Data camp versus data quest that's what we're going to be looking at in today's video we're going to be focusing on comparing data quests versus data camps data engineering career paths and seeing what they cover figuring out you know pricing comparisons as well as just trying to figure out which one might be better suited for you kind of by looking .
At what material each of them cover and trying to answer which data engineering career path platform should you use in order to learn the basis of your data engineering skill sets now before we get too into it let me just talk a little bit about my perspective of these platforms in general before going in depth which is these are great tools .
That you can use to really give you a high level understanding of these technologies in no way do i think either of these will make you a guaranteed bona fide data engineer they will give you the skills they will give you the understanding and the high level knowledge and from there you'll have to apply it on projects and network to .
Really start to up level yourself and bring yourself to be that data engineer that you are aiming to be so let's dive into comparing data camp versus data quest and look at their material pricing as well as kind of their ui just give a good idea of what exactly are the differences and which one you might want to take as you're trying to become a .
Data engineer so let's jump into that so let's start with data camp's career path and again this is their data engineering career path and they mostly focus on using python although they do go a little bit over scala so you'll learn a little bit of a lot of different tools in datacam's program and that's one thing i really like before we dive into .
It they really cover a lot of tools which is something i think that is something you'll realize as a data engineer is you will have to learn what feels like an endless amount of tools at least to some degree you know you've got everything from pi spark to airflow to postgres to mongodb to hadoop to hive to presto it's really just this endless .
List of tools whereas you know if you look at data scientists they mostly do everything in jupyter notebook okay that was a small dig at data scientists they obviously do a ton more and use other platforms as well but i do think data engineering has this unique thing where we just play this middle ground so we're just stuck with playing flat over tools .
Are laid before us and another thing i want to point out about this program before we start diving into it is the fact that they covered things like aws bodo which i actually don't know how to pronounce that it could be budo for all i know i just import it whenever i need it so i think that's just you know you look at .
This top portion you can already see that they're going to cover a ton of different components that are really valuable to you as a data engineer but let's get started oh also know that they do assume that you know at least the basics about python and sql and that's i think one of the differentiators here between dataquest and datacamp dataquest .
Is going to spend a ton of time giving you the basis for python and sql whereas datacamp already assumes you know that um so obviously they kind of start with a light two-hour kind of section here just to give you an understanding of what data engineering is before you get too deep into and committing into the concept of data engineering you know .
Figuring out what do data engineers do is the work you're going to want to do kind of what's their role in the whole data science flow they do do a little python assessment because again they're assuming you already have python in your back pocket at least to a certain degree so you know kind of where you're going .
Then of that again another kind of more introduction more focused from a tooling standpoint so they're going to kind of cover a lot of things and a lot of concept here from a very high level but so you can understand what you're about to learn in the next few sections and let's just kind of dig into this and see what they're going to cover in this very .
First intro so diving into this very first intro what you're going to see is it's one free so you can do this for free and you can kind of see the chapter details and you can see they're going to cover a lot about the high-level concepts in terms of tooling that you're going to work with right like so if i click cloud providers you're going to .
Get a pretty quick video here where they're going to intro cloud and kind of what it is and their different services that you can work with it's only about four minutes and that's pretty consistent throughout their whole program so again this is free so you can poke at it and see again this is more to give you a high level of what you're .
About to learn and less focused on actually i think digging into the nuances of data engineering going back and digging into this a little more there's also stuff where they're going to cover things like etls which i'm really glad that they're kind of starting to right off the bat cover the things that you're going to need to know .
In terms of again cloud etls concepts that are very data engineering centric and from here they're now going to start covering some more data engineering specific topics so scrolling back down again they cover a little bit about pandas i'm actually not the biggest pandas fan i tend to write things more in sql when it comes to transformations .
But there's a lot of stuff you can do there you know some people do a lot of data framework some people prefer doing sql work i this is some interesting split that i've definitely seen both provide benefits both provide good use cases so i wouldn't stray away from pandas but i also still i think prefer sql um but that's probably because .
That's what i learned on they also go over a little bit more in writing efficient python code which is great and writing functions which is great but i think you should hopefully have that under your belt before getting into the next few sections which i think are the key portions which they start with an introduction to the shell which .
Personally i think all of us need i always tell people you know spin up some sort of ec2 instance or something similar get used to running things in command line or something similar because that's where we spend a lot of our work even today sure these days with vs code i honestly just ssh into a remote vm somewhere and then end up .
Coding there rather than in vim i still don't know how people code in vim like all of their work but people do i'm still more of a vs code person but i still spend a ton of time in command line so the fact that they give you an introduction here as well do some data processing i think that's great also let me be real with you some people still .
Have data pipelines that are written in bash so don't be surprised if you come up into some company and they're running all of their data processing and data pipelines through some bash scripts or at least managing it all through that so i think this is great because it's just going to give you that breadth of reality that hey there are all different .
Ways you can create data pipelines and here's some of the ways you can even if you don't think people are doing it this way so they're going to have three sections on that just so you have a good foundation i think this section after that is a little bit of a cop-out they do unit testing for data science which i get it maybe they think it's about the .
Same i think there's definitely some differences because unit testing to me doesn't just impact in terms of like functions that you're building for python i think there's a lot in terms of like table development sql queries there's a lot of other things that you need to unit test along the way that is very unique i think to data engineering .
That's not as i think prevalent in data science so i'd love to see them make a data engineering specific course here just to kind of cover a lot of the different layers that we cover because we do so many different things from programming to sql to you know even maybe dashboards where unit tests i think play a role because data is so .
Fickle and can be wrong and i always like to say you know if i write a sql query i can ask someone else to write the same sql query and they will write it in a completely different way so you know i get that unit testing for data science may sound the same but i think it's different and i think they should have prepped a course for that um again .
I'm just going to programming personally i think that's stuff you should already have but if you haven't they cover that as well and this is where they start getting into the meat and potatoes which i'm really excited about because i didn't see this as much in data quest and i started seeing this in data camp and i .
Was really excited as soon as i did which is they give you an introduction to airflow and pi spark pretty much right next to each other just so you can kind of see two different ways that people are managing their data pipelines airflow is the way that i use a lot but pi spark is also great when you're dealing with larger data sets and maybe .
Dealing with more programmatic situations whereas airflow's great for like batch etls and now that amazon has managed workflows for apache airflow that's where i'm putting all of my work right now as i'm consulting because it just makes it so much easier for clients to manage their airflow instances without having to actually manage their .
Airflow instances so this was really great personally again the fact that these covered these concepts which are very key i think to data engineers because people always ask like what should i learn is like well a little bit of everything because you don't know where you're gonna go and then when you figure .
Out a company you wanna work at learn that skill that they have on their uh job requirements or something because honestly every company i work at uses different tooling and it's just really hard to say which one's gonna be best some companies use five trans some companies use ssis some companies use azure data factory some use pi spark .
Some use airflow some use luigi it's so many different options that it's best to kind of just be ready for anything and at least have a high level knowledge of what the components are and then as you kind of work drill into the specifics so as we keep going again they talk about building data engineering pipelines here so if we were to click into this they do .
Some basics in terms of like setting up airflow let me go down again they've got a free section here so they're going to talk a little bit with working with json communicating with apis which are both very common if you're a data engineer i spend a lot of my time doing both um json can be a pain so you know especially with nested .
Objects i think it's great that they covered it um then they end up covering a little bit more on pi spark and creating pipelines there as well as then having more orchestrated kind of workflows which is you guessed it airflow and i like this because they're doing this comparison .
Where you can see the difference and i think that's really important as a data engineer or if you're what i like to build myself as is like a data solution architect which is you need to know all the different tools you have at your disposal and which ones work best for which situation and why you might want to only pick one or the other and i .
Think it's great to figure that out by comparing them side by side and figuring out what's one good for what's the other good for and the fact that they've kind of got this set up that way i think just shows you that they really did put a lot of thought into how they structured this course rather than just giving you a bunch of materials that you .
Can you know go into on udemy and probably find courses that might even be better for possibly cheaper at least you could arguably do it cheaper you can do it here where they kind of give you a little bit better of a flow so the great thing about datacamp is they keep having like hits in terms of the fact that they kept hitting courses and sections that i .
Really thought made a lot of sense um for example next they talk about aws and boto and just the fact that this is where you're gonna spend a lot of time at least aws or maybe gcp or if they're doing microsoft whatever it might be you're going to be working with cloud components and you might as well get .
Used to it so the fact that they kind of covered things like multiple clients um how to deal with things like buckets and things of that nature that are very common for data engineers to do in the kind of like modern cloud world whether you want to call it modern data stack or modern kind of cloud architecture the fact that they put a .
Section just for this i think again blows a lot of other courses out of the water because they show that they're actually aware of what data engineers do i think it's really unique i don't think a lot of places do that well and i was really actually happy with this i'm sharing files securely again another thing that's very important especially .
With like s3 it's really easy to set up s3 improperly without setting it up securely so i love this again it was just hit after hit for me in these sections as i was going through so once you finish boto you'll have the ability to do a skills assessment again for sql i think it's great because if you're doing interviews you're to be doing .
Similar sql queries that they're going to ask you here which are a little more on the analysis side and not purely on the operational or structural side often it does depend on the company you're working for um some might do more storage procedure work some might do more analysis work it really does depend but i think this was great from there .
They do an introduction to relational databases in sql as well as just database design which where they cover things like snowflake as well as first second third normal form so really important concepts if you're doing data modeling i really again enjoyed it and i think i covered a lot of important components here in these next like eight .
Hours of courses and then if we keep scrolling down they gave you an intro to scala which i think is great you know you might use it you may not use it but i think it's great to have in your back pocket again just so the very least you have an idea of how it works and operates because again i don't think everyone has to use it that often but if .
You are the only person that knows how to use it it can be very helpful in your future then they go over some big data fundamentals with pi spark which is i think you really unlocking pi spark to do a lot more than just you know the base level of what they did earlier in the introduction section um as well some cleaning with data spark although this .
Kind of felt a little more data science-y i still think it's you know helpful in terms of setting up that kind of work and then this is where i actually think most of the world is going anyways which is my assumption is eventually we're just gonna be doing um spark sql for a lot of everything just because sequel tends to be a little more .
Friendly for most people and doing things in pi spark just gives you the ability to almost do anything that you want which is almost too much whereas sequel i think kind of limits you more and i think it's the way most people think about data um in the data world more in data sets rather than kind of this like functional approach which i .
Think what most a lot of people sometimes do with pi spark and then once you finally feel like you've learned all the tools you need they bring up sql server which is again i think great because it's just another perspective what you've looked at so far is like postgres a little bit some spark you can also go over mongodb photo which is all .
These different components but sql server is kind of this classic mainstay where people are still using things like sql server in a lot of places for data warehouses or maybe they're using something from like synaptic analytics like their data warehousing component that they've built um in their kind of data platform but overall it's a .
Microsoft product so there's at least some similarity there where you can get familiar with these concepts like you're gonna see things like error handling they're also gonna cover triggers here later if i keep scrolling down they're gonna be talking about triggers here and like building optimized kind of databases in sql server which i think is .
A very common database that people still use today so i'm kind of glad that they covered it and that's really what most of the rest of this is for the most of this course is they're talking about like improving query performance on sql server and then they do i think mongodb yep they do mongodb and then they close off could they have maybe added in a .
Snowflake component here i think that would be great i think doing some snowflake or bigquery would have really sealed the deal for this because you would have been doing some sort of cloud data warehouse which also is very important but you know you can find a course for that i think that's the one thing missing from this course i would .
Like to see like one cloud data warehouse because that's where we're all going anyways but i just love the fact that they were very rich in terms of like the breadth that they covered and i think that's very accurate to what you'll be dealing with in your data engineering day-to-day it's like you're gonna be using a ton of tools .
Get used to it just just get used to it now don't feel like you're gonna be stuck with one tool so that was uh data camp i really liked this course overall i think they just did again a great job of covering a lot and covering a lot of things that are very pertinent like it felt like if i would set up a course i think they hit a .
Lot of the points that i would set up myself and so i really agreed with this and i i'm i was i was really happy with kind of the overall flow now let's compare that to data quest so right off the bat the thing that i kind of wasn't hugely a fan of in terms of data quests was they really spent a lot of time first focusing on basically the .
Fundamentals of python again in data camp they already assume you should probably know this stuff i think they were assuming you understand things like lists loops and so forth like the fact that they call this kind of python for data engineering i mean this is just basic python maybe they put it more for use cases that are .
Data engineering-esque but at the end of the day you should just know basic python one way or the other and the fact that you have to put this into the section kind of seems silly and that kind of continues for the first three steps here so they're going to cover over dictionaries here as well and a few other kind of more intermediate .
And advanced concepts below and they're going to do things here with like sorting algorithms and algorithmic complexity which is great if you're like a software engineer which is part of what data engineers do and it probably will help you when you're trying to pass an interview but i don't think it's exactly what data .
Engineers do and the fact that they spend the next about three sections or three steps here just covering those concepts so now again things like sorting algorithms and space complexity and now they're gonna go into like sql fundamentals which again it's great you do need it but datacamp already assumed that you would have at least i think .
This basic probably top portion and probably even this bottom portion like joining intermediate joins things of that nature i think that should have been assumed that you have that already going into this data engineering camp because to me there's just so much extra stuff on top of data engineering that has nothing to .
Do with coding and sql or at least it's how you apply that coding in sql and not knowing coding in sql um that makes it different it's how you actually apply it towards data engineering versus how you apply it towards software engineering versus how you apply it towards data science is what makes these kind of three disciplines different um so they .
Basically kind of are covering things that you should also know as a data scientist here and not showing you how it applies differently and so they spent the first three steps just getting you up to speed so now if you've finished this section you can now go to step four which is when they first start introducing you to the concept of .
Databases kind of database management um some things with like database modeling here but this is really again pretty light in terms of what you need to know as a data engineer we're four steps in here and we haven't really gotten into much what i would consider data engineering concepts this is pretty general database and programming .
Concepts which again if you're a software engineer or a data scientist you probably know to some degree now we start getting into that pandas kind of numpy area which again you could then apply to maybe using something like das or something similar because you're using data frames but again this is also something that could just as easily work .
Well for data scientists and maybe they just took honestly data scientist sections and mixed them in here i think that's not 100 true because they've got things like processing data frames and chunks so i think they're trying to apply it toward data engineering but i just don't think they did as good of a job at developing .
Data engineering tracks because they now in your final step which is almost hard for me to believe like maybe there's steps that are missing from here but in this final step they finally cover building a data pipeline and it took me six steps and i'm finally going over building a data pipeline and that's that's it so i don't know if i'm missing .
Steps here if i'm off here data camp and i'm just missing some steps here let me know but like it's almost hard to believe that this is where you end um you didn't cover again things like pi spark air flow and maybe you've got a little bit of that in pipeline tasks but you didn't really dig into it not the same way that .
I think data camp did i think this is a miss i'd love to see it like if dataquest now adds in those sections like things like airflow pi spark all of that into this and maybe goes over a little bit of sql server some more data modeling like snowflake schema and star schema i think then i would be much more behind this product so i think that's .
Something where they might be going in the future but i think there's a lot of room for improvement in this specific product and especially when you look at the pricing it's basically the same so let's dive into pricing just to see that it's pretty much about the same right you've got the premium for data camp uh here 33.25 and then let's just before we .
Dig into the what see over here oh 3325 for the premium it's almost like they copy pasted their prices but forgot that they provided less product at least in my mind now the one truth is that these are kind of i think monthly fees so in a weird way you're getting as much as you actually have time for anyways so maybe it's .
Worth it for you to still do data quests because at the end of the day usually you get to that point where you know that baseline of skills of programming and things of that nature and it's 33.25 and it's gonna take you two months to learn anyway so it doesn't really matter in that regard but if you're talking about content and which one i think .
Makes more sense i think datacamp does not because of price so much as it just has that fuller curriculum in general that is focused around data engineering that feels like a data engineer built the course so i'll also add that it looks like maybe data camp tried to separate itself by adding a 25 option here as well where they pretty .
Much cover most of their courses um the difference here is like some project limitations and uh you don't get tableau power bi and oracle content which you can you know that's actually kind of cool i think it's interesting that they're adding that as well because oracle is still something that is around not as much but it's there and tablet .
And power bi i think if you're a data analyst or just a data engineer that likes uh doing data vis i think it's also great price wise again pricing is about the same and from that perspective it's on you so you know if you're not even a person that knows anything on python or sql it doesn't really matter in terms of which one you go with .
Datacamp or dataquest but i think once you start diving into really trying to learn more about data engineering concepts i think data camp does do a better job at creating a complete curriculum again they could add i think cloud data warehousing that would be amazing just some snowflake and bigquery because that's where things are going i .
Think and there's honestly so much they can add there they can talk about snow pipes streams there's so many unique features that snowflake has that makes it kind of a cut above the rest for most other options that it'd be really cool to see them build a whole program around that or maybe they can't because of licensing .
I don't know maybe snowflake has to like give them the okay to give this section i don't know it'd be cool to see them do something with snowflake so from there let's kind of compare the uis because honestly they're also kind of the same there so if we compare the uis i think the one thing that really stands out is it just looks like data camp maybe has a .
Slightly better like color scheme going on um when you look at this right like this actually looks very pretty and very well put together whereas if we compare it to dataquest it's just a little bit more looking like a command line that's not what it is but it just looks a little bit more like that so it's barely even noticeable personally personally .
The uis are about the same run code submit answer it looks like they basically just copy pasted their solution in fact i'm pretty sure if you've ever done any form of like code camp or anything it always looks exactly this way so i'm sure there's just a template that you pay for somewhere that gives you this exact same format every .
Time um so overall i think this is the same again if you're if you're just planning to learn like python or data science i'm sure dataquest is fine i just think datacamp from a data engineering standpoint currently is the winner like that's just my perspective dataquest if i'm wrong and i missed some courses correct me but again currently .
You end on data pipelines that's what i see and that's kind of a miss because there's just so much more to cover so hopefully that was helpful to you and if this video was take a moment to be helpful to me and just smash that like button it does seem to improve my standings in the youtube algorithm and i really do appreciate it again we're .
Pretty much at 10k and i'm gonna keep making videos because this is a lot of fun for me i'm really enjoying getting to review different products as well as talk about things like consulting and data engineering so i really enjoyed it thank you guys so much for just your time i really do appreciate all of you good luck out there on your data .
Engineering journeys and i hope you figure out which tool you like better data camp or dataquest i will see you next time and goodbye