Hannover Messe 2024: Generate business value from your SAP data
Summary
TLDRこのビデオスクリプトでは、製造業向けのSnowflake for SAPデータ分析についてDavid Rickerが語っています。市場動向としてM&Aの増加とそれに伴う迅速な財務報告の必要性、BW(Business Warehouse)の廃止とその遅れ、顧客が求めるイノベーションとスケーラビリティ、データコラボレーションの重要性が説明されています。SnowflakeはSAPシステムと異なるデータソースを統合し、大規模なデータを効率的に分析・管理できるプラットフォームとして紹介されています。また、Snowflakeのパートナーシップと生態系、特にAWSとの連携が強調されています。スクリプトは、製造業におけるデータ分析の課題とSnowflakeが提供する解決策、そして実際の顧客事例を通じてその価値を説くものとなっています。
Takeaways
- 📈 スノウフレークは製造業向けのSAPデータ分析に適しており、企業は急速に統合や分割を遂げ、財務諸表を迅速に作成する必要があると述べています。
- 🔄 市場動向として、企業はM&Aを通じて急速に拡大し、SAPシステムが異なるブループリントを持つ多数の企業が統合されるケースが増えています。
- 📊 SAPのBWシステムは徐々に廃止されており、2029年までに段階的に切られる予定です。顧客はイノベーション、スケーラビリティ、安定性、データコラボレーションを求めています。
- 🚀 スノウフレークは最大40ペタバイトのデータを扱える大規模なデータベースであり、SAPの最大BWシステムの277倍以上のスケールを提供しています。
- 🔗 スノウフレークはデータコラボレーションを促進し、サプライチェーンにおける顧客とのデータ共有を容易にします。
- 💡 ERPとSAPデータを統合し、分析するための課題は、多数のERPシステムからデータを集約し、効率的に分析する必要性です。
- 🛠️ スノウフレークは非構造化データや半構造化データを容易に取り込み、管理し、分析することができます。
- 💼 スノウフレークはビジネス価値の迅速提供に貢献し、エンタープライズの可視性を高めるだけでなく、ビジネス改善プロセスを強化します。
- 🌐 スノウフレークはクラウドベースのシステムであり、パートナーシップのエコシステムを通じて顧客間のデータ共有を可能にしています。
- 🔧 スノウフレークはAWS、Microsoft、Googleなどとの互換性があり、様々な技術パートナーシップを通じて強化されています。
- 🔄 大規模企業はSAP BWからスノウフレークへの移行を通じて、コスト削減と効率化を実現しています。
Q & A
スノーフレークは製造業向けのSAPデータ分析にどのように役立つか説明してください。
-スノーフレークは製造業向けにSAPデータ分析を提供し、企業が迅速な合併や事業譲渡に対応する際に財務諸表を迅速に作成し、データの統合と分析を容易に行うことを支援します。
スノーフレークが提供する市場動向にはどのようなものがありますか?
-スノーフレークは市場からの要望に応えるために、企業がより迅速に合併や事業譲渡を行う必要があるという動向に加え、SAP BWのフェーズアウトとそれに伴う顧客のイノベーションニーズに対応しています。
スノーフレークはSAP BWと比較してどのような利点がありますか?
-スノーフレークはSAP BWよりもスケーラブルで安定しており、大量のデータを扱うことができる利点があります。スノーフレークは最大40ペタバイトのデータを扱うことができ、SAP BWの最大容量をはるかに上回ります。
スノーフレークにおけるデータコラボレーションとは何を意味しますか?
-データコラボレーションは、企業がサプライチェーンにおける顧客やパートナーとデータを共有することができることを意味しており、ネットワークを介してデータを移動する必要なく、ペタバイト規模のデータを効率的に共有できます。
スノーフレークはどのような課題を解決するのに役立ちますか?
-スノーフレークはERPシステムからのデータの統合、分析、およびAI/MLの活用を支援し、企業が経済的な方法でデータをエンリッチし、効率的に分析することができるようにします。
スノーフレークにおけるゼロコピークローンとは何ですか?
-ゼロコピークローンはスノーフレークの機能で、データをコピーする際に追加のストレージを必要とせず、同じサイズのデータを別の環境で使用可能にします。これにより、開発やテスト環境を効率的に作成・破棄できます。
スノーフレークはどのようなビジネスプロセス改善に貢献していますか?
-スノーフレークはエンタープライズの可視性を高めるだけでなく、ビジネス改善プロセスに貢献しています。それは予測能力の強化、供給連のリスク管理、在庫可用性の改善などを通じて行われます。
スノーフレークはどのようなパートナーと提携していますか?
-スノーフレークはAWS、Microsoft、Googleなどのクラウドプロバイダーと提携しており、さらにシステムインテグレーターやデータサービスプロバイダーと協力して顧客のニーズを満たしています。
スノーフレークにおけるSAPデータアーキテクチャの代替としてどのような機能がありますか?
-スノーフレークはSAPデータアーキテクチャの代替として、データを取り込み、モデル化し、レポート作成することができる機能を提供しています。これは従来のSAP BWやHANAアーキテクチャとは異なるアプローチです。
スノーフレークはどのような技術的なユースケースを解決していますか?
-スノーフレークはSAP Enterprise Data Warehouseの代替としてコストを大幅に削減し、またSAP BWからワークロードを移動してコストを節約するのに役立ちます。さらに、リアルタイムでのデータの取り扱いも可能です。
スノーフレークはどのようなSAPデータを取り扱うことができますか?
-スノーフレークはSAPから提供される様々なデータを取り扱うことができます。それは透明テーブル、プールテーブル、クラスターテーブルなどを含むSAPのデータを指し示します。
スノーフレークでのリアルタイムデータ分析はどのように実現されますか?
-スノーフレークではトリガーベース、バッチベース、マイクロバッチベースの方法でリアルタイムデータ分析を実現しており、さまざまなパートナー企業がこのプロセスをサポートしています。
スノーフレークにおけるデータのセキュリティはどのように確保されていますか?
-スノーフレークはデータのセキュリティを確保するために包括的なセキュリティ機能を提供しており、これはデータの取り扱い、格納、およびアクセスを管理する機能を含みます。
スノーフレークはどのようなSAPサポートツールを使用していますか?
-スノーフレークはSAPサポートツールとしてデータサービスを使用しており、これらのツールはSAPとスノーフレークの間のデータを効率的に移動させるために設計されています。
スノーフレークでのデータ分析にはどのような方法がありますか?
-スノーフレークでは、SQLだけでなくPython、Scala、Javaなどのプログラミング言語を使用してデータ分析を行えます。また、機械学習やAIのライブラリも利用可能です。
スノーフレークでのデータ分析の利点は何ですか?
-スノーフレークでのデータ分析の利点は、モデルに依存しない柔軟性、豊富な分析機能、および他のツールとの統合性です。これにより、複雑なデータ分析を効率的に行うことができます。
スノーフレークはどのような顧客事例を挙げていますか?
-スノーフレークは顧客事例として、SAPデータをスノーフレークに移行し、分析ユーザー数を数千人規模に拡大する企業を挙げています。これにより、供給リスクの低減やコスト効率の向上が実現されています。
スノーフレークでの分析環境の自動化とはどのようなものですか?
-スノーフレークでの分析環境の自動化とは、ユーザー向けのスノーフレーク環境の自動生成と展開を意味しており、これは従来の手動でのプロセスよりも効率的でスケーラブルです。
スノーフレークへの移行の最初のステップは何ですか?
-スノーフレークへの移行の最初のステップは、現状の分析を評価し、使用されていない機能を特定することであり、その後必要な機能のみをスノーフレークに移行する戦略を立てます。
Outlines
📊 SAP データ分析に関する市場動向
スノーフレークのDavid Rickerは、製造業向けのSAPデータ分析について語ります。市場の力学やCOVID-19後の企業の合併・分割がデータに与える影響について説明し、SAPシステムの統合と財務データの統合の難しさを強調します。また、BWシステムの段階的廃止と、それに対する顧客のニーズについても触れています。さらに、SAPのデータ量の増加とそれに対応するための技術的な課題についても議論しています。
🔍 データ統合と管理の重要性
Davidは、SAPシステムとスノーフレークの統合について詳述します。データの統合、ガバナンス、中央集権化の重要性を強調し、企業がより効果的にデータを活用するための方法を紹介します。AIやMLを活用したデータの強化や、SAPデータと非構造化データの統合の必要性についても説明しています。
🌐 ビジネスプロセスの改善と予測強化
ビジネスプロセスの改善と予測の強化について話します。さまざまなデータソースを統合することで、税務情報やサプライチェーンリスクなどの重要なビジネス質問に答える能力を高めます。システムの高可用性を維持しながら、効率的なデータ処理を実現するためのスノーフレークの利点を強調しています。
🤝 パートナーシップとデータ共有の利点
スノーフレークのエコシステムとパートナーシップの重要性について議論します。クラウドベースのシステムにより、データの共有が容易になり、サプライチェーン全体での効率が向上します。さらに、SAPシステムとの統合と、データのリアルタイム取得に関する技術的な側面についても説明しています。
🚀 AWSとの統合による効率向上
製造業向けのAWSとの統合アーキテクチャの利点について解説します。経済的で効率的なデータ統合を実現するための方法を示し、スノーフレークのスケーラビリティとコスト効率の良さを強調しています。また、複雑なSAPシステムの統合を簡素化するための具体的なステップや、ビジネス上の利点についても触れています。
Mindmap
Keywords
💡Snowflake
💡SAP BW
💡データ分析
💡M&A
💡データコラボレーション
💡スケーラビリティ
💡データウェアハウス
💡SAP S/4 HANA
💡マージ
💡分散型アーキテクチャ
Highlights
Snowflakeは製造業向けのSAPデータ分析に大きな進展をもたらします。
Snowflakeは最大40ペタバイトのデータを扱うことができ、SAP BWの最大システムを大幅に上回ります。
Snowflakeは、異なるSAPシステム間でのデータ統合を容易にします。
SAP BWは長年にわたって段階的に廃止されていますが、Snowflakeは新しいデータ管理ソリューションを提供します。
Snowflakeは、企業が迅速に財務データを統合し、分析するための強力なツールを提供します。
データのコラボレーションと共有が容易で、特にサプライチェーンの効率化に役立ちます。
Snowflakeのゼロコピークローン機能により、開発とテストのためのシステムを迅速に作成できます。
Snowflakeは、従来のETLプロセスを簡素化し、データレイクとデータウェアハウスの両方の機能を兼ね備えています。
大規模な企業は、Snowflakeを使用して数百のERPシステムを統合し、分析ユーザーの数を大幅に増加させています。
Snowflakeは、AIおよびML機能を活用して、データの価値を高めます。
Snowflakeの高可用性と冗長性により、システムのダウンタイムを最小限に抑えることができます。
Snowflakeは、コスト効率の高いデータストレージおよび分析ソリューションを提供します。
Snowflakeのパートナーエコシステムにより、さまざまなデータソースからの統合が容易になります。
Snowflakeは、製造業におけるデータ統合と分析を大幅に簡素化します。
Snowflakeは、SAPシステムとその他のデータソースからのデータを効率的に統合します。
Snowflakeは、企業が迅速に市場変化に対応できるよう支援します。
Transcripts
[Music]
[Applause]
[Music]
[Applause]
the next couple minutes I'll be talking
about the snowflake for manufacturing
sap data analytics I'm David Ricker uh
part of snowflake part of the field CTO
office based out of
Geneva uh we have some future statements
so have a safe harbor
piece so if we take a look at what some
of the market forces are happening on I
don't know if you saw the SAP stand it's
a little small stand at the corner in
the back one or two people go say hi to
them they feel a bit lonely so why do
they feel lonely so what are some of the
things that are happening um at least a
little bit in the market conditions um
as we're coming out of the covid area
and the recovery is that there's a lot
of um companies that are U not only
merging right and Acquisitions but they
have to merge more quickly and they have
to get these uh financial statements out
as as quickly as possible but then
there's also divestures and if we look
at that um that has a huge implication
as concerning uh data and how it works
so take for example there's
traditionally lots of um companies out
there that have made mergers after
mergers after mergers and you have 30 or
40 companies that are into one and they
all have SAP systems but they're on a
different blueprint how do you bring
that financial data together uh easily
mix it enrich it and whatnot um second
point is BW they've been phasing it out
well they've been phasing it out since
like
2012 um Hassel plaer it's like BW is
dead and yet it keeps coming back and
it's eventually getting phased out back
in 2019 they said it we'll phase it out
in 2024 and now it's 2027 that it's 2029
and if you pay just a little bit more
you can get a bit more but okay that
makes people think a bit on the uh sap
side but what customers are actually
looking for is innovation uh sizing
scalability
stability and then the third part data
collaboration po pi uh all these things
you know this transactional pieces
between uh company's great but it's only
a megabyte you know in today's day and
age uh we're not even talking about
gigabytes anymore we're talking about
terabytes and pedabytes and how many how
many uh petabyte BW systems are out
there it's compressed no okay so there's
you know the the biggest every I've t I
talk to the biggest companies on the
planet and they all have the biggest sap
system there there's literally 10 of
them but um the largest BW system I've
heard of uh it's not even running on a
Hannah system but it's something around
250 it's running uh IBM db2 somewhere in
Canada but it's around 300 300
terabytes any guess what the largest
snowflake customer
is how big it
is 40 pedabytes
what's that even mean it's huge so if
you if you take if you take the largest
BW system on Hannah which is about 120
terab it's 277 times bigger than that so
that's comparing Venus to the Sun so
it's just literally a different Market a
different way of working with that we
you know we call it Big Data and then we
call it data and then we call it Big
Data again but being able to collaborate
that means sharing pedabytes uh Gab
terabytes pedabytes of data is super uh
important in your supply chain uh with
customers and without having to push
this data back and forth across the
networks so what are some of the
challenges um on the Erp side again if
you have 40 50 erps the some of the
biggest companies on the planet uh
getting that data together there's
either you spend 15 years getting one
blueprint onto that for example the
globe project at nestate or you do uh
that that consolidation piece either on
the BW side or in a system like
snowflake uh and you bring it together
and you report on it the second part is
being able to analyze that data it's
fantastic you know if it's coming off an
sap system why not use sap if you have a
100%
sap there's not a lot of value add on
the snowflake side on the other hand if
you have nonsp systems and you have
semi-structured data or unstructured
data and you want to be able to bring it
in just e omally it's not it's doesn't
make any sense to do this into a Hannah
system you want something that can store
that data that can govern the data that
can centralize the data where you can
access the data and you only pay for
what you use um the third part um okay
we you know expensive complex difficult
to support but hey we you know we we all
made our you know a lot of uh how do you
say supported my family for a long time
on that um but some of these challenges
if we get down to the business side of
things what kind of questions are we
trying to answer we've been trying to
answer these questions for 20 years but
now we're talking about what's the most
economical way to answer it what's the
most um effective way of answering that
what if I want to enrich that data what
if I want to get that AI uh ml piece I
have to say that at least 40 times or I
get kicked off right AI ml um Global
spend improving production sales Trends
external data supplier variant uh
inventory availability uh take an
example of a company in in Germany tea
they say okay we're going to take our
sap data and we're going to take our uh
shop floor data we're going to take that
conveyor belt data and say hey um if
there's a Slowdown in production or
something stops is because I now combine
that data in Snowflake I can tell oh
this is com this is connected to this
customer and I can send them a message
right
away um typical sap data architecture uh
I like the slide I built no I didn't buy
I didn't build the slide but having
worked it right you have your
transactional system and then you need
to bring that into um you know
traditionally as extractors and then
bring that into uh for 20 years it was
BW and then it's like oh wait we have to
move into the cloud um oh wait we need
to be in memory so we now you have a
choice of BW or now I have a choice of
Hannah okay I'll go BW oh wait I can't
fit everything in so I need to use um
hot warm cold storage and so I have the
BW piece I have the Hannah piece I have
the cbase piece I have the hadu lake
piece and somehow it's all managed and
work and somebody's going to partition
it and somebody's going to push back and
forth the data and I'm eventually going
to get the data and report on it um and
then just to add to that it's like okay
we're moving to the cloud now that I've
got the cloud um you want want your BW
data into uh sap system well okay let's
you need a another BW BW bridge and then
then bring it
into the inmemory reporting which is on
Hannah which again has certain
restrictions
um
no if we move and for example take
what's the value at of of having
snowflake for example surprise um one is
that from any source of data any type
volume variety velocity of data uh you
can bring that into snowflake as a first
class citizen it's very easy so
traditionally what did we do we have
extraction uh you wouldd have to
transform that it have to be structured
you have a lot of logic in your ETL
process uh and then you land it into
your Target and then if you have to
change it you have to go back
dependencies Etc um with modern data
architecture right you extract the data
as raw as possible and land it into you
oh data Lake well snowflake is a
combination it does both the data Lake
and does the data warehousing and it's
been doing it for 10 years now it's not
you know it's been it's one of the first
in the market to to do both that so one
increase Enterprise
visibility the second thing is that now
hey I don't have to have siloed systems
for my it data transactional data and my
OT data right OT data shop floor sensor
this can be 100 times or a th thousand
times more data than your transactional
systems and so you need to have an
economical way of bringing that across
and bble to store it and distort it into
one place snowflake is a great place um
deliver value faster how do you do that
if you can land that data in one place
and uh conform it and model it and
report on it um because it's in one
place it's easier but secondly there's
just basic functions in Snowflake we
call zero copy clone um we couldn't do
this before so say I have a 100 terte uh
system and I need to do some I I want to
stress test it well I'm not going to
stress test my production system so we
have something called zero copy clone it
makes a uh a zero copy clone so it
doesn't take any more space than 100
terabytes I can run different compute
against it I can stress test all my data
inside that and then go that and I can
also zero copy clone it into Dev and
test and things like that so instead of
maintaining four systems right Dev test
sandbox production
I can generate and Destroy environments
uh at
will what are some of the things that we
do on the business improvement process
one enhanced forecasting because you can
bring all the different data pieces um
you can bring you know tax information
it information that to bring it across
uh sourcing supply chain risk um how
many times do has your system your
analytics systems fallen over end a
month end a year as I I have zero
visibility it is extremely hard to make
a snowflake system fall over why because
it's distributed because you have
different we call them virtual
warehouses for the computation we have
the data stored in triplicate uh in the
storage piece and and and whatnot so
there's a a huge amount of redundancy
and resiliency that's built into it for
if you want a high availability in in
sap system it only costs twice as much
because you have to have two systems
right snowflake is just built in
um making right uh machine learning
we're coming out with the co-pilot and
whatnot uh delivery custom fulfillment
uh you know use use that transactional
data but be able to mix it with the
other pieces uh and services being able
to uh detect on your iot side on your
sensor data uh it just enables you to
react more quickly as opposed to right
traditionally having these Silo
systems who are some of the partners um
that we work with right I I think one of
the the fundamental uh key value pieces
for snowflake is the ecosystem of
partners that we have and because we
have a cloud um based system is that any
s snowflake customer can uh share data
with any other customer and this can
allow you in Upstream Downstream supply
chain uh business partners to be able to
share not megabytes of data but
pedabytes of data this is super
interesting on the master data side uhi
spare spare parts for example uh Cold
Storage these kinds of things um and so
on the manufacturing piece we have a lot
of c um a lot of uh snowflake Partners
who are sharing their data either for
free or as part of the marketplace and
it's also a great way if you want to
monetize your data really easily you
immediately have access to over 9,000
other uh customers on the technology
Partners you know a AWS we're the number
one
isv for AWS this is this is a massive
thing for us and and a great thing for
for AWS on that side but we also work on
you know Microsoft and and Google um
powered by snowflake you can we first
started out on the data side but now we
have something called native
applications and and we have lots of
companies that are building applications
uh directly on snowflake first it was
analytical loads um and now we're
bringing in our htap or uh um oltp piece
uh as well into Snowflake and then
systems integrators right especially on
something as complex as as sap the
partners you're working with today uh
they also work with
snowflake what are some of the steps the
two big technical use cases that we see
is that these large companies that have
AWS U sorry Hannah Enterprise data
warehouse um right costs Millions you
have you know Max maybe 60 terabytes 100
terabytes on there for only you know 16
20 million a year is that a terabyte in
Snowflake is 23 23 per terabyte per
month so it's it's literally hundreds of
times cheaper and that's just on the
storage piece of course there's a
computation piece but we've seen uh
reductions in costs uh for equivalent uh
features and even right improved
features 40 50% and we have uh customer
references on that so one is just
complete replace of of Hannah Enterprise
data warehouse uh it's an anql database
at its foundations uh snowflake is as
well and the second piece is is BW it's
always a it's always a harder play right
because it's been around for a long time
there's a lot of custom code in it um
and there's also right planning
consolidation etc etc is that what if
you look at the sap road map though
they're pushing that planning piece out
of BW into sap Analytics cloud and
they're pushing the consolidation piece
into group reporting on the S4 piece so
one it's already moving out of BW second
piece is that you can save a lot of
money reducing that size of BW uh moving
the workloads into Snowflake and then uh
for the other
piece uh you know why not keep a bit bit
of BW around everybody everybody loves a
little bit of
that um you you know what are the basics
moving Erp data at sap to snowflake
systematically everybody ask me how do
you get data out of
sap there's 12 interfaces and we will go
through each one no sorry um
so there's there's some Partners there
some Partners in the room that have been
doing this for years and years and years
uh sap silver partner uh they know how
to get the data out they know how to get
um what about transparent tables yes
pool tables yes cluster table all that
stuff okay that what about the security
yes of course
uh and how do I get that in real time
yes uh you can get that out trigger
based batch based micro batch um it's
all there it's been done it's been done
thousands of times um and then
acquisition so there's uh partners that
push data directly into snowflake tables
there's other ones that use files um
depends on how fast you want it depends
on some of the economics transformation
some
people snowflake is a is a fully
functional not only on the SQL feature
side but python Scala Java all these all
these and the and the libraries that are
behind it you can use all these
different tools they're at your disposal
inside of that uh centralized governed
snowflake um uh ecosystem platform as it
were uh reporting analysis right you can
we're model agnostic I was talking to
customers using Data Vault bring across
30 different BW systems into to that
other ones that saying I'm going to use
BW it's already modeled I use that as a
source and so I have my 65 dimensions
and my three fact tables for my Global
spend analysis and and it's it's there
already uh reporting say obbc
jdbc uh again if you want to use python
or ml AI all that all those interfaces
are there uh and then at the end of
course right you want to be able to
eventually turn off uh some of other
stuff and get those cost savings we have
a uh a breako right you're going to have
both systems running at the same time so
that's going to cost a bit more but then
you have a massive uh decrease in spend
on the snowflake side and as you turn
off that BW
side um some of the uh uh partners that
we work with I'll just talk about um on
the acquisition side there's S&P glue
can you raise your hands so these two
guys S&P glue us be data data glue uh
worked really closely with us over the
past year we came out with uh it's
called a streaming SDK and so previous
at snowflake you would have to batch
load things staging area bring it across
they worked with us and so you can now
stream data directly from an sap system
uh directly into a snowflake system it
is the most direct path on the market
for the data is is these guys here um
Informatica they came in a bit uh the
other ones might work with some other uh
cases and they're absolutely
available um if we take a look at some
of the uh reference
architectures uh pretty simple there's
you know you need to have some kind of
uh tool between SAP systems and
snowflake uh and beyond that is like
what's the interest of snowflake is like
take over the data Lake component data
engineering component uh ml component AI
component um the other other workloads
we also have you know um security logs
and and whatnot but you land it uh you
can transform it within snowflake you
can use a tool of choice or you can use
uh Native features that we have in
Snowflake we have something Dynamic
tables say you have you use an S&P glue
you land that you have that PSA that raw
layer and then a dyamic tables you can
do any kind of arbitrary uh sequels that
combine these five tables uh to have
this uh uh reference table at end and we
manage the Delta we manage uh the
loading behind it
automatically some of the replication
tools some of the uh uh and we'll leave
this as as reference because it gets a
bit a bit complicated on what can you
hit against uh what does things natively
what does things at the database level
and whatnot but and um and this is just
on the sap side right what are the what
the sap supported tools uh or sap tools
that have um certified snowflake
connectors so data services
um data
services and so they can use also these
other tools that have that and bring it
across uh third party tools is it a Ab
App application that's putting pushing
data out like the glue is it a some
agent that's sitting outside of that um
is it hitting against database logs is
it how are you dealing with the sap
licensing if we take a look at the AWS
so this is a a general reference
architecture that I built up with the uh
AWS Architects and I think it's one of
the especially for manufacturing one of
the most compelling architectures out
there why is because you can bring in
not only that it data transactional
Oracle sap or whatnot but also that OT
data in one place an economical
efficient effective way of bringing it
across um
and it goes through a lot of the
different uh pieces that we have um
maybe a bit of you know snowflake 101 is
that we land the data once it's in
triplicate for uh resiliency and then we
have compute clusters on top of that and
you only pay by the second say I run a
query uh we snowflake have a hot pool of
compute clusters that are running um so
it attaches in a second runs the query
and then it can turn off and so you're
only paying for that number of seconds
that that compute cluster is running
plus you know average storage cost you
look at Legacy systems transactional
systems even Cloud solutions that are
supplied other things it takes the other
Solutions right 15 minutes to start then
I can run my query and then I don't want
to turn it off to this cash you're
paying for that all the time that's not
the case with snowfl like turn it on off
it's like a utility it's like
electricity AB they've been my C so I've
worked for snowflake for for four years
worked sap before that for 18 years uh
ABB has been my customer for 10
years we'll do the math um they have 40
uh erps and you think okay it's all sap
uh just bring it across and it's easy to
report no because it's 40 different
blueprints
and so they spent um literally you know8
years uh building up a semantic layer on
the BW side because all the fields
weren't same matching and things like
that a lot of uh manual effort there and
now you know now it's easy enough the
IPS are easy enough to shift over uh to
snowflake bring together have a modern
uh platform that's scalable not to 60
terabytes but to hundreds of terabytes
you know to pedabytes um um some of the
savings that they have right 200 200
million savings annually on the U
inventory purchases uh Revenue growth
they have to have this to support it how
else can you go from a couple hundred
analytics users to thousands and
thousands of analytic
users it's snowflake it it can scale it
scales horizontally it scale vertically
within seconds it's not a it's not a
question like oh I need to order uh
another machine it will come in weeks I
need to transfer the No it's it's in
seconds it's like make it make it bigger
make it extra large make it small
um the next one uh near and dear to my
heart seens anybody here seens no no so
it's a and some two people from Seaman
are right here um right uh absolutely
huge Hannah Enterprise data warehouse
deployment and within months uh they
replaced that with snowflake talking
over 50 uh
erps uh they're using a combination of
uh sap technology and S&P glue to get
the data across so SLT plus SNP uh land
it and snowflake all the Transformations
now they're able to serve up tens of
thousands of users uh they're able they
build an application to uh automatically
generate and deploy snowflake
environments for for their users and
this is it automating this creation
which unheard of um so it reduces supply
chain risk
uh improved cost and efficiency gains um
secure self-service tools which we just
mentioned so how do you get
started um do you want to no okay so one
of the things to go is like what's
what's your status quo what for example
if you're using BW what are you not
using and wouldn't it be nice if you're
not using 30% of it that you're not
going to uh migrate it across
and so uh S&P for example gives a uh
we're going to change the name to
Fitness tal or like you know let let BW
die nicely but um maybe you won't but
the uh um you install it runs four or
five weeks you see what's what's being
used what's not being used and then you
can decide uh hey if we're not using
that we can turn it off and maybe BW
will live a little bit longer or hey
these are the things that are super
important let's bring those across um
thank you I'll open up to questions we
have a couple minutes
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