357 private links
- Organizations don't use that much data.
Of queries that scan at least 1 MB, the median query scans about 100 MB. The 99.9th percentile query scans about 300 GB.
but 99.9% of real world queries could run on a single large node.
I did the analysis for this post using DuckDB, and it can scan the entire 11 GB Snowflake query sample on my Mac Studio in a few seconds.
When we think about new database architectures, we’re hypnotized by scaling limits. If it can’t handle petabytes, or at least terabytes, it’s not in the conversation. But most applications will never see a terabyte of data, even if they’re successful. We’re using jackhammers to drive finish nails.
As an industry, we’ve become absolutely obsessed with “scale”. Seemingly at the expense of all else, like simplicity, ease of maintenance, and reducing developer cognitive load
Years it takes to get to 10x:
10% -> ~ 24y
50% -> ~ 5.7y
200% -> ~ 2.10y
Scaling is also a luxurious issue in many cases: it means the business runs well.
- Hardware is getting really, really good
In the last decade:
SSDs got ~5.6x cheaper, 30x more on a single SSD and 11x faster in sequential reads and 18x in radom reads.
CPUs core count went up 2.6x, price went down at least 5x per core, each Turin core is also probably 2x-2.5x faster.
Distributed systems are also overkill as hardware progresses faster.
Auto-complétion d'adresses? Oui
Avec une image docker de https://photon.komoot.io/: https://github.com/rtuszik/photon-docker
ENISA is mandated to develop and maintain the European vulnerability database.
Storing the raw blobs data has one advantage: no data is lost and they can be refined by need.
IDE references can be thrown into postgres in order to retrieve them.
Handling chinese characters in a JSONB column and a dictionnary.
or (of course) temperature changes
Let's check this :D
Cette liste (non exhaustive) recence les principales sources de données accessibles en ligne utiles dans des travaux de diagnostic et d'analyse des territoires (aménagement, urbanisme, mobilité, environnement,…)
Un recensement des violences policières
- 3D flow diagram for relationships and connections
- Card Diagram to highlight and select information or data in relation to its surrounding data and information
- Pyramid graph: Being great at showing two categories of information and comparing them horizontally, they are an alternative to typical horizontal or vertical bar graphs.
Pyramid graph - Sankey Flow Diagram: show the progression and the journey of information and data and how they are connected in relation to their data value.
- Stream graph: a great way to show the data and how it relates to the other data
- Tree map: It’s a great way to show the data spatially and how the data value relates, in terms of size, to the rest of the data.
- Waterfall chart: showing the data and how it relates in a vertical manner to the range of data values.
- Doughnut chart: show the data against the other data segments, and value within a range of data.
- Lollipop chart: excellent method to demonstrate percentage values that also integrates the label and data value well.
- Bubble Chart: illustrate data values in terms of size and sub-classification in relation to the surrounding data.
A 1.5GB csv file to retrieve all video names
Plus de données disponibles!
L'accueil du site: https://www.opendatarchives.fr/
(fichiers datant du 12 septembre 2018)
Pour un jour X, je veux connaître les fruits et légumes correspondant à la période de production.
Pour une période X, je veux connaître les fruits et légumes correspondant à la période de production.
Projets existants: