Apache Parquet
Apache Spark
Apache Arrow
Amazon S3
DuckDB
Apache Avro
Apache Kafka
Hugging Face
GitHub Pages
Vercel
Netlify
Jekyll
Cloudflare Pages
surge.sh
Neocities
tiiny.host
Apache Parquet GitHub PagesNo Apache Parquet videos yet. You could help us improve this page by suggesting one.
Based on our record, GitHub Pages seems to be a lot more popular than Apache Parquet. While we know about 504 links to GitHub Pages, we've tracked only 31 mentions of Apache Parquet. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Apache Iceberg fits these requirements well. Iceberg stores data as immutable Apache Parquet files and adds them through atomic commits, so readers always see a consistent snapshot. A separate metadata layer prunes files by their statistics before the data itself is ever read, and those statistics can be extended to match an observability filtering profile. - Source: dev.to / 25 days ago
Depends on the domain. There's a bunch of sciences using large datasets served up efficiently using static file formats, e.g., https://zarr.dev/ and https://parquet.apache.org/. - Source: Hacker News / about 2 months ago
The data files themselves are still standard Parquet or ORC. The table format adds a metadata layer on top that gives those files the properties of a database table. - Source: dev.to / 3 months ago
The dataset is huge - in parquet conversion - it is total 9gb. And in raw PNG image nested folders - it is 67 gigabytes. Huge... - Source: dev.to / 4 months ago
The solution is to standardize on columnar formats like Apache Parquet. Parquet stores data in columns, not rows, which immediately enables column pruning. If a query is SELECT avg(price) FROM sales, the engine reads only the price column and ignores all others. This can reduce storage footprints by up to 75% compared to raw formats and is a cornerstone of modern analytics performance. - Source: dev.to / 9 months ago
The site itself is a statically generated Next.js app, built in CI and deployed to GitHub Pages via actions/deploy-pages. No server to manage, no hosting bill. - Source: dev.to / 4 months ago
Static sites are fast and cheap to host, but your data goes stale the moment you deploy. This post shows how a SvelteKit portfolio site serves live data from five external sources while still deploying as static HTML to GitHub Pages. - Source: dev.to / 4 months ago
All three themes are designed for accessible deployment. You can host them for free on Netlify, GitHub Pages, Vercel, or Cloudflare Pages. The only cost is a domain name (which can be as cheap as $5/year on Porkbun). - Source: dev.to / 6 months ago
This action can store collected benchmark results in GitHub pages branch and provide a chart view. Benchmark results are visualized on the GitHub pages of your project. - Source: dev.to / 10 months ago
But that's not the case. The blog is a simple static generated website using Jekyll, it is built and served through GitHub Pages. With that in mind it makes more sense to use tools and leverage tool calling. - Source: dev.to / 11 months ago
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Vercel - Vercel is the platform for frontend developers, providing the speed and reliability innovators need to create at the moment of inspiration.
Apache Arrow - Apache Arrow is a cross-language development platform for in-memory data.
Netlify - Build, deploy and host your static site or app with a drag and drop interface and automatic delpoys from GitHub or Bitbucket
Amazon S3 - Amazon S3 is an object storage where users can store data from their business on a safe, cloud-based platform. Amazon S3 operates in 54 availability zones within 18 graphic regions and 1 local region.
Jekyll - Jekyll is a simple, blog aware, static site generator.
