Microsoft Fabric is a new, end-to-end, unified data and analytics platform designed to solve the complexities and inefficiencies of traditional fragmented data architectures, offering a comprehensive solution for all data personas.
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over the next 40 minutes I'm going to
present to you everything you need to
know about Microsoft fabric Microsoft's
new endtoend data and analytics platform
the key learning outcomes for this
course are that I want you to walk away
with a solid understanding of what
fabric is the problem it solves and how
it solves them but more than just the
boring what why and how of fabric really
what I want to share with you is some of
my passion and excitement for Microsoft
fabric so you leave with an appreciation
for the huge opportunity that fabric
represents for your organization and for
you personally in your own career we'll
Begin by telling the story of Houston
Electric a fictional electrical Goods
e-commerce company that is struggling
with its existing data infrastructure
and workflows through the case study we
will explore the problems that fabric
was built to solve we will then dive
into the big Vision behind fabric I'll
be showing you exactly how Microsoft
fabric solves these typical indust
industry problems and you begin to see
why I believe fabric is such
transformational technology now if you
want to truly understand fabric then
there are some fundamental concepts that
you will need to learn so in part two of
the course we will cover what I believe
are the most important Concepts that you
need to understand those being one Lake
the seven experiences of fabric and the
four compute engines together we will
then look in more detail at the seven
experiences of fabric so that you gain a
proper appreciation for what exists
and how you can use it to round off this
course I'll be showing you how you can
get started using fabric for yourself
and signpost some of my favorite
resources for learning fabric that I
think can really help you in your own
Journey so if you're wondering is this
course really for me should I invest the
time to sit through this course well the
great thing about fabric is that it's an
endtoend unified solution which means
that it caters for every data Persona in
your organization so if you're working
in any of these roles currently maybe
outside of fabric then this presentation
will definitely be highly relevant and
interesting for you or if you want to be
working in one of these roles within
Fabric in the future then this course
will also be a great foundation for you
either way no knowledge of fabric is
assumed there's not going to be any code
examples or too technical in the weed
stuff this is quite a high level course
if you want a more detailed explanation
and code examples feel free to look at
my other videos on YouTube where there's
lots of that stuff on there and I'll be
using plenty of practical examples
throughout to solidify your
understanding of the platform I've spent
over 200 hours preparing this course so
I believe it would definitely be worth
your while to stick around on top of
that I've been using fabric nearly every
day since it was released so I've got a
good understanding of how it works and
I've been a data professional for the
last eight plus years working across
pobi data engineering data science data
warehousing and realtime analytics which
you're about to see covers nearly every
aspect of Fabric's functionality so all
that effort knowledge and experience has
been condensed into this introduction to
fabric course which I'm releasing for
free on this YouTube channel to give
back to the community that has helped me
so much in my own Journey as well as on
YouTube this course is hosted for free
in my online community built especially
for people like you to learn Microsoft
fabric even faster I recommend you
follow the link in the description
because as well as the video lessons you
also get access to the course notes and
links to further resources that I'll be
mentioning throughout this video
throughout the course and you can use
the community feature to ask me and the
community any questions you might have
about the course and Microsoft fabric
generally and it's completely free so
there's nothing to lose so without
further Ado let's begin so to understand
why fabric is such a transformative
technology it helps to properly
understand the problems that fabric has
been built to solve and to illustrate
this I want to tell you about the story
of Houston Electric now Houston
electrics is a fictional electrical
goods online retailer based in the US
but it ships electrical parts around the
world the company's grown to over 400
employees to service a growing demand
and the company Prides itself and being
Innovative and uses data to make better
decisions in 2015 Houston electrics
hired their first Chief data officer who
launched a digital transformation
program to make better use of technology
to drive business growth Central to this
strategy was the establishment of a
central data department now with more
than 50 employees and the company has
invested in a number of cloud
Technologies in Azure and Amazon web
services and they've started using
powerbi looking at what the company is
using today we can see that the compan
is split into the following main
departments at least from a technical
point of view each department has a
number of data technologies that they
use to store and manage and analyze
their data and if you're not familiar
here with all of the logos on the screen
don't worry the point here is that the
data landscape in the company has grown
organically over the last 8 years with
little strategic Vision this is a common
situation that a lot of companies find
themselves in each department uses the
tools they prefer and each department
relies on data from other departments to
do their job for example the customer
success team manages the order reviews
database which keeps track of all the
reviews that customers leave about their
products now let's look at a particular
workow that uses this you orders review
database to see how it works and see how
the company functions so when a customer
purchases a product they leave a review
on the website and the review data is
stored in an Azure SQL database managed
by the customer success team and the
data engineering team and the company
have built a data pipeline that copies
this data every morning into a data Lake
for data scientists to analyze and the
data scientists then take this review
data they perform sentiment analysis on
the review basically to gauge whether
the customers are liking or hating
certain products based on the text they
write and these sentiments are then
written into a data Lake container
finally the bi team have created a
powerbi report that communicates these
findings back to the product teams to
help them improve the products going
forward now they've got this workflow to
a point where it's it's working but it
was a pain to set up and it's a pain to
maintain and data just for this one
workflow is scattered across four
different locations in four different
data formats some of which are
proprietary meaning you can't really
access it very easily from other
tools and this is just one workflow the
company operates many data processing
pipelines like this copying data between
different departments and between
different storage locations and products
the complexity of the data landscape has
grown so much that the maintenance of
these existing systems and processes
becomes an almost full-time job so to
analyze what might be going wrong here
let's map out some of the tools that the
company is currently using under these
four broad buckets of data ingestion
data storage data engineering and data
science and business intelligence now
let's analyze the current situation
against these nine considerations listed
down the left hand side and to analyze
them we're going to speak to a number of
key employees in the company so if you
speak to the chief data officer they're
going to tell you that the data
architecture has grown organically and
now there are data silos all over the
place I'm the chief data officer and I
don't want to be the chief integration
officer so instead of focusing his time
on how the company can generate more
value from data here's role is focused
on system integration which is quite a
massive headache for him and for his
team and for the company so in our
visual map here we can plot different
blocks that represent all the different
systems that create different data silos
then then if we speak to the data
engineering lead he tells us that we're
maintaining hundreds of pipelines
copying data between lots of different
data stores for different departments
and it's messy and to get around all
these silos of data within and between
departments and data products the data
engineering team have been flat out
building data pipelines that copy data
from here to there and now they're in to
deep the pipelines are failing regularly
as there's just too much to manage and
maintain within a small team so on our
diagram now we can see all of the
different copies between each system and
product and how this is causing
confusion and maintenance and problems
with data quality as well it's not just
the data engineering team this causes
issues for imagine being a powerbi
developer how on Earth would you know
which data sets to connect to with an
organization and how can you know if
they're up to dat and whether you can
trust them the data science team faces
similar issues they mention that we have
data scattered in so many formats in
different places
and it takes me days to get clean data
sets just to begin an analysis and even
then I don't know if I can trust the
data and on top of that just the fact
that so many systems exist and they're
all different creates a cognitive
overhead for this team they added that I
had to learn the intricacies of many
different data Technologies and each one
is different we had a new starter and it
took three months to upskill them in all
the different data platforms we use so
let's add those problems onto our visual
here the data scientists mentioned that
there's no uniformity in data formats
across the organization which makes it
difficult to get everything into a
common format needed to begin analysis
on top of that they have to work every
day across five six maybe seven data
products each with their own learning
curve user interface user experience and
this is inefficient especially for new
joiners so the different color blocks
here show how the user experience is
different in each of the tools the
company uses next the IT director is not
happy too he's saying we're using too
many systems all have different security
profiles and requirements to keep data
at rest and data in transit secure and
it's a nightmare and it's true it's not
just the people on the front end of
these tools that find it difficult spare
thought for the IT director and his team
responsible for managing access to each
of these tools independently securing
them and the data stored within them
governing the data and then monitoring
and maintaining the systems generally
and as if that wasn't bad enough enough
he also gets the finance director on the
phone every month to complain about the
Azure Bill he added I dread getting our
Azure bill every month it's so
unpredictable and sometimes scary each
data product has their own pricing
structure so it's difficult to predict
how much we will be charged month to
month so now we add billing and
Licensing to our visual here and we can
see that each product has a different
licensing and a different billing
structure which makes it really
difficult for companies to manage and
finally it's the poor old dashboard user
they don't really know much about all
the palava that's going on behind the
scenes here but they can sense that
something isn't right they say I'm not
sure I can trust the data that's being
presented to me here doesn't always
reflect reality and they're right to be
nervous all of this complexity means
that the data teams have failed to
really get a grip on data governance and
data quality and the dashboard users at
the end of the process are losing trust
and when they lose trust in the
visualizations and the data being
presented to them then what's the point
in all of this anyway so let's just
pause there for a second and it was at
this point when the head of Houston
electrics the smart guy that he is was
watching the learn Microsoft fabric
YouTube channel wow what a coincidence
he learned about Microsoft Fabric and he
knew that Microsoft fabric could help
solve the problems that they were
experiencing as a business and he was
right you see within Microsoft fabric we
have a family of data products available
to us which Loosely fall under the four
buckets that we were looking at before
data ingestion storage data engineering
data science and business intelligence
now these tools when looked at in
isolation they serve a similar function
to the tools that the company was using
previously and it's a common myth that
you sometimes hear people say about
Microsoft fabric is that oh it's just a
remarketing exercise they've just put a
new badge on existing technology but
this Viewpoint fundamentally
misunderstands that fabric has been
completely built from the ground up to
address all of the problems that we saw
in the previous slides it's not just a
marketing exercise but a complete
rethink and re architecture of how data
is managed in your organization and
let's look at what we mean by that in
more detail so in fabric all of your
company data resides in one place and
it's called one Lake and it's a
fundamental concept that we'll explore
in more detail a little bit later on how
one Lake eradicates data silos so data
across all these different products is
actually stored in one place and because
of this it also eradicates the need to
create multiple copies of a data set a
fundamental principle in fabric is that
a data set should only ever exist in one
place and then be referenced throughout
fabric using a clever feature called
shortcuts so all your organization's
data is stored in one Lake and in one
Lake all data is stored in the same
format called Delta paret and this is an
Open Standards format and this solves so
many problems that we highlighted
previously most importantly it solves
the data integration problem so data
scientists data engineers and data
analysts might all be using different
tools maybe different languages that
they're familiar with but under the hood
they're all working on the same data
which is in the same format and they're
not wasting hours and days trying to get
that data into suitable format so they
can begin their analysis next fabric
provides a unified user experience we
heard previously that people were sick
of having to log into multiple different
platforms each with a different look and
feel when in fabric you log in Via a web
portal and the user experience is
designed to feel similar to that of
Microsoft 365 so you log in once and you
can navigate to any fabric experience
and each experience has a similar look
and feel this means you can spend more
time thinking about how you're going to
get value from your data and not
worrying about how to navigate through
the application and for people
administering fabric there's so many
benefits too for starters access control
and security is drastically simplified
in fabric there's one access control
method and one security model applied
across all tooling and experiences and
access to these resources is principally
managed through workspaces which is just
a collection of fabric items that makes
sense for a particular security boundary
that you want to set up and because all
of our data lives in one Lake data
governance and discoverability becomes a
much easier task and in fact fabric has
many features built in for governing
your data set next up is a really
important one fabric comes with a single
built-in monitoring Hub which monitors
all fabric activity across your
experiences so you only have one place
to look if you want to monitor all of
your different data pipelines your
notebook runs any kind of processes that
you're running within Fabric and last
but not least billing and Licensing in
fabric is Unified meaning that when you
purchase some fabric capacity for your
organization you immediately get access
to all of the features and items there's
no longer a separate license or billing
structure for all of the different
products listed above now there's
different levels of capacity you can buy
depending on the intensity of your usage
and something to bear in mind here is
that if your company is currently paying
for power bi premium capacity you get an
f64 fabric capacity which is a lot of
capacity in fabric for free with your
powerbi premium capacity this means you
can create and use any fabric items
today without paying any extra and all
of these things I've mentioned here are
kind of a topic within themselves that I
could go into a lot more detail on but
what I've done is if you go through to
the Community Link here and go through
to the classroom tab I'll leave a link
in the description there's links to a
lot more resources where you can learn
about each of these specific features in
Fabric in more detail okay so now we're
starting to understand the power of
fabric and you've heard about one Lake
and the experiences and the compute
engines and how powerful these things
are when they work together let's look
at how it works in more detail so in
fabric there are seven experiences and
they cover the whole endtoend data analy
workflow that a company might face now
an experience is just a logical grouping
of tools that make sense for specific
personas for example in the data
engineering experience you'll find easy
access to tools that a data engineer
would use frequently and in fabric
you'll find three main data stores
places where you can create and manage
data the data warehouse the lakeh house
and the kql database the important point
is that any tabular data that you create
in any of these stores is under the hood
automatically stored in one Lake and
crucially all the data stored in one
lake is is stored in this format the
Delta paret format which as I said
previously Is An Open Standards format
so to interact with data in any of these
stores we can write tsql scripts in the
data warehouse we can write python r or
Scala in the notebooks in data
engineering and data science experiences
or we can write kql in the real-time
analytics experience and there's also a
lot of low code and no code options for
beginners in each of these experiences
and Microsoft co-pilot is now tightly
integrated into fabric so you don't need
to worry if you don't know much coding
fabric is really built to cater for
people with no coding experience all the
way through to professional developers
and these are features that they're
constantly adding more of into fabric to
make it a bit less intimidating for you
know beginner level people entry-level
people to get up and running quickly in
fabric now the ability to run script in
many different languages against the
underlying Delta paret format of one
lake is made possible by the four
compute engines in fabric now these
compute engines act like the integrator
the user writes some code in a language
they're familiar with and then the
relevant engine converts that into a
query of the underlying Delta tables in
one Lake and Returns the data to the
user that they're expecting in the
experience that they're currently using
now once the data is stored in one Lake
it's directly accessible by all the
other engines without needing any import
or export and all the compute engines
have been fully optimized to work with
Delta paret as their native format as
well as data stores and scripting tools
there are more items available a lot
more items available to us in each
fabric experience so now we have a good
understanding of how the overall
architecture Works let's review each
experience one by one starting with data
Factory so the data Factory experience
in fabric is primarily focused on moving
and transforming your data and a core
use case is using data Factory to get
new data into fabric perhaps from an
external API or by connecting to one of
your organizational systems you could
describe the data Factory experience as
a set of tools to help you with extract
transform and load functions now it's
built for Enterprise scale too so if you
have a lot of data or a high frequency
of refresh then this shouldn't be a
problem now the fabric items that you
can create within this experience are
the data flow and the data flow
basically allows citizen developers to
connect to more than 300 data sources to
bring data into fabric transform it
using a kind of familiar low code no
code power query interface so if you're
a powerbi developer or you've used Excel
then this will be very familiar to you
and the data can then be written into
one of the fabric data storage solutions
like a Lakehouse or a data wouse we also
have the data pipeline now this is an
orchestration tool and it's used to
trigger different data processing
workflows normally on a schedule for
example triggering the Run of a fabric
Notebook on a particular schedule or
maybe triggering a stored procedure in
your data warehouse and pipelines can be
used also to bring data into fabric this
set of tools provides similar
functionality to existing tools that you
might be familiar with like azid data
fact Factory synapse pipelines and
powerbi data flow gen one and the main
personas who might be using the
datafactory tools would be data
Engineers analytics engineers and also
powerbi developers now the data
warehouse experience consists of the
data warehouse unsurprisingly which
provides a familiar transactional data
warehouse solution with tables schemas
views stored procedures and all that
good stuff and it's obviously queriable
using tsql and it provides features to
make it more accessible for citizen
analysts so there's visual scripting low
and no code Solutions built into this
experience as well and it's Lake Centric
meaning that under the hood it's highly
scalable architecture so it's not a
traditional SQL Server although you can
use tsql to query it under the hood it's
actually built on top of a completely
different engine called the pois engine
now the fabric items you can create
within this experience are as we
mentioned a trans action or data
warehouse built on top of the Polaris
engine for scalability so this is where
users can create tables schemas views
store procedures functions all this good
stuff now it provides a similar set of
functionality although not exactly the
same to some of these existing tools so
it's similar in a way to SQL server or
an azra SQL database because it uses
tsql it allows the user to interact with
it using tql but it's more similar to
synapse SQL serus or dedicated pools
because it us is the same underlying
engine the Polaris engine and if you're
using tools outside of the Microsoft
ecosystem similar to snowflake I mean
obviously there's lots of other tools I
could mentioned here that similar to but
these are just a selection and the main
personas who might be using the data
warehouse experience could be database
administrators data Engineers data
analysts those kind of people next up we
have the data engineering experience and
data Engineering in Microsoft fabric
enables users to design build build and
maintain infrastructures and systems
that enable their organization to
collect store process and analyze large
volumes of data so the fabric items that
you can create in this experience are
the Lakehouse which allows you to store
and manage both unstructured data so
files and convert them into structured
data so Lakehouse tables and we do that
using notebooks and other tools as well
but the notebook is where a user can
write write and run scripts in a variety
of languages to perform data engineering
tasks like cleaning data or validating
your data or whatever you like really
and the languages available to you are
python or r or Scala and the notebook is
built on top of Apache spark which is a
big data processing framework commonly
used in the data industry now we also
have the spark job definition and this
is a set of instructions typically
written in Python in P spark that Define
how to execute a job on the spark
cluster and this is more for advanced
users who want a bit more control over
how their data is going to be processed
by The Spark engine now this set of
tools provides similarish functionality
to some of these tools that you might be
familiar with like ADLs Azure data Lake
Services data bricks or Snowflake and
obviously the main personas who might be
using the data engineering experience
would be data Engineers or analytics
Engineers next up we have the data
science experience so the data science
experience provides a complete set of
tools to support the entire data science
workflow within an organization right
the way through from data exploration
preparation cleansing and
experimentation modeling model scoring
and the serving of your insights and
your predictive insights within a
powerbi report now the fabric items you
can create in this experience are are
the notebook so the notebook is a key
tool for data scientists and they use
them to explore the data through code
typically through python or R and the
notebooks are used for data exploration
running experiments training ml models
machine learning models and other stuff
that data Sciences do as well another
tool in this experience are experiments
so when a data scientist is training a
machine learning model typically they
will run a lot of experiments to
optimize the model and the experiments
item in fabric provides functionality to
track each of these different iterations
and logs things like the parameters that
are being used the code versioning and
evaluation metrics for that particular
model run now experiments use mlflow
which is basically the industry standard
for this kind of logging of machine
learning model training and
experimentation finally we have machine
learning models so during the experiment
ation phase specific versions of machine
learning models can be registered using
ml flow and fabric provides
functionality for managing and reviewing
these models using the machine learning
model item in Fabric and you'll find
similar functionality within here that
you might find in other tools like Azure
machine learning synapse notebooks and
datab Bricks notebooks as well and
finally the main personas that going to
be using the data science experience is
not surprisingly data scientists the
real time analytics experience in fabric
provides a set of tools to ingest manage
and analyze real time invent data and
event data is a completely different
Paradigm really in data analytics and
requires a different mindset and also a
different set of tooling the fabric
items you can create within this
experience are the kql database which is
a data store for your streaming data
sets it's built on top of the kql the
custo query language engine and and if
you're familiar with Azure data Explorer
for things like logging then this is the
same engine that being used in the kql
database within fabric we also have
event streams and this is a no code tool
to register streaming data sets process
them and then rout them to various
destinations in fabric we also have the
kql query set now this basically allows
you to query data in a kql database
using the custa query language and this
set of tools provides a similar
functionality to the existing tools as I
mentioned it's very similar to Azure
data Explorer now there are some new
things like the event stream that's
completely new but it's built on top of
the same engine and the personas well it
could be a DAT engineer or an analytics
engineer or if you have real time or iot
Internet of thing Engineers or perhaps
security Engineers because this is used
quite a lot with security event logging
and tracking that sort of thing as well
so powerbi is Microsoft's business
intelligence solution that allows you to
create reports to present visual
insights to business users within fabric
there's quite a lot of items that you
can create under this experience I'm
going to be talking about two of the
main ones first one being the report so
the report is basically a business
facing business intelligence report and
you use it to visualize business data
and insights and show them back to to
your user your business and when you
publish a report you also get an
Associated semantic model and these are
what used to be called a data set in
powerbi until not too long ago and the
semantic model is a collective term that
refers to all the things that make up
kind of like the back end of a powerbi
report so things like your tables your
relationships your Dax measures now
things like calculation groups as well
all these different things make up the
semantic model this experience provides
a set set of tools that you know similar
to maybe you've used Tableau or looker
obviously it's not exactly the same
there's lots of differences but it's
these kind of business intelligence
tools if you're coming from a different
platform and the main users and personas
who might be using this experience will
be well the business users you know not
necessarily data professionals but
people who are consumers of your reports
then you have powerbi developers and
maybe bi analysts data analysts
all going to be using powerbi now the
final experience I want to touch on here
is data activator and data activator is
currently in preview still and it's a
very new experience but it's a no code
experience in fabric for automatically
taking actions for example running a
power automate script when patterns or
conditions are detected in changing data
and this could be data in a powerbi
report or it could be event streams as
we've just seen in the real time
experience and in data activator you can
create what's called a reflex now a
reflex lets you define a specific data
point that you're tracking maybe it's
the temperature of a fridge in an iot
system for example and then it also
allows you to define a condition so
whenever the temperature of your fridge
gets greater than 4° C or whatever that
is in Fahrenheit it's going to trigger
an action and your action can be a
variety of different kind of actions it
could be a power automate script that
runs or it could be an email
notification or it could be a HTTP
request to some other Azure function for
example or all these different options
but basically it monitors your data in
real time and then when the data reaches
some Condition it's going to perform an
action data activator i' would say is
quite unique in the data analytics
Market I don't think there's too many
companies doing a similar thing at least
at this scale so you might have used
tools like power automate or Azure
functions which has a similar kind of If
This Then That Type Paradigm but
obviously in Azure functions you have to
create that logic yourself most of the
time and power automate is not really
built for Enterprise scale data
processing but these are some of the
tools that you could describe it as
similar to although it is unique in its
own way and the people who going to be
using this
tool are again business users because
this is an entirely no code experience
so it's built for people in the business
to set up these things without you
having to do it so if you've got someone
in marketing who really wants to track
you know social media Impressions and
when they get above a certain level they
want to be notified you know if one of
your posts is going viral you don't want
to be sitting reading the powerbi report
day after day hour after hour really you
you want to set a threshold and once it
goes above a th000 Impressions then you
want to be alerted to that fact and then
you can do something about it so that's
why data activator is so powerful
because people in the business can set
these up for themselves in a no code way
so we've covered a lot of ground there's
probably information overload going on
inside your heads so let's just pause
here for today but before we finish I
just want to let you know how you can
get started using Fabric and then show
you lots of links to more resources that
I find really helpful for learning
Fabric and I think would help you with
your journey as well okay so here I am
inside of the school Community learn
Microsoft Fabric and if you haven't been
here before I recommend you go here you
know we've got 15 members currently but
it's likely to go up a lot more I
haven't even advertised it yet so follow
the link in the description here I
basically answer any questions that
you've got and you can ask me any
questions or other people in the
community as well and and generally we
have lots of discussions about fabric so
if you want to learn fabric I recommend
you go here and in here I've got this
classroom content and it's called
introduction to Microsoft Fabric and
within this course you'll find all of
the notes about what I've just been
describing in this course and at the
bottom there's this getting started link
and here I describ two methods for how
you can get started with Microsoft
fabric the first one is to follow
through to the documentation that's the
Microsoft documentation to set up a free
Tri now this is good if you have admin
privileges within your existing company
or within your Microsoft tenant now if
you're just someone at home who wants to
set up their own fabric instance
especially with its own admin privileges
then there is a way to do this via the
M365 developer account I've linked to a
blog post here on data witches. comom
and this is a really good blog post
because it shows you how to set up an m
365 developer account and it is a little
bit long- winded but basically allows
you to get a fabric free trial with
admin privileges so really you can use
it to do whatever you want for 60 days
and in this blog post it tells you
exactly how to do that so I recommend
following these instructions and then
getting your sandbox environment set up
for fabric on top of that I've also got
this links to more resources and some of
these links are provided by Microsoft so
in their introduction to fabric
presentation they list these resources
so these are all Microsoft resources at
the top so links to the documentation
links to the ebook links to Microsoft
learn fabric modules some endtoend
scenarios and fabric notes if you're
more of a visual learner these are some
revision cards for Microsoft fabric that
you might find useful now on top of that
I've listed these personal
recommendations of what I think are good
resources for you to learn from so one
of the best ones that I use quite regular
regular
is the Azure synapse YouTube channel so
they've kind of rebranded let's say and
they're doing quite a lot of content on
Microsoft fabric particularly there's a
series called fabric espresso and what's
good about the is that they interview
the people working on uh the fabric
product teams about how their particular
product works or how a new feature works
so I definitely recommend you going
there next up is advancing analytics
YouTube channel and this is really good
particularly if you're interested in
learning more about lakeh House Lake
House principle
Simon there has got a lot of experience
in building and designing lake houses
and The Spark engine in particular as
well so if you looking to learn that
kind of stuff I would recommend
advancing analytics we also got katos bi
run by Chris Vagner which is an awesome
place to learn more about Fabric and
they have every Friday they have like a
live stream every well for me it's in
the mornings I think if you're in the US
it would be around lunchtime and yeah
it's just really good resource to learn
about fabric next up we have Tales from
the field and tales from the field again
is like a regular discussion and Roundup
about what's happening in the fabric
data community so if you don't have much
time to get your head around all the
different news and events and ways that
people are working with fabric and I
recommend Towers from the field it's a
great bunch of guys all working for
Microsoft all working with clients on
kind of figuring this stuff out as well
so there's some good insights in that
one as well I'd also recommend Fab bri.
Guru which is a Blog run by Sandy Po and
this is a really good resource for
helping you understand a wide range of
topics um especially Sandy loves going
into the some of the more technical
stuff figuring out how these systems can
integrate working out cool new workflows
that are available to us in Fabric and
he documents them really well in a lot
of detail so I definitely recommend you
checking that out another one is data
Mozart focusing mainly on powerbi but
but now is producing a lot more content
on fabric particularly recently around the
the
dp600 which is one of the certification
exams it's the only fabric certification
exam currently that allows you to become
a certified fabric analytics engineer so
if that you're interested in that I
definitely recommend checking out data
moar and the final one is a Blog by Sam
de bruy and this is basically a
collection of articles about fabric he
focuses I think previously he was
focusing on azure and kind of the
Microsoft data stack in general but now
Sam's been producing some of the best
like blog posts that I've read come from
Sam so I definitely recommend if you
like reading blog posts about fabric to
check this one out as well if you've
made it this far I want to say thank you
very much for watching and I would love
it if you leave a fire Emoji in the
comments just to let me know that you've
made it all the way to the end if you
have any questions then I recommend that
you join our learn Microsoft fabric
community and ask away the link is in
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