Schema.org usage statistics reveal TVSeason vs TVSeries adoption

Data-driven transparency in the web standards world and it's about time. Google Search Central just highlighted something the structured data community has quietly needed: Schema.org's usage statistics dataset. It provides monthly aggregate data drawn from millions of domains, showing which Schema.org terms are actually being used in the wild and how that adoption shifts over time. The raw .csv is on GitHub, and trends are surfaced directly on individual schema term pages. For years, structured data decisions were made on best guesses and anecdotal experience. Now there's real signal. Practically, this changes things for: Developers & SEOs: benchmark your implementation against what the ecosystem is actually doing, not just what's theoretically recommended. Tooling builders: prioritize schema types that deserve richer support based on real adoption data. The standard itself: maintainers can see what's thriving, what needs better documentation, and what's quietly being abandoned. And yes to settle the question Google raised TVSeason vs TVSeries? The data says TVSeason sits in the 1K–10K domain bucket. Now go check TVSeries. 🙂 This is how the open web gets better incrementally, transparently, with evidence. #StructuredData #SEO #SchemaOrg #WebStandards #OpenData #GoogleSearch

This is a big deal for structured data practitioners. You can now see exactly how widely adopted any Schema.org type is across millions of real domains. No more guessing whether it is worth implementing. The data is right there. #SEO #StructuredData #SchemaOrg #TechnicalSEO

Google just released Schema.org's usage stats. If you are curious to check the data, you will see Itemtype vs Predicate. Itemtype is a thing, like a class or entity type that describes what something "is". While, Predicate is a property, which describes an attribute or relationship "of" a thing. A simple analogy: if itemtypes are database tables, predicates are the columns. Most used schema markup is the usual, no surprises here. BreadcrumbList, EntryPoint, ImageObject, ListItem, Organization, Person, etc.

Nice public dataset. Interested to see what trends are like when this dataset is updated. If you want to see this 'visualised' there's a streamlit here: https://lnkd.in/eFvxQwiy

New video! For 99% of databases and 99% of the time, the default settings for the optimizer will be fine for your performance needs. But occasionally, you might need to divert to get the most out of it. https://lnkd.in/gsAex_5m

🌟 Latency is the delay. How long it takes for a single packet to travel from sender to receiver. If your ping shows 40 ms round-trip, that's latency. Throughput is the actual delivery rate. How much data is successfully transferred per second. If your download shows 62 Mbps, that’s throughput. Bandwidth is the maximum capacity of the link. For example, a 100 Mbps connection is the upper limit under ideal conditions. Similarly, low latency doesn't always mean high throughput. Small payloads, single connections, and tight window sizes can all keep throughput low, which is why fast responses don't guarantee you're sending a lot of data. Another way to understand these three concepts: Bandwidth is the highway width. Throughput is the traffic flow. Latency is how long it takes a car to go from A to B. All three matter, but they solve different problems. Great breakdown by ByteByteGo on this topic, worth reading the full piece: https://lnkd.in/gMirwXmX Credit: ByteByteGo #DatabasePerformance #SystemDesign #BackendDevelopment #SQL #Latency #TechCommunity #SoftwareEngineering

Embeddings are primarily used to: A) Encrypt data B) Represent information in numerical vector space C) Compress databases only D) Generate source code automatically

An R code for big data if the file is too large to load into RAM with dplyr and arrow scrips: library(arrow) library(dplyr) ds <- open_dataset("large_stock_data_parquet/") summary_data <- ds %>% filter(year >= 2000) %>% group_by(permno, year) %>% summarise( avg_return = mean(ret, na.rm = TRUE), volatility = sd(ret, na.rm = TRUE) ) %>% collect()

In advanced timeseries analysis there is a new challenge to handle I(2) series. In this context, I provide a tutorial on Testing for cointegration between I(1) and I(2) series in timeseries data. https://lnkd.in/g4AhnGND

All of Maple also gets tracked and observed on Maple. Here are a few dashboards I use to track Query Engine and Ingest service perfomance