New Amazon S3 Tables: Storage optimized for analytics workloads | Amazon Web Services Amazon S3 Tables optimize tabular data storage (like transactions and sensor readings) in Apache Iceberg, enabling high-performance, low-cost queries using Athena, EMR, and Spark.

This announcement is really interesting, Iceberg in S3 🤯

Learn more: github.com/Titan-System...

Titan Core helps simplify RBAC alignment, save time, and maintain consistency across environments.

Defining granular permissions for data products with consistent security models, including data access roles, functional roles, and role hierarchies.

I’m exploring Titan Core for two key use cases:
Deploying base infrastructure for new Snowflake accounts, using templates for databases, roles, warehouses, integrations, and account configurations.

Titan Core lets you define roles, privileges, and resources with YAML or Python, generating SQL to ensure consistency and reusability.

RBAC (Role-Based Access Control) is key for managing permissions in Snowflake, but scaling it with SQL scripts can get complex. Titan Core created by
@teej.bsky.social simplifies this process.

Code example showing how to configure Iceberg tables in Snowflake using dbt, including materializations like Table, Incremental, and Dynamic Table with parameters such as table_format, external_volume, and base_location_subpath.

Testing Iceberg tables in Snowflake with dbt! Curious to see if it becomes the standard for open table formats #databs

Hello World from Python SDK

In the Chicago open data portal there are quite a few datasets very well documented and exposed via API.

In particular I'm using a dataset that has all the crimes in the city since 2001, it's quite interesting:
data.cityofchicago.org/Public-Safet...