What Tools Should Data Engineers Know In 2024 - 100 Days Of Data Engineering

Summary

The video script discusses the multitude of tools and skills necessary for a successful career as a data engineer. It emphasizes the importance of understanding programming languages like SQL and Python, working with Linux, and mastering version control with Git. The speaker also highlights the significance of working with databases, cloud data platforms, and ETL/data pipelines, as well as the evolving nature of data engineering tools. The video serves as a guide for those looking to break into the field, stressing the value of a solid foundation in both tools and best practices for data management and processing.

Takeaways

• 🛠️ The landscape of data engineering tools is vast and constantly evolving, requiring adaptability and continuous learning.
• 🔧 Core programming languages and technologies like SQL, Python, and Linux are fundamental to a data engineer's skill set.
• 📚 Understanding the basics of object-oriented programming and writing efficient functions is essential for effective data engineering.
• 🖥️ Familiarity with version control systems like Git is crucial for managing code and collaborating with teams.
• 🔐 Knowledge of secure file transfer protocols (SFTP) and encryption tools (PGP) is necessary for data security and compliance.
• 💾 Working with databases, both traditional RDBMS and NoSQL, is a key responsibility of data engineers for data extraction and manipulation.
• 🌐 Cloud data platforms and warehouses like Snowflake, Databricks, and BigQuery are becoming increasingly important in modern data engineering.
• 🔄 Data orchestration and pipeline tools such as Airflow and Azure Data Factory help automate and manage data workflows.
• 🔧 A basic understanding of containerization (Docker) and orchestration (Kubernetes) can be beneficial, even if managed by a devops team.
• 🚀 The ability to choose the right tool for the job, whether it's a data warehouse, ETL, or data pipeline solution, is a valuable skill for data engineers.
• 🎯 Focusing on building a solid foundation in data engineering principles and tools can lead to a successful and adaptable career in the field.

Q & A

What are some of the core programming languages and technologies a data engineer should be familiar with?

A data engineer should have a strong understanding of SQL, Python, and Linux. They should also be comfortable working with Bash scripts and have a basic knowledge of networking.

How have the tools used in data engineering evolved over time?

Data engineering tools have changed significantly over the years. Initially, engineers had to manually manage solutions like Hadoop and Spark by setting up their own infrastructure. Nowadays, cloud-based services like Databricks, Athena, and others have simplified the process.

What is the importance of version control in data engineering?

Version control is crucial for managing code changes, collaborating with other engineers, and maintaining a record of the development process. Familiarity with tools like Git is essential for any data engineer.

What are some of the basic technical tools and skills that a data engineer should possess?

Basic technical skills for a data engineer include understanding SFTP for secure file transfers, using PGP for encryption, and having a foundational knowledge of object-oriented programming and writing functions in Python.

How do different databases play a role in data engineering?

Data engineers often interact with various databases, both traditional relational databases like PostgreSQL and MySQL, as well as NoSQL databases like MongoDB. Understanding how to pull data from these sources and manipulate them is a key part of the job.

What is the role of cloud data platforms and warehouses in data engineering?

Cloud data platforms and warehouses like Snowflake, Databricks, and Big Query are used to build data lakes or data warehouses. They offer different architectures and features compared to traditional databases, and a data engineer must understand these differences to effectively use them.

Why is it important for a data engineer to understand both tools and the underlying concepts?

Understanding both tools and concepts allows a data engineer to make informed decisions about which tools to use for specific tasks, optimize their work, and troubleshoot issues effectively. It also helps them adapt to new technologies and stay current in the field.

What are some orchestration and ETL tools that a data engineer might use?

Orchestration and ETL tools like Airflow, SSIS, Azure Data Factory, and Informatica are used to automate data workflows, extract data from various sources, transform it into the desired format, and load it into target systems.

How does a data engineer decide which cloud platform to learn?

A data engineer should consider the popularity and prevalence of cloud platforms in the job market, as well as the specific needs of the companies they want to work for. AWS is often a safe bet due to its widespread use, while Azure may be preferred by large enterprises.

What additional tools might a data engineer need to know for containerization and infrastructure management?

For containerization, a data engineer might need to understand Docker and Kubernetes. For infrastructure management, tools like Terraform can be useful. However, these are often managed by devops teams, so data engineers might not need to be as deeply knowledgeable in these areas.

What advice would you give to someone looking to break into the field of data engineering?

Focus on building a strong foundation with the core tools and technologies, and don't feel rushed to learn everything at once. It's more important to understand the concepts and how the tools fit into the bigger picture. As you gain experience, you'll naturally learn more advanced tools and techniques.

Outlines

00:00

🛠️ Introduction to Data Engineering Tools

This paragraph introduces the vast array of tools available to data engineers and acknowledges the challenge of keeping up with the constantly evolving landscape of data engineering. It reflects on how tools like Hadoop and Spark have changed over time, moving from self-hosted solutions to managed services like Databricks and Athena. The speaker aims to create a video series to help viewers understand which tools are essential for a data engineer, emphasizing the importance of foundational skills like programming languages (SQL, Python), operating systems (Linux), and basic scripting (Bash).

05:00

🔧 Core Skills and Tools for Data Engineers

The speaker delves into the core skills and tools that a data engineer should possess, starting with programming languages and basic system interactions. It highlights the necessity of understanding networks and having a baseline of coding skills, including object-oriented programming and writing functions in Python. The paragraph also introduces version control systems like Git as essential tools for managing code, along with other technical tools like SFTP and PGP for secure file transfers.

10:01

💾 Working with Databases and Data Platforms

This section focuses on the interaction with databases, both traditional relational databases and NoSQL counterparts, as a key aspect of data engineering. It discusses the importance of understanding different database systems and their nuances, such as indexing and data manipulation. The paragraph then transitions into cloud data platforms and warehouses like Snowflake and Databricks, emphasizing the need to grasp the differences between these services and traditional databases to effectively build data solutions.

15:01

🔄 Orchestration, ETL, and Data Pipelines

The speaker addresses the realm of orchestration, ETL processes, and data pipelines, highlighting the various tools and platforms that data engineers may encounter. It mentions the use of Apache Airflow for workflow orchestration and how it can be utilized as an ETL or data pipeline solution. The paragraph also touches on the importance of understanding different data processing engines like Spark, Presto, and Trino, and the decision-making process behind choosing the right tool for the job.

🌐 Cloud Services and Advanced Tools

In this part, the speaker discusses the importance of cloud services in data engineering, suggesting AWS as a good starting point due to its popularity and wide usage. It also mentions other cloud providers like Azure and GCP, and their specific use cases. The paragraph further explores additional tools like Docker and Kubernetes, suggesting that while they may not be an immediate focus, having a basic understanding of these technologies is beneficial for managing infrastructure and containers in a data engineering context.

Keywords

💡Data Engineer

A data engineer is a professional responsible for designing, building, and maintaining the systems that handle and process data. In the context of the video, the speaker discusses the skills and tools necessary to be successful in this role, emphasizing the importance of understanding various technologies and programming languages to manage and analyze data effectively.

💡Tools

In the context of data engineering, tools refer to the software, programming languages, and systems used to manage, process, and analyze data. The video highlights the vast array of tools available, such as SQL, Python, Linux, and specific database systems, and how they are essential for a data engineer to perform their job effectively.

💡SQL

SQL, or Structured Query Language, is a domain-specific language used to manage and manipulate relational databases. The video mentions SQL as a fundamental tool for data engineers, as it is used to query, update, and manipulate data stored in databases, which is a core responsibility of the role.

💡Python

Python is a high-level, interpreted programming language that is widely used in the field of data engineering. The speaker in the video notes that Python is a crucial tool for data engineers due to its versatility and powerful libraries that facilitate data analysis, automation, and the creation of data pipelines.

💡Linux

Linux is an open-source operating system that is widely used in servers and data centers. In the video, the speaker mentions Linux as a necessary tool for data engineers, as they often need to interact with server systems, write bash scripts, and manage data processing tasks on Linux environments.

💡Version Control

Version control is a system that records changes to a file or set of files over time, allowing developers to track and manage these changes. The video emphasizes the importance of understanding version control, specifically Git, for data engineers to manage their codebase and collaborate with other team members effectively.

💡Data Platforms

Data platforms refer to the cloud-based services and technologies designed to manage and analyze large volumes of data. The video discusses various data platforms such as Snowflake, Databricks, and BigQuery, highlighting their role in modern data engineering practices and how they provide scalable and efficient solutions for data storage and processing.

💡ETL

ETL, or Extract, Transform, Load, is a process used in data engineering to move data from one system to another, often involving data cleaning and transformation. The speaker in the video mentions ETL as a core component of data engineering, where tools like Airflow and SaaS can be used to automate and manage the ETL process.

💡Data Pipelines

Data pipelines are the infrastructure that facilitates the movement and processing of data within an organization. The video explains that data engineers need to understand how to build and maintain data pipelines, which may involve using various tools and technologies to ensure data flows efficiently and reliably from source to destination.

💡Cloud Computing

Cloud computing refers to the delivery of computing services, such as storage, processing power, and databases, over the internet. The video discusses the relevance of cloud computing in data engineering, noting that understanding cloud platforms like AWS, Azure, and GCP is essential for leveraging their services for data storage, processing, and analysis.

💡DevOps

DevOps is a set of practices that combines software development and IT operations to shorten the system development life cycle and provide continuous delivery of value to end users. In the video, the speaker touches on the importance of having at least a basic understanding of DevOps tools like Docker and Kubernetes for data engineers, as they may be required to manage infrastructure or work closely with teams that do.

Highlights

The ever-changing landscape of data engineering tools

The importance of having a foundational understanding of programming languages like SQL, Python, and Linux

The evolution from self-hosting solutions like Hadoop and Spark to managed services on platforms such as Databricks and Athena

The necessity of understanding basic networking concepts for a data engineer

The role of version control systems like Git in data engineering workflows

The importance of learning and applying basic coding principles even for using drag-and-drop tools

The use of SFTP and PGP for secure data transfer and encryption

The need for familiarity with various databases, both traditional RDBMS and NoSQL

The distinction between data engineers, software engineers, and data scientists based on the tools they use

The concept of cloud data platforms and data warehouses, and how they differ from traditional databases

The learning curve associated with understanding the nuances of different cloud platforms like AWS, Azure, and GCP

The importance of not rushing through learning tools and taking the time to understand their intricacies

The role of orchestration tools like Airflow in ETL and data pipeline processes

The potential need for data engineers to understand and work with Docker and Kubernetes

The value of having a baseline understanding of tools to be competitive in the job market

The importance of continuous learning and growth in the field of data engineering

Transcripts

there are what feel like an infinite

amount of tools you can pick from as a

data engineer and likely if you've

worked in the industry for a while

you've maybe worked with some and heard

of others and are always wondering what

do you actually need to know to be a

successful data engineer and the funny

thing is and I think the challenge is

what we're working on today will

probably change a little bit tomorrow

you know when I first broke into the de

and data engineering world uh Hadoop and

Spark were all the rage and you would

have to figure out how to host it

yourself and spin up zookeeper and it

would be like 30 different solutions

just to get it working and now you know

we're just running things on data bricks

or Athena or something other than uh you

know us manually managing some of these

Solutions so the tools that we use

changed drastically uh over the years

but I wanted to create a video that was

in conjunction with my 100 days of data

engineering video that helps you guys

understand what tools you need to know

as a data engineer and we are taking a

quick pause from uh the AWS cloud videos

but I'll be back on those here shortly

if you haven't watched those uh give

those a checkout later if you'd like to

learn more about how data Engineers can

work with Cloud but for now let's talk

about tools from a high level let's

first cover the basics and this is one

of the challenges is like where do tools

start and where do tools end in terms of

solutions I think it's fair to say that

programming uh and certain languages and

basic solutions kind of fit in the tool

aspect and Tool World right like they

are tools we've built as humans as tool

Builders to help us automate and and

build processes so with that I think the

tools you'll definitely need as a dat

engineer even with things like chat GT

obviously uh SQL python Linux I say

Linux more more overarchingly um most

likely you're going to have to write

your fair share of bash scripts or at

least interact with um servers right you

might not need to be an expert but you

need to be able to interact with those

systems so yeah python SQL Linux some

level of understanding how to work with

networks all of that will likely come

into play and you need these Baseline

skills like they seem basic but you need

them like you can't get around them uh

yeah there's lots of dragon drop tools

but I recall recently I was working with

someone who was working on ssas and they

were like oh yeah I don't do the c um

blocks in ssas cuz I don't know how to

do it which to me is a little bit of a

copout because yeah you kind of should

be able to do at least some baseline

coding doesn't have to be fancy but at

least you know some level of

understanding you know of

object-oriented programming how to write

functions just your Baseline uh

understanding of python now along with

those Basics come kind of your other

technical basic Solutions and tools

right uh things like git right you

probably think of it as GitHub but git

is a broader solution and broader tool

that you will need to know you will need

to know how to Version Control uh all of

your various code that you will be

putting into places whether it's in a

Lambda or in a larger system that you're

developing you know in air flow Etc it

needs to go somewhere and so at least

understanding the four or five commands

in git that you will likely use all the

time you know things like get ad get

commit and get push uh at the very least

at least to understand how those

actually operate and if you want to go

deeper there are plenty of articles that

explain to you how this tool operates

but just being able to understand like

how to create branches and these small

things is very vital to being successful

as a data engineer and again these

skills kind of really build up your

Baseline and I think this is why it can

be hard to break into Data engineering

is because these are the tools that can

take time just in themselves to become

decent T you can probably in the 100

days that I've set up get a good idea of

these Solutions but maybe becoming

really good at them is hard and honestly

I'm still working on these Solutions and

constantly finding new things that I

maybe don't know fully um in these

Solutions uh another kind of basic

technical set of tools that you will

likely need to know is things like SFTP

and pgp these again are kind of this

interesting space I haven't started

talking about like actual tools yet like

uh people probably think of like airf

flow or snowflake but these are Baseline

skills and Baseline tools you will

likely need to work with you know you

likely will have somewhere uh that sstp

will come into play it still exists

today even with data sharing I have to

do it to Facebook I had to use SFTP or

secure file transfer protocol in order

to push files out to external Partners

who would then ingest that data and then

do analytics on it and then give us back

some sort of reporting on it and

similarly as we're going through that

process often we'd encrypt that file uh

with some set of keys so using something

like pgp uh or some similar uh protocol

will likely be required as well and now

you've got a baseline set of tools and I

probably Ed skills and tools here

interchange l in all fairness some of

this is tools some of this is skills but

I think you can't avoid these right like

these are things you have to know how to

work with you're going to have to know

to program you're going to have to know

use SQL you're going to likely have to

interact with a Linux box somewhere

whether it's an ec2 instance or a gcp

cloud compute somewhere you're going to

be interacting with these Solutions and

these tools now you are a data engineer

so you can't just know obviously these

These are tools that likely depending on

how you apply them either make you more

of a software engineer a data scientist

or a data engineer which why we break

these uh names up uh I've seen some

people I think recently kind of poke fun

at these names or be like you know

you're not really an engineer that's

less of the point to me to me it breaks

up the difference of why these different

jobs exist and what they do right sure

we're a data plumber that's fine

plumbers still have specific sets of

tools and plumbers also solve very hard

problems I know I've had them have to

fix a few around my house so as a data

engineer there are specific tools that

we use heavily first often at least

you'll have to interact with databases

in particular likely you'll pull a lot

of things from certain Source databases

and these Source databases tend to be

your traditional relational database

Management systems or something more on

the like nosql side so things that are

maybe document databases uh like mongod

DB uh so that's always great or cander

DB you'll also likely need to know

obviously again the traditional

postgress MySQL you don't have to know

every database that exists right there's

IBM db2 there's you know your Oracle

databases more than likely as long as

you get two or three under your belt and

they're kind of uh you know using some

different uh dialect of SQL you will be

familiar enough to pull from various

sources in the future yes they might all

interact slightly differently like you

they one will use change data Capture

One Way one will do it another way one

will have bin logs one won't but as long

as you get I think three or four that

you're comfortable with that you can

build a basic schema on you can

understand how to insert data into that

you can understand how to update data

you'll likely be okay here you don't

have to be again an expert but you need

to be familiar enough to build on them

to understand why someone might put an

index somewhere it will take again time

these aren't things you have to rush

through don't let 100 my 100 days of dat

engineering make you feel like you have

to run through any of this stuff again

if you don't learn it now if you happen

to get a job as a data engineer

somewhere you'll be learning it there uh

so just make sure you don't run too too

fast otherwise you're going to be

stressed out in your actual job so again

now that you've kind of got your

databases under underway right you've

kind of built a good understanding of

how databases operate this will kind of

give you the next layer of knowledge so

that when you go into what now people

kind of call like cloud data platforms

or cloud data warehouses honestly

there's so many different terms now that

people use for these Solutions because

you look at them and they aren't

actually set up like sometimes

traditional databases which is why it's

good to actually understand how I think

traditional databases operate so that

when you look at snowflake uh you don't

think they're the same thing you don't

think like oh this operates 100% exactly

the same way um as my traditional

database or same thing with data brakes

which is even farther removed from your

traditional databases um and harder to

probably grasp that hey there is a a

compute engine here or there's some sort

of you know uh query engine here sort of

with spark and there is kind of storage

but the some of the traditional stuff

that exists is kind of all peace Meed

out right it doesn't exist in the same

framework and so that's why it's really

important I think to build these steps

slowly so that you understand these

differences um I always remember when I

first uh interacted with my First Data

Warehouse because I had taken like your

tradition relational database course um

in school and I was literally uh

interning at the same time while I was

taking that course uh and looking at

this data warehouse I was like oh these

are kind of the same right like you've

got something called a key here and an

ID here they're the same thing right is

in my mind and obviously a few months

later I I eventually learned that no

these are different things and I had to

like dig into that and start reading

things like Kimble and actually dig into

the differences and that's why I think

again the more you can kind of

understand and see when when things are

different it it just makes you uh more

valuable as a data engineer moving

forward so the next one I'm sorry for

that die tribe but the next one is

really these data platforms so snowflake

data bricks we're going to throw a big

query in there again it doesn't fit

maybe as much of the data platform is

space but I guess if you add in

everything else gcp has it kind of can

so if you add in all the gcp data flows

and things like that it kind of fits but

these are the traditional Solutions you

might have to know um you can also again

throw in red shift there in Azure synaps

analytics which actually does fit more

of that data platform space but those

are kind of your key um data platforms

that you'll likely be building on and in

general building some sort of data

warehouse or dat lake house if that's

your cup of tea um on obviously it's

going to depend on which one of these

Solutions you pick they all do operate

slightly differently um the way I often

feel it is gcp tends to feel to me like

it's very a little more limited in terms

of like what I can find- tune whereas

snowflake tends to be a happy medium and

and data breaks like it gives you a lot

of uh control but then you have to

understand how that operates almost kind

of like the old or orle days where it's

like Oracle gave you a ton of control

but that's why you'd pay a lot for

Oracle Consultants cuz they'd have to

know how to like set up control files

and all this stuff um as you were

loading data and as well as fine-tuning

a lot of other stuff whereas you could

just use SQL Server which I often found

a little easier to work with versus

Oracle now as you're learning these

Solutions you're going to again be

layering more and more of your skills on

top of each other think about it what

are you going to be likely writing when

you are working on snowflake or big

query likely SQL that is how you're

going to intera act with these VAR

Solutions hopefully on top of these

tools you also have the skills and best

practices to build a data warehouse or

data lake house but those that's where I

definitely draw the line in terms of

like what's a tool what's more of a

skill and a best practice right that's

going to fall more in terms of like how

you build data pipelines how you build

data warehouses that comes more into

skills and best practices and design

versus actual tools that can help you um

Implement these uh best practices and

and designs and along with that if

you're on a a data bricks uh fan person

you will have to learn a spark and how

it operates and how you can best

interact with it including when you're

writing things like SQL instead of maybe

uh python or Scala in order to interact

with spark you know what's the best way

to run joins things like that um are

really important to understand why you

might want to use something like an

engine like Spark versus maybe Presto or

trino and where in fact at Facebook we

even had the ability to switch in

between spark and Presto depending on

the job cuz sometimes it was more

efficient to use Presto or trino and

sometimes it was more efficient to know

or to use spark and you'd have to know

why and so it's really good to know um

at least a little bit about all these

tools if not a deep understanding

because it will become valuable as

you're going along I think the important

thing is as you're going through these

steps of learning again you don't need

to feel rushed you will learn all of

this stuff through time as long as

you're putting in the effort you know if

you're putting in 10 minutes a day

probably won't learn it but if you're

putting in hours a day like most of us

have at some point if not still today

you will pick up these Solutions you

will learn them and you will feel

confident in actually being able to

deliver with them all right so now

you've again you've kind of built all of

this Baseline the next set of tools

you'll often see that you need to know

are things like orchestration ETL and

data pipelines you can throw an elt in

there these all kind of fit in this

similar space and I know some people

would get mad if I said that but I say

that because airflow which um obviously

fits in this workflow orchestrator space

often when I see it implemented gets

used as an ETL type solution or data

pipeline

to run a very basic um extraction of

data and then load it somewhere and then

maybe add in snowpipe or something

similar that can just pick up a trigger

of one file dropping into S3 but you're

really going to see that there are a lot

of different ways you can do pipelines

and honestly what you often find is

there's a few kind of types of tools you

can do things very custom you can build

it yourself people love doing that for

some reason even though we we've built a

ton people love having um open- Source

Solutions again airflow and Mage are

examples of that these kind of fit again

in that Orchestra Trader world but also

often just get used as data pipelines or

ETL type flows as well and then you have

things that are maybe fully you know

managed things like ssas very drag and

droppy um so ssas Azure data Factory and

a few others that all involve you know

dragging and dropping and and automating

tasks that way and so those are kind of

the various tools you'll see um there's

a few others that like very much focus

on maybe like just extract and load most

of those tend to be very easy to work

with so I don't think you need to put a

ton of effort learning them off the bat

I think it's very much worth to at some

point but more than likely you'll need

to pick some of these Solutions because

you will uh likely use them and they

tend to be the easiest to learn cuz

there are others even again we could go

on forever in terms of orchestrators

data pipelines Informatica and a few

others that often cost a lot of money to

get access to but for now I think just

understanding the concept and getting a

few of these tools under your belt maybe

one or two is good enough generally to

at least make you uh hirable which is

your first goal is just get hired in a

junior position and then eventually you

can go from there and again like I kind

of referenced earlier the cloud is

another set of tools that you will

eventually learn there are a ton of

clouds you don't need to learn all the

clouds generally I tell most people AWS

is a safe bet because most people use it

if you do want to learn Azure understand

that most of that is going to be uh at

large Enterprises that use it whereas

AWS T to be a broader range and I find

that most people that use gcp use it for

big query because they like big query so

if you are going to pick AWS tools

that's where I'd start and I do have

this AWS video that you can go through

to actually look at all the various uh

tools you'll likely need to know I think

I go through like eight or nine um cuz

you don't need to know every solution

and so the cloud is always a baseline

that you need to know and then there's a

ton of one-off tools that you may or may

not need to know like honestly I have

mixed feelings I will say that it's

worth at least digging into Docker cuz

you probably will have to occasionally

start up a Docker instance here or there

and same thing with kubernetes at least

understand how it operates understands

how to kill you know a pod occasionally

but more than likely you will have a

devops team that manages it and if not

you are the devops team and that's now

your new job like you it's very hard to

like build data pipelines and manage a

bunch of infrastructure that you've

developed similar thing can be said

about terraform right like it's worth

knowing but in theory you should have a

devops team that does that obviously

nowadays people are um starting to I

feel like reduce the amount of people

that work on these teams so maybe maybe

you will be a one One Stop Shop for all

of this stuff but these tend to be the

things you can maybe learn last uh you

don't need to put a ton of effort in

immediately um some of it will just come

through uh you just naturally doing your

work but also you don't want to be

running or trying to figure out how

Docker Works while you're pushing code

to production so make sure you've at

least run it a few times and if it is

your first time seeing it in production

and you've never run it in production

try to find someone to help you out

there cuz it's just there's a lot of

ways it can go wrong and those are most

of the Baseline tools you need to know

I'm sure there's others that people feel

like I've missed please comment below if

you think uh I'll pin it if I think it's

a good tool I'm like yeah that's

actually true I should have covered this

but I think that's the Baseline it takes

a little bit of time to become a data

engineer and I think this is part of it

like you don't have to know all of these

tools super in- depth but you need to

know what they do in an interview

someone likely might ask like where

would you maybe use one of these

Solutions versus another um how do joins

work uh on one solution versus another

uh does red shift have merge if it

doesn't how can you you know end up

running something similar to a merge

statement all of this stuff is important

to at least understand and have touched

here and there I don't want this to be

discouraging again you have long careers

um it took me a few years to get to a

point where I had a title of data

engineer and and even now whether that's

the title whether the title is data

plumber I think is less the point I

think the point is how you do your work

so hopefully this was helpful for you

out there whether you're an analyst an

engineer data scientists um to

understand the tools that a data

engineer will likely need know with that

guys thanks so much for watching and I

will see you guys in the next one thanks

all

goodbye a