Introducing Crunchy Data Warehouse: A next-generation Postgres-native data warehouse. Crunchy Data Warehouse Learn more
Marco Slot
Marco Slot
We are excited to release Crunchy Data Warehouse, a modern data warehouse for Postgres. Crunchy Data Warehouse combines Postgres with Iceberg, Parquet, and data lake formats for fast analytics queries and cost efficient storage.
Elizabeth ChristensenChristopher Winslett
Elizabeth ChristensenChristopher Winslett
Histograms were first used in a lecture in 1892 by Karl Pearson — the godfather of mathematical statistics. With how many data presentation tools we have today, it’s hard to think that representing data as a graphic was classified as “innovation”, but it was. They are a graphic presentation of the distribution and frequency of data. If you haven’t seen one recently, or don’t know the word histogram off the top of your head - it is a bar chart, each bar represents the count of data with a defined...
Read MoreGreg Nokes
Greg Nokes
Today's release of Crunchy Postgres for Kubernetes, version 5.8, is a substantial update that introduces a range of features designed to revolutionize your data infrastructure and observability. Whether you are a seasoned DevOps engineer, a database administrator, or an application developer seeking a reliable Postgres environment, this version offers enhancements to streamline your workflows and enhance the overall efficiency of your deployments. The developer-focused improvements are: • Enhanc...
Read MoreCraig Kerstiens
Craig Kerstiens
Today I'm excited to announce the release of Crunchy Data Warehouse on premises, which provides one of the easiest and yet richest ways to work with your data lake in the environment of your choosing. Built on top of Crunchy Postgres for Kubernetes, Crunchy Data Warehouse extends Postgres with a modern data warehouse solution, giving you: • The ability to easily query data where it resides in S3 or S3 compatible storage (like MinIO). With a variety of data formats supported including CSV, JSO...
Read MoreCraig Kerstiens
Craig Kerstiens
As a database service provider, we store a number of logs internally to audit and oversee what is happening within our systems. When we started out, the volume of these logs is predictably low, but with scale they grew rapidly. Given the number of databases we run for users on Crunchy Bridge, the volume of these logs has grown to a sizable amount. Until last week, we retained those logs in AWS CloudWatch. Spoiler alert: this is expensive. While we have a number of strategies to drive efficiency...
Read MoreElizabeth ChristensenDoug Hunley
Elizabeth ChristensenDoug Hunley
The Center for Internet Security (CIS) releases security benchmarks to cover a wide variety of infrastructure used in modern applications, including databases, operating systems, cloud services, containerized services, and even networking. Since 2016 Crunchy Data has collaborated with CIS to provide this security resource for those deploying Postgres. The output of this collaboration is a checklist for folks to follow and improve the security posture of Postgres deployments. The PostgreSQL CIS...
Read MoreÖnder Kalacı
Önder Kalacı
Today we're excited to announce built-in maintenance for Iceberg in Crunchy Data Warehouse . This enhancement to Crunchy Data Warehouse brings PostgreSQL-style maintenance directly to Iceberg. The warehouse autovacuum workers continuously optimize Iceberg tables by compacting data and cleaning up expired files. In this post, we'll explore how we handle cleanup, and in the follow-up posts, we'll take a deeper dive into compaction. If you use Postgres, you are probably familiar with tables and ro...
Read MoreGreg Sabino Mullane
Greg Sabino Mullane
Someone recently asked on the Postgres mailing lists about how to remove unwanted duplicate rows from their table. They are “unwanted” in that sense that the same value appears more than once in a column designated as a primary key. We’ve seen an uptick in this problem since glibc was kind enough to change the way they sorted things. This can lead to invalid indexes when one upgrades their OS and modifies the underlying glibc library. One of the main effects of a corrupted unique index is allowi...
Read MoreCraig Kerstiens
Craig Kerstiens
Citus is in a small class of the most advanced Postgres extensions that exist. While there are many Postgres extensions out there, few have as many hooks into Postgres or change the storage and query behavior in such a dramatic way. Most that come to Citus have very wrong assumptions. Citus turns Postgres into a sharded, distributed, horizontally scalable database (that's a mouthful), but it does so for very specific purposes. Citus, in general, is fit for these type of applications and only the...
Read MorePaul Ramsey
Paul Ramsey
What's your favourite infinite sequence of non-repeating digits? There are some people who make a case for e , but to my mind nothing beats the transcendental and curvy utility of π, the ratio of a circle's circumference to its diameter. Drawing circles is a simple thing to do in PostGIS -- take a point, and buffer it. The result is circular, and we can calculate an estimate of pi just by measuring the perimeter of the unit circle. Except, look a little more closely -- this "circle" seems to...
Read MoreAykut Bozkurt
Aykut Bozkurt
Seamless integration of dbt with Crunchy Data Warehouse automates data movement between Postgres and Apache Iceberg. dbt’s modular SQL approach, combined with Iceberg’s scalable storage, and Postgres’ query engine means you can build fast, efficient, and reliable analytics—with minimal complexity. Today let’s dig into an example of using dbt with Postgres and Iceberg. The steps will be: 1. Set up Iceberg tables in Crunchy Data Warehouse using real-world real-time data from GitHub events 2. Confi...
Read MoreElizabeth Christensen
Elizabeth Christensen
Working on big data loads and or data type changes can be tricky - especially finding and correcting individual errors across a large data set. Postgres versions, 16, 17, and newer have a new function to help with data validation: . is a sql function that can be queried that will determine if a given input can be parsed into a specific type like numeric, date, JSON, etc. Here’s a super basic query to ask if ‘123’ is a valid integer. This function gives a t-true and f-false response. So if I as...
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