In 2010 I just kept forgetting to fill out the census. My census guy left notes on my apartment door begging me to call him. Finally, one day, he literally popped out of the fucking bushes with the census form.
I kept getting some mid-census domestic abuse questionnaire a couple years ago. I had zero interest in filling it out but they were persistent about that too.
So, I thought genetic mutations were random. If that's the case, why does the amount of antibiotics prescribed matter? Won't these mutated bacteria excel regardless?
Simplified, to survive one antibiotic, you need only mutate to resist that one antibiotic. To survive n antibiotics, you need to mutate to resist n antibiotics _at once_. Being vulnerable to just a single of them will kill you.
If the chance to get the right mutation to resist one antibiotic is, say 0.01 (1%). Then the chance to mutate to resist three antibiotics at once is 0.01^3, thats 1e-6 (0.0001%).
I'm going to ship you a item. Luckily I have multiple copies of the item. I ship using UPS, Fedex, and USPS. You'll probably get three copies, but to get no copies of the item I'm shipping you something has to go ridiculously wrong in three different companies.
* Genetic drift vs fixation. As bacteria reproduce and die, the relative frequency of genes tends to remain fixed in the absence of selection. In layman's terms: if 0.0000001% of bacteria in a population have antibiotic resistance, they will likely all die before passing it on. If 10% have resistance, there's a much better chance of it sticking around. The more antibiotics prescribed, the more you enrich the population for resistance, the greater chance it sticks.
* Many antibiotic resistance genes put bacteria at a disadvantage relative to the same bacteria without resistance. If antibiotics are not present, then resistance will eventually be selected against and disappear. Overprescription of antibiotics ensures that this doesn't happen.
The mutations might be random but not every mutation is fit to survive any given environment. As such only "the fittest bacteria"* with "the best mutations"* survive in the end. So it's not as random as it might appear, it's actually quite focused.
for that given environment
That's why antibiotics should generally be used as conservatively ass possible because overkilling bacteria does nothing but accelerate the evolution of the few surviving into "super bacteria" that much faster by giving them a harsh environment they can adapt to.
This looks very cool. Although, I'm not sure I totally understand how it can be used to replace batch ETL processes. So, PipelineDB eliminates ETL batch processing by incrementally inserting data into continuous views, but the documentation says that it's not meant for ad-hoc data warehouses as the raw data is discarded. So, does that leave me still using batch processes to load my data warehouse? Is PipelineDB going to be my data warehouse as long as I only want the resulting streamed data? Just trying to figure out what this would look like and where its place is in a data warehouse environment.
Hey Chad, PipelineDB co-founder here. PipelineDB certainly isn't intended to be the only tool in your data infrastructure. But whenever the same queries are being repeatedly run on granular data, those are the types of situations in which it often makes a lot sense to just compute the condensed result incrementally with a continuous view, because that's the only lens it's ever viewed through anyways (dashboards are a great example of this). Continuous views can be further aggregated and queried like regular tables too.
In terms of not requiring that raw data be stored, a typical setup is to keep raw data somewhere cheap (like S3) so that it's there when you need it. But granular data is often overwhelmingly cold and never looked at again so it may not always be necessary to store it all in an interactively queryable datastore.
As I mentioned, PipelineDB certainly doesn't aim to be a monolithic replacement for all adjacent data processing technologies, but there are areas where it can definitely introduce significant efficiency.
Great. Thank you for the clarification. What you just described definitely sounds like something PipelineDB would be great for. I can see it being especially useful for quickly standing up dashboards and maybe even datamarts when considering new data sources. I just wanted to make sure that I wasn't missing something.
So what's the best practice for when you want a real time dashboard but also want the ability to compare data overtime. E.g., ave. bounce rate this month vs last? Is Pipeline still ideal in this case?
Jeff (PipelineDB Co-Founder, here) - Yes, PipelineDB is great for this use case. One powerful aspect of PipelineDB is that it is a fully functional relational database (a superset of PostgreSQL 9.4) in addition to a streaming-SQL engine we have integrated the notion of 'state' into stream processing, for use cases exactly like this.
You can do anything with PipelineDB that you can do with PostgreSQL 9.4, but with the addition of continuous SQL queries, sliding windows, probabilistic data structures, uniques counting, and stream-table JOINs (what you're looking for here, I believe.)
