,092 | 10 | — |
| 78 | Anthony Shaw DevOps; Microsoft | 41,329 | 9 | 5,166 | 5 | — |
| 79 | Tyler Hannan ClickHouse | 41,292 | 7 | 6,882 | 4 | 1 |
| 80 | Mridul Seth UCLouvain; NetworkX | 41,282 | 8 | 5,897 | 6 | — |
| 81 | Angie Jones Block; Agentic AI Foundation | 41,202 | 10 | 4,578 | 8 | 2 |
| 82 | Stephen Frost Crunchy Data | 40,987 | 12 | 3,726 | 6 | — |
| 83 | Zhamak Dehghani Nextdata; Thoughtworks | 39,561 | 10 | 4,396 | 3 | — |
| 84 | Louis Liu Shengli Technologies | 39,510 | 9 | 4,939 | 5 | 1 |
| 85 | Michael Meskes credativ; Instaclustr | 39,418 | 8 | 5,631 | 5 | — |
| 86 | Charly Batista Percona; EDB | 38,628 | 14 | 2,971 | 6 | 1 |
| 87 | Javier de la Torre CARTO | 38,598 | 12 | 3,509 | 2 | — |
| 88 | Timothy Spann StreamNative; Cloudera | 38,531 | 14 | 2,964 | 5 | — |
| 89 | John Morehouse Denny Cherry & Associates Consulting | 38,400 | 12 | 3,491 | 5 | — |
| 90 | Sridhar Ramaswamy Snowflake | 38,384 | 8 | 5,483 | 4 | — |
| 91 | Hannu Krosing Google | 38,340 | 8 | 5,477 | 6 | — |
| 92 | Chang She LanceDB; Eto Labs | 38,222 | 12 | 3,475 | 4 | — |
| 93 | Ásgeir Gunnarsson Datheos; North Insights ApS | 37,872 | 16 | 2,525 | 6 | — |
| 94 | Erin Stellato Microsoft; SQLskills | 37,741 | 10 | 4,193 | 5 | — |
| 95 | Stephen Chin Neo4j | 37,563 | 11 | 3,756 | 4 | — |
| 96 | Jarek Potiuk Polidea; Apache Software Foundation | 37,475 | 12 | 3,407 | 6 | — |
| 97 | Luca Mezzalira DAZN; Amazon Web Services | 37,090 | 11 | 3,709 | 2 | — |
| 98 | Gabor Szarnyas DuckDB Labs; Budapest University of Technology and Economics | 37,013 | 16 | 2,468 | 8 | — |
| 99 | Brian A. Randell MCW Technologies | 36,954 | 27 | 1,421 | 1 | 1 |
| 100 | Joe Conway Crunchy Data; Amazon Web Services | 36,653 | 12 | 3,332 | 6 | — |
How this is counted
- Miles are great-circle distances between the cities of consecutive in-person events in date order, not actual flights: a speaker who flies home between two European events still counts only the hop between them.
- Left out: online and cancelled events, and events with no known city. Speakers need at least three located in-person events.
- Turnarounds use the days from the last day of one event to the first day of the next. Events run several days, so a speaker may have had more time than this if their talk came late. Far-apart events with no feasible gap (overlapping, the same day, or under two days for 8,000+ miles) are treated as remote or streamed talks and left out of the turnaround figures.
- "Speaks mostly in" is the country where the person gave most of their in-person talks, not where they live; the index has no residence data.
- Coverage varies. Conferences that publish full speaker lists (Postgres, SQLSaturday, PyCon, STAC) are fully counted; many vendor flagships list only keynotes.