INDUSTRY WATCH performance helps us run very complex SQL queries , sometimes sourced from domain experts in engineering , to help the support team quickly resolve issues for our merchants .
How does Snowflake provide a clear picture of what is going on within the payments infrastructures of your customers ?
Every piece of data about the tenant ’ s infrastructure already is or can be ingested into Snowflake . This means that whenever there is an active issue with one of the merchant ’ s terminals , or we want to check the impact of a particular future change , all we have to do is log into a BI tool and run a query .
Mike Gouline , Data Architect , mx51
process . However , Snowflake provides an admin panel to self-serve creating new accounts and thanks to an open-source Terraform provider , we can automate all configuration and security settings through the same means as all the other AWS infrastructure without resorting to many manual steps .
Can you explain how Snowflake allows queries to be quickly answered ?
Answering queries , whether they are business questions answered via visualisation tools ( e . g . Metabase or Power BI ) or low-level queries surfaced via the customer-facing dashboards , the biggest challenge is getting all the data from disparate sources in one place and up to date ready to be combined , joined and aggregated any way the task requires . This part of the data workflow is aided by Snowflake ’ s loading features , such as Snowpipe , streams and tasks , which enable us to easily load anything we extract from the relational databases at scale .
Once everything is in Snowflake , in semi-structured or structured form , scalable compute warehouses help run queries as quickly as we need and in parallel , meaning that a sudden spike in data analytics activity before a board meeting has no impact on any production processes in the background . Not having to wait a long time for results also means that users are encouraged to ask more complex questions of the data , instead of having to make do with basic summaries if digging deeper means waiting 10 minutes for each result .
When you have thousands of payment terminals generating millions of diagnostic events , trying to find out what happened to a particular one on a particular afternoon can be daunting . Snowflake ’ s
For example , when the support team identifies a particular problematic payment terminal configuration after troubleshooting on the phone with a merchant , they can immediately check how many other merchants have this configuration and put together a plan with engineering to fix it behind the scenes or proactively contact the businesses with a fix .
Moving beyond descriptive and diagnostic analytics , Snowflake allows us to seamlessly transition into the predictive and prescriptive realm , running ML-based anomaly detection and feeding the alerts back to the customer-facing dashboards well before human users identify the problem , or clustering merchants based on their usage patterns to recommend the tenant ’ s relevant features and products to them .
Why did you choose to use Snowflake ?
There is no shortage of data products on the market . Our aim is to keep the product offering as cloud agnostic as possible , so we wanted to store the data somewhere that works on all three major clouds : AWS , Azure and Google Cloud .
Similarly , we try to avoid deep vendor lock-in when it comes to consuming the data . Part of it is the front-end tool support discussed earlier , but it ’ s also about the amount of training and effort required for new team members to become familiar with querying the data . Snowflake is based on SQL , which means that anyone with some experience in other relational databases can start using it within minutes or hours , not days or weeks . We have team members with no engineering or scientific background happily writing SQL queries to answer their own questions . p
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