14 articles on analytics — what drives the cost, how it is priced, and where the savings actually are.
ClickHouse on your own instances can serve analytical queries for a fraction of per query warehouse pricing, but only above a certain query volume. Here is where the crossover sits and what it costs to cross it.
Switching a table from CSV to columnar Parquet typically cuts storage by 70 percent and query scan volume by 90 percent or more. On per TB scan pricing that is the difference between $500 and $40 a month.
On scan priced engines you pay for every byte the engine reads, not every byte you use. Partitioning, clustering, and file layout decide which of those two numbers you get billed for.
A streaming and batch pipeline ingesting 1 TB a day costs about 5,721 dollars a month on AWS, which is 19 cents per gigabyte. Here is the breakdown, and why the transform step is the cheapest part of it.
Athena charges $5 per terabyte scanned, so the whole cost game is scanning less. Partitioning, columnar formats, and compression can cut the data a query reads by 10 to 100 times. Here is how each lever works and how they stack to slash an Athena bill.
Glue crawlers keep your Data Catalog in sync with S3, but they bill per DPU-hour with a 10-minute minimum and a 2-DPU floor, so frequent crawls of many tables cost more than teams expect. Here is the pricing and how to crawl less to spend less.
Synapse offers a serverless SQL pool billed per terabyte scanned and dedicated SQL pools billed per hour of provisioned capacity. Which is cheaper depends on how often you query and how much data each query touches. Here is the comparison.
Azure Databricks bills a DBU rate for its platform on top of the underlying VM cost, and the DBU rate varies by workload type and tier. Understanding DBUs is the key to controlling Databricks spend. Here is how they work.
dbt Core is free and open source but you run and orchestrate it yourself. dbt Cloud is a paid managed service with scheduling, IDE, and collaboration. The cost comparison is subscription fees versus your own compute and engineering time. Here is how to choose.
Data warehouse cost is compute (queries and transformations) plus storage, whether Snowflake credits, BigQuery bytes scanned, or Redshift nodes. Scanning less, right-sizing compute, and auto-suspend cut it. Here is how.
Azure Data Explorer (Kusto) bills the compute of its cluster VMs (with a markup) plus storage, running continuously unless stopped, or serverless-style with autoscale. Idle clusters and over-provisioning drive the bill. Here is the model.
QuickSight bills per author (a monthly subscription) and per reader (session-based with a monthly cap), plus SPICE in-memory capacity beyond the included allowance. Matching author and reader licensing to real usage is the main lever. Here is the model.
Athena bills $5/TB scanned and costs nothing idle; Redshift bills for a provisioned cluster (~$793/month per node) or per RPU. Here's the crossover by query volume and how table design cuts both.
BigQuery on-demand bills ~$6.25/TB scanned; capacity bills for slots. Here's which model is cheaper for your query volume, and why partitioning and never SELECT * is the biggest saving on either.