Cloud and Data Analytics Architecture: Data Everywhere for Everyone

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Big Data Analytics in Power and Utilities Industry Summit Amsterdam 2022

Données & analyses

Cloud and Data Analytics
Architecture
Data Everywhere for Everyone
Big Data Analytics in Power and Utilities Industry Summit
Amsterdam 2022

innogy CZ market position: Market leader in natural gas, fastest-growing in
electricity and #1 on the CNG market.

innogy · Presentation title · DD month YYYY

4
Our cloud story year by year

Cloud will handle all your
responsibility on security and
data protection

innogy · Presentation title · DD month YYYY 6

7
• AWS OnPremise
• Private Cloud (single-tenant):
̶ customer specific development
• Public Cloud (multi-tenant):
̶ custom development
• Customer AWS account
• Vendor AWS account
innogy cloud application landscape

8
innogy Shared Responsibility Model

WHY DOESN’T MODERN
CLOUD RESULT IN A
MODERN DATA
EXPERIENCE?

10
Analytical Database at its Center
• ETL data pipelines that extract
all the data to database or
datawarehouse at its center
Full-blown Data Streaming
• A set of microservices
commonly configured as a
messaging queue
Full-blown Data Mesh
• A set of microservices that are not
product-oriented like streaming
architectures, but are domain-focused
on the teams and products
Data Lake at its Center
• ELT pipelines that store everything in a
data lake, which gets later integrated
into an approved data model.

innogy · Presentation title · DD month YYYY 11
We integrate so many and focusing more on the integration, administration, and
maintenance of those tools instead of building data products.
- Who’s going to use the tool?
- What data problems is the tool trying to solve?
- What are you data governance requirements?
Challenges of the Modern Data Stack?

TOOLS

1
Engineering principles
testing, code review, CICD
2
SQL
well-known language
3
Out-of-the-box testing
data integrity, referential
constraints, and semantic
validity
4
Documentation
knowledge repository,
data lineage
5
Versioning
integrate with git
6
Deployment
DataOps practices
Source: 7 reasons why we integrated dbt into Keboo

DATA
ACADEMY

MODERN DATA
ARCHITECTURE

innogy · Presentation title · DD month YYYY 16
Modern Cloud Data Architecture

DATA MESH
As a new approach to sourcing, sharing,
accessing, and managing analytical
data at scale

Data & Analytics Modernization Days 2022:
How to leverage AWS Modern Data
Architecture to Accelerate your Data Strategy
18
Data
Producers
Domain expertise
Data ownership and governance
Data Quality
Metadata Management
Data
Mesh
Build security controls
Build and run the platform
Simplify on-boarding
Training and community
Data
Consumers
Execute business priorities
Business analytics development
Data Discovery
Data Pipeline development
Creation of new insights

Think Big,
start Small
and Be Prepared

Thank you!

Contenu connexe

Tendances

This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems. Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/) Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.

Data Mesh Part 4 Monolith to Mesh

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Jeffrey T. Pollock

Data catalogs are in wide use today across hundreds of enterprises as a means to help data scientists and business analysts find and collaboratively analyze data. Over the past several years, customers have increasingly used data catalogs in applications beyond their search & discovery roots, addressing new use cases such as data governance, cloud data migration, and digital transformation. In this session, the founder and CEO of Alation will discuss the evolution of the data catalog, the many ways in which data catalogs are being used today, the importance of machine learning in data catalogs, and discuss the future of the data catalog as a platform for a broad range of data intelligence solutions.

Data Catalog as the Platform for Data Intelligence

Data Catalog as the Platform for Data Intelligence

Data Catalog as the Platform for Data Intelligence

Delta has been powering many production pipelines at scale in the Data and AI space since it has been introduced for the past few years. Built on open standards, Delta provides data reliability, enhances storage and query performance to support big data use cases (both batch and streaming), fast interactive queries for BI and enabling machine learning. Delta has matured over the past couple of years in both AWS and AZURE and has become the de-facto standard for organizations building their Data and AI pipelines. In today’s talk, we will explore building end-to-end pipelines on the Google Cloud Platform (GCP). Through presentation, code examples and notebooks, we will build the Delta Pipeline from ingest to consumption using our Delta Bronze-Silver-Gold architecture pattern and show examples of Consuming the delta files using the Big Query Connector.

Building End-to-End Delta Pipelines on GCP

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Synthesis Webcast with Eric Kavanagh and Tamr DataOps is an emerging set of practices, processes, and technologies for building and automating data pipelines to meet business needs quickly. As these pipelines become more complex and development teams grow in size, organizations need better collaboration and development processes to govern the flow of data and code from one step of the data lifecycle to the next – from data ingestion and transformation to analysis and reporting. DataOps is not something that can be implemented all at once or in a short period of time. DataOps is a journey that requires a cultural shift. DataOps teams continuously search for new ways to cut waste, streamline steps, automate processes, increase output, and get it right the first time. The goal is to increase agility and cycle times, while reducing data defects, giving developers and business users greater confidence in data analytic output. This webcast examines how organizations adopt DataOps practices in the field. It will review results of an Eckerson Group survey that sheds light on the rate and scope of DataOps adoption. It will also describe case studies of organizations that have successfully implemented DataOps practices, the challenges they have encountered and benefits they’ve received. Tune into our webcast to learn: - User perceptions of DataOps - The rate of DataOps adoption by industry and other demographic variables - DataOps adoption by technique and component (i.e., agile, test automation, orchestration, continuous development/continuous integration) - Key challenges organizations face with DataOps - Key benefits organizations experience with DataOps - Best practices in doing DataOps - Case studies and anecdotes of DataOps at companies

Best Practices in DataOps: How to Create Agile, Automated Data Pipelines

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Elastic APM helps you deliver better digital experiences by providing complete visibility into the health of your apps — no matter how they are built, where they run, or which dev stage they’re in. Learn about the evolution of Elastic APM, the problems it solves, and where it’s headed. See how it connects traces, logs, and metrics to help you quickly get to the root cause. Plus, hear from customers using Elastic APM to improve their applications.

Monitor every app, in every stage, with free and open Elastic APM

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With so many new technologies it can get confusing on the best approach to building a big data architecture. The data lake is a great new concept, usually built in Hadoop, but what exactly is it and how does it fit in? In this presentation I'll discuss the four most common patterns in big data production implementations, the top-down vs bottoms-up approach to analytics, and how you can use a data lake and a RDBMS data warehouse together. We will go into detail on the characteristics of a data lake and its benefits, and how you still need to perform the same data governance tasks in a data lake as you do in a data warehouse. Come to this presentation to make sure your data lake does not turn into a data swamp!

Big data architectures and the data lake

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This describes a conceptual model approach to designing an enterprise data fabric. This is the set of hardware and software infrastructure, tools and facilities to implement, administer, manage and operate data operations across the entire span of the data within the enterprise across all data activities including data acquisition, transformation, storage, distribution, integration, replication, availability, security, protection, disaster recovery, presentation, analytics, preservation, retention, backup, retrieval, archival, recall, deletion, monitoring, capacity planning across all data storage platforms enabling use by applications to meet the data needs of the enterprise. The conceptual data fabric model represents a rich picture of the enterprise’s data context. It embodies an idealised and target data view. Designing a data fabric enables the enterprise respond to and take advantage of key related data trends: • Internal and External Digital Expectations • Cloud Offerings and Services • Data Regulations • Analytics Capabilities It enables the IT function demonstrate positive data leadership. It shows the IT function is able and willing to respond to business data needs. It allows the enterprise to meet data challenges • More and more data of many different types • Increasingly distributed platform landscape • Compliance and regulation • Newer data technologies • Shadow IT where the IT function cannot deliver IT change and new data facilities quickly It is concerned with the design an open and flexible data fabric that improves the responsiveness of the IT function and reduces shadow IT.

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The top questions we get about ChatGPT-powered enterprise scenarios are all about using the company's own data as the basis for the responses. In this session, we'll explore various options starting from simply injecting data into the prompt to the advanced architectures with multiple Cognitive Services chained together and fine-tuning models - all for you to choose the flexible, scalable, and cost-efficient solution that works the best for you.

Using the power of OpenAI with your own data: what's possible and how to start?

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Watch full webinar here: https://bit.ly/3FF1ubd In the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, we have discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important. In this session, you will learn how your organization can apply a logical data fabric and the associated technologies of machine learning, artificial intelligence, and data virtualization can reduce time to value. Hence, increasing the overall business value of your data assets. KEY TAKEAWAYS: - How a Logical Data Fabric is the right approach to assist organizations to unify their data. - The advanced features of a Logical Data Fabric that assist with the democratization of data, providing an agile and governed approach to business analytics and data science. - How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self-service.

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The presentation describes my views around the data we encounter in digital businesses like: - Looking at common Data collection methodologies, -What are the common issues within the decision support system and optimiztion lifecycle, - Where are most of failing? and most importantly, "How to connect the dots and move from Data to Strategy?" I work with all facets of Web Analytics and Business Strategy and see the structures and governance models of various domains to establish and analyze the key performance indicators that allow you to have a 360º overview of online and offline multi-channel environment. Apart from my experience with the leading analytic tools in the market like Google Analytics, Omniture and BI tools for Big Data, I am developing new solutions to solve complex digital / business problems. As a resourceful consultant, I can connect with your team in any modality or in any form that meets your needs and solves any data/strategy problem.

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Data Lakehouse Symposium | Day 4

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Many organizations are immature when it comes to data and analytics use. The answer lies in delivering a greater level of insight from data, straight to the point of need. There are so many Data Architecture best practices today, accumulated from years of practice. In this webinar, William will look at some Data Architecture best practices that he believes have emerged in the past two years and are not worked into many enterprise data programs yet. These are keepers and will be required to move towards, by one means or another, so it’s best to mindfully work them into the environment.

Data Architecture Best Practices for Advanced Analytics

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Introducing Databricks Delta

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NEED FOR CHANGE: Data is changing the world. We all know that. The real challenge will be to keep up with those changes by hiring the right team to help you take on the data that is already in your organization.  STAFF FOR SUCCES: Make sure you have an executive sponsor that has a vision for how the organization can become data-driven; hire experienced team members to lead the data engineering, and architecture teams; and adopt agile methodologies to allow for quick experimentation and quick failures. SKILL UP: In a recent survey focused on Spark, over 60 percent indicated that the skills/training gap was their biggest organizational challenges with Spark, but 65% of respondents indicated that they either did not know or had no future plans for training. Cloudera University to get them ramped up quickly. Cloudera University helps organizations tackle the skill gaps issue they encounter when growing their teams and helps them stay up to date on the latest supported technologies.

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Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.

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Tendances (20)

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Data Lake - Multitenancy Best Practices

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Data Lakehouse Symposium | Day 4

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Watch full webinar here: https://bit.ly/33yYuQm Data virtualization has become an essential part of enterprise data architectures, bridging the gap between IT and business users and delivering significant cost and time savings. This technology revolutionizes the way data is accessed, delivered, consumed and governed regardless of its format and location. This 1.5 hour discovery session will show help you identify the benefits of this modern and agile data integration and management technology for your organisation.

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Big Data LDN 2018: CONNECTING SILOS IN REAL-TIME WITH DATA VIRTUALIZATION

Big Data LDN 2018: CONNECTING SILOS IN REAL-TIME WITH DATA VIRTUALIZATION

Big Data LDN 2018: CONNECTING SILOS IN REAL-TIME WITH DATA VIRTUALIZATION

Watch full webinar here: https://bit.ly/2JuD9NC Organizations are adopting cloud at a fast pace and migration of critical enterprise information resources could be a challenge when dealing with a complex and big data landscape. Building the right data services architecture can help alleviate the pain points, whereby data virtualization comes to the rescue by enabling the companies to gain maximum benefits from cloud initiatives in form of agility, cost savings, and more. In this webinar, you'll learn: - How Denodo Platform's multi-location architecture can simplify and accelerate cloud migration. - Best practices of deploying the Denodo Platform in the cloud. - Leverage Denodo's virtual data services layer to address and augment cloud solutions such as data warehouse modernization, data science, and data lakes in the cloud. - Watch a demo showcasing data virtualization and analytics in the cloud.

Accelerate Migration to the Cloud using Data Virtualization (APAC)

Accelerate Migration to the Cloud using Data Virtualization (APAC)

Accelerate Migration to the Cloud using Data Virtualization (APAC)

Watch full webinar here: https://buff.ly/3JC8gCS Accelerating cloud adoption and modernizing analytics in the cloud has become a necessity to facilitate timely, insightful, and impactful decision making. However, with the widespread data in an organization across disparate hybrid cloud data sources poses a challenge with real time and well governed analytics. Data Virtualization is a modern data integration technique in which a single semantic layer can be built to help drive data democratization and speed up the analytics in an efficient and cost-effective manner. Watch this session to learn: - How various AWS services (Redshift, S3, RDS) can be quickly integrated using Denodo Platform’s logical data management by implementing a logical data fabric (LDF) - How LDF helps you manage and deliver your data for data science and analytics programs, supporting your business users. - How governed Data Services layer enables self-service analytics in your complex AWS data landscape

Data Driven Advanced Analytics using Denodo Platform on AWS

Data Driven Advanced Analytics using Denodo Platform on AWS

Data Driven Advanced Analytics using Denodo Platform on AWS

Watch full webinar here: https://bit.ly/3juxqaw In a world where machine learning and artificial intelligence are changing our everyday lives, digital transformation tops the strategic agenda in many private and government organizations. Data is becoming the lifeblood of a company, flowing seamlessly through it to enable deep business insights, create new opportunities, and optimize operations. Chief Data Officers and Data Architects are under continuous pressure to find the best ways to manage the overwhelming volumes of the data that tend to become more and more distributed and diverse. Moving data physically to a single location for reporting and analytics is not an option anymore – this is the fact accepted by the majority of the data professionals. Join us for this webinar to know about the modern virtual data landscapes including: - Virtual Data Fabric - Data Mesh - Multi-Cloud Hybrid architecture - and to learn how to leverage the Denodo Data Virtualization platform to implement these modern data architectures.

Agile Data Management with Enterprise Data Fabric (ASEAN)

Agile Data Management with Enterprise Data Fabric (ASEAN)

Agile Data Management with Enterprise Data Fabric (ASEAN)

For organisations to successfully adopt data mesh, setting up and maintaining infrastructure needs to be easy. We believe the best way to achieve this is to leverage the learnings from building a ‘central nervous system‘, commonly used in modern data-streaming ecosystems. This approach formalises and automates of the manual parts of building a data mesh. This presentation introduces SpecMesh; a methodology and supporting developer toolkit to enable business to build the foundations of their data mesh.

The Enterprise Guide to Building a Data Mesh - Introducing SpecMesh

The Enterprise Guide to Building a Data Mesh - Introducing SpecMesh

The Enterprise Guide to Building a Data Mesh - Introducing SpecMesh

IBM & Cloudera: Hybrid Cloud & the Power of Possibilities

IBM & Cloudera: Hybrid Cloud & the Power of Possibilities

IBM & Cloudera: Hybrid Cloud & the Power of Possibilities

omkar_nimbalkar

Similaire à Cloud and Data Analytics Architecture: Data Everywhere for Everyone (20)

Belgium & Luxembourg dedicated online Data Virtualization discovery workshop

Belgium & Luxembourg dedicated online Data Virtualization discovery workshop

Belgium & Luxembourg dedicated online Data Virtualization discovery workshop

Bridging the Last Mile: Getting Data to the People Who Need It

Bridging the Last Mile: Getting Data to the People Who Need It

Bridging the Last Mile: Getting Data to the People Who Need It

451 Research + NuoDB: What It Means to be a Container-Native SQL Database

451 Research + NuoDB: What It Means to be a Container-Native SQL Database

451 Research + NuoDB: What It Means to be a Container-Native SQL Database

Speak to Your Data

Speak to Your Data

Speak to Your Data

Denodo Partner Connect: A Review of the Top 5 Differentiated Use Cases for th...

Denodo Partner Connect: A Review of the Top 5 Differentiated Use Cases for th...

Denodo Partner Connect: A Review of the Top 5 Differentiated Use Cases for th...

Bridging the Last Mile: Getting Data to the People Who Need It (APAC)

Bridging the Last Mile: Getting Data to the People Who Need It (APAC)

Bridging the Last Mile: Getting Data to the People Who Need It (APAC)

Multi-Cloud-Datenintegration mit Datenvirtualisierung

Multi-Cloud-Datenintegration mit Datenvirtualisierung

Multi-Cloud-Datenintegration mit Datenvirtualisierung

The power of orchestration - Inside Cisco IT - DC Cloud from IaaS to Fast IT

The power of orchestration - Inside Cisco IT - DC Cloud from IaaS to Fast IT

The power of orchestration - Inside Cisco IT - DC Cloud from IaaS to Fast IT

Maximizing Oil and Gas (Data) Asset Utilization with a Logical Data Fabric (A...

Maximizing Oil and Gas (Data) Asset Utilization with a Logical Data Fabric (A...

Maximizing Oil and Gas (Data) Asset Utilization with a Logical Data Fabric (A...

A Key to Real-time Insights in a Post-COVID World (ASEAN)

A Key to Real-time Insights in a Post-COVID World (ASEAN)

A Key to Real-time Insights in a Post-COVID World (ASEAN)

Connecting Silos in Real Time with Data Virtualization

Connecting Silos in Real Time with Data Virtualization

Connecting Silos in Real Time with Data Virtualization

2022 Trends in Enterprise Analytics

2022 Trends in Enterprise Analytics

2022 Trends in Enterprise Analytics

Accelerating the Path to Digital with a Cloud Data Strategy

Accelerating the Path to Digital with a Cloud Data Strategy

Accelerating the Path to Digital with a Cloud Data Strategy

Data Virtualization: An Introduction

Data Virtualization: An Introduction

Data Virtualization: An Introduction

Big Data LDN 2018: CONNECTING SILOS IN REAL-TIME WITH DATA VIRTUALIZATION

Big Data LDN 2018: CONNECTING SILOS IN REAL-TIME WITH DATA VIRTUALIZATION

Big Data LDN 2018: CONNECTING SILOS IN REAL-TIME WITH DATA VIRTUALIZATION

Accelerate Migration to the Cloud using Data Virtualization (APAC)

Accelerate Migration to the Cloud using Data Virtualization (APAC)

Accelerate Migration to the Cloud using Data Virtualization (APAC)

Data Driven Advanced Analytics using Denodo Platform on AWS

Data Driven Advanced Analytics using Denodo Platform on AWS

Data Driven Advanced Analytics using Denodo Platform on AWS

Agile Data Management with Enterprise Data Fabric (ASEAN)

Agile Data Management with Enterprise Data Fabric (ASEAN)

Agile Data Management with Enterprise Data Fabric (ASEAN)

The Enterprise Guide to Building a Data Mesh - Introducing SpecMesh

The Enterprise Guide to Building a Data Mesh - Introducing SpecMesh

The Enterprise Guide to Building a Data Mesh - Introducing SpecMesh

IBM & Cloudera: Hybrid Cloud & the Power of Possibilities

IBM & Cloudera: Hybrid Cloud & the Power of Possibilities

IBM & Cloudera: Hybrid Cloud & the Power of Possibilities

Dernier

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Discover Why Less is More in B2B Research

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This project aims to predict whether a loan application will be approved or denied based on various factors such as applicant's income, credit score, loan amount, etc. Using a dataset containing historical loan application data, we employed machine learning algorithms to build a predictive model. The model was trained on features such as applicant's income, credit history, loan amount, loan term, and others. After training the model, we evaluated its performance using metrics like accuracy, precision, recall, and F1 score. The insights from this project can help financial institutions streamline their loan approval process and make informed decisions. Visit for more information: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

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Predicting Loan Approval: A Data Science Project

Predicting Loan Approval: A Data Science Project

Boston Institute of Analytics

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1. Cloud and Data Analytics Architecture Data Everywhere for Everyone Big Data Analytics in Power and Utilities Industry Summit Amsterdam 2022

2. innogy CZ market position: Market leader in natural gas, fastest-growing in electricity and #1 on the CNG market.

3. innogy · Presentation title · DD month YYYY

4. 4 Our cloud story year by year

5. Cloud will handle all your responsibility on security and data protection

6. innogy · Presentation title · DD month YYYY 6

7. 7 • AWS OnPremise • Private Cloud (single-tenant): ̶ customer specific development • Public Cloud (multi-tenant): ̶ custom development • Customer AWS account • Vendor AWS account innogy cloud application landscape

8. 8 innogy Shared Responsibility Model

9. WHY DOESN’T MODERN CLOUD RESULT IN A MODERN DATA EXPERIENCE?

10. 10 Analytical Database at its Center • ETL data pipelines that extract all the data to database or datawarehouse at its center Full-blown Data Streaming • A set of microservices commonly configured as a messaging queue Full-blown Data Mesh • A set of microservices that are not product-oriented like streaming architectures, but are domain-focused on the teams and products Data Lake at its Center • ELT pipelines that store everything in a data lake, which gets later integrated into an approved data model.

11. innogy · Presentation title · DD month YYYY 11 We integrate so many and focusing more on the integration, administration, and maintenance of those tools instead of building data products. - Who’s going to use the tool? - What data problems is the tool trying to solve? - What are you data governance requirements? Challenges of the Modern Data Stack?

13. 1 Engineering principles testing, code review, CICD 2 SQL well-known language 3 Out-of-the-box testing data integrity, referential constraints, and semantic validity 4 Documentation knowledge repository, data lineage 5 Versioning integrate with git 6 Deployment DataOps practices Source: 7 reasons why we integrated dbt into Keboo

14. DATA ACADEMY

15. MODERN DATA ARCHITECTURE

16. innogy · Presentation title · DD month YYYY 16 Modern Cloud Data Architecture

17. DATA MESH As a new approach to sourcing, sharing, accessing, and managing analytical data at scale

18. Data & Analytics Modernization Days 2022: How to leverage AWS Modern Data Architecture to Accelerate your Data Strategy 18 Data Producers Domain expertise Data ownership and governance Data Quality Metadata Management Data Mesh Build security controls Build and run the platform Simplify on-boarding Training and community Data Consumers Execute business priorities Business analytics development Data Discovery Data Pipeline development Creation of new insights

19. Think Big, start Small and Be Prepared