Redefining Data Engineering with AI (for Duc Ka) (First Early Release) (Ashok Singamaneni, Sarath Chandra Bandaru etc.)（Z-Library）

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Redefining Data Engineering with AI Vibe Engineering Your Way to Data Democracy With Early Release ebooks, you get books in their earliest form—the author’s raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles. Ashok Singamaneni, Sarath Chandra Bandaru, Phani Vemuri, and Aditya Chaturvedi

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Redefining Data Engineering with AI by Ashok Singamaneni, Sarath Chandra Bandaru, Phani Vemuri, and Aditya Chaturvedi Copyright © 2027 Ashok Singamaneni, Sarath Chandra Bandaru, Phani Vemuri, and Aditva Chaturvedi. All rights reserved. Published by O’Reilly Media, Inc., 141 Stony Circle, Suite 195, Santa Rosa, CA 95401. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (https://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com. Acquisitions Editor: Aaron Black Development Editor: Corbin Collins Production Editor: Katherine Tozer Interior Designer: David Futato Interior Illustrator: Kate Dullea January 2027: First Edition Revision History for the Early Release 2026-03-03: First Release See https://oreilly.com/catalog/errata.csp?isbn=9798341672840 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Redefining Data Engineering with AI, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc.

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The views expressed in this work are those of the authors and do not represent the publisher’s views. While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights. 979-8-341-67280-2 [LSI]

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Brief Table of Contents (Not Yet Final) Chapter 1: Data Engineering for the Enterprise (available) Chapter 2: Understanding AI Evolution and Tools (available) Chapter 3: Planning with Intent (unavailable) Chapter 4: Designing with Context (unavailable) Chapter 4: Building the Foundation (unavailable) Chapter 4: Building with Insight (unavailable) Chapter 4: Validating with Trust (unavailable) Chapter 4: Deploying with Confidence (unavailable) Chapter 4: Supporting in Real Time (unavailable)

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Chapter 1. Data Engineering for the Enterprise Amid AI Disruption A NOTE FOR EARLY RELEASE READERS With Early Release ebooks, you get books in their earliest form—the author’s raw and unedited content as they write—so you can take advantage of these technologies long before the official release of these titles. This will be the 1st chapter of the final book. Please note that the GitHub repo will be made active later on. If you’d like to be actively involved in reviewing and commenting on this draft, please reach out to the editor at ccollins@oreilly.com. Enterprise organizations of the 21st century (we are a quarter of the way into it now) look quite different today than they did at the turn of the century. In fact, if you compare the list of the market leaders today with the lists from any time of the past, there are some clear and startling patterns: According to a study by Harvard Business Review, over 50% of the Fortune 500 list has churned out each decade from 1955 through the early 1990s, and nearly 80% turned over per decade by the late 1990s and 2000s. (Source) Fortune’s analysis reveals that only 49 companies have appeared on every Fortune 500 list for over 70 years. (Source) Only 10% of the 1955 original cohort remains on the list by 2019. One of them is IBM. (Source)

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There are many reasons for this turnover, spanning from market realities to fundamentals to resilience to change. But there is one more factor that draws a clear line between pre-1995 and post-1995: the rise/evolution of “digital” companies. So what are these digital companies? In our view, there are three broad categories: Digital disruptors Firms that led the digital revolution, brought computers and the internet out of research labs into the mainstream, and helped with the diffusion of technology into everyday lives. Examples: Apple, Microsoft, Hewlett-Packard, Xerox, and IBM. Digital natives Businesses born in the digital era with technology as their foundational core, not an add‑on. Technology shaped their business model, operating model, culture, and customer experience from day one. Examples: Yahoo, Amazon, and Google Digital evolutionaries Companies that originated with pre‑digital business models but have deliberately and continuously reshaped their strategy, operations, culture, and value proposition around digital technologies over time. Examples: Walmart, Disney, and Nike.

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NOTE Disclaimer: We will use the term enterprise several times in this chapter. For the context of the book, an enterprise (company, software, teams) means any reasonably sized organization with multiple departments and teams collaborating and depending upon each other to achieve the company’s stated mission and goals in line with their brand. Examples: Apple, Microsoft, and Starbucks. Code Is the How, Data Is the What Let’s work from the first principles. There are only three core functions for the majority of software code: 1. Create data 2. Consume data 3. Transform data That’s it!! That’s all software code is used for, ultimately. Everything worthwhile is just different state machines derived from these three fundamental functions, arranged in a specific order and repeated over and over again. Whether it meets the need depends upon how you write the logic (code) and how you model information (data). Drawing from the principles of the object-oriented programming system (OOPS), let’s recall what is really represented by code and data. Data represents the state of an object: the facts about a thing in your domain at a given moment. These are typically the fields/properties of a class (e.g., balance in a BankAccount, position in a Queue). Conceptually, this is your domain model: data encodes the “what” of your problem space (attributes, relationships, invariants).

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Code, on the other hand, represents the behavior associated with that state: the valid operations that can be performed on an object. These are the methods/functions (e.g., deposit(), withdraw(), move()). Conceptually, methods encode the how: how state may change over time, under which rules, and in response to which messages/events. Code vs. data is less a separation and more a partition of responsibility within each object: data embodies the current state of truth about the object, whereas code embodies the allowed transitions and interactions on that truth. WARNING Coding without considering how this data will move and connect across the ecosystem is like designing a Formula 1 car without any care for the fuel, the inlet, the exhaust, and anything that connects them together. Good luck racing in that! So, as you can see, it is equally important to think about the shape and nature of data as it is to think about the logic and nature of your software code. You will fail without either. What Is Data Engineering? Now that we have a clearer understanding of the role and value of data in the enterprise ecosystem powered by software, let’s try and understand the concept of data engineering. Data engineering in the enterprise is the organizational functional discipline responsible for building, operating, and governing the infrastructure and processes that transform raw, distributed operational data exhaust into reliable, high-quality data products. Data engineering catalyzes the practice of insights, intelligence, and application to operate safely and efficiently at scale across the organization through an empirical, scientific approach to

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ingestion, transformation, storage design, quality assurance, and observability. TIP Let’s dispel the myth that data engineering is only for analytics and AIML. That is just plain false. Data engineering is very much a core part of the craft and practice of software engineering. You design software — you design code and data together to build systems. No one part can be an afterthought. Strategic Positioning In the modern enterprise, data engineering occupies a critical architectural position: it sits as a bridge between core transactional and operational systems (ERP, CRM, product platforms, third-party SaaS tools, logs, IoT sensors, and other data sources) and the intelligent consumers that depend on trusted data, including business intelligence platforms, predictive models, marketing automation, agentic AI systems, personalization engines, and mission-critical applications. The data engineering bridge is essential for an organization to be able to create a perpetual feedback loop between its operational systems and the value these systems creates. The information being carried is data in one direction and insights and intelligence in the other. Data engineering’s role is foundational in building and keeping this bridge functional and useful. Core Functions and Responsibilities This section looks at data engineering’s core functions and responsibilities. Data ingestion and pipeline engineering Data engineering builds and maintains the automated pipelines that reliably extract data from diverse operational sources, such as the following: Applications

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SaaS (Software-as-a-Service) platforms Event logs IoT (Internet-of-Things) sensors Third-party feeds Data engineering funnels these into enterprise data storage systems, such as data lakes, warehouses, and lakehouses. These pipelines must be designed to be resilient, scalable, and maintainable to keep up with complexities of real-world data that invariably shows up as requirements of the business evolve, for example: Unintentional upstream / downstream schema changes Latency variations for both ingestion and consumption Unplanned source / destination system outages Uncertain arrival of data volumes. Rather than a one-time build, pipeline engineering must be treated as an ongoing practice of monitoring, optimization, and evolution. Data cleaning and transformation Raw operational data inherently reflects the messy interactions and behaviors of software operators who are human. This data is more than likely to be duplicated, incomplete, inconsistent, and difficult to interpret without context. Data engineering applies systematic transformation logic to standardize, enrich, and reshape this data into consistent, interpretable formats. This logic includes deduplication, handling missing or unexpected values, data type conversions, business logic application, and enrichment from multiple sources. The goal is to convert operational “exhaust” into intentional, usable assets that downstream consumers can trust and build upon. Storage architecture and data modeling

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Data engineering designs and implements the storage infrastructure and logical data models that enable efficient, consistent querying and analysis across the organization. This infrastructure encompasses the following: Physical storage architecture decisions These may include data lakes providing raw storage for massively parallel data processing, warehouses for curated analytics data, and lakehouses for hybrid approaches. These determine your physical storage format, compression algorithm, storage destination, and strategies. For example, columnar formats optimized for analytics (Apache Parquet, ORC) vs. row-oriented formats optimized for streaming and transactions (Apache Avro, CSV); Snappy vs ZSTD compression, Delta lake vs Apache Iceberg, and AWS S3 vs Minio object storage systems. Logical data models These include dimensional schemas like star and snowflake models, semantic layers, and entity-relationship designs that reflect business concepts and optimize query performance. Data organization strategies Partitioning, bucketing, and indexing balance query efficiency with storage costs. Storage architecture, data modeling, and organization strategies form the foundation of analytical performance. These strategies determine not just query latency, but whether data scientists can iterate rapidly, whether applications can scale reliably, and whether analysts can discover insights without navigating unnecessary complexity. Poor decisions here compound downstream: for example, a suboptimal compression choice can cascade into slow queries, expensive compute, and ultimately delay business outcomes.

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Quality assurance and observability Data engineering must implement a comprehensive quality and observability framework that ensures that data is trustworthy and traceable throughout its lifecycle. This framework includes the following components: Data quality testing Validation rules, anomaly detection, completeness checks, accuracy assessments Observability Telemetry such as pipeline health, data arrival SLAs, and quality metric deviations, monitoring, and notifications. Lineage and cataloging Metadata tracking, data provenance, and documentation that establishes clear traceability from source to use. Compliance and auditing Access, security, and privacy that demonstrate data governance adherence to regulatory and organizational standards. This function transforms data from a liability - something that cannot be trusted without deep investigation - into an asset with transparent, verifiable quality characteristics. Why this matters Without disciplined data engineering, organizations face several compounding risks: Quality decay

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Data moves through pipelines without validation, leading to corrupted analytics and failed AI systems. Trust erosion Downstream teams cannot confidently use data, duplicating effort through manual verification or avoiding data-driven decisions entirely. Scaling friction Ad-hoc, undocumented data processes break as query volume or data complexity increases. Compliance exposure Untracked data flows and quality issues create audit and regulatory vulnerabilities. Organizational siloing Without shared, trustworthy data products, teams build isolated datasets and duplicate infrastructure Data engineering, when executed right, addresses these risks by establishing data as a managed organizational asset with clear ownership, quality guarantees, and transparent usage patterns. The Product Perspective A key insight embedded in modern data engineering is the product mindset: data engineers should treat the data they produce into various systems and layers - data lakes, warehouses, curated datasets, semantic layers - as products with users, service-level commitments, documentation, versioning, and feedback loops. Let’s consider this framing for outcomes — from “Move data from point A to point B” to “deliver trustworthy data products that power decision

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systems.” This fundamentally shifts engineering priorities: instead of optimizing for pipeline throughput and uptime alone, teams now must optimize for data contracts that don’t break, schemas that evolve gracefully, and quality guarantees that downstream consumers can depend on. The product mindset embeds accountability: when data engineering owns outcomes rather than infrastructure, reliability becomes a feature specification, not an operational afterthought. Enterprise data engineering is not and should not be treated as a supporting function that “keeps the lights on.” Rather, it must be viewed as a strategic capability that determines whether an organization can effectively monetize its data, compete on analytics and AI, and operate with data-driven decision-making. By bridging the inevitable gap between how operational systems generate data and how humans and machines can safely consume it, data engineering enables organizations to make faster decisions on reliable information, train models on validated datasets, and scale analytics without degrading production systems. Table 1-1 summarizes. Complimenting Development Operations (DevOps) Complementing Insights (DataOps) Complementing Intelligence (MLOps) Model Transactional Behavior Operational Observability Message Passing Analytical Models PIpelines Engineering (Streaming / Batch) Data Transformation (Constructive, Destructive, Aesthetic, and Structural) Data Governance Feature Engineering Pre-Training ML

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WARNING If data is not put to work through strategic curation, it will actively work against business interests. Think of it like hoarding data and adding to the bottom line of the business through storage, processing, and computational costs — but no ROI because no active decisions are being made through feedback loops. It’s just … there. What Is Data Governance? Let’s draw from first principles of governance and apply it to data. The earliest known form of governance is civic governance, and it comes with three branches: 1. Legislative: Make the policy. 2. Executive: Enforce the policy. 3. Judicial: Determine when an action is considered policy violation and what must happen as a result. If you apply the same fundamental principles to any other governance problem space — technology, data, AI, financial, political — the branches, which we might call primitives, hold true. The methods and means of executing these primitives in the context of managing data for an enterprise is called data governance. This is not meant to be an overly reductive definition. Most, if not all, governance frameworks are not monolithic, but multi-faceted, and are constrained by the environment they are operating under. This is a good starting point as a mental framework, however. The Compliance Chess: Value of Data Governance to Enterprise Enterprises operating across borders face an increasingly complex governance environment shaped by proliferating data protection and localization requirements in jurisdictions worldwide. Global organizations

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must navigate overlapping and sometimes conflicting obligations, such as the EU’s GDPR, which permits fines of up to €20 million or 4% of global annual revenue, China’s PIPL with stringent domestic data controls, and California’s CCPA. With these ever-evolving geopolitical conditions, governance becomes an operational necessity with measurable returns. Organizations report 30-40% reductions in compliance costs and multimillion-dollar recoveries from prevented data quality incidents — but the strategic value of data governance extends beyond cost avoidance. Let’s evaluate the architectural implications with an example: a pharmaceutical company operating across EU, US, and Chinese markets must satisfy GDPR’s consent requirements, FDA’s audit trail mandates, and China’s data localization rules - simultaneously! Without governance infrastructure — unified consent management, cross-border transfer controls, automated retention policies — each regulation may spawn bespoke solutions that fragment data architecture and multiply engineering overhead. Mature governance converts this regulatory complexity into reusable patterns: consent as an enforceable data contract, compliance as a pipeline stage, and sovereignty as a routing decision. This doesn’t just result in a point-in-time regulatory compliance. It’s an architectural framework that supports market expansion without replatforming, protecting brand reputation and identity through systematic quality controls, and enabling innovation on trustworthy data. Governance, when properly implemented, can transform constraint into competitive infrastructure. Universal Value Creation by Cost Avoidance and Propelling Strategic Growth Organizations without governance experience predictable failure modes. Let us consider the cascading effects for this: revenue KPIs diverge across departments because no authority reconciles conflicting calculation rules. Finance counts bookings, sales counts contracts signed, and executive

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dashboards report whichever number tells the better story. Data pipelines break silently: schema changes propagate from source systems, ETL jobs continue running, and analysts build models on subtly corrupted datasets that passed outdated validation rules. AI models deploy to production without bias testing because no process mandates validation of datasets or model cards. Does that sound familiar at all? Data breaches trace back to access controls that accumulate over time: service accounts granted temporary elevated privileges that were never revoked, analysts who changed roles but retained access to sensitive data, copies of production databases in unsecured development environments. There is a common thread across all of this: 65% of data breaches stem from inadequate governance practices because perimeter security cannot prevent misuse by authorized users. Breach prevention requires governance infrastructure, including systematic access reviews, data classification policies, and audit trails, not just hardened perimeters. Mature governance shifts from cost center to operational accelerator. There is a clear velocity impact: analysts spend 60-80% of their time hunting for data, validating quality, and reverse-engineering undocumented transformations. Governance infrastructure — centralized catalogs with ownership metadata, automated quality metrics, documented lineage — reduces this discovery overhead. Teams access trusted data in hours instead of days, which compounds: faster analytics iteration, faster A/B test cycles, faster response to market signals. The 33% velocity improvement is the difference between shipping a feature next quarter versus next week. Strategic leverage extends itself into AI development as well. Without governance, AI teams face an impossible demonstration problem: regulators ask, “What data trained this model?” and the answer is, “We think it came from these fifteen sources, but we can’t prove consent or verify quality.” Governance makes this auditable: data lineage traces from model back to source systems, consent records document legal basis for each data element, and validation datasets prove bias testing occurred. This transforms AI from liability into defensible competitive infrastructure. Organizations with governance ship models that regulators approve, not because they’re more

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compliant, but because they can prove what compliant organizations can only claim. Organizations with strong governance frameworks demonstrate 33% faster analytics velocity because teams can quickly access trusted, well- understood data without spending days chasing down data provenance or quality issues. This accelerates decision-making and time-to-insight, enabling faster responses to market opportunities. More strategically, governance enables responsible AI development: without governance, organizations cannot demonstrate that AI models are trained on appropriate, consented data with proper lineage, or that models were validated for bias and accuracy. This creates regulatory and liability risk. With governance, organizations can build trustworthy AI that regulators approve and stakeholders trust. For multinational enterprises specifically, governance facilitates data-driven expansion into new markets because the organization already possesses the compliance infrastructure and data standardization to operate in new jurisdictions. For financial institutions, governance enables accurate risk management and capital optimization, allowing for more sophisticated portfolio management and regulatory capital efficiency. For healthcare providers, governance enables care coordination and quality improvement by ensuring that patient data is reliable and accessible. For life sciences, governance accelerates regulatory approval by demonstrating data integrity from the outset. Return on Investment The financial case for data governance investment is well-documented: Direct cost avoidance Organizations lose an average of $12.9 million annually due to poor data quality, which can be substantially eliminated through governance. Bad data costs enterprises 30% of their annual revenue when accounting for wasted employee time,

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delayed automation, and incorrect decisions. (Source 1 and 2) Compliance cost reduction Banks implementing data governance have achieved 30% reductions in compliance-related IT costs. Organizations can avoid massive regulatory penalties. For example, GDPR fines reach 20 million euros or 4% of global turnover, HIPAA penalties reach $100 million+, CCPA penalties reach $2,500-$7,500 per violation. Operational efficiency: Studies of data governance and compliance automation report 20–40% reductions in data errors and roughly 30–40% reductions in compliance-related costs when governance is embedded and automated across the organization. (Source) Revenue enablement Data governance enables new product development, market expansion, and data monetization by providing trusted data foundations that support advanced analytics, AI/machine learning initiatives, and intelligent process automation. Typically, organizations measure data governance ROI through four key metrics: Data quality improvements (fewer duplicate records, fewer manual fixes), time saved (faster report creation, quicker data onboarding) Reduction in compliance issues or audit findings Increase in data usage across teams. These metrics collectively demonstrate that data governance actively drives business value.