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emailEfficient data transformation and modeling framework that is backwards compatible with dbt.
SQLMesh is a next-generation data transformation framework designed to ship data quickly, efficiently, and without error. Data teams can efficiently run and deploy data transformations written in SQL or Python with visibility and control at any size.
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Data Runs Better on SDF. Transform Data Better with SDF.
SDF is the fastest way to build a scalable, reliable, and optimized data warehouse.
SDF is a developer platform for data that scales SQL understanding across an organization, empowering all data teams to unlock the full potential of their data.
SDF is a multi-dialect SQL compiler, transformation framework, and analytical database engine. It natively compiles SQL dialects, like Snowflake, and connects to their corresponding data warehouses to materialize models.
Cross-Language Serialization for Relational Algebra.
A cross platform way to express data transformation, relational algebra, standardized record expression and plans.
Substrait is a format for describing compute operations on structured data. It is designed for interoperability across different languages and systems.
RSS-lambda transforms RSS feeds without RSS client lock-in.
There are RSS clients that can perform transformations on RSS feeds, e.g. only keep entries with certain keywords, or translate texts of the entries
However, using those features from the RSS clients will create RSS client lock-in that prevents you from moving to another RSS client if you desire
RSS-lambda is an application that perform transformations on the server-side instead so that you can freely move to another RSS client while keeping the transformations. It's also self-hostable so that you don't even need to rely on the official server instance!
Transform Data in Your Warehouse. Build trusted data products faster.
Accelerate your data transformation process with dbt Cloud and start delivering data that you and your team can rely on. dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications. Analysts using dbt can transform their data by simply writing select statements, while dbt handles turning these statements into tables and views in a data warehouse.
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