Say you have ingestion pipelines; what are you primarily using? Are you using Databricks or Snowflake? Was the whole platform considered?

Ingestion pipelines typically involve extracting data from source systems, transforming it, and loading it into a target data store. The choice of platform depends on the data volume, velocity, and use case. Databricks excels at large‑scale batch and streaming ETL using Spark; it provides a unified environment for data engineering, data science, and machine learning. It is ideal when you need complex transformations, real‑time analytics, or integration with ML workflows. Snowflake, on the other hand, is a cloud data warehouse that offers elastic compute, automatic scaling, and strong SQL support. It is well suited for analytical workloads, ad‑hoc querying, and scenarios where you want a fully managed, pay‑as‑you‑go data warehouse without managing clusters. Many organizations adopt a hybrid approach: use Databricks for data ingestion and transformation, then stage the cleaned data into Snowflake for downstream analytics and BI. The decision also hinges on cost, governance, and existing cloud infrastructure. When evaluating a platform, consider factors such as data size, transformation complexity, latency requirements, and the skill set of the team.