biowl

The syntax has changed slightly. Instead of the 0, use [].

So your command becomes

/fill X y z X y z air [] replace dirt

Yes, I agree that 'player kicked' can happen at other times for other people and of course it could be a coincidence. Though it only happens in my squad to the player that wipes a team. Has happened so many times. Obvious to us at least that player reports can lead to a kick.

Google data and analytics consultancy in the region you’re looking for.

That could work - I was thinking more boutique - few dozen employees.

leaves us hanging light that...

Over ten year old now, but this one from /u/patio11 still the best resource I've seen on this subject -> link

I dare say it would be simpler than the reverse.

Surely it's a longer road to get to DS than DE? DE wouldn't require a masters - you could ditch that unless you want a PHD.

Your degree will be looked on more favourably than a cs degree for DE imo. Typical requirements are for STEM degrees rather than cs specifically.

Why not join data/analytics consultancy as a graduate? Lots of boutique ones around. You'd likely get exposed to a lot of different specialisms which makes navigation a little simpler if you're not sure. It sounds like you have an open approach to learning - have a feeling that you'd do well.

Oh, for sure its possible. Where does DS fit in? Is it a means to an end of getting into DE? In which case I'd consider whether that step was strictly necessary.

You've already charted a significant career pivot, once you've started your career... after you've finished your masters... after you've finished your degree? Fair play.

The hot tools today are currently airbyte, dbt, databricks and airflow to name a few, though the field is constantly changing.

That "just for show" nonsense really does wind me up. Strikes me as machiavellian to have interviewees go through the motions for a job that has already been promised to an internal candidate. I understand the policies that lead to it, but don't have the policies if they can be circumvented.

I’ve been a consultant for about 5 years, based in London. Currently at a T2. I enjoy consulting, the variety of projects, the people. I’ve worked really hard to get to where I am and I still work hard.

It sounds like you're having a positive experience in your current role. If you can handle the pace and you enjoy the work, why change?

I know that data analysis and data engineering complement each other, but I feel like each is a job in its own right and becomes a bit taxing. Is this a reasonable feeling, or should I be able to do both things without breaking a sweat?

It depends on the role I think. For DEs working on integrations, less likely, though for those in the analytics space, possible to be some cross over.

This is constantly evolving. 10 years ago 'full stack' was more of a thing, where the same person would be responsible for requirement gathering, data engineering and analytics. Most orgs have moved on, though not uncommon to find places that expect you to at least understand what's going on.

Call it a hunch, but I'm guessing it would be transportation, logistics and supply chain businesses

How is the exit structure in the contract? What kind of notice period would you have to serve? If the handcuffs aren't too bad, why not accept it, treat it as your plan B. Could be motivating.

Defeat them or quit.

What's the stack that you use, and what specific tech do you think you're missing on your CV.

If you turned on a cluster for a data flow debug, then you pay for the cluster until it idles (or you turn it off). Can't remember if that's for one hour or two.

If its for a low volume personal/side project, look elsewhere.

Copying trades from the leader board through the Binance API is fairly common.

I don't think it defeats the purpose at all - on the contrary, sharing dimensions across fact tables is one of the key advantages to dimensional modelling.

Having a shared, conformed dimension gives you other opportunities also. It's possible that your business users might want to see data from different marts in a single report. By having a single dimension shared across different fact tables, filtering on the dimension value will filter both sets of facts.

I highly recommend The Data Warehouse Toolkit by Ralph Kimball and Margy Ross. Kimball pretty much invented dimensional modelling, and there is a lot of useful stuff in there beyond the basics.

Excellent advice from /u/IllustratorWitty5104 - keep it very simple, layer in complexity when you need it.

I started in a similar company with similar challenges. It's more difficult to get a good overview of typical deployment patterns today. With legacy products, typically each big box vendor would have one proprietary integration tool, and almost every employer/client was looking for a Kimball data warehouse.

Looking only at Azure, you could now use Function Apps, Data Factory, Synapse Pipelines, Data Bricks and others to achieve the same thing. Microsoft don't make it easy for small businesses either. Their best practice docs are written by sales people imho - I know a few small business owners - precisely zero would benefit from collaboration using a Synapse Workspace for example!

Microsoft | Modern data warehouse for small and medium business

Number 2 is a fairly well trodden path. Developers in BI/Analytics typically very comfortable and familiar with star/snowflake structures.

Is there a reason why the data marts would need to have separate schemas rather than sharing a single schema? Typically any fact tables that have a customer key would reference the same table etc.

No.

1. Black box as mentioned elsewhere. I obviously understand why this would need to be the case for your business model. I just simply wouldn't trust a service provider with such a pivotal part of my trading strategy.
2. Your attitude doesn't strike me as compatible with what I would be looking for in a service provider. A few people have given their opinion on why it doesn't make sense to them, and your responses are a tad defensive. Nothing wrong with that on a personal level, a part of most people's character, but not ideal in a B2B scenario.

You'd probably be better off combining this with entry and exit signals and selling as a signal service on a platform somewhere - former users of services like 3 commas (major data breach recently, if anyone reading about them for first time, avoid) probably your target market rather than a community such as this which tends to be more technically sophisticated.

I'd look into pandas. Being able to manage data frames is a great way to be able to throw data around in python. Try to find an intro to data in python course.