Using pg_parquet you can trivially export data to S3, and using Crunchy Data Warehouse you can just as easily query or import Parquet files from PostgreSQL.
Using pg_parquet you can trivially export data to S3, and using Crunchy Data Warehouse you can just as easily query or import Parquet files from PostgreSQL.
The common denominator for all of these is that you have to be *doing* (modeling, writing SQL, and wrangling data) to get better.
still.visualmode.dev/blogmarks/6
The common denominator for all of these is that you have to be *doing* (modeling, writing SQL, and wrangling data) to get better.
still.visualmode.dev/blogmarks/6
www.crunchydata.com/blog/postgre...
www.crunchydata.com/blog/postgre...
- the partnership between @cncf.io and Andela
- the release of Akamai App Platform cc/ @mikemaney.bsky.social
- the rebranding of Akka
- the partnership between @gitlab.com and AWS
- the release of Crunchy Data Warehouse cc/ @craigkerstiens.com @crunchydata.com
- the partnership between @cncf.io and Andela
- the release of Akamai App Platform cc/ @mikemaney.bsky.social
- the rebranding of Akka
- the partnership between @gitlab.com and AWS
- the release of Crunchy Data Warehouse cc/ @craigkerstiens.com @crunchydata.com
Incremental pipelines come to Postgres via Crunchy Data! This is like "dbt incremental", not true incremental view maintenance like @materialize.com or Snowflake's dynamic tables, but it's a neat step towards IVM.
Incremental pipelines come to Postgres via Crunchy Data! This is like "dbt incremental", not true incremental view maintenance like @materialize.com or Snowflake's dynamic tables, but it's a neat step towards IVM.
There's raw events table and a summary table containing view counts.
You then define a pipeline using an insert..select command, and keeps running that to do fast, reliable, incremental processing in the background.
There's raw events table and a summary table containing view counts.
You then define a pipeline using an insert..select command, and keeps running that to do fast, reliable, incremental processing in the background.
I decided to build an extension that just keeps running the same command in Postgres with different parameters to do fast, reliable incremental data processing.
That's pg_incremental.
1/n
www.crunchydata.com/blog/pg_incr...
I decided to build an extension that just keeps running the same command in Postgres with different parameters to do fast, reliable incremental data processing.
That's pg_incremental.
1/n
www.crunchydata.com/blog/pg_incr...
www.crunchydata.com/blog/pg_incr...
TIL about the ROLLUP keyword that can go after GROUP BY which will create subtotals of numeric data!
TIL about the ROLLUP keyword that can go after GROUP BY which will create subtotals of numeric data!
Please commence the flame wars in the comments below.
www.bigdatawire.com/2024/12/03/h...
Please commence the flame wars in the comments below.
www.bigdatawire.com/2024/12/03/h...
www.crunchydata.com/blog/postgre...
www.crunchydata.com/blog/postgre...
A useful pattern is to keep track of loaded files in a Postgres table the same transaction that loads the file into Iceberg, such that each file is loaded exactly once
www.crunchydata.com/blog/iceberg...
A useful pattern is to keep track of loaded files in a Postgres table the same transaction that loads the file into Iceberg, such that each file is loaded exactly once
www.crunchydata.com/blog/iceberg...
https://www.crunchydata.com/blog/crunchy-data-warehouse-postgres-with-iceberg-for-high-performance-analytics
[comments] [10 points]
https://www.crunchydata.com/blog/crunchy-data-warehouse-postgres-with-iceberg-for-high-performance-analytics
[comments] [10 points]
https://www.crunchydata.com/blog/crunchy-data-warehouse-postgres-with-iceberg-for-high-performance-analytics
https://www.crunchydata.com/blog/crunchy-data-warehouse-postgres-with-iceberg-for-high-performance-analytics
It's like an Iceberg starter kit that bundles storage with the catalog and compaction. Systems looking to support Iceberg writes will have a relatively easy time targeting S3 tables catalog, because they don't need to add compaction.
1/n
It's like an Iceberg starter kit that bundles storage with the catalog and compaction. Systems looking to support Iceberg writes will have a relatively easy time targeting S3 tables catalog, because they don't need to add compaction.
1/n
Will be interesting to see if the same occurs with awareness around Iceberg. Having been pitching for a year now, people are still vastly unaware of Iceberg.
Will be interesting to see if the same occurs with awareness around Iceberg. Having been pitching for a year now, people are still vastly unaware of Iceberg.
Iceberg is a high-performance, open table format designed for managing large-scale data workloads in a #dataLake. Now, why does that matter? 🧵
Iceberg is a high-performance, open table format designed for managing large-scale data workloads in a #dataLake. Now, why does that matter? 🧵