32 articles on data — what drives the cost, how it is priced, and where the savings actually are.
Redshift, BigQuery, and Synapse price data warehousing on different axes, provisioned nodes or serverless, per-TB-scanned or per-slot, per-DWU. The cheapest depends on query pattern and volume, not the platform. This compares the models.
BigQuery storage bills active data (recently modified) at about $0.02/GB-month and long-term data (untouched 90 days) at about half that, with an option to bill physical (compressed) bytes instead of logical. Here is how to cut storage cost.
Synapse bills dedicated SQL pools per DWU-hour, serverless SQL per TB scanned, and Spark pools per vCore-hour, plus data integration. Pausing dedicated pools and using serverless for ad-hoc queries are the big levers. Here is the breakdown.
Data Factory bills orchestration per activity run, data movement per DIU-hour, and mapping data flows per vCore-hour, plus pipeline monitoring. Chatty pipelines and always-on data flow clusters are the usual overspend. Here is the breakdown.
Dataflow bills per vCPU-hour and GB-hour of worker time, plus a data-processed charge for Shuffle (batch) or Streaming Engine. Right-sizing workers and using the managed shuffle are the main levers. Here is how the model works.
Dataproc adds about $0.01 per vCPU-hour on top of the Compute Engine instances it runs, so the cluster costs more than the VMs alone. Ephemeral clusters, preemptible workers, and Dataproc Serverless are the main levers. Here is how to cut it.
Glue bills about $0.44 per DPU-hour for jobs and crawlers, with a minimum billing per run, plus Data Catalog storage and request fees. Fewer, right-sized DPUs and less frequent crawling are the main levers. Here is the breakdown.
Athena charges $5 per TB of data scanned, so cost is decided by how much data each query reads, not how often you query. Partitioning, columnar formats, and compression cut scans by an order of magnitude. Here is how.