𝗠𝗼𝗱𝗲𝗿𝗻 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴

𝗠𝗼𝗱𝗲𝗿𝗻 𝗗𝗮𝘁𝗮 𝗪𝗮𝗿𝗲𝗵𝗼𝘂𝘀𝗶𝗻𝗴

Data warehouses moved from on-premise to the cloud. Now compute and storage are separate. You analyze terabytes of data in seconds using SQL.

Snowflake separates compute and storage. You pay for each separately. Scale compute as you need. It handles many users well. It costs more for always-on work.

BigQuery is serverless. It scales on its own. You manage no hardware. You pay for data scanned. It works well for ad-hoc analytics. Minimize scanned data to save money.

Redshift is a traditional warehouse. It uses columnar storage. It works well for predictable workloads. You manage the cluster size. Optimize distribution keys for speed.

The lakehouse combines lakes and warehouses. Databricks and Apache Iceberg use open formats on S3. They offer SQL and ACID transactions.

Pick a platform based on your work.

• Use BigQuery for ad-hoc analytics.
• Use Snowflake for variable workloads.
• Use Redshift for predictable large scale analytics.

Focus on data modeling. Star schemas work best for analytics. Use ELT pipelines with dbt for transformations.

Source: https://dev.to/therizwansaleem/modern-data-warehousing-snowflake-bigquery-redshift-and-the-lakehouse-doa Optional learning community: https://t.me/GyaanSetuAi