Do you really need big data tooling?
Distributed systems add cost and complexity, so the first question is whether you need them. A well-tuned PostgreSQL or a columnar warehouse such as BigQuery or Snowflake handles a surprising amount of data. Spark clusters, Kafka and streaming engines make sense when volumes are very large, data arrives continuously, or processing cannot fit a single machine. Often this saves a considerable amount of money and operational burden.
We size the problem from real figures: daily volume, growth, latency required, query patterns and number of consumers. Sometimes the answer is a simpler warehouse and better modeling. When it is not, we explain why, so you buy the capacity you need and not the architecture a conference talk suggested. Table formats also provide time travel and schema evolution, which make auditing and fixing historic errors far easier.
