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?”
There are a few questions we have to answer along the way:
1) Operational or Analytical?
The solution varies depending on whether or not you are trying to understand or report on trends across large datasets, or whether you are looking to improve operational processes by extending the visibility and reach of the main Salesforce platform.
2) Which Salesforce cloud?
The solution varies depending on whether or not you are talking about Salesforce Marketing Cloud, or the main Salesforce Platform. The…
In a previous story, I demonstrated that a true cloud data warehouse is capable of handling common machine learning tasks for structured data, like training tree-based models.
The gist of my argument was that if the architecture is right and the use case is common, then you shouldn’t need to transfer the data out of your database into a general-purpose compute cluster. …
Sometimes when statisticians are sampling data, they like to put each one back before grabbing the next. This is called random sampling with replacement, as opposed to random sampling without replacement, which is the more common thing.
An example of when this is done in the wild is during the training of a Random Forest, and this was the trigger for me writing this post.
All of the Snowflake built-in sampling functions revolve around sampling without replacement. This means you only have to make a decision about each row once — it’s either in or out according to chance.
The…
At Omnata, we love to watch our prospects’ amazement as we demo the use of Salesforce to navigate through, and automate on, huge quantities of data in real-time from Snowflake, linked to individual records in their CRM. If you’re using both Snowflake and Salesforce and want to combine their strengths, you owe it to yourself to check us out!
Back to the topic though, it’s always nice to make the sample data look familiar and relatable. However, generating 100 million fake things (names, addresses, whatever) and importing them into Snowflake can be time consuming and error-prone.
Enter Flaker!
At Omnata, we love to watch our prospects’ amazement as we demo the use of Salesforce to navigate through, and automate on, huge quantities of data in real-time from Snowflake, linked to individual records in their CRM. If you’re using both Snowflake and Salesforce and want to combine their strengths, you owe it to yourself to check us out!
Back to the topic though, it’s always nice to make the sample data look familiar and relatable. However, generating 100 million fake things (names, addresses, whatever) and importing them into Snowflake can be time consuming and error-prone.
Enter Flaker!
At Omnata, we love to watch our prospects’ amazement as we demo the use of Salesforce to navigate through, and automate on, huge quantities of data in real time from Snowflake, linked to individual records in their CRM. If you’re using both Snowflake and Salesforce and want to combine their strengths, you owe it to yourself to check us out!
Back to the topic though, it’s always nice to make the sample data look familiar and relatable. However, generating 100 million fake things (names, addresses, whatever) and importing them into Snowflake can be time consuming and error prone.
Enter Flaker!
In this cloud era, not only are new possibilities continually being created, but existing activities become easier and less expensive. Whenever a new paradigm emerges, those products not only disrupt their direct competitors, but often enable a wave of change in adjacent markets.
I’ll be upfront; this is one of those architecture articles that ends with a product that: a) enables the solution to solving the problem it describes, and b) the author happens to have a stake in. …
Normally I stick to writing about topics that I’m comfortably qualified in and fully understand. And without any proper training in medicine, psychology or biology, this should exclude Autism Spectrum Disorder.
Not to mention that there are already plenty of amazing resources to help those who are figuring out what autism means for their life.
But over the past few years, I’ve come to learn that we still know so little about autism (and most other mental disorders), that sharing what I’ve learned could be useful for someone who’s trying to understand this disorder in the same way I’ve been…
This article introduces a fully functioning prototype of a cloud service I’ve built, and I’m looking for your feedback on it — contact me here!
So, you’ve finally signed up for a Snowflake account and begun your data warehouse migration to the cloud.
You’ve tried out the data sharing feature and it lives up to the hype. But alas, there are those external partners who aren’t quite ready and still just want an FTP server to fetch data from. …
One of the ways we like to make sense of the world is by grouping similar things together.
In design, we group colours into shades. In sales and marketing, customers are usually segmented to accommodate their differences. And in statistics, clustering is a widely used unsupervised learning technique to help organise a dataset.
As you may have noticed from my previous stories, Snowflake can be used for way more than just retrieving or aggregating data. It can be extended to a wide range of data-related tasks at a large scale, and coexist peacefully alongside the more traditional workloads.
In this…
