>>> ALL RIGHT. GOOD AFTERNOON, EVERYONE.
MY NAME IS AMAR GOWDA. WE ARE BASICALLY
AN EXTINCTION THE ENGINEERING TEAM.
WE WORK -- AN EXTENSION OF THE ENGINEERING
TEAM. WE BRING IN THE FEEDBACK AND
INSIGHTS TO THE ENGINEERING TEAM
TO GET THINGS PRIORITIZED. ALL RIGHT,
SO TODAY WE ARE HERE TO WHAT IS
THE COMMON THING CUSTOMERS COME
IN WITH THE KAFKA REQUIREMENTS COMING
INTO AZURE. WHAT WE HAVE SEEN AS
A TREND LATELY AND KAFKA IS A HARD
TOPIC, I'M SURE THAT'S WHY YOU ARE
ALL HERE TO LISTEN WHAT THIS IS
ABOUT. I WOULD LOVE TO START WITH
WHAT KAFKA AND GO OVER COLD CON
ACCEPTS OF KAFKA. AND THEN GO OVER
AND LOOK AT TYPICAL SCENARIO EXAMPLE
OF CUSTOMERS COMING IN THIS WITH
REQUIREMENT. SO KAFKA IS AN OPEN
SOURCE SOFTWARE. SO IT'S A DISTRIBUTED
STREAMING PLATFORM. SO I SAY STREAMING,
THAT'S IMPORTANT. AND DISTRIBUTED
ON TWO TERMS AND I WILL ELABORATE
ON WHAT THAT MEANS. BECAUSE THERE'S
CONFUSION WHEN IT COMES TO MESSAGE
BROKERS AND THE SERVICE, THE ALL
OF THOSE AND HOW DIFFERENT IS KAFKA
AND HOW DO YOU BOTH IN THE STREAMING.
SO PRIMARILY DESIGNED FOR STREAMING
TYPE OF REQUIREMENTS. SO WHAT IT
REALLY MEANS IS IT'S A LOG. A DISTRIBUTED
LOG. YOU CAN CAPTURE THE LOGS AND
DO THE PROCESSING REQUIRED TO DO
THIS. IT CAN BE USED AS MESSAGING
AND I'LL GO OVER MORE DETAILS ABOUT
HOW THOSE ARE DONE. AND AS A TYPICAL
TYPE OF APPLICATION WHEN YOU BRING
IN MICRO SERVICES INTO THE PICTURE
OR TRYING TO DO PROCESSING WHICH
IS TYPICAL WITH ANY MESSAGE BROKER
SYSTEM. AND MAINLY IS THE STREAM
PROCESSING ITSELF, HOW YOU CAN DO
THE STREAMS COMING IN FROM IoT DEVICES
AND OTHER T AT AND PUSH TO THE PROFESSION.
WHAT ARE THE KEY THINGS, KAFKA COMING
IN WITH KAFKA REQUIREMENTS ARE,
SO ALL OF THE MESSAGES GO INTO A
TOPICS WHERE THE MESSAGES ARE STORED.
BEHIND THE TOPIC, THEY HAVE PARTITIONS,
THAT'S HOW YOU MANAGE WHERE THE
THINGS ARE GOING AND GIVES YOU MORE
THROUGHPUT AS WELL BASED ON HOW
MANY PARTITIONS YOU HAVE. AND A
PRODUCER IS SOMEONE SENDING A MESSAGE.
A CONSUMER IS SOMEONE PULLING THE
MESSAGE. YOU PULL FOR THE MESSAGES.
BROKERS ARE LITERALLY MORE END POINTS
WHICH YOU CONNECT TO. AND THEY COULD
BE MULTIPLE BROKERS IN A KAFKA.
AND YOU JUST ASK FOR A BROKER OR
YOU CAN CONNECT A BROKER. BUT IF
YOU ASK FOR IT, IT'S GOING TO GIVE
YOU THE LIST OF BROKERS AND THE
TOPICS AND CONNECT FROM YOUR PRODUCING
SITE. MESSAGES ARE SOMETHING STORED
IN MORE OF A KEY VALUE OBVIOUSLY.
A KEY WHERE IT SAYS, WHO YOU ARE,
WHAT TYPE OF MESSAGE, THE BODY OF
THE MESSAGE, INTO THE VALUE ITSELF.
AND WITH THAT, THERE'S A DATA AND
TIME STAMP WHICH IS IMPORTANT HOW
THE SEQUENCE OF ORDERING IS KEPT,
NO MATTER IF YOU'RE IN MULTIPLE
PARTITIONS. MOST OF THE FORTUNE
500 COMPANY, HELPTY BANKING AND
EVERYBODY ELSE HAVE EMBRACED KAFKA
AND DEPLOYED HUGE CLUSTERS BUT STARTED
FROM LINKEDIN BY THE WAY. THAT'S
WHERE IT STARTED AND CONTRIBUTING
A WHOLE LOT OF STUFF IN HERE. SO
THE KEY CONCEPTS OF WHAT KAFKA OFFERS
IS YOU HAVE A CONSUMER API. YOU
HAVE A PRODUCER APIs. THIS IS HOW.
AND STREAMS API WHICH IS VERY OPTIMIZED,
VERY HIGH THROUGHPUT TYPE OF SCENARIO
THAT YOU'RE TRYING TO TAP INTO THE
TOPICS WHICH HAVE MESSAGES COMING
IN AND YOU CONSUME THE MESSAGES.
VERY LOW LATENCY KIND. KAFKA SAY
PCP PROTOCOL BY THE WAY. JUST LIKE
OTHER EQUIVALENT OF THAT. SO IT
WORKS ON A PROTOCOL. SO IT HAS AUTOMATICALLY
FEW OTHER THINGS THAT COME. THE
CONNECTOR, CONNECTORS ARE SOMETHING
THAT HAVE A PREBUILT LODGE NICK
THEM TO RECONNECT LIKE YOU BUILD
SQL SERVER TO KAFKA TOPICS. SO WHEN
THERE'S AN INTERRUPTION, THEY CONNECT
AND RECONNECT BACK. ALL LOGIC BUILT
INTO THE CONNECTORS SO A LOT OF
EXTENSIONS YOU CAN BUILD ON AND
EXTEND YOUR APPLICATIONS TO DO MORE
FROM DIFFERENT DATA SOURCES AS WELL.
LET'S GO OVER HOW THE TOPIC AND
THE CONSUMER GROUP. AND THEN WE
CAN JUMP OVER AND HOW IT'S GOING
TO LOOK LIKE ON AZURE AND WHAT ARE
THE KEY THINGS CUSTOMERS COME WITH.
SO THE KEY THING HERE AS I MENTIONED
IS, THE TOPICS AND THE PARTITIONS
THAT GO AND HOW THINGS ARE DONE.
THE PARTITIONS WILL KEEP YOUR REAL
MESSAGES AND THE PARTITION CAN BE
IN DIFFERENT BROKERS. COULD BE BETWEEN
DIFFERENT SERVERS FOR HIGH THROUGHPUT.
AND ALSO VENUE READ BACK FROM THE
PARTITION, YOU CAN CREATE A CONSUMER
GROUP. HEY, CONSUMER GROUP A, GIVE
ME ALL THE MESSAGES THAT BE LONG
TO ME. IF YOU'RE NEW, IT WILL START
FROM WHERE IT WAS, THE START OF
THE MESSAGES AND KEEP GOING ONCE
THE CONSUMER GROUP EMPTIES SO. IF
YOU HAVE A MICRO SERVICE OR DIFFERENT
CONSUMERS LISTENING TO THE SAME
MESSAGE, PUT THEM IN THE GROUP SO
A, CONSUMER A READS A MESSAGE, THEN
IT'S ALREADY A READ MESSAGE. CONSUMER
B WILL READ THE NEXT MESSAGE IN
THE LINE. THAT'S THE CONCEPTS MEAN.
IF A CONSUMER ATTACHED TO A A TOPIC
AND BUILD ARCHITECTURE LIKE THAT,
YOU WOULD HAVE TO ATTACH ONE CONSUMER
TO ONE CONSUMER GROUP THROUGH THE
MESSAGES SO MULTIPLE SERVICES TAPPING
INTO THE SAME MESSAGE AND REACTING
TO THE MESSAGE CAN BE DONE THROUGH
HAVING DEDICATED CONSUMER GROUPS
FOR MICRO SERVICE. LET'S LOOK AT
WHAT ARE THE COOL FEATURES THAT
COME OUT OF BOX WITH KAFKA ITSELF.
AND THERE'S A LOT OF ACTIVITY FROM
OSS ADD-ONS THAT YOU CAN BRING.
SO THE KEY THING HERE IS, EXACTLY
ONE MESSAGE. SO PRODUCER WHEN THERE'S
A DISCONNECT WHEN IT TRIES TO SEND
A MESSAGE AND COMES BACK, IT'S NOT
GOING TO PRODUCE THE SAME DUPLICATED
MESSAGE AGAIN. THAT IS HANDLED BY
CALF CHASM AND NOW YOU'RE COMING
BACK -- BY KAFKA, NOW YOU'RE COMING
BAH. SO THAT'S AN IMPORTANT FEATURE.
THERE ARE OTHER IMPORTANT FEATURES
THAT COME INTO IS RETENTION WHICH
IS VERY IMPORTANT. IF YOU WANT TO
KEEP THE MESSAGES BASED ON A SIZE
AND DELETE ALL THE MESSAGES PAST
SEVEN DAY WHICH IS STANDARD OR KEEP
THE MESSAGES FOR SO LONG BASED ON
THE SIZE, YOU HAVE ALL THOSE CONFIG
RAYINGS YOU CAN DO AND -- CONFIGURATIONS
YOU CAN DO. AND YOU HAVE REPLICAS,
HEY, YOU WANT ME, I NEED TO COPY
THAT MESSAGE OVER TO MULTIPLE CLUSTER,
MULTIPLE REGIONS AND ASKING CALLED
ACTS SETTING. DO YOU WANT TO SYNC
WHICH IS GOING TO TAKE LONGER IN
GIVING BACK THE ACKNOWLEDGMENT OR
SYNC BACK INTO MULTIPLE REPLICA
PARTITIONS. THOSE ARE THE FEATURES
THAT COME WITH KAFKA ITSELF. SO
THIS IS ALL ABOUT CALF CHASM NOT
TALKING ANYTHING RELATED TO AZURE
YET. WHAT ARE THE COMMON SCENARIOS
STREAM PROCESS, IT COULD BE SOMETHING
YOU'RE TRACKING WEBSITE ACTIVITY,
TRYING TO DO ATM TRANSACTIONS OR
A LOT OF ACTIVITIES IN THE BANKING
THAT ARE HAPPENING AT DIFFERENT
REGION, TRYING TO BRING THAT INTO
ONE PIPELINE. AND TRY TO DO SOME
AI AND SOME LEARNING TO DETECT EARLY
PROBLEMS WITH YOUR CREDIT CARD.
COULD BE ANYTHING ELSE. SO THAT'S
ALL COMES INTO STREAM. EVEN IoT
COMES INTO THE SAME STREAM PROCESSING
AS WELL. DEVICES COMING IN, A SLIDING
WINDOW OF WHAT EVENTS HAPPENED.
YOU PUSH IT UP INTO A TOPIC OR YOU
BRING TOPICS FROM DIFFERENT LOCATIONS
OR DIFFERENT CENTERS AND DO DOWNSTREAM
PROCESSING. THAT'S VERY DIFFICULT.
INTERESTINGLY, PEOPLE ALSO USE KAFKA
AS A STORAGE SYSTEM ITSELF. IF YOU
HAVE A TRANSACTION LOG, YOU WANT
TO KEEP THE TRANSACTION LOG FOR
SEVEN YEARS OR BEYOND THAT BECAUSE
OF SOME COMPLIANCE REASONS, PEOPLE
TEND TO PUT IT AND KEEP IT IN THE
KAFKA TOPICS FOR LONGER AND GET
A CHANCE TO REPLAY IT. DOING THE
RECONCILIATION, YEARLY ACCOUNTING,
OR SOMETHING, ALL THE MESSAGES BACK
AND MAKE SURE THE TALLY IS HAPPENED.
SO THAT'S ANOTHER BIG SCENARIO WE
SEE WITH KAFKA. AND ENTERPRISE MESSAGING,
PEOPLE TEND TO KEEP IT WHEN THEY
COME WITH CLOUD, TYPE OF APPROACH
AND THEY WANT TO KEEP IT TO ONE
SYSTEM, ONE MESSAGE BROKER, ONE
APPLICATION DOING MULTIPLE THINGS.
THEY TEND TO GO WITH KAFKA AND DO
THE [ INDISCERNIBLE ]. AND MICROSOFT
ORBING ADMINISTRATRIX ALSO. LIKE,
HEY, I FINISHED MY MESSAGE, I'M
GOING TO DROP ANOTHER THING TO THE
TOPIC. ANOTHER SERVICE PICKS IT
UP. I PASS TO IT THE NEXT ONE. TYPICAL
MESSAGE BROKER ROUND ABOUT IS DONE
ON KAFKA. WE HAVE SEEN THAT BUT
NOT A WHOLE LOT THAN ON THE OTHER
TWO. ALL RIGHT. SO LET'S LOOK AT
SOME SCENARIOS. BEFORE THAT, I WANT
TO KIND OF INTRODUCE WHAT A LAMBDA
ARCHITECTURE IS. SO WE HAVE LAMBDA
ARCHITECTURE. AND THIS IS BASED
ON HOW YOU PROCESS THE MESSAGES
OR STREAMS. THE KEY THING HERE WITH
LAMBDA ARCHITECTURE IS YOU HAVE
A HOT PATH, AND A COLD PATH. THE
BATCH LAYER IS THE HOT PATH. WE
USE THE TERMS IN A MIXED WAY. AT
REAL-TIME AT THE SLIDING WINDOW
OF A THREE MINUTES OR FIVE MINUTE,
I AM LOOKING AT HOW THE MESSAGES
ARE COMING IN AND DOING REAL-TIME
ANALYTICS OF THE DASH BOARD TO TELL
WHAT IS HAPPENING. SO THEY CAN THROW
IT OUT INTO REFINE OR POWER BI OR
ANY OTHER SOLUTIONS FOR FRONT-END
VIEWING OF THE THINGS AND PUT SOME
AI IN THERE TO DETECT SOME THINGS
THAT YOU CAN FLAG THEM. THE BATCH
LAYER OR THE COLD PATH, I'M GOING
TO PASS THESE RECORDS KIND OF PUT
IT IN A COLUMN FOR THE DATA RETENTION
PURPOSES AND GOING TO DO SOME OTHER
OFF LINE LEARNING BASED ON THIS.
THAT'S A HOT PATH. THAT'S A KEY
CONCEPT WITH LAMBDA ARCHITECTURE.
YOU LEAVE THE SPEED LAYER AND EVERYTHING
IS IN THE SPEED LAYER. NOTHING STORING
ANYTHING ON THE SPEED LAYER. JUST
LOOKING AND REACTING TO THE THINGS.
THAT'S THE IMPORTANT CONCEPT ON
THE LAMBDA. I WILL BUILD THE SLIDE
AND THEN TALK THROUGH. OKAY. SO
THE KEY THING ABOUT STREAM PROCESSING
IS, DATA IS ALWAYS MOVING. YOU CANNOT
GO BACK AND SAY GIVE ME THIS. IT'S
NOT A SQL QUERY ON A COLUMN STORED.
AT THIS TIME, FROM WHAT HAPPENED,
COMPARE THAT TO WHAT HAPPENED MINUS
FIVE OR PLUS FIVE MINUTES AND TELL
ME AND GIVE INSIGHTS ABOUT HOW WE
CAN REACT TO IT. THAT'S WHAT IT
REALLY MEANS. SO THE KEY OTHER THING
IS THE ENGINE WOULD TELL YOU WHAT'S
HAPPENING. NOS DATA IN A VERTICAL
FORMAT, HOW DO YOU WANT TO REACT
TO MESSAGES AND PUT IT OUT THERE
IN A DOWNSTREAM PROCESS. IT'S ALL
UP TO YOU. YOU DON'T HAVE TO REACT
TO EVERYTHING COMING IN THE STREAM.
LOOK AT WHAT IS AN EXCEPTION THAT
DOESN'T FALL AND THEN ACT ON AND
IT. THAT'S WHEN YOU START AND PUTTING
THAT IN A COLUMN AND NOTE IT'S AN
EXEMPTION. SO LET'S LOOK AT HOW
TYPICAL ARCHITECTURE. THIS IS A
PLAP OF WHAT CUSTOMERS -- THIS SAY
MAP OF WHAT CUSTOMERS COME IN WITH.
AND OTHER SOLUTIONS THEY TEND TO
LEAN TOWARDS OR USE IT. AND I WILL
BRING UP THE NEXT SLIDE WHICH TALKS
ABOUT THE TYPICAL SCENARIO WITH
CUSTOMERS TO. PEOPLE TEND TO ALWAYS
HAVE A KAFKA CLUSTER ON-PREMISES.
MORE OF A COME WITH THEM EXISTING
REQUIREMENTS. NOT A GREEN FIELD.
PART OF A BROWN FIELD TYPE OF PROJECTS.
AND THEY WANT TO PUMP THOSE MESSAGES
TO AZURE TO DO SOME DOWNSTREAM PROCESSING
AND BRING THE BEST OF WHAT THE CLOUD
GIVES YOU. MORE LEAN TOWARDS MAKE
CHEER IS A POWERFUL OR TAKING MULTIPLE
TOPICS AND PUSHING IT OUT TO ANOTHER
KAFKA CLUSTER. IF YOU WANT TO DO
A DISASTER RECOVERY, YOU WOULD REPLICATE
THE MESSAGES ACROSS DIFFERENT LOCATIONS.
SO WE HAVE DIFFERENT WAYS YOU CAN
RUN. PEOPLE TEND TO GO WITH THE
EVENT HUBS FIRST, WITH THE PaaS
APPROACH AND EXPAND TO HD INSIGHT
KAFKA OFFERING OR EVEN TRY TO DO
THEIR OWN WITH VMs. PEOPLE EXPLORING
A LOT ON AKS. WE HAVE SEEN THAT
TREND WHERE PEOPLE WANT TO CONTAINERIZE
AND RUN KAFKA INTO A STAYSLESS TYPES.
SO A LOT OF NUANCES AND YOU NEED
TO WATCH OUT RUNNING AT SQUALL.
BUT IT IS DEV -- AT SCALE. BUT IT
IS DEFINITELY DO BELIEVE. AND THE
COLD PATH. YOU WOULD PUT THAT IN
THE CAPTURE, PUT IT, YOU WILL WRITE
AZURE DATA FACTORY FLOW TO TRANSFORM
THE MESSAGES AND READ THROUGH IT.
AND ALSO DATA BRICK WHICH IS EXTENSION
OF SPARK AS WELL. ON THE HOT PATH
ITSELF, AZURE STREAM ANALYTICS WHICH
DOESN'T ACTUALLY ATTACH ADD A PROTOCOL,
BUT AZURE STREAM ANALYTICS I PUT
A ASTERISKS BUT CONNECTS TO EVENT
HUBS REALLY WELL FOR DOING REAL-TIME
HOT PATH ANALYTICS AND THAT SLIDING
WINDOW AND FIRE OFF AZURE FUNCTIONS
WHAT YOU WANT TO DO AND HOW WE WANT
TO REACT TO THAT OR ANY OTHER DOWNSTREAM
APPLICATIONS. ANY OF THOSE, EXCEPTIONS
WILL GO INTO THE SERVING LAYER ITSELF
WHICH COULD BE A COLUMN STORE OR
NOT. COULD BE A DB, JUST DUMP THE
DOCUMENT WHAT HAPPENED. YOU WOULD
THROW IT OUT IN A SERVING LAYER
WHICH HAPPENS TO BE THE PEOPLE THAT
COME UP WITH, OR THEY WANT TO GO
WITH THE MORE MICROSOFT APPROACH
WITH POWER BI. SO THAT'S A VERY,
VERY TYPICAL APPROACH. AND LET'S
LOOK AT HOW THIS WOULD LOOK LIKE
WITH THE CUSTOMER WE WORK WITH.
AND AND THIS IS AN EXAMPLE. THIS
WAS ALL ABOUT DOING PREDICTIVE SCAPABLE.
SO A MEDIA SERVICES COMPANY AND
ALL THE STREAMING COMES FROM THE
AKs ITSELF. THIS IS ALL ABOUT, HEY,
SOMEONE IS GOING TO WATCH "GAME
OF THRONES. " HOW DO I SCALE IT
UP? THE TIME OF THE START? OR PEOPLE
WANT TO TUNE IN DIFFERENTLY. SO
ALL OF THE PREDICTOR SCALING, WANTED
TO PUT MACHINE LEARNING AND DO THOUSAND
TREND IS LOOKING LIKE. AND THEN
PROGRAM THE THINGS. SO THE WAY THEY
DO IT, THEY PUSHING ALL OF THOSE
MESSAGES BACK INTO ON-PREMISES.
AND OPEN THE CELLPHONE, OPEN THE
APP OR THE TV, WHERE EVER YOU'RE
WATCHING AND COMING IN TO ON-PREM.
AND I THEY WOULD AGGREGATING THAT
AND PUSHING IT TO EVENT HUBS TO
DO THE DOWNSTREAM PROCESSING. THEY
BUILT OFF A MODEL THIS WAS LEARNED
ON THE CORE PATH BUT THEY PUT IT
BACK ON THE HARD PATH TO DO REAL-TIME
SLIDING AN LYFTICS AND SAY NOW WE
NEED TO TRIGGER THE AUTO SCALER
FOR AKS. THIS WAS A VERY COMMON
EXAMPLE AND THE EXAMPLE WE HAVE
SEEN HERE IS THE WAY THEY DO IT
IN THE COLD PATH AND HOT PATH FOR
DOWNSTREAM PROCESSING. THIS CUSTOMER,
THEY WANTED TO OPEN IT UP THROUGH
INTERPHASE TO TELL THEIR CUSTOMERS,
CDM PROVIDER, THESE ARE THE BAND
WIDTH, THIS IS EXEMPTIONS, HOW MUCH
WE SCALED UP AND DOWN, AND THOSE
ARE THE REAL-TIME THINGS THEY WANTED
TO SEE. WHERE THEY WERE ABLE TO
PLUG INTO THE HOT PATH AND CONSUME
THE MESSAGES. SO THIS WAS A VERY
TYPICAL SCENARIO. SO WITH THAT --
DOING GOOD WITH THE TIME. ALL RIGHT.
THIS IS MY LAST LINE, THIS WAS 20
MINUTE AND I'LL BE OFF STAGE. IF
YOU HAVE QUESTION, ANY REQUIREMENT,
AS WELL AS TO DISCUSS MORE ABOUT
HOW WE CAN HELP YOU. SO THINGS YOU
NEED TO KNOW THAT ARE VERY IMPORTANT
FOR YOU WHEN YOU'RE THINKING OF
KAFKA IN YOUR ARCHITECTURE NOW TRYING
TO COME TO AZURE. NO BUILT-IN WAY
OF DOING IT. MAKER OR UBER HAS A
REPLY CAT OR LINKEDIN, OPEN SOURCES
IN BROOKLYN WHICH IS A VERY HIGH
SCALE MAKER TYPE OF AN APPROACH.
CONSIDER THOSE, THOSE ARE THE ONLY
OPTIONS THERE. SO THE BIG THING
ABOUT KAFKA IS THE HIGH LAY 10 SINGLE
DIGITS TYPE OF APPLICATION. SO IT
COULD BE REALLY HELPFUL IF YOU'RE
STREAMING IN A LOT OF DATA. OKAY.
SO IF YOU'RE LOOKING AT USING KAFKA
AS A PURE QUEUING, DO SOME MORE
RESEARCH TO SEE IF KAFKA IS THE
RIGHT FIT OR NOT. BECAUSE THERE
ARE DEFINITELY BETTER SOLUTIONS
BUT PEOPLE GO WITH ONE OR THE OTHER.
JUST KEEP IN MIND, ALL OF THESE
ARE GREAT OFFERINGS BECAUSE THEY
HAVE GUARANTEED DELIVERY, SO YOU
CAN LOCK THE MESSAGE WHEN YOU'RE
PROCESSING, A LOT OF GOOD THINGS
THAT THE THINGS COME WITH. HERE
YOU HAVE TO HANDLE IT YOURSELF.
AUTHENTICATION, THIS IS ALSO DIFFERENT.
DEEPER TOPIC YOU NEED TO RUN THIS
OR EXPOSING EXTERNALLY APPLICATIONS.
NOT WORKING IN A V NET OR SECURED
ENVIRONMENT, A LOT OF CONSIDERATIONS
THERE. ONE LAST THING IS PEOPLE,
IF YOU REALLY WANT TO KEEP IT TO
A CLOUD APPROACH, KAFKA IS A GOOD
ANSWER. TO START WITH. SO IF YOU
WANT TO BUILD SOMETHING THAT YOU
CAN DO A HOST OR PUSH THAT DEPLOYS
TO MULTIPLE CLOUD PROVIDERS, SAME
APPLICATION, THIS IS THE WAY TO
DO IT. AND WE'VE ALWAYS SEEN CUSTOMERS
COME IN, HEY, I WANT TO BUILD A
SOLUTION THAT IS AGNOSTIC. THAT'S
COMMON THESE DAYS, E WHETHER I THE
TOP AZURE CUSTOMERS SO. THIS IS
THE APPROACH. WE HAVE A PRE-EXISTING
REQUIREMENTS OR THEY WANT TO BUILD
SOMETHING THAT CAN HELP US DEPLOY
IN MULTIPLE CLOUD. EITHER WAY, THESE
ARE THE THINGS TO WATCH OUT FOR.
WITH THAT, THIS WAS MY LAST SLIDE.
I DIDN'T HAVE A DEMO OR ANYTHING.
A 20 MINUTE SESSION OBVIOUSLY. THANK
YOU. GET IN TOUCH WITH ME IF YOU
HAVE A REQUIREMENT OR IF YOU HAVE
CUSTOMER YOU'RE WORKING WITH WHO
HAVE THIS NEED OR THINKING OF COMING
INTO KAFKA, WE CAN HELP YOU. WE
HAVE A TEAM THAT WILL IT IS YOU
WITH, CODE WITH YOU, LEARN WHAT'S
CORK WORKING AND TAKE IT BACK TO
ENGINEERING. THANKS, EVERYONE. [