Last updated on 2024年6月19日

What are the most important cloud data architecture skills for the future?

由人工智能和领英社区提供技术支持

Cloud data architecture is the design and implementation of data systems and processes in the cloud, using various services, platforms, and tools. As more and more organizations migrate their data to the cloud, the demand for cloud data architects is increasing. But what are the most important skills that cloud data architects need to succeed in the future? In this article, we will explore six key areas of cloud data architecture skills that you should focus on developing or improving.

此文章中的业界达人

由社区从 12 条内容中精选。了解更多

Siddharth Rajagopal

Chief Architect at Informatica | Executive MBA at Quantic
Fayomi F.

I help aspiring tech professionals get well paid jobs in the cloud industry through high quality courses and…

1 Cloud Computing Fundamentals

To be a successful cloud data architect, you need to understand the basics of cloud computing, such as the different types of cloud models (public, private, hybrid, multi), the cloud service models (IaaS, PaaS, SaaS), and the cloud deployment strategies (cloud-native, cloud-ready, cloud-agnostic). You also need to be familiar with the main cloud providers (AWS, Azure, Google Cloud, etc.) and their offerings, pricing, and features. You should be able to compare and contrast the benefits and challenges of different cloud options and choose the best one for your data architecture goals.

添加您的观点

Fayomi F.

I help aspiring tech professionals get well paid jobs in the cloud industry through high quality courses and mentorship. | Senior Solutions Architect | Click follow to accelerate your cloud career
举报内容
The most underrated cloud architecture skills are around your communication skills. Your ability to speak to different stakeholder and understand what the requirements and constraints are. This is important because it really helps you to understand the problem you need to solve which means you can create the best solution

已翻译

赞
Rupali Gupta

Senior Technology Leadership | Cloud Transformation Lead | API & Data Architecture | Architecting Large Scale Platforms | FinTech | Innovator & Executor | Building High-Performing Teams | Data Science | AI& ML
举报内容
Additionally, it's crucial for a cloud data architect to have a solid grasp of security best practices and compliance requirements within cloud environments. Understanding how to implement robust security measures, such as encryption, access controls, and data governance, is essential for safeguarding sensitive data and ensuring regulatory compliance. Moreover, staying updated on emerging security threats and industry regulations will enable you to proactively address potential vulnerabilities and maintain the integrity and confidentiality of data stored in the cloud.

已翻译

赞
Omoniyi Ewete
举报内容
Knowledge of the different cloud models, deployment strategies, cloud providers and their offerings will allow the adaptation to the ever changing client needs & scenarios.

已翻译

赞
Bengt Sandberg

Lead architect, Inter IKEA Systems
举报内容
With cloud and available services it is much more relevant and easier to support with ad-hoc data and services. Ad-hoc data where SaaS user can retrieve data or stream filtered data. It is about how you design your layers (IaaS, PaaS, SaaS) and of course the software architecture. I believe a paradigm shift are here where business logic and data are treated the same and get the same governance focus already in design phase. I believe "data mesh architecture" are a concept to get data available and achieve high data quality. Example on keywords are "Data as a product", "Self-serve data platform".

已翻译

赞

2 Data Modeling and Design

Data modeling and design are essential skills for any data architect, but especially for cloud data architects, who need to deal with complex, heterogeneous, and distributed data sources and environments. You need to be able to create logical and physical data models that capture the data requirements, relationships, and constraints of your data systems. You also need to be able to design data schemas and structures that optimize data quality, performance, scalability, and security in the cloud. You should be familiar with different data modeling techniques and tools, such as ER diagrams, UML diagrams, data dictionaries, and metadata management.

添加您的观点

Fayomi F.

I help aspiring tech professionals get well paid jobs in the cloud industry through high quality courses and mentorship. | Senior Solutions Architect | Click follow to accelerate your cloud career
举报内容
Understanding how to use design software is essentials. Also knowing when to create high level designs and low level designs based on who your audience is is an essential skill

已翻译

赞
Rupali Gupta

Senior Technology Leadership | Cloud Transformation Lead | API & Data Architecture | Architecting Large Scale Platforms | FinTech | Innovator & Executor | Building High-Performing Teams | Data Science | AI& ML
(已编辑)
举报内容
Cloud data architects must also understand regulatory requirements and industry standards related to data management, privacy, and security (e.g., GDPR, HIPAA) and implement governance frameworks to ensure compliance within cloud environments. This involves defining data policies, establishing data lineage and traceability, implementing access controls and data encryption mechanisms, and conducting regular audits to monitor and enforce compliance measures. By prioritizing data governance, cloud data architects can mitigate risks, build trust with stakeholders, and uphold the integrity and confidentiality of data assets in the cloud.

已翻译

赞

3 Data Integration and ETL

Data integration and ETL (extract, transform, load) are the processes of moving, transforming, and loading data from various sources to a target destination, such as a data warehouse or a data lake. Cloud data architects need to be skilled in data integration and ETL, as they often have to deal with large volumes, velocities, and varieties of data in the cloud. You need to be able to design and implement data pipelines that are efficient, reliable, and secure, using various cloud-based tools and services, such as AWS Glue, Azure Data Factory, Google Cloud Dataflow, etc. You also need to be able to handle data quality issues, such as data cleansing, validation, and enrichment.

添加您的观点

Fayomi F.

I help aspiring tech professionals get well paid jobs in the cloud industry through high quality courses and mentorship. | Senior Solutions Architect | Click follow to accelerate your cloud career
举报内容
Your programming skills are important to be able to successfully perform ETL tasks. Currently Python is one of the best programming language to learn for this work

已翻译

赞
David Alzamendi

Managing Director. Microsoft Data Platform MVP and DAMA master. International speaker.
举报内容
Data engineers need to learn how to properly use Large Language Models, like ChatGPT, to improve their efficiency, speed of outcomes, and quality.

已翻译

赞

4 Data Analytics and Visualization

Data analytics and visualization are the processes of analyzing and presenting data to generate insights and support decision making. Cloud data architects need to be proficient in data analytics and visualization, as they are responsible for delivering data solutions that meet the business needs and expectations of their stakeholders. You need to be able to use various cloud-based tools and services, such as AWS Quicksight, Azure Power BI, Google Data Studio, etc., to perform data analysis and create interactive dashboards and reports. You also need to be able to communicate your findings and recommendations effectively, using data storytelling techniques and best practices.

添加您的观点

5 Data Security and Governance

Data security and governance are the policies and practices that ensure the confidentiality, integrity, and availability of data in the cloud. Cloud data architects need to be knowledgeable and compliant with data security and governance, as they are accountable for the protection and management of their data assets. You need to be able to apply the principles of data security, such as encryption, authentication, authorization, and auditing, using various cloud-based tools and services, such as AWS KMS, Azure Key Vault, Google Cloud IAM, etc. You also need to be able to implement data governance frameworks and processes, such as data lineage, data catalog, data quality, data ethics, and data privacy, using various cloud-based tools and services, such as AWS Lake Formation, Azure Purview, Google Cloud Data Catalog, etc.

添加您的观点

Raghu Moparthi PAHM?

Senior Solutions Architect | AWS certified | Facets SME
举报内容
Data security and governance is the key principle in the cloud security architecture. Applying industry security standards while architecting the data workload and using tools for governance is advised.

已翻译

赞

6 Data Engineering and DevOps

Data engineering and DevOps are the practices of developing, testing, deploying, and monitoring data systems and applications in the cloud, using agile and collaborative methods. Cloud data architects need to be adept at data engineering and DevOps, as they are involved in the entire data lifecycle, from design to delivery. You need to be able to use various cloud-based tools and services, such as AWS CodeCommit, Azure DevOps, Google Cloud Build, etc., to automate and orchestrate your data workflows and pipelines. You also need to be able to use various cloud-based tools and services, such as AWS CloudFormation, Azure Resource Manager, Google Cloud Deployment Manager, etc., to provision and manage your cloud resources and infrastructure.

添加您的观点

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Siddharth Rajagopal

Chief Architect at Informatica | Executive MBA at Quantic
举报内容
From a Data Architect perspective it is critical to be able to combine the different Data elements (Data Management, Data Governance, Data Infrastructure, Data Storage & Compute, Data Analytics etc.) and which aspects need to harmonize and integrate with each other, what happens if one of the layers/pieces change (such as a new Cloud Data Warehouse for instance). Connecting the dots is critical for a Data Architect.

已翻译

赞
Mosè Tronci

EMEA Data Management Practice Lead at Google
举报内容
A notable omission in this article is Artificial Intelligence, and Generative AI. Data Architectures are poised to play an increasingly pivotal role in facilitating the training and deployment of machine learning models, as well as serving as knowledge bases for Retrieval Augmented Generation (RAG) large language models. A contemporary Data Architect should possess a thorough understanding of the key concepts in this domain to effectively serve these use cases.

已翻译

赞

加载更多内容

Data Architecture

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

What are the most important cloud data architecture skills for the future?

1

2

3

4

5

6

7

1 Cloud Computing Fundamentals

2 Data Modeling and Design

3 Data Integration and ETL

4 Data Analytics and Visualization

5 Data Security and Governance

6 Data Engineering and DevOps

7 Here’s what else to consider

Data Architecture

给文章评分

感谢您的反馈

更多Data Architecture相关文章

更多相关阅读内容

What are the most important cloud data architecture skills for the future?

1

2

3

4

5

6

7

1 Cloud Computing Fundamentals

2 Data Modeling and Design

3 Data Integration and ETL

4 Data Analytics and Visualization

5 Data Security and Governance

6 Data Engineering and DevOps

7 Here’s what else to consider

Data Architecture

给文章评分

感谢您的反馈

查看其他技能