Skip to content

Home

  • 3 min read

Our Future is AI - Choosing the Companion You Want to Live With

Artificial Intelligence (AI) is not just a fleeting trend; it's a transformative force reshaping various aspects of our lives, from healthcare and agriculture to social care and beyond. But as we stand at this crossroads, it's crucial to ponder which AI future we want to embrace. This post delves into the critical considerations for choosing the AI that aligns with our values and needs.

The Imperative of AI in Healthcare

The traditional healthcare model is buckling under the weight of skyrocketing costs, outpacing GDP growth. Yet, despite these expenditures, we still face dismal survival rates for major cancers and inadequate detection rates for treatable neonatal conditions. The shortage of radiologists and consultants exacerbates the issue, leading to prolonged and costly diagnostic processes. AI offers a beacon of hope, with the potential to revolutionize healthcare by enhancing detection, diagnosis, and treatment processes.

The Endless Use Cases of AI

Beyond healthcare, AI's applications are boundless. In developed economies, where human resources are limited, AI can significantly impact sectors like building maintenance, social care, agritech, and climate change mitigation. For instance, the UK's building maintenance costs in 2020 soared to $81 billion, and public spending on adult social care reached $34 billion annually. AI can offer more efficient and cost-effective solutions in these areas.

AI has evolved from Symbolic AI in the 1950s to the Generative AI of today, powered by transformers like Google's model in 2017. Generative AI, leveraging large language models (LLMs) and other techniques, can efficiently train predictive models across various domains, from text and images to programming languages and robotics. However, this evolution also brings challenges, including ethical concerns, transparency issues, and the risk of job losses and misinformation.

The Ethics of AI: A Double-Edged Sword

While AI holds promise, it also poses ethical dilemmas. The "black box" nature of neural networks raises questions about bias, censorship, and transparency. Furthermore, the potential for job displacement, deepfakes, and cybercrime cannot be ignored. Regulation, such as the EU AI Act, may be necessary, but it's crucial to consider its implications carefully.

Generative AI: Power and Pitfalls

Generative AI, despite its capabilities, comes with its own set of challenges. Issues like hallucination, drift, and confabulation can undermine its reliability. Moreover, the provenance of training data, copyright concerns, and the potential for adversarial AI highlight the need for vigilance and responsible use.

AI and Robotics: A Synergistic Future

The integration of AI with robotics opens new horizons, from industrial and agricultural robots to personal and defense robotics. However, safety and ethical considerations remain paramount, especially as robots become more integrated into human environments.

Choosing Your AI Companion

As AI becomes an integral part of our lives, it's essential to choose wisely. Look for AI systems that prioritize privacy, ethics, and intuitiveness. They should respect individual autonomy, be accessible, and maintain a shared history. In essence, the AI companions we choose should enhance our lives without compromising our values.

Conclusion: Navigating the AI Landscape

Our future is undeniably intertwined with AI. As we navigate this landscape, it's crucial to consider not just the technological capabilities but also the ethical and social implications. By making informed choices, we can ensure that the AI future we embrace is one that aligns with our aspirations and values.

  • in zh
  • 1 min read

我們的未來是AI - 選擇你想與之共度一生的伴侶

人工智慧（AI）不僅僅是一種短暫的趨勢；它是一種改變我們生活各種方面的變革力量，從醫療和農業到社會照顧等等。但是，當我們站在這個十字路口時，思考我們想要接受哪種AI未來是至關重要的。本文深入探討了選擇與我們的價值觀和需求相符的AI的重要考慮因素。

醫療保健中AI的必要性

傳統的醫療保健模式正在承受著成本飛漲的壓力，超過了國內生產總值（GDP）的增長。然而，儘管有這些開支，我們仍面臨主要癌症生存率低落以及可治療的新生兒病症的檢測率不足的情況。放射科醫師和顧問的短缺加劇了這個問題，導致診斷過程費時且成本高昂。AI提供了一線希望，其有可能革命性地改變醫療保健，以增強檢測、診斷和治療過程。

AI的無盡用途

超越醫療，AI的應用是無窮無盡的。在發達經濟體中，由於人力資源有限，AI可以對建築維護、社會照顧、農業科技以及氣候變化緩解等行業產生重大影響。例如，2020年英國的建築維護費用飆升至810億美元，成人社會照顧的公共支出每年達到340億美元。AI可以在這些領域提供更高效且更具成本效益的解決方案。

導航AI的演進

AI從1950年代的符號AI演變為今天的生成型AI，得力於像2017年的Google模型這樣的變革者。生成型AI，利用大型語言模型（LLM）和其他技術，可以高效地訓練各種領域的預測模型，從文本和圖像到編程語言和機器人學。然而，這種演化也帶來了挑戰，包括倫理問題、透明度問題，以及工作損失和誤導信息的風險。

AI的倫理：雙面刃

雖然AI充滿希望，但它也帶來了倫理困境。神經網絡的"黑箱"性質引起了關於偏見、審查和透明度的問題。此外，工作崗位消失、深度偽造和網路犯罪的可能性也不能被忽視。像歐盟AI法案這樣的規定可能是必要的，但我們必須仔細考慮其可能引發的影響。

生成型AI：權力與陷阱

儘管生成型AI能力強大，但也帶來了自身的挑戰。像是幻覺、漂移和捏造等問題可能會損害其可靠性。此外，訓練數據的來源、版權問題以及敵對AI的潛在可能性，都凸顯出了需要警惕並負責任地使用的重要性。

AI和機器人學：協同的未來

AI與機器人學的整合開放了新的視野，從工業和農業機器人到個人和防禦機器人。然而，安全和道德問題依然優先，尤其是當機器人越來越融入人類環境的時候。

選擇你的AI伙伴

隨著AI成為我們生活的重要部分，我們必須明智地做出選擇。選擇那些尊重個人自主權，可以通用且可以保留使用歷史的AI系統。他們應該尊重個人自主權，並且要方便使用，並保持共享的歷史。從本質上說，我們選擇的AI伙伴應該在不影響我們價值觀的情況下提升我們的生活。

結論：導航AI風景

我們的未來無可避免地與AI交織在一起。當我們在這個風景中導航時，我們必須考慮的不僅僅是技術能力，還有倫理和社會影響。通過做出明智的選擇，我們可以確保我們接受的AI未來與我們的期望和價值觀相符。

  • 3 min read

Embracing the Axioms of Digital Architecture for Transformation

In the rapidly evolving digital landscape, businesses must adapt to stay ahead. This adaptation is not just about adopting new technologies but also about rethinking the way we approach architecture. The following axioms of digital architecture provide a framework for creating agile, customer-centric, and resilient systems.

1. Outside-In Thinking

Traditional approaches often start with asking clients what they need. However, to create a truly differentiated customer experience, we must go beyond this. Outside-in thinking involves discovering hidden or untold customer needs and adopting a design thinking approach that is human-centric. This ensures that solutions are not just technically sound but also deeply resonate with the end-users.

2. Rapid Feedback Loops

In the digital age, customer preferences and market dynamics can change swiftly. Rapid feedback loops are essential to verify customer needs and expectations continuously. By integrating feedback early and often, businesses can iterate quickly, ensuring that the solutions remain relevant and effective.

3. Bias for Change

Change is the only constant in the digital world. An architecture that welcomes changing requirements is vital. The architecture should be viewed as a living artifact, striking a balance between intentional (planned) and emerging (agile) aspects. Intentional architecture sets the direction but should be flexible enough to integrate new requirements without slowing down the process.

4. Organization Mirroring Architecture

The structure of digital teams should reflect the system's intentional architecture. This concept is aligned with Conway's Law, which states that the system's design will mirror the organization's communication structure. The Inverse Conway Maneuver suggests evolving the team and organizational structure to promote the desired architecture, ensuring alignment between the system and the way teams interact.

5. Autonomous Cross-Functional Teams

Empowering teams with autonomy is crucial for agility and innovation. Autonomous cross-functional teams can respond more quickly to changes and are better equipped to address complex problems. This autonomy, however, should be balanced with clear guidelines and objectives to ensure coherence and alignment with the overall architectural vision.

6. Loosely Coupled Systems

High-performing teams are often associated with loosely coupled architectures. Such systems allow for greater flexibility, enabling teams to make changes without impacting other parts of the system. This reduces dependencies and fosters a more resilient and adaptable architecture.

7. Partitioning over Layering

While layered architecture patterns are common, they tend to create silos that can hinder agility and scalability. Partitioning, on the other hand, should be market-driven at the business level and capability-driven at the operating model level. This approach promotes a more modular and scalable architecture, facilitating easier adaptation to changing market demands.

Conclusion

Embracing these axioms of digital architecture can transform the way businesses approach their digital strategies. By focusing on outside-in thinking, rapid feedback loops, a bias for change, organizational alignment, team autonomy, loosely coupled systems, and partitioning over layering, companies can build architectures that are not only robust and scalable but also agile and customer-centric. In the digital era, these qualities are not just desirable but essential for success.

  • in zh
  • 1 min read

擁抱數位建築原則以實現轉型

在迅速變化的數位環境中，企業必須適應以保持領先。這種適應不僅是採用新技術，還要重新思考我們對建築的方式。以下數位建築原則的公理提供了一個框架，用於創建敏捷、以客戶為中心和具有韌性的系統。

1. 外向內思考

傳統的方法通常從問客戶他們需要什麼開始。然而，要創造一種真正區別於眾不同的客戶體驗，我們必須超越這一點。外向內思考涉及發現隱藏的或未被告知的客戶需求，並採用以人為中心的設計思維方法。這確保了解決方案不僅技術上可靠，而且與最終用戶深度共鳴。

2. 迅速的反饋迴路

在數位時代，客戶的偏好和市場動態可能會迅速變化。迅速反饋迴路對於不斷驗證客戶需求和期望至關重要。通過提早並經常集成反饋，企業可以迅速迭代，確保解決方案保持相關並有效。

3. 變革傾向

變化是數位世界中唯一不變的東西。一種能夠接受變更需求的建築方式至關重要。建築應被看作是一種活脫的產物，在有意的（計劃的）和不断浮現的（敏捷的）方面之間取得平衡。有意的建築設定了方向，但應足夠靈活，能夠整合新的需求，而不會拖慢過程。

4. 組織反映建築

數位團隊的結構應反映系統的有意的建築。這個概念與康威定律一致，該定律指出，系統的設計將反映組織的溝通結構。反康威法則建議改變團隊和組織結構，以推動期望的建築，確保系統與團隊互動方式之間的一致性。

5. 自主的跨職能團隊

賦予團隊自主權對敏捷性和創新至關重要。自主的跨職能團隊可以更快地應對變化，並更好地應對復雜問題。然而，這種自主性應與清晰的指導方針和目標相平衡，以確保與整體建築視野的一致性。

6. 不緊密結合的系統

高性能團隊通常與不緊密結合的建築相關聯。這種系統允許更大的靈活性，使團隊能夠在不影響系統其他部分的情況下進行變化。這減少了依賴性，促進了更具韌性和適應性的建築。

7. 劃分優於分層

雖然分層的建築模式很常見，但它們往往創造出障礙敏捷性和可擴展性的孤島。另一方面，切割應該在商務層面上由市場驅動，在操作模型層面上由能力驅動。這種方法促進了更模塊化和可擴展的建築，便於適應變化的市場需求。

結論

擁抱這些數位建築的公理可以改變企業對其數位策略的方式。通過關注外向內思考、迅速的反饋迴路、變革傾向、組織對齊、團隊自主權、不緊密結合的系統和劃分優於分層，公司可以構建不僅堅固和可擴展，而且敏捷和以客戶為中心的建築。在數位時代，這些品質不僅是可取的，而且對成功而言是必不可少的。

  • 2 min read

ISO 20022 - the Global Standard for Financial Messaging

In the rapidly evolving world of financial technology, the need for standardized and efficient communication between institutions has never been more critical. Enter ISO 20022, a global standard that is revolutionizing the way financial messages are structured and exchanged. This blog post will delve into the intricacies of ISO 20022, its significance, and its impact on the financial industry.

What is ISO 20022?

ISO 20022 is an international standard for electronic data interchange between financial institutions. It provides a common platform for the development of messages, covering various financial business areas such as payments, securities, trade services, cards, and foreign exchange. The standard is designed to improve the efficiency, reliability, and security of financial messaging across the globe.

Key Features of ISO 20022

  1. Rich Data Model: ISO 20022 uses a data dictionary that defines each piece of financial information in a message, ensuring consistency and clarity.

  2. Flexibility: The standard can accommodate different message formats, including XML, JSON, and ASN.1, making it adaptable to various technologies and systems.

  3. Extensibility: New messages and data elements can be added without affecting existing messages, allowing for easy updates and enhancements.

  4. Interoperability: By providing a common language for financial messages, ISO 20022 facilitates seamless communication between diverse systems and networks.

Benefits of ISO 20022

  1. Enhanced Efficiency: Standardized messages reduce the need for manual intervention and translation, leading to faster processing and lower costs.

  2. Improved Accuracy: The rich data model minimizes the risk of errors and misunderstandings in financial transactions.

  3. Better Compliance: The standard supports regulatory requirements and helps institutions comply with anti-money laundering (AML) and know your customer (KYC) regulations.

  4. Greater Innovation: With a flexible and extensible framework, ISO 20022 paves the way for new financial products and services.

Implementation Challenges

While the benefits of ISO 20022 are clear, its implementation is not without challenges. Financial institutions must invest in updating their systems, training staff, and ensuring compatibility with their partners' systems. Additionally, the transition from legacy systems to ISO 20022 requires careful planning and coordination to avoid disruptions in service.

The Future of ISO 20022

ISO 20022 is set to become the global standard for financial messaging, with major payment systems and central banks around the world adopting it. The standard's adoption is expected to accelerate with the rise of digital currencies and real-time payment systems. As the financial industry continues to evolve, ISO 20022 will play a crucial role in shaping its future.

Conclusion

ISO 20022 is more than just a technical standard; it is a catalyst for change in the financial industry. By standardizing financial messages, it enhances efficiency, reduces risks, and opens up new opportunities for innovation. As the adoption of ISO 20022 continues to grow, it will undoubtedly transform the landscape of financial communication for the better.

  • in zh
  • 1 min read

ISO 20022 - 金融訊息的全球標準

在金融科技迅速發展的世界裡，各機構之間需要進行標準化而且高效的溝通，這一點從未如此關鍵。進入ISO 20022，這是一個正在改變金融訊息結構和交換方式的全球標準。本博文將深入探討ISO 20022的複雜性，其重要性，以及它對金融業的影響。

什麼是ISO 20022？

ISO 20022是金融機構之間進行電子數據交換的國際標準。它提供了一種共同平台來開發訊息，涵蓋了各種金融業務領域，例如支付、證券、貿易服務、卡片和外匯。該標準旨在提高全球金融訊息的效率、可靠性和安全性。

ISO 20022的關鍵特性

  1. 豐富的數據模型： ISO 20022使用一種數據字典來定義訊息中的每一項金融信息，以確保一致性和清晰度。

  2. 靈活性：該標準可以容納不同的訊息格式，包括XML、JSON和ASN.1，使其能夠適應各種技術和系統。

  3. 可擴展性：可以新增訊息和數據元素而不影響現有的訊息，容許輕鬆更新和增強。

  4. 互操作性：通過為金融訊息提供一種共同語言，ISO 20022促進了不同系統和網絡之間的無縫溝通。

ISO 20022的益處

  1. 提高效率：標準化的訊息減少了手動干預和翻譯的需要，從而加快了處理速度和降低了成本。

  2. 提高準確性：豐富的數據模型減少了金融交易中的錯誤和誤解的風險。

  3. 更好的合規性：該標準支援監管要求，並幫助機構遵守反洗錢（AML）和了解您的客戶（KYC）規定。

  4. 更大的創新：有了一個靈活和可擴展的框架，ISO 20022為新的金融產品和服務鋪平了道路。

實施挑戰

雖然ISO 20022的好處很明顯，但其實施並非沒有挑戰。金融機構必須投資於更新他們的系統、培訓員工，並確保與他們合作夥伴的系統兼容。此外，從傳統系統過渡到ISO 20022需要小心謀劃和協調，以避免服務中斷。

ISO 20022的未來

ISO 20022將成為金融訊息的全球標準，全球主要的支付系統和央行都在採用它。預計隨著數字貨幣和實時支付系統的崛起，該標準的採用將加速。隨著金融業的不斷發展，ISO 20022將在塑造其未來中起著決定性的作用。

結論

ISO 20022不僅僅是一種技術標準；它是金融業變革的催化劑。通過標準化金融訊息，它提高了效率，減少了風險，並為創新開創了新的機會。隨著ISO 20022的採用持續增長，它無疑將改變金融通訊的景觀，使之變得更好。

  • 3 min read

Microsoft Fabric - Revolutionizing Data Analytics in the AI Era

In today's fast-paced digital world, data is the lifeblood of AI, and the landscape of data and AI tools is vast, with offerings like Hadoop, MapReduce, Spark, and more. As the Chief Information Officer, the last thing you want is to become the Chief Integration Officer, constantly juggling multiple tools and systems. Enter Microsoft Fabric, a game-changing solution designed to simplify and unify data analytics for the era of AI.

From Fragmentation to Unity: The Evolution of Data Analytics

Microsoft Fabric represents a paradigm shift in data analytics, moving from a fragmented landscape of individual components to a unified, integrated stack. It transforms the approach from relying on a single database to harnessing the power of all available data. Most importantly, it evolves from merely incorporating AI as an add-on to embedding generative AI (Gen AI) into the very fabric of the platform.

The Four Core Design Principles of Microsoft Fabric

  1. Complete Analytics Platform: Microsoft Fabric offers a comprehensive solution that is unified, SaaS-fied, secured, and governed, ensuring that all your data analytics needs are met in one place.
  2. Lake Centric and Open: At the heart of Fabric is the concept of "One Lake, One Copy," emphasizing a single data lake that is open at every tier, ensuring flexibility and openness.
  3. Empower Every Business User: The platform is designed to be familiar and intuitive, integrated seamlessly into Microsoft 365, enabling users to turn insights into action effortlessly.
  4. AI Powered: Fabric is turbocharged with AI, from Copilot acceleration to generative AI on your data, providing AI-driven insights to inform decision-making.

The Transition from Synapse to SaaS-fied Fabric

Microsoft Fabric marks a significant evolution from separate products like Azure Data Factory (ADF) and Azure Cosmos DB to a unified, seamless experience. This transition embodies the shift towards a SaaS (Software as a Service) model, characterized by ease of use, cost efficiency, scalability, and accessibility.

OneLake: The OneDrive for Data

OneLake stands as the cornerstone of Microsoft Fabric, offering a single SaaS lake for the entire organization. It is automatically provisioned with the tenant, and all workloads store their data in intuitive workspace folders. OneLake ensures that data is organized, indexed, and ready for discovery, sharing, governance, and compliance, with Delta - parquet as the standard format for all tabular data.

Tailored Experiences for Different Personas

Microsoft Fabric caters to various personas, including data engineers, scientists, analysts, citizens, and stewards, providing optimized experiences for each. From executing tasks faster to making more data-driven decisions, Fabric empowers users across the board.

Copilot: AI Assistance for All

Copilot is a standout feature of Microsoft Fabric, offering AI assistance to enrich, model, analyze, and explore data in notebooks. It helps users understand their data better, create and configure ML models through conversation, write code faster with inline suggestions, and summarize and explain code for enhanced understanding.

Adhering to Design Principles

Microsoft Fabric adheres to key design principles, ensuring a unified SaaS data lake without silos, true data mesh as a service with OneLake, no lock-in with industry-standard APIs and open file formats, and comprehensive security and governance.

In conclusion, Microsoft Fabric is a transformative solution that simplifies and unifies data analytics in the era of AI. With its core design principles, it empowers business users, leverages AI power, and offers a seamless, SaaS-fied experience, making it an essential tool for any organization looking to harness the full potential of their data.

  • in zh
  • 1 min read

微軟 Fabric - 在 AI 時代革新數據分析

在今天的快節奏數位世界中，數據是 AI 的命脈，數據和 AI 工具的景象廣大，如 Hadoop、MapReduce、Spark 等等。作為首席信息官，你最不希望的就是變成首席集成官，不斷地操縱著多種工具和系統。微軟 Fabric 的出現，是一種革命性的解決方案，旨在簡化和統一 AI 時代的數據分析。

從碎片化到統一：數據分析的演變

微軟 Fabric 代表了數據分析的範疇變化，從由個別組件組成的碎片化景象轉變到一個統一、集成的堆疊。它將方法從依賴單一數據庫轉變到利用所有可用數據的力量。最重要的是，它從僅僅作為一種附加裝置將 AI 納入其中，發展到將生成性 AI (Gen AI) 深入到平台的根本中。

微軟 Fabric 的四大核心設計原則

  1. 完整的分析平台：微軟 Fabric 提供完全的解決方案，這是統一的，SaaS 化的，安全的，並受到監管，確保所有您的數據分析需求均在一個地方得到滿足。
  2. 湖心且開放：Fabric 的核心是“一湖、一份”的概念，強調一個在每一階層都開放的單一數據湖，確保靈活性和開放性。
  3. 賦權每一個商業用戶：該平台設計得熟悉且直觀，無縫集成到微軟 365 中，使用者可以毫不費力地將見解轉化為行動。
  4. AI 驅動：Fabric 用 AI 加速，從副駕駛加速到在您的數據上生成 AI，提供 AI 驅動的見解以通報決策。

從 Synapse 到 SaaS 化的 Fabric 的轉變

微軟 Fabric 標誌了從像 Azure Data Factory (ADF) 和 Azure Cosmos DB 這樣的獨立產品向統一，無縫體驗的重大演變。這次轉變體現了朝向 SaaS (Software as a Service) 模型的轉變，其特點是易於使用，成本效益高，可擴展性強和易於取得。

OneLake：數據的 OneDrive

OneLake 是微軟 Fabric 的基石，為整個組織提供單一的 SaaS湖。它自動與租戶一起提供，所有工作負載都將其數據存儲在直觀的工作區文件夾中。OneLake 確保數據組織有序，有索引，並且準備好進行發現，共享，治理和遵守，Delta-parquet 是所有表格數據的標準格式。

為不同人群提供定制的體驗

微軟 Fabric 適合各種人物角色，包括數據工程師，科學家，分析師，公民，和監管者，為每一個都提供優化的體驗。從執行任務更快到作出更多以數據驅動的決策，Fabric 賦權給各種使用者。

副駕：所有人的 AI 幫助

副駕是微軟 Fabric 的一個突出特點，提供 AI 協助來豐富，建模，分析，並在筆記本中探索數據。它幫助用戶更好地理解他們的數據，通過對話創建並配置 ML 模型，更快地寫出代碼，並彙總並解釋代碼以增強理解。

堅持設計原則

微軟 Fabric 遵循關鍵設計原則，確保一個統一的 SaaS 數據湖，無孤島，真正的數據網格作為 OneLake 的服務，無鎖定，具有行業標準 API 和開放文件格式，以及全面的安全性和治理。

總之，微軟 Fabric 是一種改革性的解決方案，大大簡化了 AI 時代的數據分析並加以統一。通過其核心設計原則，它賦權於商業用戶，利用 AI 的力量，並提供無縫的，SaaS 化的體驗，使其成為任何希望充分利用其數據潛力的組織的必須工具。

  • 2 min read

A Pragmatic Approach Towards CDK for Terraform

Infrastructure as Code (IaC) has revolutionized the way we manage and provision resources in the cloud. Terraform, by HashiCorp, has been a leading tool in this space, allowing users to define infrastructure through declarative configuration files. However, with the advent of the Cloud Development Kit for Terraform (CDKTF), developers can now leverage the power of programming languages they are already familiar with, such as TypeScript, Python, Java, C#, and Go, to define their infrastructure.

Building Blocks of CDK for Terraform

CDK for Terraform is built on top of the AWS Cloud Development Kit (CDK) and uses the JSII (JavaScript Interop Interface) to enable publishing of constructs that are usable in multiple programming languages. This polyglot approach opens up new possibilities for infrastructure management.

The foundational classes to build CDKTF applications are:

  • App Class: This is the container for your infrastructure configuration. It initializes the CDK application and acts as the root construct.
  • Stack Class: A stack represents a single deployable unit that contains a collection of related resources.
  • Resource Class: This class represents individual infrastructure components, such as an EC2 instance or an S3 bucket.
  • Constructs: Constructs are the basic building blocks of CDK apps. They encapsulate logic and can be composed to create higher-level abstractions.

When to Use CDK for Terraform

CDK for Terraform is a powerful tool, but it's not always the right choice for every project. Here are some scenarios where CDKTF might be a good fit:

  • Preference for Procedural Languages: If your team is more comfortable with procedural programming languages like Python or TypeScript, CDKTF allows you to define infrastructure using these languages instead of learning a new domain-specific language (DSL) like HCL (HashiCorp Configuration Language).
  • Need for Abstraction: As your infrastructure grows in complexity, creating higher-level abstractions can help manage this complexity. CDKTF enables you to create reusable constructs that encapsulate common patterns.
  • Comfort with Cutting-Edge Tools: CDKTF is a relatively new tool in the Terraform ecosystem. If your team is comfortable adopting new technologies and dealing with the potential for breaking changes, CDKTF can offer a more dynamic and flexible approach to infrastructure as code.

Conclusion

CDK for Terraform offers a pragmatic approach for teams looking to leverage their existing programming skills to define and manage cloud infrastructure. By providing a familiar language interface and enabling the creation of reusable constructs, CDKTF can help streamline the development process and manage complexity in large-scale deployments. However, it's essential to evaluate whether your team is ready to adopt this cutting-edge tool and whether it aligns with your project's needs.

  • in zh
  • 1 min read

對Terraform的CDK採取實用方法

基礎設施即代碼（IaC）已經使我們管理和提供雲端資源的方式進行了革命性的改變。由HashiCorp開發的Terraform在這個領域中一直領先，允許用戶通過聲明性配置文件來定義基礎設施。然而，隨著Terraform的雲端開發套件（CDKTF）的出現，開發者現在可以利用他們已經熟悉的程式設計語言的力量，例如TypeScript、Python、Java、C#和Go，來定義他們的基礎設施。

Terraform CDK的構建塊

Terraform的CDK是建立在AWS的雲端開發套件（CDK）之上的，並使用JSII（JavaScript Interop Interface）來啟用在多種程式設計語言中可用的構建塊的發佈。這種多語言方式為基礎設施管理打開了新的可能性。

構建CDKTF應用的基礎類別包括：

  • 應用類別：這是您的基礎設施配置的容器。它初始化CDK應用並充當根構建塊。
  • 堆棧類別：一個堆棧代表一個包含了一系列相關資源的單一可部署單位。
  • 資源類別：這個類別代表單個基礎設施組件，如EC2實例或S3存储桶。
  • 構建塊：構建塊是CDK應用的基本構建塊。他們封裝邏輯並可以組合創建更高級別的抽象。

何時使用Terraform的CDK

Terraform的CDK是一個強大的工具，但並非每個項目都是最好的選擇。以下是一些CDKTF可能適合的情況：

  • 偏好程序式語言：如果您的團隊更熟悉如Python或TypeScript等程序式程式設計語言，CDKTF允許您使用這些語言而不是學習新的特定領域語言（DSL）如HCL（HashiCorp配置語言）來定義基礎設施。
  • 需要抽象：隨著您的基礎設施變得越來越複雜，創建更高級別的抽象可以幫助管理這種複雜性。CDKTF使您能夠創建封裝常見模式的可重用構建塊。
  • 對前沿工具的熟悉：CDKTF在Terraform生態系統中是一個相對新的工具。如果您的團隊樂於接受新技術並處理可能的重大變化，CDKTF可以提供一種更動態和靈活的基礎設施即代碼方法。

結論

Terraform的CDK為希望利用他們現有程式設計技能來定義和管理雲端基礎設施的團隊提供了一種實用的方法。通過提供熟悉的語言界面並啟用創建可重用構建塊的功能，CDKTF可以幫助簡化開發流程並管理大規模部署中的複雜性。然而，評估您的團隊是否準備好採用這種前沿工具，以及它是否與您的項目需求相符，這是至關重要的。