Canner

The growth of data innovation has exploded in recent years, largely due to the thriving cloud data warehouse communities such as Snowflake, Redshift, and BigQuery, with their main focus on SQL users and business intelligence use cases. The advent of Lake House architecture brings analytics closer to data lakes, enabling heterogeneous and distributed data processing engines to ingest sources, including diverse workloads such as data science, machine learning capabilities and near real time analytics enablement; it has also spawned thriving innovations in integrated data services that automate and unify data modeling, transformation, and metrics such as dbt and LookML. Collectively, these tools lay the foundation upon which next-generation low code or no code real time BI and analytics data applications can be constructed for various data consumers of different personas.