Modern data warehousing: Snowflake, BigQuery, Redshift and the lakehouse

Data warehousing has evolved from on-premise Oracle databases to cloud-native platforms that separate compute and storage. The modern data stack makes it possible to analyze terabytes of data with SQL queries that complete in seconds.

Snowflake popularized the separation of compute and storage. You pay for storage independently from compute, and you can scale compute up and down as needed. Snowflake's architecture handles concurrency well. The tradeoff is cost: Snowflake is expensive for always-on workloads.

Google BigQuery is serverless and automatically scales. You don't manage any infrastructure. BigQuery's pricing charges for the data scanned by each query. It excels at ad-hoc analytics on large datasets. The key optimization is to minimize the data scanned by each query.

Amazon Redshift is the most traditional data warehouse. It uses columnar storage, compression, and massive parallelism to deliver fast query performance. Redshift is great for predictable workloads. The tradeoff is that you manage the cluster size and need to optimize distribution keys.

The data lakehouse pattern combines data lake flexibility with warehouse performance. Platforms like Databricks and Apache Iceberg store data in open formats on S3 but provide SQL querying, ACID transactions, and schema enforcement.

Choose your platform based on your workload. BigQuery is ideal for ad-hoc analytics. Snowflake is best for complex workloads with variable concurrency. Redshift is cost-effective for predictable, large-scale analytics at steady state.

Invest in data modeling. Star schemas with fact and dimension tables remain the best practice for analytical querying. ELT pipelines using dbt have become the standard for transformation.

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Rizwan Saleem | https://rizwansaleem.co