Introducing Crunchy Data Warehouse: A next-generation Postgres-native data warehouse. Crunchy Data Warehouse Learn more
Craig 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 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 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 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 MoreMarco Slot
Marco Slot
PostgreSQL is commonly used to store event data coming from various kinds of devices. The data often arrives as individual events or small batches, which requires an operational database to capture. Features like time partitioning help optimize the storage layout for time range filtering and efficient deletion of old data. The PostgreSQL feature set gives you a lot of flexibility for handling a variety of IoT scenarios, but there are certain scenarios for it is less suitable, namely: • Long-te...
Read MoreMarco Slot
Marco Slot
PostgreSQL is one of the most versatile data storage and processing tools available. We enhanced it even further by adding Iceberg tables to PostgreSQL in Crunchy Data Warehouse with a fast analytical query engine. What is Iceberg? Iceberg tables are stored in a compressed columnar format for fast analytics in object storage (S3). This means storage is cheap and there are no storage limits. Yet the tables are still transactional and work with nearly all PostgreSQL features. Crunchy Data Wareho...
Read MoreMarco Slot
Marco Slot
PostgreSQL is the bedrock on which many of today’s organizations are built. The versatility, reliability, performance, and extensibility of PostgreSQL make it the perfect tool for a large variety of operational workloads. The one area in which PostgreSQL has historically been lacking is analytics, which involves queries that summarize, filter, or transform large amounts of data. Modern analytical databases are designed to query data in data lakes in formats like Parquet using a fast vectorized...
Read MorePaul Ramsey
Paul Ramsey
The Overture Maps collection of data is enormous, encompassing over 300 million transportation segments , 2.3 billion building footprints , 53 million points of interest , and a rich collection of cartographic features as well. It is a consistent global data set, but it is intimidatingly large -- what can a person do with such a thing? Building cartographic products is the obvious thing, but what about the less obvious. With an analytical engine like PostgreSQL and Crunchy Bridge for Analy...
Read MoreMarco Slot
Marco Slot
Crunchy Data is excited to announce the next major feature release for Crunchy Bridge for Analytics : Geospatial Analytics . We have developed a variety of features to connect Postgres and PostGIS to S3 and public web servers to make spatial data access easier than ever. This release includes: • Creating an analytics table directly from a geospatial data set by providing only the URL, for ad-hoc queries and data transformations. • Creating a regular PostGIS table directly from a URL. • Automat...
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