Migrate and modernize your database applications (L300) | AWS Events

Summary

ビデオスクリプトは、AWSのデータベースサービスの現代化について解説しています。Barry Morrisが主導するこのトピックでは、データがAIの基礎であり、クラウドへの移行がビジネスの機敏性、スケール、および可用性を高めると語られています。Amazon.comがリレーショナルデータベースからノンリレーショナルデータベースへの移行を通じて、検索、顧客フィードバック、推奨エンジンなどの機能を強化した経験が紹介されています。また、クラウドネイティブのデータベースサービスを使用することで、アプリケーションの柔軟性とスケーラビリティを高め、ビジネスのイノベーションサイクルに再投資することができると強調されています。

Takeaways

• 🌟 AWSのデータベースサービスは、非関係型データベースを含む多岐に渡るデータベースサービスを提供しており、クラウドへのデータベースの移行とモダン化の過程で重要な役割を果たしている。
• 🚀 Amazon.comは、従来のリレーショナルデータベース技術から、検索、キーバリュー、グラフ技術などの多様なデータベースに移行し、クラウド上で高いスケーラビリティを実現している。
• 🔄 クラウドデータベースの移行とモダン化は、企業のビジネスの敏捷性、スケール、および24/7の可用性を高めるために重要なプロセスである。
• 🛠 従来の3層アプリケーションアーキテクチャから、マイクロサービスやコンテナ化されたアプリケーションへの移行が、クラウドネイティブなアーキテクチャを実現している。
• 🔑 データベースの選択は、アプリケーションの要件に応じて最適なものを選ぶことが重要で、これによりパフォーマンスと柔軟性が得られる。
• 🔍 クラウドへの移行において、マルチテナントアーキテクチャを検討することは、コスト効率とリソースの最適化を可能にする。
• 📈 Amazon Auroraは、従来のMySQLやPostgreSQLに比べて大幅なパフォーマンス向上を提供し、ストレージの自動スケーラビリティを備えている。
• 🛑 Babble Fishは、SQL ServerからPostgreSQLへの移行を容易にし、ダウンタイムを最小限に抑えることができるサービスである。
• 📊 Amazon TimestreamやNeptuneなどの特別なデータベースは、特定のユースケースに対応し、ハイパースケーラビリティやリアルタイムのデータ処理を可能にしている。
• 🔌 AWSでは、データベース移行サービス（DMS）やスキーマ変換ツールなどのツールが提供され、データの移行と分析のためのETLプロセスを簡素化している。
• 📚 現代のアプリケーションでは、データの分析と機械学習への活用が重要で、AWSはこれらのプロセスをサポートする包括的なサービスを提供している。

Q & A

AWSのデータベースサービスのどの部分をBarry Morrisが担当していますか？

Barry MorrisはAWSで非関係型データベースを含む多数のデータベースサービスを担当しています。

データベースの現代化とはどのような意味を持っていますか？

データベースの現代化とはクラウド上でデータベースを最新の技術に合わせて更新し、パフォーマンスや可用性、スケーラビリティを高めることを指します。

Amazon.comはどのようにしてデータベースの限界に達しましたか？

Amazon.comはリレーショナルデータベース技術をベースにいても、その限界に達し、その後にDynamo DBなどの新しいデータベース技術を導入してスケーラビリティを高めることを決めました。

AWSはどのようにしてデータベースの選択と使用を最適化していますか？

AWSは適切なデータベースを適切な用途に使用することで、パフォーマンスとアジャイル性を確保しています。これにより、データベースの選択がビジネスのニーズに合わせて最適化されます。

マイクロサービスとは何で、なぜクラウド上でスケーラブルですか？

マイクロサービスはアプリケーションを小さく、独立したサービスに分割することで、それぞれのサービスが独立してスケールできるアーキテクチャです。クラウド上で実行することで、リソースの柔軟な割り当てが可能でスケーラビリティが高まります。

AWSのデータベース移行サービスのデータコレクターとは何ですか？

データコレクターはAWSのデータベース移行サービスの一環で、自動的にデータベースのインベントリを収集し、分析することで、移行の計画を容易にします。

Amazon Auroraは何で、どのような利点がありますか？

Amazon Auroraは互換性のあるリレーショナルデータベースサービスで、MySQLやPostgreSQLと同様のSQLをサポートし、高性能とスケーラビリティを提供します。また、ストレージが自動的にスケーラブルであることが大きな利点です。

AWSではどのようにしてデータベースの多テナンシーを管理していますか？

AWSでは、単一のアプリケーションイメージとデータベースインスタンスを共有する完全な多テナンシーから、各顧客に専用のインスタンスを提供するまでのスペクトルで管理しています。顧客のニーズに応じて適切なアプローチを選択します。

Amazon Dynamo DBはどのような種類のデータベースですか？

Amazon Dynamo DBはキーバリューストア型のデータベースで、非常に高いスケーラビリティとパフォーマンスを持ち、大きなデータセットに対する高速アクセスに適しています。

AWSのデータベースサービスはどのようにしてビジネスの迅速なイノベーションサイクルに貢献していますか？

AWSのデータベースサービスは、データベースの管理や保守の重労働を自動化し、開発者がビジネスロジックやイノベーションに集中できるようにすることで、迅速なイノベーションサイクルに貢献しています。

Outlines

00:00

😀 クラウドデータベースの移行とモダン化

Barry Morris氏はAWSのデータベースサービスのリーダーとして、データがAIの基礎であり、オペレーションデータベースがデータの基礎だと語りました。顧客の多くがクラウドに移行し、クラウドデータベースへの移行を経験しています。Amazon自身もリレーショナルデータベースの限界に達し、多くのデータベースサービスに移行しました。また、現代のデータベースアプリケーションには、 Agility、Scale、および24/7の可用性が求められます。AWSはモノリスではなく、マイクロサービスアーキテクチャに移行し、スケールとコスト管理の両面で利点を享受しています。

05:01

😉 クラウドへの移行とマルチテナンシーの課題

クラウドへの移行は、データセンターの終了やコスト削減、デジタルトランスフォーメーションなどが理由として挙げられます。クラウドへの移行を通じて、顧客はコストを削減し、イノベーションサイクルへの再投資を望んでいます。ISVにとっては、クラウドへのサービス提供に伴いマルチテナンシーの問題があります。単純なマルチテナンシーでは、小規模な顧客に対して過剰なプロビジョニングが行われ、コストがかさんでしまう一方で、完全なマルチテナンシーでは、顧客間でアプリケーションとデータベースを共有するため、複雑さが増します。

10:01

🎯 データベースの分析と移行戦略

データベースの移行は、分析から始まるプロセスです。顧客は新しいアプリケーションを構築するか、既存のアプリケーションを分析し、削減、統合、リフトアンドシフト、またはモダン化を選んで移行します。AWSは、データベース移行サービスのデータコレクターツールを提供し、手動での監査を自動化して、データソースのインベントリを作成します。これにより、顧客はどのデータベースから始めるかのインフォーメーションを得ることができます。

15:03

🛠 データベース移行ツールの活用

AWSのデータベース移行サービス(DMS)とスキーマ変換ツールは、移行プロセスを支援します。スキーマ変換ツールは、顧客が最適なターゲットデータベースを選択し、スキーマ変換やコードオブジェクト変換を支援します。DMSは、データの移動と最小限の停止時間またはほぼゼロの停止時間のためのデータレプリケーションを支援します。また、AWSでは、Aurora PostgresやAurora for MySQLなどのマネージドサービスを提供しており、顧客はこれにより、インフラの管理から解放され、イノベーションに注力できます。

20:05

📈 パフォーマンスとスケーラビリティを兼ね備えるデータベースサービス

AWSは、さまざまなスケーラビリティとパフォーマンスを提供するデータベースサービスを提供しています。Amazon Auroraは、従来のMySQLやPostgreSQLに比べて大幅な性能向上を提供し、ストレージの自動拡張性を備えています。また、DynamoDBはキーバリューストアとして、膨大なスケールでの要求に対応できる能力を有しています。DocumentDBは、ドキュメントデータベースとして、MongoDBと互換性があり、スキーマの柔軟性を提供します。

25:08

🔄 データのリアルタイム分析と移行

リアルタイムデータ分析と移行は、ビジネスにとって重要です。AWSでは、ストリーミングデータのためのKinesis、MSK、バッチ処理のためのEMR、Glueなどのサービスを提供しています。これにより、顧客はデータパイプラインを構築し、データの移動と分析を効率化できます。また、Amazon QuickSightなどのビジネスインテリジェンスツールや、Amazon SageMakerのような機械学習サービスを活用して、データから洞察を得ることができます。

30:09

🚀 ベクターデータストアと生成的AIアプリケーション

生成的AIアプリケーションは、ベクターデータストアを活用して、新しいデータと既存のデータの間のギャップを埋めています。AWSでは、様々なデータベースサービスにベクターデータストアのサポートを提供しています。これにより、開発者は新しい機能を構築する際に、データストアとベクターデータストアを簡単に統合できます。

🛠 移行とモダン化のためのツールとサービス

AWSは、移行とモダン化のプロセスを支援するツールとサービスを提供しています。DMS、Fleet Advisor、Schema Conversion Toolなどがその例です。これらのツールは、データの分析、インベントリの作成、スキーマの変換を支援し、顧客が移行プロセスを円滑に進めることができます。また、AWSは、データ戦略チームやデータドリブンイノベーションセンターなどのプログラムを通じて、顧客のビジネスケースをサポートしています。

Keywords

💡データベース

データベースとは、大量のデータを効率的に管理するためのシステムであり、このビデオでは特にクラウド上で動作するデータベースサービスに焦点が当てられています。データはAIの基礎であり、データベースはデータの基盤を提供します。ビデオでは、AWSのデータベースサービスを率いるBarry Morrisが、顧客がクラウドに移行し、データベースを最新化する過程について話しています。

💡クラウド移行

クラウド移行とは、企業が自社のデータやアプリケーションをインターネット経由でアクセス可能なリモートサーバーに移すプロセスを指します。ビデオでは、クラウドへの移行がデータベースの最新化と密接に関係しており、顧客がクラウドデータベースに移行することで、柔軟性、スケーラビリティ、および可用性の向上が実現可能であると説明されています。

💡NoSQLデータベース

NoSQLデータベースは、非関係型のデータベースであり、ビデオでは特にDynamoDBというAWSのNoSQLデータベースサービスが紹介されています。NoSQLはスケーラビリティや柔軟性に優れており、ビデオではAmazonがリレーショナルデータベース技術からNoSQLへの移行を行い、その利点を強調しています。

💡マイクロサービス

マイクロサービスは、大規模なアプリケーションをより小さな、独立したサービスに分割するアーキテクチャスタイルです。ビデオでは、マイクロサービスがアプリケーションのスケーラビリティや開発者の迅速なイノベーションを可能にする重要な要素として触れられています。

💡スケーラビリティ

スケーラビリティとは、システムが負荷の増加に伴い、より多くのリソースを効果的に活用してパフォーマンスを維持または向上させる能力を指します。ビデオでは、スケーラビリティがクラウドデータベースの主要な利点として強調されており、DynamoDBがこの特性を持ち、膨大なアクセス負荷にも対応できると紹介されています。

💡可用性

可用性は、システムが必要な時に利用可能であることを保証するシステムの性質です。ビデオでは、現代のアプリケーションでは24/7の可用性が求められており、クラウドデータベースはシステム障害や極端な負荷下でも高い可用性を維持する能力を持っていると説明されています。

💡データマイグレーション

データマイグレーションとは、データをあるシステムから別のシステムに移動させるプロセスです。ビデオでは、AWSのDatabase Migration Serviceがデータマイグレーションのプロセスを自動化し、顧客がクラウドに移行するのを助けるツールとして紹介されています。

💡サーバーレスアーキテクチャ

サーバーレスアーキテクチャは、開発者がバックエンドインフラストラクチャの管理に関わる詳細を心配する必要なく、アプリケーションをビルドできるようにするアーキテクチャです。ビデオでは、サーバーレス技術が新しいアプリケーションの構築において技術的な負担を軽減し、開発者のイノベーションを促進する上で役立つと説明されています。

💡キャッシュ

キャッシュは、データのコピーを高速アクセス可能な場所に保存しておくことで、データへのアクセス時間を短縮する技術です。ビデオでは、Elastic CacheやMemoryDBがマイクロ秒レベルのレスポンスタイムを提供し、アプリケーションのパフォーマンスを向上させるために使われると紹介されています。

💡マルチテナンシー

マルチテナンシーは、ソフトウェアアプリケーションが複数の顧客（テナント）のデータを同じインスタンス上で独立して管理できるアーキテクチャスタイルです。ビデオでは、クラウドサービスにおいてマルチテナンシーがコスト効率とリソースの共有を提供し、クラウドへの移行において重要な概念であると説明されています。

Highlights

Barry Morris介绍了自己在AWS数据库服务中的职责，特别是非关系型数据库。

数据是AI的基础，操作数据库是数据的基础。

Amazon自身也经历了从关系型数据库到多种数据库技术的迁移。

现代数据库应用需求包括敏捷性、可扩展性和24/7可用性。

传统应用堆栈正在向微服务和解耦应用转变。

多租户架构是云服务提供商需要考虑的重要问题。

Amazon.com的数据库现代化之旅，包括从单体架构到微服务架构的转变。

客户迁移到云数据库的典型旅程，包括从EC2开始的迁移。

Venki介绍了数据库现代化的不同路径和模式。

使用数据库迁移服务（DMS）和模式转换工具简化迁移过程。

Amazon Aurora提供了与传统数据库相比更高的性能和可扩展性。

Babble Fish工具帮助减少从SQL Server迁移到PostgreSQL的复杂性。

Amazon Dynamo DB能够处理极高规模的请求，如Prime Day。

Amazon Document DB为文档模型提供了高性能的数据库解决方案。

Amazon Neptune和Timestream针对特定数据集提供了专业的数据库解决方案。

数据现代化不仅是数据库的迁移，还包括从交易型数据库到分析型数据库的转变。

AWS提供了多种工具和服务来支持数据库的现代化和迁移。

向量数据库在支持生成性AI应用方面变得越来越重要。

AWS和合作伙伴提供的工具和服务可以帮助规划迁移和现代化之旅。

AWS提供不同的计划和团队来帮助客户在数据现代化方面取得成功。

Transcripts

um so um just want to welcome you uh my

name is Barry Morris uh I I head up a

number of the database services in na

AWS uh notably non-relational databases

work very closely with a number of the

other uh database or all the other

database leaders in the

organization um so I just wanted what

we're going to be talking about today is

about databases of course data is the

foundation of AI and operational

databases are the foundation of data um

uh and most of our customers are going

through a a sort of a journey of

migration onto on on onto the cloud and

onto Cloud databases um and um and this

is quite a fun thing for us to talk

about because uh because Amazon's been

through this ourselves um amazon.com not

too many years ago was running

essentially on relational database

technology um and it got to its limits

and uh and so what we've done

subsequently if you look at the at the

at the Amazon

uh web page today the amazon.com page

behind that application is many

databases there's search technology

that's driving the search there's

there's there's key value technology

that's that's driving the the customer

feedback stuff there's graph technology

that's driving the recommendations and

so on and we'll talk a little bit more

about that in a minute so this is a

journey that we've been on in ourselves

um we want to talk through really sort

of what it means to modernize databases

on on the cloud I'm going to give you a

little bit of an overview and Van is

going to give us a bit of a a deep dive

so um excuse me the first thing is about

the these modern modern database

applications uh the requirements are are

these uh first of all agility because

your business is agile you've got a need

to move your business forward you need

your unlike old style applications that

would be built rather like a bridge

they'd be built and you move on and

build the next Bridge today applications

are live you keep working on them you

move as far as as you can um and so

agility is absolutely key to

it um the second piece scale is actually

the primary thing that drove Amazon to

move to notably adding uh document this

Dynamo DB into into amazon.com because

uh every time that we every time that we

came to a big uh retail day um we had to

do enormous planning to make sure that

the relational databases could keep up

with the likely Peak load um since we

put Dynamo DB in there we've never had

to even plan for it um because it just

scales and so that's really the the

essence of this whole kind of

modernization thing if you use the right

database for the right thing um you get

the performance you get the agility and

so forth and lastly of course um getting

to to to 247 availability even under

situations of of system failure or of

extreme load

Etc so these are the sorts of

requirements for mod modern applications

um and traditional applications don't

typ tyly do

that uh on the left you see the sort of

typical application stack that's been

around for for a couple of decades uh

and it's and it's you've got a you've

got a presentation layer and you've got

a business logic layer and you've got a

storage layer uh kind of three tier type

applications these are exactly the

applications that people are running on

premises and uh and which don't tend to

scale very well on the cloud of course

where we're going to is what's on the

right you've still got those same basic

layers they're all very different

presentation layer is probably much more

mobile oriented it's much more it's it's

it's it's voice oriented it's natural

language oriented perhaps it's targeting

special devices set top boxes in in car

and so forth um but it's the middle

layer that's that's had the big change

which is that we've moved towards

microservices uh and to so disaggregated

applications uh running on kubernetes or

whatever uh and the goal there is that

every piece of the application can not

only scale independently but that it can

have separate teams that are working on

it and they can be going through their

cicd deployment pipelines independently

and so you start getting that that kind

of application velocity it's absolutely

at the Key of it and of course at the

bottom each of those microservices will

tend to use not only their language of

choice and their Frameworks of choice

and so forth but they'll tend to use

that the database of choice and for some

of them it's going to be a mongodb style

document database and for some of them

it's going to be relational and some of

them it's going to be key value for many

of them there's going to be a cach on

top of it um and the and and what you've

got is an architecture that doesn't look

anything like that monolithic

architecture that you had uh on

premises additionally um there are if if

you're if you if if you've historically

been selling software to on premises

customers uh so you're an isv in other

words uh and you start wanting to Ser to

serve th th those that application as a

service on the cloud you've got this

issue of multi-tenancy and uh and really

this sort of multi tendency thing is

that the naive way is that you're going

to simply fire up an instance of that

application stack uh for every customer

and that's what you've got depicted here

lots of people do this it's got various

strengths and weaknesses single biggest

weakness is that you you probably going

to over provision for your smallest

customers and it's going to cost you a

lot of money um the alternative at the

other end of the spectrum um is to is to

have multi-tenancy full multi tendency

where all your customers are sharing the

same application image and database

instances and so forth um that's hard to

do as you're going through this whole

process that's something if you're going

to be doing that that's a big piece of

work that's going to be part of your

modernization in fact a lot of customers

end up with uh a lot of a lot of of our

customers will end up having um a mix of

those two things because their largest

customers have got so much more in terms

of their needs um that it's quite it

makes sense for them to have dedicated

infrastructure and then the smaller ones

to share and so there are advantages and

disadvantages to to these two sides of

it but the main message from the point

of view of modernizing applications and

certainly moving on premises to the

cloud is think about uh multi- tendency

up front it is a big part of of the

need we mentioned Amazon uh amazon.com

um this as I say is something which uh

um is is is enormous scale um the you

know this the the numbers of

transactions that we have to deal with

on a sort of on a busy day or measured

in in trillions per day um so very very

large uh data loads um and uh and we

moved from this monolith we moved to

microservices style architecture

organizationally as well of course what

we did is the typical Amazon thing of

having small teams what we call two

Pizza teams a team that can be fed by

two pizzas um driving each of these

microservices and so now you've got

something where as I mentioned earlier

we don't really have concerns about

scale where like we had before uh we

feel that we can cope with whatever the

Peaks are on on a particular day um

because of the of the modernization that

we've

done and then lastly just before I hand

over to Vani let me just sort of picture

for you what a typical Journey looks

like for a

customer um you know typically people

are still from the bottom left and

they're and they're moving from on

premises to probably to ec2 um that'll

be a kind of a lift and shift movement

they're running essentially identical

technology but now on the cloud they got

a lot of benefit from that because they

can do things like provisioning quickly

or move to a bigger machine or add

another location or whatever um and they

they get onto both of these arrows the

one of which is the arrow of innovation

velocity and the other of which of

course is cost management um and they're

onto that but and and there's some

customers that are happy to stop there

but really what we've been talking about

in terms of Amazon and and and and and

and some of the more advanced customers

they're getting to the far end they've

moved through going through moving to

manage databases where they no longer

have to do the the the sort of what we

call undifferentiated heavy lifting of

running those database services and

doing the backups and the patching and

everything else we'll take care of that

for you uh once they've done that quite

often people that came onto the cloud

running on a commercial database will

feel well I don't need to do that I can

actually run on postgress or my SQL or

something so let's me move let me move

away from that and ultimately moving to

modernization which is to adding elastic

Ash to make to because you can do that

on the cloud and it's much harder to do

on premises or adding Dynamo or adding

whatever it is um moving document DB in

because you've got content management to

do and so forth so really that's what I

wanted to do by way of introduction I'm

going to hand over to venki um and he

can take us into a deep

dive thank you thank you

Barry so perfect let's let's take a look

into the pathways for modernization so

what you have learned is customers would

like to modernize as they migrate into

the cloud right and as you all know

modernization is not a process it's a

journey and you may have different

comping reasons to migrate and most of

the common reasons we hear from the

customers are data center exit or end of

Hardware software licensing or simply

for cost

savings and the board of directors CTO

CFOs

are no longer expect uh they no longer

expect cost savings although you do

expect to save at least 25 to 50% when

they move into on uh when they move to

AWS when you compare it with on

Prem they would also want to know

transform the customer experience they

would want to digitally transform the

business and most importantly they would

want to reinvest that cost savings back

into the Innovation cycle and that is

very important and for that you cannot

modernize just the database alone right

you know you have to start with the

application no matter what so when you

start building right you know you have

to analyze your existing portfolio of

applications now a lot of customers you

know we have learned that they start

from the scratch by building you know

brand new applications mostly they use

serverless as their building blocks and

basically they are technical depth free

right you know and then they look into

the existing portfolio of applications

they will try to to either Reduce by

returing their applications that they no

longer want or they try to consolidate

by moving into multi tency they try to

safy their

applications then most of uh the

customers would like to do a lift and

shift or they migrate to AWS and the

last pattern is the modernization right

now this is where you would see

refactoring and re-platforming and this

is where we going to spend some time on

it so here is a slide which is probably

quite busy but then let's start from the

left hand side or right hand side to me

uh so that's the application you have

databases and windows right you know the

first step that most of the customers

would like to do is rehost right you

know you can rehost the application into

the cloud on ec2 for example but then

there are requirements from the customer

where they do not want to move into

Cloud probably due to some stringent

requirements uh they can go for public

CL if public cloud is not an option then

you can go for AWS Outpost you know that

is an option for customers to run AWS

infrastructure on Prim to get a full

hybrid

experience and then when you want to

completely refactor and rep platform

your applications you know you can move

into more of a cloud native architecture

as you see on the

screen so let's say you have to build an

event driven architecture or or

applications that are already being even

driven you can go for Lambda right and

then you can go for containers

especially if you have any VMS you want

to move into Cloud you can go for

container especially they're great for

monolithic applications or let's say

that you want to run and deploy long

running

applications then let's say you want to

have uh more of a controlled environment

where you do not want to spend time on

managing you could go for ECS which is

again a managed container service where

you can run deploy your cized

applications you also have foget which

is a computer engine for managing your

uh conization then you have eks where

you can again uh manage your conization

applications again that is based on open

Service open source kubernetes so that's

there so from a developer perspective

you know they would like to have more

agility right you know we do have

applications like codar where developers

can build deployment workflows on AWS

and for continuous delivery you have

code builds uh code Pipeline and so on

then moving into the data layer you know

this is where you have mve to managed

and mve to open source where either

you're going to move from let's say SQL

Server to audio SQL or let's say you

want to move away from SQL Server to

Aurora postgress so we will spend some

time into the next few slides

there so what what are the common

patterns that we normally see from the

customers right so let's say you have uh

like thousands of enrollments at your on

Prem and you want to make an inventory

the most challenging part is like you

know you need to do a manual auditing to

find out what your data sources are how

many databases you have and so on and so

forth that could be a task which could

take months right in order to make that

job easier we have a tool from the

database migration service called data

collector so with that tool you know

it's automated it's going to collect all

your inventories probably in Hearts

you're going to get a list and that list

will have all your environment with the

versions the database size and so on so

you could make an informed decision of

like you know okay which database I need

to start with and so on and so forth

then let's say you have commercial

engines like SQL server and Oracle

server and you want to make a decision

okay I want to do

migrate to an ec2 or I want to move into

RDS the first option most of the

customers see is ec2 obviously they use

native tools to migrate but let's say

they have some restrictions and they're

unable to use uh Native tools then you

have DMS and S what it stands for is

database migration service and schema

conversion tool so the schema conversion

tool is probably the first step where

you know it's going to create an

assessment report and if you're unsure

let's say I don't know where to migrate

to from s you know should I pick my SQL

or should I pick postris then schema

conversion tool would give you an

assessment report and that tool is going

to tell you okay this is the best Target

because the level of effort for you to

migrate is probably the least so with

that you're going to make a decision and

not only that it it's going to also help

you do schema conversion it's going to

do code objects conversion and the most

important part is the data migration

which is taken care of by TMS and for

minimal downtime or near zero downtime

it also helps with data replication as a

part of DMS what we call it as

CDC the first pattern is pretty obvious

that is M to manage so let's say you

have an nonpr SQL you then to uh RDS SQL

servers which is pretty straightforward

you can use DMS in or native tools of

your choice to migrate which is

great and uh just before going there

like you might wonder you know what is

the benefit of moving to a manage

service you know like Barry said manage

services are great if you don't want to

spend your time on undifferentiated

heavy lift like you know provisioning

patching upgrades security and so on so

R is as a managed service we give you

that experience so that you can focus on

Innovation the next pattern which we

commonly see from the customer is breakf

free that is you know I want to move

away from SQL to something else and this

is where sat is going to help you you

know it's going to create your this

assessment report and you're going to

move from there so in this case you know

I picked Amazon Aurora so for those who

is not aware of arur uh so this is

compatible with both my SQL and postris

which gives you five times more

performance than uh vanilla my SQL and

three times more performance than

postris and the best part is you know

the storage is distributed um you know

it's automatically uh scalable and so on

and so forth so I have two more uh

elements there which I will cover after

this so that's the babble fish again for

those who is not aware of Babble fish

you know moving away from SQL Server to

let's say aor postris is a task on its

own right you know it's timeconsuming is

resource

intensive but with Babble fish we have

reduced the downtime or reduced the

effort so what it means is uh let's say

you can keep your applications that are

written for the SQL Server post can able

to understand so basically it can able

to understand both tsql and psql so for

the new codes that you're developing you

can continue writing it on psql and for

the existing codes you can take your

time to convert and until then you can

continue to use your tsql codes so it's

basically a machine that can understand

both

languages and the last most popular

option is the purpose B database because

what we understood after working several

with the customers is that there is not

one database that suits for all your use

cases right you that isn't a database so

although Amazon Dynam DB is what I've

used in the screen uh there are multiple

databases which I will be talking about

in the next few screens and before I go

there I want to talk about Aurora

Limitless for a bit so you know

relational databases can one scale to an

extent right whereas aora Limitless

gives you an option to scale almost

infinitely right you know you with the

writer in node for example you can

almost able to achieve million

transactions per second you know that is

unbelievable for a relational database

yes you can able to achieve that with

purpose build databases but from a

relational perspective normally you are

bound to hit some of the uh capacity or

the limitations which we have taken a

feedback and worked on a new tool that

is Aurora

Limitless So within this modernization

uh pattern right you know what we' have

seen is adding this caching yes you can

work with the databases uh normally what

happens is the data is going to be

fetched from the disk which is an

expensive process we going to put it

into the memory and then you're going to

read it from the memory which is all

right but then as you keep doing you

know that is going to add performance

penalty to your applications but let's

say your developers need microsc latency

as a basic requirement then the

relational databases can't do

justification right and this is where

AWS has given you an option that's

elastic cache in memory DB now Amazon

elastic cache is R is and M cache uh

compatible so persistence is an option

so you can enable or disable and it

gives you microsc uh response times and

on the other hand uh Amazon memory DB is

fully compatible with redis and it's a

persistent inmemory data store right so

you get uh you know microsc latency for

reads and millisecond latency for rights

and keep in mind you can use this

database as a persistent inmemory data

store especially where the application

sensitive application is Sensi to

latency this is great and as you see you

know you can achieve scalability and

Agility with the help of a purpose build

database like elastic cache and memory

TV then the second subpattern again for

non- relational uh I mean for the

purpose buil is a non-relational query

patterns yes you know you can scale to

an ex with the relational databases but

what if you have uh a requirement where

you have to have an high performance

application that you know that has to

scale infinitely right such as

amazon.com you know when when you have

this

amazon.com uh sales like Prime day for

example uh just to give you a stats

Amazon Dynamo DB was able to serve 45

trillion requests a day so that is 45

million request per second sorry 7

trillion million requests uh a day and

then 45 million requests per second so

that's a lot imagine you know we don't

expect customers to be scaling of that

size but what I'm trying to say is you

know you have a service that can able to

scale that much uh so Dynamo DB is built

for key value database like I said it

can able to perform at any scale and uh

so if you have an use case where your

developers needed uh you needed you can

go for 10B

and the second popular engine is the

document TB in this diagram uh so again

this is a purpose buil database for the

data document model so let's say you as

a company that's working on a lot of

documents be it nested documents that

you'd want to query the document or

index the document uh then doing it on a

relational uh database is probably timec

consuming right you know you have to

normalize that you have to fit it on a

relational table and that's not going to

scale well and this is why we have come

up with uh Amazon document DB which is

fully compatible with mongodb so which

means any apis drivers application codes

that you're using already with mongodb

you can able to use it with Amazon

document DB and some of the benefits

again same as what we seen before

scalability agility and

performance and uh the last uh is the

specialized data sets what we call so

these are like unique cases right so

let's say you as a company that uh that

is working with highly connected data

sets uh let's say you are a company

which has millions of followers and you

want to establish a relationship between

the followers and let's say an athlete

for a sports store then uh you know

social or sorry the graph database such

as Amazon Neptune is a great fit for

your use case and for uh time stream

imagine you I have a data set uh based

on time that's constantly changing and

you have like uh GBS of data that's

getting ingested per minute uh obviously

in relational database can't do

justification for that especially when

you want to create index on a relational

database for that size the indexes are

going to be bit clunky right so you

don't uh you know it's not going to

scale well so this is where Amazon time

stream is going to come in really handy

and this is a database where you know

you can ingest tens of GBS of data

permanent and you can able to create

that in fraction of

second so that's the whole uh uh in a

purpose built diagram with different use

cases and now let's see what customers

do let's say after migrating right so

you know you have your applications now

as a customer you have made your case

you have migrated you have probably

modernized some of your

databases uh the next step

is what to do with this data right now

data is a foundation right so you would

want to move some of the data from the

transactional engine to the analytical

engine and probably beyond for AML

Innovation and for that what you seen is

you know customers normally uh have

these developers they would want to use

new services but most of the times when

they want to use this new service or a

plugin they would have to

rearchitecturing

a service anywhere you can build a

pipeline and you can move the data

around so for streaming we have Amazon

kindnesses and uh

msk and for batch Eng you have uh

services like TMS you have Amazon EMR

and you can use open source Big Data

Frameworks like Hive spark Presto and

you also have glue where you want to do

an ETL kind of you know extract

transform and

load and then you move your data build

your analytics or your data L of that

sort and then from there you know you

can do a visualization using Amazon

quick site which is a business

intelligence tool or you can like you

know stream it back to other backend

applications or you could basically use

this data to build train and model your

uh machine learning llms right and

that's where Amazon sagemaker comes in

uh handy and you do also have Bedrock

know we have a separate track for that

so I'm not going to dive deep into that

and one other area which I would want to

highlight is the ZL uh here so ZL is

another platform that's going to help

you move your data with a click of a

button from your analytical database

sorry from your relational database to

the analytical database so from Amazon

Aurora to Red shift uh you have the zero

zero ETL integration that works uh

smoothly uh our next session is on zero

ETL so I'm not going to jump too much

into it so you can wait and listen to

the next session to uh get more about it

so these are some of the zero ETL

features that's available as of

today and again now it's easy you don't

your developers don't need to spend time

building this whole Pipeline and

something breaks you know you don't want

to spend time on investigating and

fixing it uh we made your life easier

and what's even interesting is quite

recently we have made uh a new feature

where you can take control over your

Source database on like which tables

that you want to migrate it from olp to

oap so that's more on the next

session and if you want to go seress

obviously you have a lot of options on

the application and here is a snapshot

of the applications from the database

side you have all this that's

highlighted in white uh or the database

layer of course so you can use uh Sous

offerings

here so what's next yes you have

migrated you have modernized you have

innovated like you know uh what's the

next popular pattern that we have seen

right you know with this whole whole

generative era we have seen Vector

databases so before I come in here I

want to talk about this slide I'll go to

that one later so probably have seen

this uh slide before but I would want to

focus just on the vector data store

right you know so Vector data store is

an emerging thing uh especially that

supports this generative AI

applications so again we have a separate

topic running on this Vector but just to

give you an overview let's say you as a

customer or present with a prompt you

enter uh a question and you're looking

for an answer so what it's going to do

is it's going to look into the llm and

it's going to fetch and it's going to

provide a context as an output but what

if the data that you have trained uh was

trained on let's say 3 months ago right

know and there's a lot of data that's

probably has been populated right after

that and if you asked any question of up

toate data probably you're not going to

get any up toate information so how are

you going to tackle it and what we call

it as h ination in in a world so in

order to you know tackle this

hallucination problem you have this rag

based solution or retrival augumented

generation and to support that you have

Vector data store so basically you're

going to create embeddings and you're

going to store into a vector database

Ting is nothing but a format that

machine can understand to train uh so

with this what it's going to happen is

uh with all the new data that you're

getting from the right hand side we're

going to create embeddings we're going

to put it into the vector data store and

for the questions that's being asked

from the user we're going to use the uh

context and we going to do a similarity

search we're going to get the context

and with the given input we're going to

add this additional context to the

ground Alum so that it gives you an

accurate information and this is where

Vector databases are popular so I'm

going to go back to the previous slide

and then I'm going to jump onto that so

you might wonder like you know why

having this vectors and data store uh

are important like why do you want to

keep it together yes you do have a

separate uh Vector data store but then

as a developer you don't want to build

another pipeline to move your data from

your data store to another Vector data

store right you want to keep everything

in sync so that you don't you avoid this

data moment your developers probably

will have to learn a new language to or

create a new apas and so on and so forth

so you're going to avoid all of this if

you are keeping your vector and your

databases together so to make that

easier I'm going to move now on to the

next one sorry for

that so we have all of these services

that you on the screen that supports

Vector database now many of you have

already used or some of you have already

used the service so we have Vector data

store support in all of it right so you

can continue using your database as you

do and then if you have a new

functionality of you are building a new

generated U application then you can

continue using the vector data store uh

in this

service so

now what are the tools that's available

you know we have spoken about everything

now what are the tools that's available

so you have DMS Fleet adviser which you

already seen that helps you basically

doing an inventory analysis and you can

create a business use case basically to

perform this migration and you have

schema conversion tool that helps you

assessment report schema conversion code

conversion and so on and the database

migration service itself which would

help you to remove the data and helps

you replicate then you know we have

folks like us essay Partners in the room

and outside and uh again internally we

have dmas then you have a lot of other

programs which you'll be hearing

throughout the session here in other uh

areas so I'm not going to spend much

time on

it uh and we spoke about schema

conversion tool so I still add a slide

here so why is it used like I said you

know create this assessment convert

schema code objects and not only for

relational database as you see on the

far right it also helps you extract data

warehouse uh data to Amazon red

shift so what SS and Target to DMS

support as of today so here is a

snapshot of that uh and this list is

growing so you can take a picture and

when we show this probably you'll have

new sources next

time so when we speak with the customers

right now we start with the data

flywheel I mean you may have a different

data flywheel but then we work with the

customers we create uh you know their

own custom data flywheel and if you Noti

is you know it's made up of few key

initiatives and one key initiative

doesn't work on its own it has to be a

combination for the business to grow in

long term uh so the businesses that

actually Focus is on each part of this

flywheel has a long momentum right you

know this is where AWS will work with

you Partners will work with you in

creating this flywheel and we help you

succeed so what are the takeaways you

know like I initially said modernization

is not a process but rather a journey

and you can use the pathways and decide

like you know where do I start and where

I am and then from there you can move

your next step because you know it's

it's it's a journey like we said and

with tools that we have shown from AWS

as well as from Partners you can start

planning your migration and

modernization

journey so we have a lot of programs uh

again you would be seeing this in almost

all the sessions but just to give you a

high level overview if you need P level

executive guidance we have data data

strategy team so they are basically a

former cxos from large Enterprises we

will team you up with those uh members

and we can help you create a business

use case and we have D2 or data driven

everything which is again a program

where we'll work with your C Level uh

and we'll create a business use case or

ideation day and we will help you uh

achieve that uh for a long-term

progress then you have reimagine data

and you have AWS generative AI

Innovation Center where we will again

team you up with uh you know folks with

the skill set on that particular domain

and we will help you achieve what you

want to build in the uh next in ation

cycle on the generative

Ai and with that I'm I'm going to

conclude the presentation thank you very

much for listening to our special thing

I appreciate that