Omnata drastically simplifies integration tasks, from Snowflake to Salesforce and BigQuery to Salesforce. Previously, this required middleware with complex setup and constant maintenance. The simplicity of this new approach rests on two key components, cloud-native data warehouse design and Salesforce Connect external objects.
We thought about the priorities of data teams and the existing tools that they use and concluded that outbound data integration could be further simplified. We built a data-engineer friendly experience that uses our native Salesforce app and the native capabilities of Snowflake and dbt, an increasingly popular combination.
By the end of this article, regardless of your stack, hopefully, you’ll have an appreciation for the general challenges when taking analytics projects into production and some ideas on how to overcome them. Or at the very least, some new things to consider.
A very common (and very reasonable) question I hear, is "how is Omnata different to Tableau/Einstein/Fivetran/Mulesoft/Workato/etc with respect to Salesforce?" The answer depends on a few things. Operational or Analytics? Which Salesforce cloud? Which direction? In 2021, there are more options than ever so we boil them down for you.
As we enter 2021, it feels as if organisations who have successfully adopted the modern data stack are finally in a position where they can truly harness the value of data being produced.
Expert tips for Salesforce and Snowflake, plus, open-source and community contributions. Read insights about analytics, machine learning, enterprise architecture and data-engineering.
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