Selected Data Engineering Posts . . . September 2024

The most popular of my posts on data engineering in September 2024 ...

Welcome to the latest edition of "Selected Data Engineering Posts". In this issue, we explore the major trends and advances shaping the data landscape.

Discover how Generative AI has the potential to equalize society by benefiting less skilled workers and bridging the digital divide. Learn the importance of adopting a "Data as a Pure Structure" approach to ensure data flexibility and accessibility.

Explore the challenges and opportunities facing Tech-Forward Boardrooms, as highlighted in Deloitte's 2023 study. Discover how AI Data Catalogs can enhance data management and decision-making.

Understand the evolving structure of Data Architecture Teams in response to modern data ecosystems. Learn about the benefits of AI Database Schema Generators in automating database creation and management.

Finally, explore the implications of the EU AI Act, which addresses high-risk AI systems and establishes regulations for providers and users.

These curated posts provide valuable insights to help you stay informed and navigate the evolving data engineering landscape.

Each post is accompanied by carefully curated references to further reading, allowing you to delve deeper into these informative topics at your own pace.

Subscribe now to stay updated with our monthly issues and unlock the full potential of data engineering. By staying informed about the latest trends and advancements, you can make more informed business decisions and expand your data engineering expertise.

This issue:

Generative AI Opportunities: Generative AI, with its user-friendly interface and potential for broad productivity gains, could act as a societal equalizer, benefiting less skilled workers and narrowing inequality. This technology may help bridge the digital divide, enhance social mobility, and democratize access to well-paying jobs.

Importance of Data Structures: The "Data as a Pure Structure" approach should be prioritized over "Data as an Application" when designing data products. This ensures that data remains flexible, independent, and accessible without reliance on specific applications, preserving its versatility, lineage, and business context.

Tech-Forward Boardroom: Deloitte's 2023 study shows that while 67% of boards now include technology-experienced members, a disconnect persists between boards and technology leaders. Key challenges include ineffective communication, insufficient integration in strategy, and inadequate measurement of technology’s business impact.

AI Data Catalogs: Over 80% of organizational data is unused for analytics. AI data catalogs enhance data management by improving accessibility, governance, and productivity. Decube’s AI Data Catalog automates metadata management, simplifies data discovery, and helps organizations make better decisions by streamlining data organization.

Data Architecture: The structure of data architecture teams depends on the data ecosystem. With the shift from monolithic databases to scalable streaming architectures, distributed teams offer greater flexibility and efficiency. This decentralized model enhances scalability, adaptability, and responsiveness, especially in medium-sized organizations with established Data Mesh frameworks.

AI Database Schema Generator: An AI database schema generator automates the creation and management of database structures, defining data organization, relationships, and integrity. By integrating with large language models (LLMs) and tools like Retrieval-Augmented Generation (RAG), it enhances data accessibility and query efficiency while ensuring security and relevance.

EU AI Act: The AI Act defines AI systems broadly, prohibiting specific practices like real-time biometric identification for law enforcement. It establishes dual categories for high-risk AI systems, outlines strict obligations for providers and users, and mandates impact assessments for specific sectors.

We hope these insights inspire you and support your data-driven journey.

Enjoy reading!

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Generative AI holds the promise of expanding knowledge, skills, and productivity across various sectors.

In this article, Ravi Kumar S points out that Generative AI presents both significant challenges and opportunities as it begins to integrate into mainstream use.

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• ???????????????????? ???? ?????? ??????????????????: Generative AI is expected to impact up to 90% of jobs over the next decade. This widespread disruption may affect roles from entry-level positions to executive roles, requiring workers to adapt to new ways of performing tasks.
• ???????????????????? ?????? ?????????? ??????????????????????: Unlike previous technologies, generative AI might disproportionately benefit less skilled workers by accelerating their learning and productivity. This shift could reduce inequality among workers, but also heighten competition for jobs.
• ???????? ?????? ????????????????????: To mitigate potential negative effects, reskilling programs must become integral to work life. Partnerships between businesses, educational institutions, and policymakers will be essential for creating new training programs and job tracks.
• ???????????????? ?????????? ?????? ????????????????????????: It is crucial to increase public understanding and trust in generative AI. Transparent deployment and clear communication about its benefits and limitations are necessary for its broader acceptance and effective use.

If managed properly, it could bridge socio-economic gaps and democratize access to well-paying jobs, potentially becoming a new societal equalizer.

Go to Article

Further Reading

Why generative AI could be society's new equalizer

The Great Equalizer is Here: How Generative AI Will Reshape Competitiveness

The impact of generative artificial intelligence on socioeconomic inequalities and policy making

Generative AI: The Great Equalizer for Medium-Sized Businesses

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In his article, Bernd Wessely points out that delivering data as a product, not just a table or file, is essential for effective data engineering. ?????? ???????????? ???????????? ?????????????? ???????????????????? ???????? ?????? ???? ?????????????????????? ?????????????? (??????) ???? ???? ?? ???????? ???????? ?????????????????? ???? ??????????????????????. This post discusses why "Data as a Pure Structure" is preferred over "Data as an Application."

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• ???????????????????? ???????? ???????? ????????: Ensures data products can exist without applications.
• ???????????????? ???????? ???? ??????????????????: Preserves all transformations, ensuring complete lineage.
• ???????????????????? ???????????? ???????? ????????????????????????????: Allows flexibility in deriving data models.
• ?????????? ?????????????? ???????? ????????????????????: Simplifies data management and integration.

?????????????????????? ?????????????? ????????????????????:

• ???????? ???? ???? ??????????????????????: Requires an API for access, necessitating a running server and often complicating data management.
• ???????? ???? ?? ???????? ??????????????????: Data exists independently, accessible without an application, promoting versatility and direct access.

???????????????? ???? ???????? ???????? ????????????????????:

• ??????????????????: Data can outlast applications, ensuring accessibility over time.
• ??????????????????????: Easier to transfer and integrate across different environments.
• ???????????????????? ????????????????????: Avoids the complexities of managing application dependencies.

?????????????????????? ???????? ??????????????:

• ??????????: In-memory, column-oriented.
• ??????????????: Disk-optimized, column-oriented.
• ????????: Disk-optimized, row-oriented.
• ????????: Lightweight, text-based.

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Go to Article

Further Reading

Data as a product vs data products. What are the differences?

Data Mesh Principles and Logical Architecture - Data as a Product

Designing Data Products

Towards Universal Data Supply

Shifting mindsets: why you should treat data as a product

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This Deloitte Insights article points out that boards are increasingly appointing members with technology skills as technology increasingly drives business transformation.?Deloitte’s 2023 study shows that 67% of boards now have at least one technology-experienced member, up from 56% in 2020. However, there remains a ?????????????????????? ???????????????????? ?????????????? ???????????? ?????? ???????????????????? ??????????????. Only 36% of board members have full confidence in their technology leaders, and over 40% of C-suite executives find the board’s oversight of technology insufficient.

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• ???????? ???? ?????????????????? ??????????????????????????: Technology leaders often struggle to translate complex technical details into business terms, leading to inadequate understanding and decision-making.
• ???????????????????????? ??????????????????????: Technology discussions are often limited to risk management rather than being integrated into broader strategic discussions.
• ???????????????????? ??????????????????????: There is difficulty in quantifying the impact of technology investments on business outcomes.

??????????????????????????????:

• ???????????????? ?????????????????????????? by avoiding technical jargon and focusing on business needs.
• ?????????????????????? ???????? ???????? to articulate technology's impact in financial terms.
• ?????????????? ?? ???????????????????? ?????????????????? ???????????????????to track technology metrics aligned with business goals.

????????????????????: Effective technology conversations in the boardroom are critical to leveraging technology to drive strategic business outcomes. Bridging the communication gap between technology leaders and boards will improve decision-making and support successful transformations.

Go to Article

Further Reading

Elevating technology on the boardroom agenda

Four lessons for boards in overseeing emerging technology

‘Not a ceremonial position’: The need for tech-savvy boards

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... facilitating more efficient data usage and compliance.

This article by Jatin Solanki points out that over 80% of organizational data remains unused, highlighting a critical need for effective data management. An AI Data Catalog offers a solution by ?????????????????????? ???????????????????? ???????????????????????? ???? ?????????????? ???????? ????????????????????????, ??????????????????????????, ?????? ????????????????????. These tools automate metadata management, making it easier to track and utilize data across departments.

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• ???????? ??????????: Unifying data sources into a single catalog resolves the issue of fragmented data views.
• ???????? ????????????????: Implementing training programs and user-friendly interfaces can encourage effective use of the data catalog.
• ???????? ???????????????????? ??????????: Utilizing catalog features to track data access and usage helps enforce clear data management policies.

AI data catalogs are changing the way data is handled by automating tasks that were previously done manually, using machine learning for better data classification and natural language processing for easier searching. These improvements lead to better data management and more efficient use of data.

Go to Article

Further Reading

AI Data Catalog: Exploring the Possibilities

An Introduction to AI-Powered Data Catalogs

Data Catalog vs. Data Dictionary

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In his article,Arup Nanda points out that data architecture has evolved significantly, transitioning from monolithic databases to modern decoupled pub-sub approaches. Understanding this evolution is essential for structuring data architecture teams effectively.

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• ?????????????????????? ????????????????: Monolithic designs allowed all applications to read and write to central data stores. Database design was crucial due to the heavy interaction between multiple applications and data assets.
• ???????????????????? ???? ???????????????????? ????????????????????: Analytical applications necessitated reading data faster than writing, causing conflicts. Analytical stores were developed, using ETL processes to transform transactional data into analytical formats. This reduced the need for deep subject matter expertise in analytical systems.
• ??????????????-???????????????? ????????????????????????: Introduced to mitigate the complexity of managing central data stores. Data was hidden behind specialized services or microservices, simplifying data architecture and allowing technology changes without widespread impact.
• ?????????????? ????????????: Microservices often led to data redundancy. This was addressed by creating domains where services shared common data tables, reducing redundancy and aligning data architecture with domain knowledge.
• Evolution of ETL: Despite advances, ETL processes remained essential for analytical stores, highlighting the need for data architects to possess domain-specific knowledge.
• ???????? ????????????????: As analytical use cases grew, self-service models and data products were developed. Data products, managed by data stewards, provide comprehensive metadata, reducing reliance on data architects for user guidance.
• ??????????????????: A shift to streaming systems allowed publishers to stream data to consumers, eliminating the need for direct ETL processes. Data architects focus on schema definition and transformation, ensuring accurate data flow.

Further reading: It’s Time for Streaming Architectures for Every Use Case

Data architecture and team structures should align with the chosen data ecosystem. A decoupled streaming architecture offers scalability and practicality for medium-sized organizations with established Data Meshes, while a monolithic ETL-based approach may benefit from a centralized architecture team.

Go to Article

Further Reading

What Is Data Architecture: Best Practices, Strategy, & Diagram

The Ultimate Guide to Data Architecture

What is Data Architecture? Types, Strategies & Principles

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... ?????? ???????? ???????????????? ?????? ???????????????????????? ????-???????????? ????????????????????????

In this article, Iris Zarecki points out that integrating an AI database schema generator can optimize data storage and retrieval, which is crucial for effective Language Learning Models (LLMs).

An AI database schema generator is a tool that uses artificial intelligence to automate the creation and management of database schemas. These schemas define the structure, organization, and relationships of data within a database.

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• ????????????: Collections of related data.
• ??????????????: Attributes or properties of the data.
• ????????: Unique identifiers for records.
• ??????????????: Structures to improve data retrieval speed.
• ??????????????????????: Rules ensuring data integrity.

LLMs rely on structured data and need schema information to generate accurate SQL queries. Without schema awareness, LLMs can struggle with fragmented data from multiple sources, impacting the reliability of their outputs.

??????????????????????????????:

• ???????????????? ???????????? ????????????????????: Use AI tools to efficiently create and manage database schemas.
• ?????????????? ???????? ???????? ??????: Combine LLMs with Retrieval-Augmented Generation (RAG) to improve the quality of responses.
• ?????????????? ???????? ??????????????????????????: Ensure LLMs are aware of all data sources and schemas to avoid inaccuracies in SQL generation.
• ???????????? ???????????????? ??????????: Consider security implications when integrating LLMs with private company data.

Utilizing AI database schema generators alongside LLMs enhances the accuracy and efficiency of data handling and response generation. This integration supports more reliable and personalized AI-driven applications while addressing data management challenges.

Go to Article

Further Reading

Introducing Schema AI: Revolutionizing Database Design with AI-Powered Simplicity

Generate Model Schemas with ClickUp Brain (AI Assistant)

Top 10 AI Tools for Database Design in 2024

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Compliance and Innovation in the new age of AI Regulation

???????????????????? ??????????????????:?The EU AI Act faces major challenges and offers recommendations to address the risks posed by AI systems. The broad definition of AI systems in the Act, with a focus on autonomy and inference, ensures its relevance but leads to difficulties in distinguishing AI from traditional software. The closed list of prohibited AI practices, especially in the context of real-time remote biometric identification, introduces nuanced rules that require thorough assessments and technical measures.

????????-???????? ???? ??????????????????????:?A critical aspect of the Act is its dual definition of high-risk AI systems, which imposes extensive regulatory obligations. These obligations include risk assessments, documentation, human oversight, and cybersecurity measures. However, the Act introduces an important exception where certain AI systems may not be considered high-risk if they do not pose significant risks to health, safety, or fundamental rights. This exception could be contentious, as AI providers might try to avoid the regulatory burdens by leveraging this clause.

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• Thorough documentation is crucial, especially for systems seeking high-risk exceptions.
• Providers must ensure AI systems comply with transparency and accountability standards.
• Public sector bodies must conduct fundamental rights impact assessments before deploying high-risk AI systems.

????????????????????: The AI Act establishes a comprehensive framework to regulate AI systems while balancing innovation and safety. Its broad scope and layered compliance mechanisms make it a landmark regulation with significant implications for AI deployment across various sectors.

Go to Article

Further Reading

EU AI Act Published: Key Takeaways

The EU AI Act Is Here: 10 Key Takeaways for Business and Legal Leaders

European Union: The EU Artificial Intelligence Act - Key takeaways for HR

The EU AI Act: The Key Takeaways

Understanding the EU AI Act: Key Takeaways and How to Comply

The EU AI Act: An Opportunity for better Data and Governance

Udemy: EU AI Act Compliance Introduction

Takeaways

Here are the key takeaways from this month's edition, providing you with essential strategies and insights to excel in data engineering:

Generative AI Opportunities: Harness the potential of generative AI to increase productivity and support less-skilled workers. Focus on transparent practices, ethical deployment and comprehensive retraining programs to ensure that the benefits of AI are accessible and equitable across all social and economic sectors.

Importance of Data Structures: When designing data products, prioritize the "Data as a Pure Structure" approach to maintain data independence, flexibility, and accessibility. This ensures long-term usability, preserves business context, and avoids dependency on specific applications for accessing or managing the data.

Tech-Forward Boardroom: Boards should prioritize improving communication with technology leaders by simplifying tech discussions, integrating technology into strategic decisions, and collaborating on measurable outcomes. This will enhance decision-making and ensure technology investments effectively drive business transformation.

AI Data Catalogs: Organizations should adopt AI data catalogs to improve data management, accessibility, and governance. Automating metadata management and enhancing data discovery allows businesses to make better decisions, increase productivity, and ensure compliance with data regulations. Decube's solution offers these benefits effectively.

Data Architecture: To maximize efficiency in data architecture, adopt a decentralized team structure aligned with a streaming-based architecture. This approach supports scalability, adaptability, and flexibility, making it ideal for medium-sized organizations or those implementing Data Mesh frameworks to handle complex, distributed data systems.

AI Database Schema Generator: To enhance data organization and retrieval, organizations should adopt AI database schema generators. Integrating these tools with LLMs and frameworks like RAG will improve data-driven applications, ensuring accuracy, security, and efficiency in querying structured data.

EU AI Act: Organizations should understand the broad definition of AI systems, comply with high-risk categorization requirements, implement necessary impact assessments, and fulfill stringent obligations to ensure transparency, accountability, and adherence to legal standards in AI deployment.

Conclusion

As we conclude this edition of Selected Data Engineering Posts, it's clear that the data landscape is undergoing rapid transformation. Generative AI offers immense potential for increased productivity and social equity, but its deployment must be ethical and inclusive. Data Structures play a pivotal role in data management, and organizations should prioritize flexibility and independence. Tech-Forward Boardrooms are essential for effective decision-making, requiring improved communication and collaboration between technology leaders and boards. AI Data Catalogs offer significant benefits in enhancing data management and accessibility, while Data Architecture should be tailored to the specific needs of organizations. AI Database Schema Generators can streamline data organization and retrieval, and the EU AI Act sets important standards for responsible AI development and deployment. By understanding and embracing these trends, organizations can position themselves for success in the data-driven era.

Don't miss our next issue, where we'll explore cutting-edge trends and insights shaping the data landscape.

See you next month ...

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