"Here’s why: There is absolutely no benefit for you to gain by talking in an exit interview, and plenty of negative consequences to come out of it. At best you’ll be remembered as a complainer, and you may make enemies."
I guess I would counter with if I have friends there, I would like their lives to be better. If my exit interview is able to do that, then I would take that as a net positive.
The only possible way to help is by providing positive reinforcement. “I loved working with X. Y is really killing it on her KPIs.” I am otherwise in agreement with TFA.
This, for sure. I pump up good people doing good things. No one cares that I think our new time tracking sucks, or that an HR policy sent me over the edge, and they definitely don’t care that I think an SVP in a different department is going to tank the company because their metric strategy is all about finding fractions of fractions that make them look good.
But they’ll, even subconsciously, remember that I said Joe and Jane were absolute rock stars.
Then count the costs. If it is worth more to you to leave such feedback and improve their world, it's always your choice.
However, you should also be either convinced that HR gives a crap, or that any potential outcomes are acceptable, including but not limited to being moved into "unregulated attrition" status, losing the ability to be hired by the same company in the future, having your words potentially turned into a lawsuit against you, etc. Unless you have actual, legal, signed documentation in place giving you such assurances, these are all on the table.
It is this sort of fear that holds society back. Individualistic thinking and a belief that one cannot make a difference anyways allows so much bad behavior to take place. With everything in life, you should always try to leave a place better than how you found it.
>I guess I would counter with if I have friends there, I would like their lives to be better. If my exit interview is able to do that, then I would take that as a net positive.
If you had any confidence your feedback would be listened to and actioned on, why would you be leaving?
To be precise, it takes 1 employee to say "used in X". It takes corporate decision to say "used by X". And it takes a written agreement to be able to use the trademarked logo of X on your page. (I know, because I have collected more than 60 such agreements to show logos on a page).
Here was my situation. Occasional queries. Over a couple petabyte of data. Customer facing so response in seconds per SLA but >
95 percent of the time the warehouse isn’t running. Cached queries from within 24 hours which don’t require the warehouse to even spin up. Our snowflake costs were significantly less than an FTE.
Would that potentially be a situation which “running your own” doesn’t make sense?
>Would that potentially be a situation which “running your own” doesn’t make sense?
Look into datalake architectures. RDBMS based data warehousing is obviously not economical at the petabyte scale. But storing all that data in S3 with Delta Lake/Iceberg format and querying with Spark changes things entirely. You only pay for object storage, and S3 read costs are trivial.
> RDBMS based data warehousing is obviously not economical at the petabyte scale.
I never said it was .. I'm simply responding to "I simply cannot understand how anyone chooses this over running your own Spark clusters with Jupyterlab". I'm trying to help you understand why folks would choose a SaaS over run your own.
> But storing all that data in S3 with Delta Lake/Iceberg format and querying with Spark changes things entirely. You only pay for object storage, and S3 read costs are trivial.
No. You don't just pay for object storage + minor S3 read costs.
You pay for operations
You pay for someone setting up conventions
You pay to not have to optimize data layouts for streaming writes
You pay to not have to discover race conditions in s3 when running multiple spark clusters writing to same delta tables
You pay to not have to discover that your partitions/clustering needs have changed based on new data or query patterns
But look .. I get it. You have chosen to optimize for cost structures in one way .. and I've chosen to optimize in a different way. In the past I've done exactly as you've said as well. I think being able to seeking to see _why_ folks may have chosen a different path may help you understand other areas to consider in operations.
Or I guess, what data size do you think it's talking about? If you only have gigabytes of data, none of this matters, you can use anything pretty cheaply and easily. So is this article just for "terabytes" or does it go up to "hundreds of terabytes" but not "petabytes"?
Hmm, I suppose it's a bit challenging to say. I initially thought that it wasn't for the 80% smallest companies and petabytes of data is probably puts you in the top 20%. (Most businesses are small businesses after all.)
However, I now realize that th biggest companies probably should manage their own data. If you're Google why would you use Snowflake?
So I don't know if you are the target audience for this blog post. It's pretty ambiguous.
I guess I'll say what I think. I do think it is targeted at that smallest 80% of companies with some digital footprint, and also at most of the top 20%. Or more specifically, I think maybe it's targeted at like the 5th percentile to the 99th percentile. That bottom 5% probably just needs Excel, and that top 1% is probably writing or heavily modifying all their own tools.
But I'm not sure the advice is very good from the 5th percentile up to ... maybe that top 20%? A lot of the stuff in the article assumes the availability of sophisticated data architects and mature infrastructure groups that I really don't think the median company has.
Super hard to say, so ... 80th or 90th? With very low confidence.
But I do have very high confidence that the 99th percentile is much larger than petabytes (think: what's next after "exa"), and I believe that many companies these days crack into "peta" territory.
But as I saw another comment mention, I think another, probably more important, consideration besides size in bytes is cardinality and structure. So maybe this whole classification we're doing is kind of beside the point :)
Yeah, it's hard to say with any certainty. I agree that the far end is the curve probably looks nothing like the "neighborhood" a couple percent away, relatively speaking.
I also agree that the variety of data plays a big part in its complexity. If you have a few petabytes of data, but it's really only a handful of tables you can real hone in on the relationships. If it's a wide array of sources with many tables between them then you have some nasty problems like entity resolution.
All happy data sets are alike; each unhappy data set is unhappy in its own way.
“I can’t imagine being a traditional teacher, and having to teach kids that don’t care.”
What I’ve seen is igniting that spark and moving folks from not caring to caring (or realizing they can affect change) can be even more rewarding than the other mentoring being discussed
Exactly. I saw that thing tucked away in the diagram for Snowflake when surveying the so-called lakes - by all accounts FDB is an excellent piece of tech.
Let’s say you work for a SaaS doing analytics. Your boss says “hey! We need to start reporting on new logos. Can you snag those from the DB?”
But what counts as a new logo? Does a pro serve engagement that doesn’t use the product count? What about a business using the SaaS but still in a trial period? Etc.
A semantic layer helps provide common agrees upon definitions to the business. So any one looking for common data entities can just look those things up… and can come to published definitions (which are backed by queries to databases, data lakes, etc).
Does that help? Another example of this would be dbt for example
I guess I would counter with if I have friends there, I would like their lives to be better. If my exit interview is able to do that, then I would take that as a net positive.