vlahmot

Their prices are often 2-4x cheaper than the big chains, sometimes for the exact same item. “Fancy” cheeses that are $10 a block at the big chains are $2.50 at Aldi. Produce can be a bit iffy sometimes but if you’re flexible and spend a bit of time picking through it it’s fine.

The self checkout machines are also much better (even though they are annoyingly insistent about “scanning the next item”) and I’ve never had to wait for someone to come unblock it vs the big chains where they always seem to get stuck when I move some items around.

I’m also a big fan of the culture changes they embody - 25 cent deposit for your cart so the parking lot isn’t filled with carts from lazy people and allowing your cashiers to sit down while they work vs making them stand for no reason other than a power move.

It’s so much better than competitors that I’ve become a marketing employee for them anytime high food prices come up in conversations to the point where my partner jumps in with “get ready for the Aldi speech”

No fancy buzzwords, to the point and speaks to things we all know are true but are typically not addressed or are wrapped up in spin. The first and last paragraphs are great examples.

The authenticity of it leaves me with a strong sense of trust and respect.

I would recommend using docker containers and writing it in a way that you can swap out lambda/ecs/k8s/etc as the backend infrastructure. That way you you can punt on doing any fancy work to try and break the jobs up across lambdas under that 15 min constraint.

All of the things you mentioned have helped me as well. I also have a couple of hemorrhoid cushions that I sit on to reduce pressure.

I’ve found that when I start to feel pain in my tailbone immediately taking some anti inflammatory medicine and getting antibiotics ASAP before the inflammation cuts off blood flow has stopped them in their tracks!

https://aspe.hhs.gov/poverty-guidelines

I was surprised to learn that the earth is about as massive as Mercury, Venus, Mars, the moon and a few more moons combined.

Do you have any information on “ the Harvard professor” and how JIT interacted with domestic lean manufacturing?

This allows you to retain the data as raw as possible and empowers your analytics teams to move faster by not having to wait for engineers to transform the data into a usable form.

Thank you!

Write all of your emails into a kafka topic from your webapp. Read from the topic to do processing. Use flume to sync results back to your webapp db.

1) For this I would probably use something like Chef/Ansible but I don't know the first thing about configuring email servers. You could have something that wakes up, reads the latest config off a topic, and then applies that config via a config management tool.

2) You can throw Apache Spark on to the kafka stream to calculate these aggregations.

3) Flume can read the emails and then save them back to wherever you need (this is typically s3/postgres for me). Flume can scale out over the kafka topic naturally using the same consumer group id.

I like this approach because you can scale it cheaply and easily by starting with kinesis streams instead of kafka if you don't have the ops resources to run kafka and running spark in stand alone mode until you need a cluster.

With spark you can do your statistics in there (streaming over a time window or batch) and then sink them over to your stats db.

With the flume/kafka combo you can treat kafka as the "channel" and you get some nice transaction functionality out of flume that makes handling failures a breeze.

It does take some tooling/monitoring to run confidently and the whole apache "big data" ecosystem is daunting at first but its well worth it in my opinion.

I feel like everyone framing this as "We won't pre-review movies until you lift the block" gave them too easy of an out.

From what they said multi-tenant is about a month a way and self service cloud early next year.

The linear algebra series from 3blue1brown has some great explanations and visualizations for thinking about linear transforms.

The real value for me besides time saved (and the insane coupons/free meals) is using ingredients that I wouldn't have otherwise picked up. I'll just grab button mushrooms most of the time but getting some different variety is nice.

I also usually grab the store brand meat because its cheaper. It's not like the grass fed organic whatever isn't there, I just usually won't pick it up.

I'd throw the data on S3, do the processing in spark(you can likely run on one node in local mode for now at that scale and scale as the data does), write the data back to s3, load that processed/aggregated data from s3 into mysql since you running that already and can just plug in your BI tools.

Much easier to process data not in the db, s3 as a source of truth is great in AWS, and much cheaper.

Many of our jobs operate on low TBs and growing but even if the data for a job is super small I'll write it in Hadoop (Spark these days) so that the build, deployment, and schedluing of the job is handled for free by our curent system.

Sure spending more time listing files on S3 at startup than running the job is a waste but far less than the man hours to build and maintain a custom data transformation.

The main benefit of these tools is not the scale or processing speed though. The main benefits are the fault tolerance, failure recovery, elasticity, and the massive ecosystem of aggregations, data types and external integrations provided by the community.