1891
Kafka Is Not a Database
Get insights on Materialize's latest updates & trends in streaming databases & data products.
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The latest blog posts to take off on Hacker News. 4,224 posts.
1892
How to Think about Correlation?
I am an economist at the Federal Trade Commission with a very basic statistics question, one that I have put to several fairly high-powered econometricians, and to which no one has had a satisfying answer.
1893
If All You Have Is a Database, Everything Looks Like a Nail
Databases are fabulous resources. Of course, we’ll abuse them in ways that cause challenges.
1894
Scaling Datastores at Slack with Vitess
From the very beginning of Slack, MySQL was used as the storage engine for all our data. Slack operated MySQL servers in an active-active configuration. This is the story of how we changed our data storage architecture from the active-active clusters over to Vitess — a horizontal scaling system for MySQL. Vitess is…
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VisiData in 60 Seconds
Download faa-wildlife-strikes.csv , a dataset of all aircraft-wildlife collisions reported to the Federal Aviation Administration between 2010 and mid-2016.
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TimescaleDB vs. Amazon Timestream
Our TimescaleDB vs Amazon Timestream results surprised us, but after intensive testing, we found Timestream slow, expensive, and missing key capabilities.
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Using Bloom filters to efficiently synchronise hash graphs
This blog post uses MathJax to render mathematics. You need JavaScript enabled for MathJax to work.
1899
Materialize Raises a $32M Series B
Materialize secures Series B funding to enhance its Operational Data Warehouse with real-time streaming capabilities for immediate data action.
1900
Amazon S3 Update – Strong Read-After-Write Consistency
When we launched S3 back in 2006, I discussed its virtually unlimited capacity (“…easily store any number of blocks…”), the fact that it was designed to provide 99.99% availability, and that it offered durable storage, with data transparently stored in multiple locations. Since that launch, our customers have used S3…
1901
Feature Casualties of Large Databases
Databases shed important RDMS features as they get big. Examining why this tends to be the case, and some ideas for preventing it.
1902
Babelfish: SQL Server-to-Postgres Translation Layer
“The greatest force in legacy databases is inertia,” a widely regarded industry analyst once told me. Not superior functionality. Not better performance. Not lower cost. None of the above. Just inertia. Developers might say they prefer to run PostgreSQL to proprietary alternatives (and they do), but enterprises have…
1903
AWS Managed Workflows for Apache Airflow
As the volume and complexity of your data processing pipelines increase, you can simplify the overall process by decomposing it into a series of smaller tasks and coordinate the execution of these tasks as part of a workflow. To do so, many developers and data engineers use Apache Airflow, a platform created by the…
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1905
SQLite briefing for Linux kernel hackers (2019)
This is a briefing on SQLite intended for Linux kernel hackers, and especially those working on Linux filesystems.
1906
Writing a Postgres Foreign Data Wrapper for Clickhouse in Go
Postgres(hereinafter mentioned as PG) is a pretty cool database with lots of nice features, one of them little known ones is the ability of having Foreign data wrappers (hereinafter mentioned as FDWs). Clickhouse(hereinafter mentioned as CH) is another amazing database with an altogether different set of features…
1907
HopsFS: 100x Times Faster Than AWS S3
Many developers believe S3 is the "end of file system history". It is impossible to build a file/object storage system on AWS that can compete with S3...
1908
US Postal Service data suggests significant population decline in San Francisco
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PengelKeluaran SGP Hari Ini Resmi Togel Singapore Pools Tersedia Melalui Data SGP Prize dan Menyalin Pengeluaran SGP Hari Ini Untuk Pemain Toto SGP.
1909
Google Cloud SQL now supports PostgreSQL 13
Fully managed Cloud SQL cloud database service now supports PostgreSQL 13.
1910
Psql command line tutorial and cheat sheet
The least you need to know about Postgres
1911
Personal Data Warehouses: Reclaiming Your Data
I gave a talk yesterday about personal data warehouses for GitHub’s OCTO Speaker Series, focusing on my Datasette and Dogsheep projects. The video of the talk is now available, and …
1912
Migrating Large Heroku Postgres Instances to AWS Aurora Without Downtime
In this article I discuss a general process I used recently to migrate a large multi-terabyte Heroku Postgres Database from the Heroku Platform to Amazon Aurora Postgres on a live Heroku based application architecture with near zero downtime and builtin failover during the process.
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CQRS: Command Query Responsibility Segregation (2011)
CQRS (Command Query Responsibility Segregation) is the notion that you can use a different model to update information than the model you use to read information
1914
InfluxDB is betting on Rust and Apache Arrow for next-gen data store
InfluxData Founder & CTO Paul Dix lays out a vision for the future of InfluxDB and introduces a new project that will form its basis: InfluxDB IOx.
1915
Analyzing the Limits of Connection Scalability in Postgres
Why it’s important to improve Postgres connection scalability, plus an analysis on the limits of connection scalability today.
1916
Looking for Voter Fraud (In Old Elections) with Data Visualization
The 2020 US election is finally over, and all the election excitement of the last week reminded me of something I had been meaning to look into: Sergey Shpilkin’s data visualizations that cle…
1917
Parsing the Infamous Japanese Postal CSV
Late last year I released posuto, a package presenting Japanese postal code data in an easy-to-use format. It's based on data released by Japan Post, which is infamous for being widely used but hard to parse.
1918
Marketers are addicted to bad data
Modern marketing is all about data and however hard you might try, you can't spend any time around marketers online without being subjected to endless
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Mendoza: Use stack machines to compute efficient JSON diffs
Mendoza is a new, super efficient format for expressing differences between JSON documents.