I’ve found a lot of the coverage about the Trump administration’s of Signal messages to The Atlantic frustrating. To read most of the coverage, the major mistake was Mike Waltz’s inclusion of Jeffery Goldberg in the conversation, and the big questions are about the impact of this particular leak. I have two alternative takeaways, one about the general security attitude, and one about Signal itself.
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With the recent news that Adam Selipsky is leaving AWS, I thought I’d share my funny Adam story.
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Here’s a reasonably elegant solution to a fun problem I stole from Timothy Gowers: prove that for every positive integer n, there do not exist positive integers a, b, c, d with ad=bc and n^2 < a < b < c < d < (n+1)^2.
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Just reading this blog, you might guess that all I do is leave jobs. First leaving Tableau, and now, four years later, departing Snowflake. Here are some thoughts on the future.
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Last time we saw how to create and use simple Java functions in Snowflake using Snowpark to detect and mask PII. But this gets awkward and tedious if we need to mask many columns. Let’s see how we can use Snowpark to iterate over metadata to make this simple and repeatable
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I wanted to share some code that I’ve used previously in demos. The examples here do basic detection and masking of personally-identifying information (PII) in Snowflake using Snowpark and Java’s built-in regular expression support.
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I won't bury the lede: My last day at Tableau was July 6th, and tomorrow I start a new gig at Snowflake.
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When we built Tableau Prep, we put a premium on ensuring composability of operations: you can take the operations Prep supports and string them together in any combination you need. There are no restrictions based on where the data came from, or what operations came before.
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I've been a bit quiet lately, but Tableau Prep out the door and it's time to make a little noise.
Clark recently wrote an excellent post on the basic UX architecture of Prep. Here I'd like to cover a key concept underlying Prep that may be a bit foreign to people coming from Tableau: the flow.
This isn't the most glamorous part of Prep, but it is one of the most fundamental concepts in the tool, so it seems worth spending some quality time on.
Strap on your life jacket and read on for more.
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When using Tableau, taking an extract is always better than using a live query, right?
Well, no.
Of course. Obviously, when your data are changing and you want to get all of the latest updates in your viz, you'll want to use a live query. But if that's not the case, then an extract is clearly better, especially with Hyper in 10.5, right?
Well, no!
Shoot! This is complicated? When will live beat an extract? Let's take a look at a few cases.
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