Observed Signal · May 5, 2026 · Book Excerpt · Source: The Pragmatic Engineer · Impact: 2/5 · Sentiment: Neutral
Designing Data‑Intensive Applications: Cloud and Ethics Excerpt
An excerpt from the second edition (2026) of Designing Data‑Intensive Applications by Martin Kleppmann, co‑authored with Chris Riccomini, was published via The Pragmatic Engineer on 2026-05-05. The excerpt includes material from Chapter 1 (trade‑offs in data systems architecture) and Chapter 14 (ethics). It reviews cloud versus self‑hosting tradeoffs, cloud‑native design patterns (object storage, virtual disks, storage/compute disaggregation, multitenancy) and operational shifts (DevOps/SRE). The ethics chapter discusses responsibilities around predictive analytics, algorithmic bias, accountability for automated decisions, surveillance risks, and societal feedback loops. The piece highlights how the second edition updates the book for a cloud‑and‑AI era while retaining core principles for designing reliable data systems.
The second edition updates a widely‑used engineering reference for modern cloud‑native and AI contexts and highlights ethical implications of data systems—useful guidance for engineers and data teams but not an industry‑shifting announcement.
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Key Takeaways & Evidence Grounding
- Martin Kleppmann published the original Designing Data‑Intensive Applications in 2016.
- A second edition (copyright 2026) was released and co‑authored by Chris Riccomini.
- The newsletter excerpt covers Chapter 1 (cloud vs self‑hosting tradeoffs) and Chapter 14 (doing the right thing / ethics).
- The text discusses cloud‑native concepts and specific services such as Amazon S3, Azure Blob Storage, Cloudflare R2, Snowflake, and virtual disk services (Amazon EBS, Azure managed disks).
- The excerpt was published on The Pragmatic Engineer newsletter/webpage on 2026-05-05 and the book is published by O’Reilly Media, Inc.
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Martin Kleppmann on Data‑Intensive Systems (Podcast)
A Pragmatic Engineer podcast episode (Apr 22, 2026) features researcher and author Martin Kleppmann discussing the newly released second edition of Designing Data‑Intensive Applications. The conversation covers lessons from building systems (Rapportive, LinkedIn), how Kafka influenced the book, cloud-driven changes to scaling, the decreasing need for manual sharding vs. continued importance of replication, and why MapReduce coverage was reduced in the new edition. Kleppmann also discusses research directions: LLMs enabling more practical formal verification, challenges of local‑first software and decentralized access control, and using cryptography for supply‑chain transparency. The episode is available on YouTube, Spotify and Apple Podcasts.
Best Practices for Building a Data Analytics Platform
This technical guide outlines practical best practices for designing and building a scalable data analytics platform. It defines four analytics maturity levels (descriptive, diagnostic, predictive, prescriptive) and a five-layer architecture (data ingestion, storage/data warehouse, transformation, business intelligence, security & compliance). The article recommends modern patterns such as ELT, modular/multi-tenant architectures for SaaS, and a technology stack centered on Python and SQL for data work plus Node.js and TypeScript/React for application layers. It highlights essential features—scalable ingestion, governance (RBAC, lineage, audit), high-performance querying, extensibility (APIs/SDKs), and tailored visualization/UX. A Seedium case study (AllClinics) describes using asynchronous Python ingestion, Google BigQuery, Docker/Kubernetes orchestration, and an interactive React front end to consolidate large healthcare datasets (millions of procedures across thousands of hospitals). The piece also recommends testing, cloud deployment (AWS/GCP/Azure) and establishing a central metrics system.
Keep Analytics Data Off the Cloud with Local AI
An opinion/analysis piece by Rıdvan Tülünay (posted May 15, 2026) argues that sending business analytics data to cloud AI services creates real compliance and privacy risks (citing KVKK and GDPR). The article recommends running AI models locally inside company infrastructure so sensitive inputs never leave internal systems. It describes local-AI deployment benefits for reporting, forecasting, ERP and executive workflows, highlights tools that simplify on-prem model hosting (Ollama, LM Studio), and frames the future as hybrid: cloud for non-sensitive workloads and local/self-hosted AI for compliance-critical analytics. The piece also notes operational trade-offs of local AI, including hardware, model selection, and infrastructure management responsibilities.
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