
The Snowflake community is rife with information dumps on how to optimize expensive queries. We know because we combed through a ton of them. What we present here are three tactical ways in which we’ve done this at Toplyne.
Toplyne’s business involves extracting real-time insights from real-time data. This data is currently sourced from our customers’ Product Analytics, CRM, and payments system.
CRM and payment data volumes are mostly manageable. A product will have a limited set of paying customers and marginally more who are tracked in a CRM. However, product analytics data is much higher in volume.
Toplyne’s POC (proof-of-concept) and MVP (minimum viable product) were built on product analytics data. We knew right from the beginning we needed to use a Data Warehousing solution to handle the data. The solution had to pass two clear requirements:
- It should easily ingest a…
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