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This work is part of a collaboration between O’Reilly and Microsoft.
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978-1-098-13886-8
[LSI]](https://image.slidesharecdn.com/81467-250322184225-d45b5119/75/Data-Management-at-Scale-Piethein-Strengholt-8-2048.jpg)
![This ebook is for the use of anyone anywhere in the United
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Title: The Festival of Spring, from the Díván of Jeláleddín
Author: Maulana Jalal al-Din Rumi
Contributor: W. Hastie
Release date: April 29, 2018 [eBook #57068]
Language: English
Credits: Produced by Turgut Dincer, Chris Pinfield and the Online
Distributed Proofreading Team at http://www.pgdp.net
(This
book was produced from images made available by the
HathiTrust Digital Library.)
*** START OF THE PROJECT GUTENBERG EBOOK THE FESTIVAL OF
SPRING, FROM THE DÍVÁN OF JELÁLEDDÍN ***](https://image.slidesharecdn.com/81467-250322184225-d45b5119/75/Data-Management-at-Scale-Piethein-Strengholt-50-2048.jpg)
![Poems'—his Divan, properly so called—'is regarded by them as of still higher
value; it is practically the Law Book and the Ritual of all these Mystics. These
outbursts of the highest inspiration of its kind deserve to be more closely
considered, as it is from them that we see shining forth as in clear splendour
the essence of the Oriental Mysticism, the cardinal Doctrine that All is One—
the view of the ultimate Unity of all Being—and giving with it Direction and
Guidance to the highest goal of Perfection by the contemplative Way of
Divine Love. On the wings of the highest religious enthusiasm, the Sofi, rising
above all the outward forms of positive Religions, adores the Eternal Being,
in the completest abstraction from all that is sensuous and earthly, as the
purest Source of Eternal Light. Mevláná Jeláleddín thus soars, not only like
other Lyrical Poets, such as Hafiz, over Suns and Moons, but even above
Space and Time, above the world of Creation and Fate, above the Original
Contract of Predestination, and beyond the Last Judgment, into the Infinite,
where in Eternal Adoration he melts into One with the Eternal Being, and
infinitely loving, becomes One with the Infinite Love—ever forgetting himself
and having only the great All in his view.'[1]
Thus far the learned Von Hammer. But let us also hear the
judgment of the East itself, of which this is only a Western echo, as
it may be gathered from Devletshah, the greatest native biographer
—the Dr. Johnson we may appropriately say—of the Persian Poets.
Of Jeláleddín, he says:—
'His pure Heart is filled with Divine Mysteries, and through his eradiating
Soul streams the Infinite Light. His View of the World leads the thirsty in the
Vale of the Contemplative Life to the refreshing Fountain of Knowledge; and
his Guidance leads those who have wandered in the Wilderness of Ignorance
into the Gardens where Truth is really known. He makes plain to the Pilgrim
the Secrets of the Way of Unity, and unveils the Mysteries of the Path of
Eternal Truth:
As when the foaming Sea high swells in Wave upon Wave,
It casts out Pearls upon Pearls on every Shore they lave.'
And to cite only one Turkish Authority—for the Turks claim
Jeláleddín as their own, although a Persian of royal race, born at
Balkh, old Bactra, on the ground of his having sung and died at
Qoniya, in Asia Minor (the Iconium of Paul and Barnabas and
Timothy and St. Thecla), whence he was called Rumi 'the Roman,'
usually rendered 'the Greek,' as wonning within the confines of old
Oriental Rome. This is how Fehîm Efendi, the Turkish Historian of the](https://image.slidesharecdn.com/81467-250322184225-d45b5119/75/Data-Management-at-Scale-Piethein-Strengholt-61-2048.jpg)
![Persian Literature, himself a Poet, begins his Sketch of the Life of the
great poetic Mystagogue:—
'As the ideal of Searchers after Truth here below, as the pattern of the
Pure, the Mevlana is honoured by great and small among the people, by the
aristocrat and the common man. In all circles his words are held in high
honour; among all the wise his knowledge is greatly esteemed; and no pen
has had the power to praise him, and to celebrate his excellence worthily, or
to describe it in fitting terms.
And should the fancy hold it can
His praise completely reach;
Mevlana's praise it ne'er shall scan—
How say it then in speech?'
Rosen, who gives this quotation, and an excellent rhymed German
translation of part of the Mesnevi, refers to that poem as not only
'one of the most celebrated productions of the Persian Mysticism,
but as being regarded by many Mohammedans as almost equal in
holiness to the Koran and the Sunna.' Being attached, at the time he
wrote, to the German Embassy at Constantinople, Rosen also
mentions that not only did the educated Oriental regard the Mesnevi
as the most perfect Book of Edification, which when its contents
were received into his mind and heart, made him certain of
Salvation; but that even the poor Persian retailers of the products of
their home industries, on the streets, could recite with enthusiasm
long passages from the poems of Jeláleddín. We believe that this
holds true to-day, more or less, of the whole Mohammedan world.[2]
But coming to more familiar names, we might gather a whole
cloud of the most approved witnesses in this connection. Thus Sir
William Jones, the first great Anglo-Indian Scholar, the Columbus of
the new Old World of Sanskrit and Persian Literature, enters with
wonderful sympathy and insight into possession of the Persian and
Hindu Mystical Poetry; he refers to their great Maulavi, and his
astonishing work, The Mesnevi; and he translates the celebrated
opening passage in rhyming couplets which would not have been
unworthy of Pope himself.[3] Sir William Jones did not, indeed, touch
Jeláleddín's Lyrics, but he rendered some precious morsels of Hafiz,
'Odes,' as they are called, both in English and French, in a way that](https://image.slidesharecdn.com/81467-250322184225-d45b5119/75/Data-Management-at-Scale-Piethein-Strengholt-62-2048.jpg)
![made young European students and poets, like Herder and Goethe,
turn again to the East with yearning expectant eyes. Similar
testimony might be adduced from Henry Thomas Colebrooke, one of
the very greatest of the successors of Sir W. Jones. The chief
Historian of Persia, and the best informed Persian scholar of his day,
Sir John Malcolm (of Langholm), if less sympathetic than Sir W.
Jones in his painstaking account of the Persian Mystics, gives
likewise the first place to Jeláleddín.[4] And then much more
definitely Sir Gore Ouseley, the first English Biographer of the
Persian Poets, gives Jeláleddín due recognition in connection with
the unrivalled Mesnevi.[5] The Journal of the Asiatic Society, an ever
valuable Magazine of Oriental learning, and the parent of many
others of its kind, has been enriched by the contributions of many
enthusiastic English scholars following in the footsteps of Sir W.
Jones, and it contains the earliest fragments of English translations
of Jeláleddín.[6] Robert Alfred Vaughan, in his Hours with the Mystics,
1856, a popular, sympathetic, and still attractive work, appreciates
Jeláleddín, and compares him with Angelus Silesius and Emerson,
but all his knowledge of the Persian Mystic was derived from Tholuck
and Sir W. Jones. At last competent scholars began to deal worthily
with Jelál's poetry in English. Sir James W. Redhouse has translated
the First Book of the Mesnevi in rhyming couplets, with the utmost
fidelity and care; and another distinguished Persian scholar, Mr.
Whinfield, the most faithful English translator of Omar Khayyám, has
given an abridged version of the whole immense work, which in the
Persian original contains about 70,000 lines.[7] The Mesnevi has thus
come now to be pretty well known by English readers interested in
the subject; and in the last edition of the Encyclopædia Britannica,
Professor Hermann Ethé, an unquestionable authority, in his valuable
Articles on Persian Literature and Jeláleddín Rumi, sums him up as
'the greatest Pantheistic Writer of all ages,' and speaks of 'his
matchless Odes in which he soars on the wings of a genuine
enthusiasm, high over Earth and Heaven, up to the Throne of
Almighty God.' Be it noted, in passing, that it is at least remarkable
how two such different writers as the Turkish Devlet Shah and the
learned German Orientalist should both write of Jeláleddín in terms](https://image.slidesharecdn.com/81467-250322184225-d45b5119/75/Data-Management-at-Scale-Piethein-Strengholt-63-2048.jpg)
![that undesignedly, but irresistibly, recall by their very
superlativeness, the famous lines of Dr. Johnson on Shakespeare:—
'Each change of many-coloured Life he drew,
Exhausted Worlds and then imagin'd new;
Existence saw him spurn her bounded reign,
And panting Time toil'd after him in vain.'
All this makes it now intelligible that the late lamented Editor of
the Encyclopædia Britannica, Dr. W. Robertson Smith, when
Professor of Arabic at Cambridge, with the fine insight of the far-
seeing scholar, should have directed the attention of a young,
enthusiastic student to the 'Lyrical Poetry of Jeláleddín Rumi'; and it
is to the loyal devotion of this young scholar that we owe the first
appearance from an English Press of a Volume of forty-eight
'Selected Poems' of Jeláleddín, in a critical Persian Text and with
accurate and elegant prose renderings.[8] Mr. Reynold A. Nicholson
has thus established a right to pronounce judgment on the merits of
Jeláleddín, and we now listen to him with deference, and no longer
with astonishment, when he deliberately characterises him as 'the
greatest Mystical Poet of any Age.'
As the object of this Introduction is only to determine, in some
measure, the literary interest of the Lyrical Poetry—the Díván, as it is
technically called—of Jeláleddín, space need not be taken up by
narrating again what is traditionally known of his Life, and it is the
less necessary as excellent accounts are now easily accessible. Sir
James W. Redhouse gives in somewhat abridged translation El
Eflākī's interesting narrative, with its romantic wreath of legend, and
its quaint anecdotes and racy sayings. Mr. Nicholson furnishes an
excellent summary. Professor Hermann Ethé's notice in the Encycl.
Brit. has been already referred to, and reference may also be made
to his Morgenländische Studien, and his popular Lecture in the
Virchow-Holtzendorff Series, 1888, on 'The Mystical, Didactic, and
Lyrical Poetry, and the later Literature of the Persians,' with its fine
characterization, which we would fain have quoted. Rosen translates
into German the Biographical Sketches of Devletshah and Jāmi.](https://image.slidesharecdn.com/81467-250322184225-d45b5119/75/Data-Management-at-Scale-Piethein-Strengholt-64-2048.jpg)
![Professor E. G. Browne's recent 'Literary History of Persia,' which
carries the subject down to A.D. 1000, and is undoubtedly so far the
best History of Persian Literature yet produced, contains appreciative
references to Jeláleddín, with a masterly account of the Sufi
Mysticism; and we look forward with much interest to a
comprehensive and judicial summing up of the great Mystic Poet, by
this high authority upon the whole subject.[9]
II.
The interest of the writer in Jeláleddín has been from the first, and
all through, philosophical and theological rather than specially
historical or textual. This interest was awakened in him by Hegel. In
early student days, when to him as to so many then, the Hegelian
Philosophy was the all in all of his thought, he was startled by the
unwonted enthusiasm with which the great thinker at the climax of
his severest exposition, paused to pay a warm tribute to 'the
excellent Jeláleddín,' when he came into view in the light of the
Supreme Idea of his own System.[10] This passage in Hegel, seems
always to have impressed the students of his own writings, and it
has been frequently referred to both by his German and English
expounders. The greatest speculative Thinker of the Nineteenth
Century, seems to have felt a deep satisfaction in recognising the
affinity of the greatest speculative Poet of the East to his own
deepest thought, while at the same time carefully distinguishing the
clearer and higher form of his own conception. Nay more, although
parsimonious to the utmost of his space and words, in this, the most
condensed and compacted Text Book of Philosophy written in any
European language since Aristotle, the stern German Dialectician in
a comparatively long Foot Note, says he 'cannot refrain' from
quoting several passages from the Poet, in order that the reader
may get a clearer knowledge of his ideas; and he quotes them from
Rückert's Versions, to give, at the same time, some specimens of
'the marvellous Art of the translation.' The Reader who is not
acquainted with German will find Hegel's words accurately translated
by the late Dr. W. Wallace, who also gives an English version of the](https://image.slidesharecdn.com/81467-250322184225-d45b5119/75/Data-Management-at-Scale-Piethein-Strengholt-65-2048.jpg)
![[DSC Europe 24] Josip Saban - Buidling cloud data platforms in enterprises](https://cdn.slidesharecdn.com/ss_thumbnails/josipsaban-buidlingclouddataplatformsinenterprises-250217194546-5568421d-thumbnail.jpg?width=640&height=640&fit=bounds)
Data Management at Scale Piethein Strengholt
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- 6. Data Management atScale Modern Data Architecture with Data Mesh and Data Fabric SECOND EDITION Piethein Strengholt
- 7. Data Management atScale by Piethein Strengholt Copyright © 2023 Piethein Strengholt. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com. Acquisitions Editor: Michelle Smith Development Editor: Shira Evans Production Editor: Katherine Tozer Copyeditor: Rachel Head Proofreader: Piper Editorial Consulting, LLC Indexer: nSight, Inc. Interior Designer: David Futato Cover Designer: Karen Montgomery Illustrator: Kate Dullea April 2023: Second Edition
- 8. Revision History forthe Second Edition 2023-04-10: First Release See https://oreilly.com/catalog/errata.csp?isbn=9781098138868 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Data Management at Scale, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. The views expressed in this work are those of the author, and do not represent the publisher’s views. While the publisher and the author have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the author 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. This work is part of a collaboration between O’Reilly and Microsoft. See our statement of editorial independence. 978-1-098-13886-8 [LSI]
- 9. Foreword Whenever we talkabout software, we inevitably end up talking about data—how much there is, where it all lives, what it means, where it came from or needs to go, and what happens when it changes. These questions have stuck with us over the years, while the technology we use to manage our data has changed rapidly. Today’s databases provide instantaneous access to vast online datasets; analytics systems answer complex, probing questions; event-streaming platforms not only connect different applications but also provide storage, query processing, and built-in data management tools. As these technologies have evolved, so have the expectations of our users. A user is often connected to many different backend systems, located in different parts of a company, as they switch from mobile to desktop to call center, change location, or move from one application to another. All the while, they expect a seamless and real-time experience. I think the implications of this are far greater than many may realize. The challenge involves a large estate of software, data, and people that must appear—at least to our users— to be a single joined-up unit. Managing company-wide systems like this has always been a dark art, something I got a feeling for when I helped build the infrastructure that backs LinkedIn. All of LinkedIn’s data is generated continuously, 24 hours a day, by processes that never stop. But when I first arrived at the company, the infrastructure for harnessing that data was often limited to big, slow, batch data dumps at the end of the day and simplistic lookups, jerry-rigged together with homegrown data feeds. The concept of “end-of-the-day batch processing” seemed to me to be some legacy of a bygone era of punch cards and mainframes. Indeed, for a global business, the day doesn’t end. As LinkedIn grew, it too became a sprawling software estate, and it was clear to me that there was no off-the-shelf solution for this kind
- 10. of problem. Furthermore,having built the NoSQL databases that powered LinkedIn’s website, I knew that there was an emerging renaissance of distributed systems techniques, which meant solutions could be built that weren’t possible before. This led to Apache Kafka, which combined scalable messaging, storage, and processing over the profile updates, page visits, payments, and other event streams that sat at the core of LinkedIn. While Kafka streamlined LinkedIn’s dataflows, it also affected the way applications were built. Like many Silicon Valley firms at the turn of the last decade, we had been experimenting with microservices, and it took several iterations to come up with something that was both functional and stable. This problem was as much about data and people as it was about software: a complex, interconnected system that had to evolve as the company grew. Handling a problem this big required a new kind of technology, but it also needed a new skill set to go with it. Of course, there was no manual for navigating this problem back then. We worked it out as we went along, but this book may well have been the missing manual we needed. In it, Piethein provides a comprehensive strategy for managing data not simply in a solitary database or application but across the many databases, applications, microservices, storage layers, and all other types of software that make up today’s technology landscapes. He also takes an opinionated view, with an architecture to match, grounded in a well-thought-out set of principles. These help to bound the decision space with logical guardrails, inside of which a host of practical solutions should fit. I think this approach will be very valuable to architects and engineers as they map their own problem domain to the trade-offs described in this book. Indeed, Piethein takes you on a journey that goes beyond data and applications into the rich fabric of interactions that bind entire companies together.
- 11. Jay Kreps Cofounder andCEO at Confluent
- 12. Preface Data management isan emerging and disruptive subject. Datafication is everywhere. This transformation is happening all around us: in smartphones, TV devices, ereaders, industrial machines, self-driving cars, robots, and so on. It’s changing our lives at an accelerating speed. As the amount of data generated skyrockets, so does its complexity. Disruptive trends like cloudification, API and ecosystem connectivity, microservices, open data, software as a service (SaaS), and new software delivery models have a tremendous effect on data management. In parallel, we see an enormous number of new applications transforming our businesses. All these trends are fragmenting the data landscape. As a result, we are seeing more point-to-point interfaces, endless discussions about data quality and ownership, and plenty of ethical and legal dilemmas regarding privacy, safety, and security. Agility, long-term stability, and clear data governance compete with the need to develop new business cases swiftly. We sorely need a clear vision for the future of data management. This book’s perspective on data management is informed by my personal experience driving the data architecture agenda for a large enterprise as chief data architect. Executing that role showed me clearly the impact a good data strategy can have on a large organization. After leaving that company, I started working as the chief data officer for Microsoft Netherlands. In this exciting new position, I’ve worked with over 50 large customers discussing and attempting to come up with a perfect data solution. Here are some of the common threads I’ve identified across all enterprises:
- 13. An overarching datastrategy is often missing or not connected to the business objectives. Discussions about data management mostly pivot to technology trends and engineering discussions. What is needed is business engagement: a good strategy and well-thought-out data management and analysis plan that includes tangible value in the form of business use cases. To make my point: the focus must be put on usage and turning data into business value. Enterprises have difficulties in interpreting new concepts like the data mesh and data fabric, because pragmatic guidance and experiences from the field are missing. In addition to that, the data mesh fully embraces a decentralized approach, which is a transformational change not only for the data architecture and technology, but even more so for organization and processes. This means the transformation cannot only be led by IT; it’s a business transformation as well. Enterprises find it difficult to comprehend the latest technology trends. They’re unable to interpret nuances or make pragmatic choices. Enterprises struggle to get started: large ambitions often end with limited action; the execution plan and architecture remain too high-level, too conceptual; top-down commitment from leadership is missing. These experiences and my observations across a range of enterprises inspired me to write this second edition of Data Management at Scale. You may wonder why this book is worth reading, over the first edition—let’s take a closer look.
- 14. Why I WroteThis Book and Why Now The first edition was founded on the experience I gained while working at ABN AMRO as chief data architect.1 In that role, my team and I practiced the approach of federation: shifting activities and responsibilities in response to the need for a faster pace of change. We used governance for balancing the imperatives of centralization and decentralization. This shift was supported by a central data team that started to develop platforms for empowering business units to meet their goals. With platforms, we introduced self-service and aligned analysts to domains, supporting them in implementing their use cases. We experimented with domain-driven design and eventually switched to business architecture for managing the architectural landscape as a whole. I used all these experiences as input for writing the first edition. The term data mesh as a description of a sociotechnical approach to using data at large was coined at around the time the manuscript for the first edition was being finalized. When Zhamak Dehghani’s article describing the concept appeared on Martin Fowler’s website, it revealed concrete names for concepts we’d already been using at ABN AMRO for many years. These names became industry terms, and the concept quickly began to resonate with large organizations as a solution to the friction enterprises encounter when scaling up. So, why write a second edition? To start with, it was the data mesh concept. I love the ideas of bringing data management and software architecture closer together and businesses taking ownership of their data, but I firmly believe that, with all the fuss, a more nuanced view is needed. In my previous role as an enterprise architect, we had hundreds of application teams, thousands of services, and many large legacy applications to manage. In such situations, you approach complexity differently. With the data mesh architecture, artist, song, and playlist
- 15. are often usedas data domain examples. This approach of decomposing data into fine-grained domains might work well when designing microservices, but it isn’t well suited to (re)structuring large data landscapes. A different viewpoint is needed for scale. Next, a more nuanced and pragmatic view of data products is needed. There are good reasons why data must be managed holistically and end-to-end. Enterprises have reusability and consistency concerns. They’re forced by regulation to conform to the same dimensions for group reporting, accounting, financial reporting, and auditing and risk management. I know this might sound controversial, but a data product cannot be advocated to be managed as a container: something that packages data, metadata, code, and infrastructure all together in an architecture as tiny as a microservice. This doesn’t reflect how today’s big data platforms work. Finally, the data mesh story isn’t complete: it focuses only on data that is used for analytical purposes, not operational purposes; it omits master data management;2 the consumer side must be complemented with an intelligent data fabric; and it doesn’t provide much data modeling guidance for building data products. Another incentive for publishing a second edition was concerns about the book’s practicality. The first version was perceived by various readers as too abstract. Some critical reviewers even left comments questioning my hands-on experience. In this second edition I’ve worked hard to address these concerns, providing many real-world examples and concrete solution diagrams. From time to time, I also refer to blog posts that I’ve written about how to implement designs. One final note on this: there are a large number of very complex topics to cover, which are also highly context- sensitive. It would be impossible to provide examples of everything in a single volume, so I’ve had to use some discretion. I’m excited to share my thoughts on best practices and observations from the field, and I hope this book inspires you. Reflecting on my time working at ABN AMRO, there are lots of good lessons to be
- 16. taken from otherenterprises. I’ve seen a lot of good approaches. There’s no right or wrong when building good data architecture; it’s all about making the right trade-offs and discovering what works best for your situation. If you’ve already read the first edition, you should find this one significantly different and much improved. Structurally it’s more or less the same, but every chapter has been revised and enhanced. All the diagrams have also been revised, new content has been added, and it’s much more practical. Within each chapter you’ll find many tips, starting points, and references to helpful articles.
- 17. Who Is ThisBook For? This book is intended for large enterprises, though smaller organizations may find much of value in it. It’s geared toward: Executives and architects Chief data officers, chief technology officers, chief architects, enterprise architects, and lead data architects Analytics teams Data scientists, data engineers, data analysts, and heads of analytics Development teams Data engineers, data scientists, business intelligence engineers, data modelers and designers, and other data professionals Compliance and governance teams Chief information security officers, data protection officers, information security analysts, regulatory compliance heads, data stewards, and business analysts How to Read or Use This Book It’s important to say up front that this book touches upon a lot of complex topics that are often interrelated or intertwined with other subjects. So we’ll be hopping between different technologies, business methods, frameworks, and architecture patterns. From time to time I bring in my own operational experience when implementing different architectures, so we’ll be working at different levels of abstraction. To describe the journey through the book, I’ll use the analogy of a helicopter ride.
- 18. We’ll start witha zoomed-out view, looking at data management, data strategy, and data architecture at an abstract and higher level. From this helicopter view, we’ll start to zoom in and first explore what data domains and landing zones are. We’ll then fly to the source system side of our landscape, in which applications are managed and data is created, and circle until we have covered most of the areas of data management. Then we’ll fly over to the consumer side of the landscape and start learning about the dynamics there. After that, we’ll bring everything we’ve covered together by putting things into practice. To help you navigate through the book, the following table gives a high-level overview of which subjects will be intensively discussed in each chapter.
- 19. Table P-1. Keytopics in each chapter Ch. 1 Ch. 2 Ch. 3 Ch. 4 Data management x Data strategy x x x Data architecture x x Data integration x Data modeling x Data governance Data security Data quality x Metadata management MDM Business intelligence
- 20. Ch. 1 Ch.2 Ch. 3 Ch. 4 Advanced analytics Enterprise architecture Chapter 1 introduces the topic of data management. It gives a contextual view of what data management is, how it’s changing, and how it affects our digital transformation. It provides an assessment of the state of the field in recent years and guidance for working out a data strategy. In Chapter 2, we’ll jump into the details of managing data at large, exploring domain-driven design and business architecture as methodologies for managing a large data landscape using data domains. Next, Chapter 3 focuses on topologies and data landing zones as a way of structuring your data architecture and aligning with your data domains. The following chapters discuss the specifics of distributing data. Chapter 4 focuses on data products, Command Query Responsibility Segregation (CQRS), and guiding principles, and presents an example solution design. Chapter 5 discusses API management, and Chapter 6 covers event and notification management. Chapter 7 brings it all together for a comprehensive overview, complemented with architecture guidance and experience. Next, we delve deeper into more advanced aspects of data management. Chapter 8 examines how to approach data governance and security in ways that are practical and sustainable for the long term, even in rapidly changing times. Chapter 9 is a deep dive into the use, significance, and democratizing potential of metadata. Chapter 10 offers guidance on using master data
- 21. management (MDM) tokeep data consistent over distributed, wide- ranging assets, while Chapter 11 addresses turning data into value. Chapter 12 concludes the book with an example of making it real and a vision for the future of data management and enterprise architecture. Conventions Used in This Book The following typographical conventions are used in this book: Italic Indicates new terms, URLs, email addresses, filenames, and file extensions. Constant width Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords. TIP This element signifies a tip or suggestion. NOTE This element signifies a general note. WARNING This element indicates a warning or caution.
- 22. O’Reilly Online Learning NOTE Formore than 40 years, O’Reilly Media has provided technology and business training, knowledge, and insight to help companies succeed. Our unique network of experts and innovators share their knowledge and expertise through books, articles, and our online learning platform. O’Reilly’s online learning platform gives you on-demand access to live training courses, in-depth learning paths, interactive coding environments, and a vast collection of text and video from O’Reilly and 200+ other publishers. For more information, visit http://oreilly.com. How to Contact Us Please address comments and questions concerning this book to the publisher: O’Reilly Media, Inc. 1005 Gravenstein Highway North Sebastopol, CA 95472 800-998-9938 (in the United States or Canada) 707-829-0515 (international or local) 707-829-0104 (fax) We have a web page for this book, where we list errata, examples, and any additional information. You can access this page at
- 23. https://oreil.ly/data-mgmt-at-scale-2e. Email bookquestions@oreilly.com tocomment or ask technical questions about this book. For more information about our books, courses, conferences, and news, see our website at http://www.oreilly.com. Find us on Facebook: http://facebook.com/oreilly. Follow us on Twitter: http://twitter.com/oreillymedia. Watch us on YouTube: http://youtube.com/oreillymedia. Acknowledgments I would like to acknowledge Jessica Strengholt-Geitenbeek for allowing me to write this book. She has supported me throughout this journey, taking care of the kids and creating room to allow me to work on this, and she’s the love of my life. I also would like to thank ABN AMRO, and especially Santhosh Pillai for his trust and for guiding me throughout my career at the company. Many of the initial ideas for this project originated in his mind. Without the countless discussions he and I had, this book wouldn’t exist. Next, I would like to thank Microsoft for providing the support I needed to write this second edition. In addition, many others provided support and feedback on the book: thanks to Tim Ward (CEO at CluedIn), Batuhan Tuter, Nasim Merhshid, Rob Worrall, Frank Leisten, and all the others who contributed in various ways. Thanks also to the book’s technical reviewers, John Mallinder and Ole Olesen-Bagneux. Your valuable insights and feedback helped validate the technical content and make this a better book. Finally, I would like to thank all the fantastic crew members from O’Reilly for their support and trust. Shira, thank you for taking care of me. I enjoyed our conversations, and I’m grateful for your constructive feedback. Katie, thank you for your continuous support
- 24. and transparency. Tomy fantastic copyeditor Rachel Head, thank you for your hard work to review and edit all content. You really have done an outstanding job by debugging the content and connecting my sentences. 1 The statements and opinions expressed in this book don’t necessarily reflect the positions of ABN AMRO or Microsoft. 2 The terminology “master/slave” is clearly offensive, and many organizations have switched to alternatives like “source/replica” or “primary/subordinate.” We strive to be as inclusive as possible, but will use “master data management” in this book because the industry hasn’t yet adopted an alternative.
- 25. Chapter 1. TheJourney to Becoming Data-Driven The pre-COVID-19 world was already fast and highly data-driven, but the pace of change has accelerated rapidly. Fierce competition, a digital-first era, ever-increasing customer expectations, and rising regulatory scrutiny require organizations to transform themselves into modern data-driven enterprises. This transformation will inevitably result in future organizations being more digital than those of today, and having a different view of data. Tomorrow’s organizations will breathe data and embrace a philosophy that places it at the heart of their business. They will manage data as a product, make strategic decisions based on data analysis, and have a culture that acts on data. Data-driven isn’t just a buzzword.1 Being data-driven provides an organization with a significant competitive advantage over other organizations. It can be proactive, and it can predict what will happen before it does. By using data correctly, organizations can quickly react to changes. Using data leads to greater confidence because decisions are based on facts, not intuition. With data, new industry trends and business opportunities can be spotted sooner. Customer retention and satisfaction are improved as well, because data tells organizations what customers think, how they behave, and what they want. With data, organizations can be more flexible, agile, and cost effective, because data provides insights into measured results, employee loyalty, dependencies, applications, and processes. So, the imperative for organizations to transform themselves into data-driven enterprises is definitively there. Before we jump into the transformation itself, we’ll explore the present-day challenges that require us to reevaluate how data must
- 26. be managed. We’llestablish a common definition of data management, encompassing all the disciplines and activities related to managing data as an asset. After that, we’ll zoom in on several key technology developments and industry trends, and consider their impact on data management. We’ll look at some best practices from the last decade of data management, providing insights into why previous-generation architectures are hard to scale. Finally, we’ll consider what a next-generation data architecture might look like, and I’ll present a set of action points that you will need to address while developing your data strategy. Recent Technology Developments and Industry Trends Transforming an organization to become data-driven isn’t easy. It’s a long-term process that requires patience and fortitude. With more data available, traditional architectures can no longer be scaled up because of their size, complexity, monolithic designs, and centralistic operating models. Enterprises need a new data strategy and cloud- based architecture. A paradigm shift and change of culture are needed, too, because the centralized data and IT operating models that work today will no longer work when applying federated data ownership and self-serve consumption models. This requires organizations to redefine how people, processes, and technology are aligned with data. Recent technology developments and industry trends force us to reevaluate how data must be managed. We need to shift away from funneling all data into a single silo toward an approach that enables domains, teams, and users to distribute, consume, and use data themselves easily and securely. Platforms, processes, and patterns should simplify the work for others. We need interfaces that are simple, well documented, fast, and easy to consume. We need an architecture that works at scale.
- 27. Although there aremany positives about evolving into a truly data- driven organization, it’s important to be aware of several technology developments and industry trends that are impacting data landscapes. In this chapter, I’ll discuss each of these and its influence on data management. Firstly, analytics is fragmenting the data landscape because of use case diversity. Secondly, new software development methodologies are making data harder to manage. Thirdly, cloud computing and faster networks are fragmenting data landscapes. In addition, there are privacy, security, and regulatory concerns to be aware of, and the rapid growth of data and intensive data consumption are making operational systems suffer. Lastly, data monetization requires an ecosystem-to- ecosystem architecture. The impact these trends have on data management is tremendous, and they are forcing the whole industry to rethink how data management must be conducted in the future. Fortunately, new approaches to data management have emerged over the past few years, including the ideas of a data mesh and data fabric: The data mesh is an exciting new methodology for managing data at large. The concept foresees an architecture in which data is highly distributed and a future in which scalability is achieved by federating responsibilities. It puts an emphasis on the human factor and addressing the challenges of managing the increasing complexity of data architectures. The data fabric is an approach that addresses today’s data management and scalability challenges by adding intelligence and simplifying data access using self-service. In contrast to the data mesh, it focuses more on the technology layer. It’s an architectural vision using unified metadata with an end-to-end integrated layer (fabric) for easily accessing, integrating, provisioning, and using data.
- 28. These emerging approachesto data management are complementary and often overlap. Despite popular belief among data practitioners and what commercial vendors say, they shouldn’t be seen as rigid or standalone techniques. In fact, I expect these approaches to coexist and complement one another and any existing investments in operational data stores, data warehouses, and data lakes. In the transition to becoming data-driven, organizations need to make trade-offs to balance the imperatives of centralization and decentralization. Some prefer a high degree of autonomy for their business teams, while others prioritize quality and control. Some organizations have a relatively simple structure, while others are brutally large and complex. Creating the perfect governance structure and data architecture isn’t easy, so while developing your strategy, I encourage you to view these approaches to data management as frameworks. There’s no right or wrong. With the data mesh approach, for example, you might like some of the best practices and principles, but not others; you don’t necessarily have to apply all of them. In this book, I’ll share my view on data management—one that is based on observations from the field while working closely with many large enterprises, and that helps you to make the right decisions by learning from others. We’ll go beyond the concepts of data mesh and data fabric, because I strongly believe that a data strategy should be inclusive of both the operational and analytical planes, and that the decomposition method for both data domains and data products must be altered to fit the scale of large enterprises. To help you in your journey, I’ll share my observations on the strategies and architectures different enterprises have designed, why, and what trade-offs they have made. Before we jump into details, we need to agree on what data management is, and why it’s important. Next, we need to determine how to define boundaries and shape our landscape based on various
- 29. trade-offs. Finally, we’llexamine how current enterprise data architectures can be designed and organized for today and tomorrow. Let me lay my cards out on the table: decentralization is not a desired state, but the inevitable future of data. I therefore have a strong belief that scalability is forcing data management to become more decentrally organized. Managing data at scale requires you to federate key responsibilities, set strong standards, and properly align central and local resources and activities. This change affects multiple areas: people, processes, and technology. It forces you to decompose your architecture, dividing and grouping responsibilities. The shift from centralization to decentralization also contradicts the established best practice from the past decade: building large data silos in which all data is collected and integrated before being served. Although data warehouse and lake architectures are excellent approaches for utilizing data, these centralized models are not suited to a decentralized distributed data architecture. Now that we’ve set the scene, I ask that you take a deep breath and put your biases aside. Many of us might be deeply invested in centralized data architectures; this has been a best practice for many years. I acknowledge that the need for data harmonization and for bringing large amounts of data into a particular context remains, and that doing so brings value to organizations, but something we must consider is the scale at which we want to apply this discipline. In a highly distributed ecosystem with hundreds or even thousands of applications, is the best way of managing data to apply centralization on all dimensions? Is it best to integrate and harmonize all data? Data Management The term data management refers to the set of processes and procedures used to manage data. The Data Management Association’s Data Management Body of Knowledge (DAMA-DMBOK)
- 30. has a moreextensive explanation of data management, which it defines as “the development, execution, and supervision of plans, policies, programs, and practices that deliver, control, protect, and enhance the value of data and information assets throughout their life cycles.”2 The DAMA-DMBOK identifies 11 functional areas of data management, with data governance at the heart, as shown in Figure 1-1. It’s crucial to embed all of these deeply into your organization. Otherwise, you’ll lack insight and become ineffective, and your data will get out of control. Becoming data-driven—getting as much value as possible out of your data—will become a challenge. Analytics, for example, is worth nothing if you have low- quality data.
- 31. Figure 1-1. The11 functional areas of data management The activities and disciplines of data management are wide ranging and cover multiple areas, some closely related to software architecture.3 In this book, I’ll focus on the aspects of data management that are most relevant for managing a modern data architecture at scale. Let’s take a closer look at the 11 areas identified in this figure and where they’re covered in the book:
- 32. Data governance, shownat the heart of Figure 1-1, involves all activities around implementing and enforcing authority and control over the management of data, including all corresponding assets. This area is described in detail in Chapter 8. Data architecture involves the definition of the master plan for your data, including the blueprints,4 reference architectures, future state vision, and dependencies. Managing these helps organizations make decisions. The entire book revolves around data architecture generally, but the discipline and its activities will be covered fully in Chapters 2 and 3. Data modeling and design is about structuring and representing data within a specific context and specific systems. Discovering, designing, and analyzing data requirements are all part of this discipline. We’ll discuss these topics in Chapters 4, 7, and 11. Data storage and operations refers to the management of the database design, correct implementation, and support in order to maximize the value of the data. Database management also includes database operations management. We’ll address this in Chapter 11. Data security includes all disciplines and activities that provide secure authentication, authorization, and access to the data. These activities include prevention, auditing, and escalation- mitigating actions. This area is described in more detail in Chapter 8. Data integration and interoperability includes all the disciplines and activities related to moving, collecting, consolidating, combining, and transforming data in order to move it efficiently from one context into another. Data interoperability refers to the capability to communicate, invoke functions, or transfer data among various applications in a way that requires little or no
- 33. knowledge of theapplication characteristics. Data integration, on the other hand, is about consolidating data from different (multiple) sources into a unified view. This process, which I consider most important, is often supported by extra tools, such as replication and ETL (extract, transform, and load) tools. It’s described extensively in Chapters 4, 5, and 6. Document and content management is the process of managing data stored in unstructured (media) and data formats. Some aspects of this will be discussed in Chapters 5 and 6. Reference and master data management is about managing critical data to make sure the data is accessible, accurate, secure, transparent, and trustworthy. This area is described in more detail in Chapter 10.5 Data warehousing and business intelligence management includes all the activities that provide business insights and support decision making. This area, including advanced analytics, is described in more depth in Chapter 11. Metadata management involves managing all data that classifies and describes the data. Metadata can be used to make the data understandable, ready for integration, and secure. It can also be used to ensure the quality of data. This area is described in more detail in Chapter 9. Data quality management includes all activities related to managing the quality of data to ensure the data can be used. Some aspects of this area are described in Chapters 2 and 3. The part of the DAMA-DMBOK that needs more work, which inspired me to write the first edition of this book, is the section on data integration and interoperability. I believe this section is lacking depth: the relationship to application integration and software architecture is not clear. It doesn’t discuss decentralized architectures, and it lacks modern guidance on the interoperability of
- 34. data, such asobservability best practices and modern data pipeline management. In addition, the link to metadata management is weak. Metadata needs integration and interoperability, too, because it is scattered across many tools, applications, platforms, and environments in diverse shapes and forms. The interoperability of metadata—the ability of two or more systems or components to exchange descriptive data about data—gets insufficient treatment: building and managing a large-scale architecture is very much about metadata integration. Interoperability and metadata also aren’t well connected to the area of data architecture. If metadata is utilized in the right way, you can see what data passes by, how it can be integrated, distributed, and secured, and how it connects to applications, business capabilities, and so on. There’s limited guidance in the DAMA-DMBOK about managing your data as a whole by utilizing and connecting metadata. Another concern I have is the view DAMA and many organizations have on achieving end-to-end semantic consistency. As of today, attempts to unify semantics to provide enterprise-wide consistency are still taking place. This is called a single version of the truth. However, applications are always unique, and so is data. Designing applications involves a lot of implicit thinking. The (domain) context of the business problem influences the design of the application and finds its way into the data. We pass through this context when we move from conceptual design into logical application design and physical application design.6 It’s essential to understand this because it frames any future architecture. When data is moved across applications, a data transformation step is always necessary. There’s no escape from this data transformation dilemma! In the following chapters, I’ll return to this idea. Another view I see in many organizations is that data management should be central and must be connected to the strategic goals of the enterprise. Some organizations still believe that operational costs can be reduced by centralizing all data and management activities.
- 35. There’s also adeep assumption that a centralized platform can take away the pain of data integration for its users and consumers. Companies have invested heavily in their enterprise data platforms, which include data warehouses, data lakes, and service buses. The activities of master data management are strongly connected to these platforms because consolidating allows us to simultaneously improve the accuracy of our most critical data. A centralized platform—and the centralized model that comes with it —will be subject to failure because it won’t be able to keep up with the developments and trends that underpin decentralization, such as analytics, cloud computing, new software development methodologies, real-time decision making, and data monetization. While they may be aware of these trends, many companies fail to comprehend the impact they have on data management. Let’s examine the most important trends and determine the magnitude of that impact. Analytics Is Fragmenting the Data Landscape The most trailblazing trend is advanced analytics, which exploits data to make companies more responsive, competitive, and innovative. Why does advanced analytics disrupt the existing data landscape? With more data available, the number of options and opportunities skyrockets. Advanced analytics is about making what-if analyses, projecting future trends and outcomes or events, detecting hidden relations and behaviors, and automating decision making. Because of the recognized value and strategic benefits of advanced analytics, many methodologies, frameworks, and tools have been developed to use it in divergent ways. We’ve only scratched the surface of what artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) will be capable of in the future.
- 36. NOTE OpenAI’s new ChatGPTis a mind-blowing example of what AI is capable of. The models behind OpenAI, which include the Generative Pre-trained Transformer (GPT) series, can work on complex tasks such as analyzing math problems, writing code snippets, producing book essays, creating recipes using a list of ingredients, and much more. These analytical trends force data to be distributed across many analytical applications because every individual use case requires different data. Unique business problems require unique thinking, unique data, and optimized technology to provide the best solution. Take, for example, a marketing business unit whose goal is to identify new sales opportunities for older and younger customers. Targeting these two audiences requires different features— measurable properties for analyzing—in datasets. For example, prospects that are younger will be segmented and clustered differently than prospects that are older. Asking the marketing department to use a single dataset for both target audiences requires many compromises, and you’ll probably end up with a feature store that doesn’t add any value to each use case.7 The optimal solution for generating the most value is to give either use case its own unique set of features optimized for each learning algorithm. The increasing popularity of advanced analytics and resulting use case diversity issues lead to two problems: data proliferation and data intensiveness. With data proliferation, data is distributed and scattered across a myriad of locations, applications, and databases. This is because consuming domains need to process the data to fit it into their unique solutions. This data distribution introduces other problems. For one, when data is repeatedly copied and scattered throughout the organization, it becomes more difficult to find its origin and judge its quality. This requires you to develop a single logical view of
- 37. the same datathat is managed in different locations. Additionally, extensive data distribution makes controlling the data much more difficult because data can be spread even further as soon as it leaves any given application. This requires you to develop a framework for efficiently reusing data, while applying governance and staying compliant with external regulations. The proliferation of analytical techniques is also accelerating the growth of data intensiveness: the read-versus-write ratio is changing significantly. Analytical models that are constantly retrained, for example, constantly read large volumes of data. This impacts application and database designs because we need to optimize for data readability. It might also mean that we need to duplicate data to relieve systems from the pressure of constantly serving it, or to preprocess the data because of the large number of diverse use case variations and their associated read patterns. Additionally, we might need to provide different representations of the same data for many different consumers. Facilitating a high variety of read patterns while duplicating data and staying in control isn’t easy. A solution for this problem will be provided in Chapter 4. The Speed of Software Delivery Is Changing In today’s world, software-based services are at the core of most businesses, which means that new features and functionality must be delivered quickly. In response to the demands for greater agility, new ideologies have emerged at companies like Amazon, Netflix, Meta, Google, and Uber. These companies have advanced their software development practices based on two beliefs. The first belief is that software development (Dev) and information technology operations (Ops) must be combined to shorten the systems development life cycle and provide continuous delivery with high software quality. This methodology, called DevOps, requires a
- 38. new culture thatembraces more autonomy, open communication, trust, transparency, and cross-discipline teamwork. The second belief is about the size at which applications must be developed. Flexibility and speed of development are expected to increase when applications are transformed into smaller decomposed services. This development approach incorporates several buzzwords: microservices, containers, Kubernetes, domain-driven design, serverless computing, etc. I won’t go into detail on all of these concepts yet, but it’s important to recognize that this evolution in software development involves increased complexity and a greater demand to better control data. The transformation of monolithic applications into distributed applications—for example, microservices—creates many difficulties for data management. When breaking up applications into smaller pieces, the data is spread across different smaller components. Development teams must also transition their (single) unique data stores, where they fully understand the data model and have all the data objects together, to a design where data objects are spread all over the place. This introduces several challenges, including increased network communication, data read replicas that need to be synchronized, difficulties when combining many datasets, data consistency problems, referential integrity issues, and so on. The recent shift in software development trends requires an architecture that allows more fine-grained applications to distribute their data. It also requires a new DataOps culture and a different design philosophy with more emphasis on data interoperability, the capture of immutable events, and reproducible and loose coupling. We’ll discuss this in more detail in Chapter 2. The Cloud’s Impact on Data Management Is
- 39. Immeasurable Networks are becomingfaster, and bandwidth increases year after year. Large cloud vendors have proven that it’s possible to move terabytes of data in the cloud in minutes, which allows for an interesting approach: instead of bringing the computational power to the data—which has been the common best practice because of network limitations—we can turn it around and bring the data to the computational power by distributing it. The network is no longer the bottleneck, so we can move data quickly between environments to allow applications to consume and use it. This model becomes especially interesting as software as a service (SaaS) and machine learning as a service (MLaaS) markets become more popular. Instead of doing all the complex stuff in-house, we can use networks to provide large quantities of data to other parties. This distribution pattern of copying (duplicating) data and bringing it to the computational power in a different facility, such as a cloud data center, will fragment the data landscape even more, making a clear data management strategy more important than ever. It requires you to provide guidelines, because fragmentation of data can negatively impact performance due to data access lag. It also requires you to organize and model your data differently, because cloud service providers architected separate compute and storage to make them independently scalable. Privacy and Security Concerns Are a Top Priority Enterprises need to rethink data security in response to the proliferation of data sources and growing volume of data. Data is inarguably key for organizations looking to optimize, innovate, or differentiate themselves, but there is also a darker side with unfriendly undertones that include data thefts, discrimination, and political harm undermining democratic values. Let’s take a look at a
- 40. few examples toget an idea of the impact of bad data privacy and security. UpGuard maintains a long list of the biggest data breaches to date, many of which have capitalized on the same mistakes. The Cambridge Analytica breach and 500 million hacked accounts at Marriott are impressive examples of damaging events. Governments are increasingly getting involved in security and privacy because all aspects of our personal and professional lives are now connected to the internet. The COVID-19 pandemic, which forced so many of us to work and socialize from home, accelerated this trend. Enterprises cannot afford to ignore the threats of intellectual property infringements and data privacy scandals. The trends of massive data, more powerful advanced analytics, and faster distribution of data have triggered a debate around the dangers of data, raising ethical questions and discussions. Let me share an example from my own country. In the Netherlands, the Dutch Tax Administration practiced unlawful and discriminatory activities. They tracked dual nationals in their systems and used racial and ethnic classifications to train models for entitlements to childcare benefits. The result: thousands of families were incorrectly classified as criminals and had their benefits stopped. They were ordered to repay what they already had received, sometimes because of technical transgressions such as failing to correctly sign a form. Some people were forced to sell their homes and possessions after they were denied access to debt restructuring. This is just one example of improper use of data. As organizations inevitably make mistakes and cross ethical lines, I expect governments to sharpen regulation by demanding more security, control, and insight. We’ve only scratched the surface of true data privacy and ethical problems. Regulations, such as the new European laws on data governance and artificial intelligence will force large companies to be transparent about what data is collected and purchased, what data is combined, how data is used within
- 41. analytical models, andwhat data is distributed (sold). Big companies need to start thinking about transparency and privacy-first approaches and how to deal with large regulatory issues now, if they haven’t already. Regulation is a complex subject. Imagine a situation in which several cloud regions and different SaaS services are used and data is scattered. Satisfying regulations, such as the GDPR, CCPA, BCBS 239, and the new Trans-Atlantic Data Privacy Framework is difficult because companies are required to have insight and control over all personal data, regardless of where it is stored. Data governance and correctly handling personal data is at the top of the agenda for many large companies.8 These stronger regulatory requirements and data ethics concerns will result in further restrictions, additional processes, and enhanced control. Insights about where data originated, how models are trained, and how data is distributed are crucial. Stronger internal governance is required, but this trend of increased control runs contrary to the methodologies for fast software development, which involve less documentation and fewer internal controls. It requires a different, more defensive viewpoint on how data management is handled, with more integrated processes and better tools. Several of these concerns will be addressed in Chapter 8. Operational and Analytical Systems Need to Be Integrated The need to react faster to business events introduces new challenges. Traditionally, there has been a great divide between transactional (operational) applications and analytical applications because transactional systems are generally not sufficient for delivering large amounts of data or constantly pushing out data. The accepted best practice has been to split the data strategy into two
- 42. parts: operational transactionalprocessing and analytical data warehousing and big data processing. However, this divide is subject to disruption. Operational analytics, which focuses on predicting and improving the existing operational processes, is expected to work closely with both the transactional and analytical systems. The analytical results need to be integrated back into the operational system’s core so that insights become relevant in the operational context. I could make the same argument for real-time events: when events carry state, the same events can be used for operational decision making and data distribution. This trend requires a different integration architecture, one that better manages both the operational and analytical systems at the same time. It also requires data integration to work at different velocities, as these tend to be different for operational systems and analytical systems. In this book, we’ll explore the options for preserving historical data in the original operational context while simultaneously making it available to both operational and analytical systems. Organizations Operate in Collaborative Ecosystems Many people think that all business activities take place within the single logical boundary in which the enterprise operates. The reality is different, because many organizations work closely with other organizations. Companies are increasingly integrating their core business capabilities with third-party services. This collaboration aspect influences the design of your architecture because you need to be able to quickly distribute data, incorporate open data,9 make APIs publicly available, and so on. These changes mean that data is more often distributed between environments, and thus is more decentralized. When data is shared
- 43. with other companies,or using cloud or SaaS solutions, it ends up in different places, which makes integration and data management more difficult. In addition, network bandwidth, connectivity, and latency issues inevitably arise when moving data between platforms or environments. Pursuing a single public cloud or single platform strategy won’t solve these challenges, because decentralization is the future. This means that if you want APIs and SaaS systems to work well and use the capabilities of the public cloud, you must be skilled at data integration, which this book will teach you how to do. The trends discussed here are major and will affect the way people use data and the way companies should organize their architectures. Data growth is accelerating, computing power is increasing, and analytical techniques are advancing. Data consumption is increasing, too, which implies that data needs to be distributed quickly. Stronger data governance is required. Data management also must be decentralized due to trends like cloud services, SaaS, and microservices. All of these factors have to be balanced with a short time to market, thanks to strong competition. This risky combination challenges us to manage data in a completely different way. Enterprises Are Saddled with Outdated Data Architectures One of the biggest problems many enterprises are dealing with is getting value out of their current data architectures.10 The majority of existing data architectures use a monolithic design—either an enterprise data warehouse (EDW)11 or a data lake—and manage and distribute data centrally. In a highly distributed and consumer-driven environment, these architectures won’t fulfill future needs. Let’s look at some of the characteristics of each.
- 44. The Enterprise DataWarehouse: A Single Source of Truth Most enterprises’ first-generation data architectures are based on data warehousing and business intelligence. The philosophy is that centralization is the silver bullet to solve the problem of data management. With this approach there’s one central integrated data repository, containing years’ worth of detailed and stable data, for the entire organization. But when it comes to distributing data at large, such a monolithic design has several drawbacks. Enterprise data unification is an incredibly complex undertaking and takes many years to complete. Chances are relatively high that the meaning of data differs across domains,12 departments, and systems, even if data attributes have similar names. So, we either end up creating many variations or just accepting the differences and inconsistencies. The more data we add, and the more conflicts and inconsistencies that arise, the more difficult it will be to harmonize the data. We will likely end up with a unified context that is meaningless for the end users. In addition, for advanced analytics such as machine learning, leaving context out can be a significant problem because it’s impossible to correctly predict the future if the data is meaningless or unrecognizable. Enterprise data warehouses behave like integration databases, as illustrated in Figure 1-2. They act as data stores for multiple data- consuming applications. This means that they’re a point of coupling between all the applications that want to access the data. It’s often assumed that centralization is the solution to data management— this includes centralizing all data and management activities using one central team, building one data platform, using one ETL framework and one canonical model, etc. However, with a centralized architecture, changes need to be carried out carefully because of the many cross-dependencies between different applications. Some changes can also trigger a ripple effect of other
- 45. Another Random ScribdDocument with Unrelated Content
- 49. The Project GutenbergeBook of The Festival of Spring, from the Díván of Jeláleddín
- 50. This ebook isfor the use of anyone anywhere in the United States and most other parts of the world at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this ebook or online at www.gutenberg.org. If you are not located in the United States, you will have to check the laws of the country where you are located before using this eBook. Title: The Festival of Spring, from the Díván of Jeláleddín Author: Maulana Jalal al-Din Rumi Contributor: W. Hastie Release date: April 29, 2018 [eBook #57068] Language: English Credits: Produced by Turgut Dincer, Chris Pinfield and the Online Distributed Proofreading Team at http://www.pgdp.net (This book was produced from images made available by the HathiTrust Digital Library.) *** START OF THE PROJECT GUTENBERG EBOOK THE FESTIVAL OF SPRING, FROM THE DÍVÁN OF JELÁLEDDÍN ***
- 51. Transcriber's Note. Apparent typographical errors havebeen corrected. Variations in transliteration have been retained. References to three Notes at the end of the text have been included in the Table of Contents. The Festival of Spring PUBLISHED BY JAMES MACLEHOSE AND SONS, GLASGOW, Publishers to the University. MACMILLAN AND CO., LTD., LONDON. New York, The Macmillan Co. London, Simpkin, Hamilton and Co.
- 52. Cambridge, Macmillan and Bowes. Edinburgh,Douglas and Foulis. MCMIII. The Festival of Spring from The Díván of Jeláleddín Rendered in English Gazels after Rückert's Versions With an Introduction And a Criticism of the Rubáiyát of Omar Khayyám By William Hastie, D.D. Professor of Divinity, University of Glasgow Glasgow James MacLehose and Sons
- 53. Publishers to theUniversity 1903 'In Depth of Conception, as well as in Loftiness of Flight and Sublimity of Language, Jeláleddín surpasses all the Poets of the East.' Professor Hermann Ethé. 'The greatest Mystical Poet of any Age.' Reynold A. Nicholson. 'And all the Breeze of Fancy blows, And every Dew-drop paints a bow, The wizard Lightnings deeply glow, And every Thought breaks out a Rose.' Tennyson. This Book with its Sincere Utterances of Love and Friendship
- 54. towards the Highest isdedicated to William A. Sanderson, Esq. Byethorne, Galashiels My ever faithful Friend in Adversity, as in Prosperity Old Songs are sweetest Old Friends are best
- 55. Note The current, popularspelling of Persian Names and Words has been generally adopted in the following pages, in order to avoid any appearance of pedantry. The Turkish forms have occasionally been preferred when in place, e.g. Devlet for Daulat, and Mevlānā for Maulānā. The exact transliteration of the Persian—such as Jalálu-'d- Din, Shams-ud-Din, Umar, Ghasal, Díwán—will be found in the foot- note references to more learned Works.
- 56. Contents Introduction Jeláleddín as aPersian Poet—Judgments of Scholars and Experts in Persian Literature since Sir W. Jones—The Philosophical and Theological Interest —Hegel—Tholuck—The Poetical Form—The Gazel —The Divan—Fitzgerald's Omar Khayyám—Burns —Browning—Keats's Nightingale—Coleridge echoes the Faith of Jeláleddín. Fifty Gazels of Jeláleddín Page I. Light, 1 II. Death and Life, 2 III. Invocation, 3 IV. Faith, 4 V. Dawn, 5 VI. Allah Hu, 6 VII. Spring, 7 VIII. Spring's Festival, 8 IX. Dependence, 9 X. Mystical Union, 10 XI. Identity, 12 XII. Confession, 13 XIII. Discordia Concors, 14
- 57. XIV. Renovation, 15 XV.Revolving in Mystic Dance, 16 XVI. The Soul in All, 17 XVII. Responsibility, 18 XVIII. Action, 19 XIX. Bondage, 20 XX. Love's Freedom, 21 XXI. In My Heart, 22 XXII. Not Deaf to Love, 23 XXIII. Assimilation, 24 XXIV. Cleanliness, 25 XXV. Where is He? 26 XXVI. Love's Slavery, 27 XXVII. Psyche in Tears, 28 XXVIII. Substitutional, 29 XXVIX. God's Throne, 30 XXX. The Lion of God, 31 XXXI. Self-Realisation, 33 XXXII. Thy Hand, 34 XXXIII. The Priests, 35 XXXIV. The Pilgrims, 36 XXXV. Many Faiths, One Lord, 37 XXXVI. Love Absolute, 38 XXXVII. Renunciation, 39 XXXVIII. All Fulness, 40 XXXIX. Friendship, 41 XL. The Friend Supreme, 42 XLI. Immortality, 44 XLII. The First and Last, 45
- 58. XLIII. Mystic LoveDance, 46 XLIV. Dream Fear, 47 XLV. The Cry of Love, 48 XLVI. Night Thought, 49 XLVII. Up out of Night, 50 XLVIII. All One, 52 XLIX. O Wake in Me, 53 L. Jeláleddín, 55 Notes A. Sir William Jones on the Mystical Poetry of the Persians. 57 B. Hegel on the Character of the Persian Lyrical Poetry. 59 C. Von Hammer's Account of Omar Khayyám. 61
- 59. Introduction I. Jeláleddín Rúmí (A.D.1207-1273) is now universally recognised by 'those who know,' as the greatest of the Persian Mystical Poets. This supremacy, in his own sphere, has been unanimously accorded to him for more than six centuries, by unnumbered myriads of his own disciples and followers in the Oriental World, who have been wrapt in devoutest admiration of the great Master to whom they have owed the highest joy and inspiration of their spiritual life. And at last, in our own Western World, the great Persian scholars of Europe, looking at him without personal or national bias, and through the clear, cold light of the new time, have come more and more, as with one voice, to join in this chorus of praise. His most appreciative recent editor and interpreter in England, in presenting a few leaves plucked with reverent hand from what he calls Jeláleddín's 'wreath of imperishable Lyric Song,' offers his own careful and conscientious work to us, as a contribution 'to a better appreciation of the greatest mystical poet of any age.' And with this designation, as summing up the judgment of a capable expert and critic—strange as it may sound—we venture, in all deference and sincerity, to agree. Jeláleddín is now rising upon our literary horizon in all his native Splendour—his name appropriately signifying 'The Splendour of the Faith'—as at once the Dante, the St. Bernard, the Spenser, the Milton, the Angelus Silesius, and the Novalis of the Orient. As a religious Lyrical Poet his mellifluous music, his variety of strain, his captivating charm of words, his purity of feeling, his joyous faith, and his elevation of thought, have never been surpassed in their own kind. Taking what Matthew Arnold has called 'the lyrical cry' even in its widest range, it would be doing no one wrong—although it dare hardly be done as yet—to rank Jeláleddín,
- 60. when he comesfully before us 'with all his singing robes about him,' with the very highest—with Shakespeare, with Wordsworth and Keats and Shelley, and with Goethe and Heine! He is certainly one of the most fertile poets of Nature among the Lyrical Singers of all time, and the most exuberant, if not also the most spiritual, Hymnist the world outside of Christendom has yet produced. This estimate, however shaded or qualified, cannot but appear at first strangely exaggerated, and out of all just proportion, to those who mayhap read the name of Jeláleddín now for the first time. Let us listen, then, to the greatest students of Persian Poetry in the critical Nineteenth Century, the judges who have highest authority on the subject, and who have the best right to pronounce judgment on Jeláleddín. And let us hear in the first place, as is his due, the most learned Historian of Persian Poetry in the Nineteenth Century, who with indefatigable industry and completest knowledge has adorned his pages with Extracts from no less than Two Hundred of Persia's greatest Poets. Joseph von Hammer, the great Austrian Orientalist (known later as Baron Von Hammer-Purgstall and as the Historian of Arabic Literature in seven immense volumes, containing Accounts of nearly ten thousand Authors) says:— 'Jeláleddín Rumi is the greatest Mystical Poet of the East, the Oracle of the Sofis, the Nightingale of the contemplative life, the Author of the Mesnevi (a celebrated double-rhymed ascetic poem), and the Founder of the Mevlevi, the most famous Order of Mystical Dervishes. As Founder of this Order, as the Legislator of the Contemplative Life, and as the Interpreter of Heavenly Mysteries, he is highly revered. And as such he has to be estimated by quite a different standard from that which applies to those Poets whose inspiration has not soared, like his, to the Vision of Divine things, to the primal Fountain of Love and Light. He cannot properly be compared either with Firdusi, the greatest of the Persian Epic Poets, nor with Nizami, the greatest of the Romantic Poets, nor with Saadi, the first of the moral Didactic Poets, nor with Hafiz, the chiefest of the erotic Lyrical Poets; for all these won the Palm of Poetry in entirely different fields from his. The only two great Poets of his kind, with whom a comparison can be in place, Senayi, the Author of the Mystical 'Flower Garden,' and Attar, the Author of the Mystical 'Bird Dialogues.' But both these works stand, as regards poetic merit, far below the Mesnevi, which is the Text Book of all The Sofis, from the banks of the Ganges to the shores of the Bosphorus. The Collection of Jeláleddín's 'Lyrical
- 61. Poems'—his Divan, properlyso called—'is regarded by them as of still higher value; it is practically the Law Book and the Ritual of all these Mystics. These outbursts of the highest inspiration of its kind deserve to be more closely considered, as it is from them that we see shining forth as in clear splendour the essence of the Oriental Mysticism, the cardinal Doctrine that All is One— the view of the ultimate Unity of all Being—and giving with it Direction and Guidance to the highest goal of Perfection by the contemplative Way of Divine Love. On the wings of the highest religious enthusiasm, the Sofi, rising above all the outward forms of positive Religions, adores the Eternal Being, in the completest abstraction from all that is sensuous and earthly, as the purest Source of Eternal Light. Mevláná Jeláleddín thus soars, not only like other Lyrical Poets, such as Hafiz, over Suns and Moons, but even above Space and Time, above the world of Creation and Fate, above the Original Contract of Predestination, and beyond the Last Judgment, into the Infinite, where in Eternal Adoration he melts into One with the Eternal Being, and infinitely loving, becomes One with the Infinite Love—ever forgetting himself and having only the great All in his view.'[1] Thus far the learned Von Hammer. But let us also hear the judgment of the East itself, of which this is only a Western echo, as it may be gathered from Devletshah, the greatest native biographer —the Dr. Johnson we may appropriately say—of the Persian Poets. Of Jeláleddín, he says:— 'His pure Heart is filled with Divine Mysteries, and through his eradiating Soul streams the Infinite Light. His View of the World leads the thirsty in the Vale of the Contemplative Life to the refreshing Fountain of Knowledge; and his Guidance leads those who have wandered in the Wilderness of Ignorance into the Gardens where Truth is really known. He makes plain to the Pilgrim the Secrets of the Way of Unity, and unveils the Mysteries of the Path of Eternal Truth: As when the foaming Sea high swells in Wave upon Wave, It casts out Pearls upon Pearls on every Shore they lave.' And to cite only one Turkish Authority—for the Turks claim Jeláleddín as their own, although a Persian of royal race, born at Balkh, old Bactra, on the ground of his having sung and died at Qoniya, in Asia Minor (the Iconium of Paul and Barnabas and Timothy and St. Thecla), whence he was called Rumi 'the Roman,' usually rendered 'the Greek,' as wonning within the confines of old Oriental Rome. This is how Fehîm Efendi, the Turkish Historian of the
- 62. Persian Literature, himselfa Poet, begins his Sketch of the Life of the great poetic Mystagogue:— 'As the ideal of Searchers after Truth here below, as the pattern of the Pure, the Mevlana is honoured by great and small among the people, by the aristocrat and the common man. In all circles his words are held in high honour; among all the wise his knowledge is greatly esteemed; and no pen has had the power to praise him, and to celebrate his excellence worthily, or to describe it in fitting terms. And should the fancy hold it can His praise completely reach; Mevlana's praise it ne'er shall scan— How say it then in speech?' Rosen, who gives this quotation, and an excellent rhymed German translation of part of the Mesnevi, refers to that poem as not only 'one of the most celebrated productions of the Persian Mysticism, but as being regarded by many Mohammedans as almost equal in holiness to the Koran and the Sunna.' Being attached, at the time he wrote, to the German Embassy at Constantinople, Rosen also mentions that not only did the educated Oriental regard the Mesnevi as the most perfect Book of Edification, which when its contents were received into his mind and heart, made him certain of Salvation; but that even the poor Persian retailers of the products of their home industries, on the streets, could recite with enthusiasm long passages from the poems of Jeláleddín. We believe that this holds true to-day, more or less, of the whole Mohammedan world.[2] But coming to more familiar names, we might gather a whole cloud of the most approved witnesses in this connection. Thus Sir William Jones, the first great Anglo-Indian Scholar, the Columbus of the new Old World of Sanskrit and Persian Literature, enters with wonderful sympathy and insight into possession of the Persian and Hindu Mystical Poetry; he refers to their great Maulavi, and his astonishing work, The Mesnevi; and he translates the celebrated opening passage in rhyming couplets which would not have been unworthy of Pope himself.[3] Sir William Jones did not, indeed, touch Jeláleddín's Lyrics, but he rendered some precious morsels of Hafiz, 'Odes,' as they are called, both in English and French, in a way that
- 63. made young Europeanstudents and poets, like Herder and Goethe, turn again to the East with yearning expectant eyes. Similar testimony might be adduced from Henry Thomas Colebrooke, one of the very greatest of the successors of Sir W. Jones. The chief Historian of Persia, and the best informed Persian scholar of his day, Sir John Malcolm (of Langholm), if less sympathetic than Sir W. Jones in his painstaking account of the Persian Mystics, gives likewise the first place to Jeláleddín.[4] And then much more definitely Sir Gore Ouseley, the first English Biographer of the Persian Poets, gives Jeláleddín due recognition in connection with the unrivalled Mesnevi.[5] The Journal of the Asiatic Society, an ever valuable Magazine of Oriental learning, and the parent of many others of its kind, has been enriched by the contributions of many enthusiastic English scholars following in the footsteps of Sir W. Jones, and it contains the earliest fragments of English translations of Jeláleddín.[6] Robert Alfred Vaughan, in his Hours with the Mystics, 1856, a popular, sympathetic, and still attractive work, appreciates Jeláleddín, and compares him with Angelus Silesius and Emerson, but all his knowledge of the Persian Mystic was derived from Tholuck and Sir W. Jones. At last competent scholars began to deal worthily with Jelál's poetry in English. Sir James W. Redhouse has translated the First Book of the Mesnevi in rhyming couplets, with the utmost fidelity and care; and another distinguished Persian scholar, Mr. Whinfield, the most faithful English translator of Omar Khayyám, has given an abridged version of the whole immense work, which in the Persian original contains about 70,000 lines.[7] The Mesnevi has thus come now to be pretty well known by English readers interested in the subject; and in the last edition of the Encyclopædia Britannica, Professor Hermann Ethé, an unquestionable authority, in his valuable Articles on Persian Literature and Jeláleddín Rumi, sums him up as 'the greatest Pantheistic Writer of all ages,' and speaks of 'his matchless Odes in which he soars on the wings of a genuine enthusiasm, high over Earth and Heaven, up to the Throne of Almighty God.' Be it noted, in passing, that it is at least remarkable how two such different writers as the Turkish Devlet Shah and the learned German Orientalist should both write of Jeláleddín in terms
- 64. that undesignedly, butirresistibly, recall by their very superlativeness, the famous lines of Dr. Johnson on Shakespeare:— 'Each change of many-coloured Life he drew, Exhausted Worlds and then imagin'd new; Existence saw him spurn her bounded reign, And panting Time toil'd after him in vain.' All this makes it now intelligible that the late lamented Editor of the Encyclopædia Britannica, Dr. W. Robertson Smith, when Professor of Arabic at Cambridge, with the fine insight of the far- seeing scholar, should have directed the attention of a young, enthusiastic student to the 'Lyrical Poetry of Jeláleddín Rumi'; and it is to the loyal devotion of this young scholar that we owe the first appearance from an English Press of a Volume of forty-eight 'Selected Poems' of Jeláleddín, in a critical Persian Text and with accurate and elegant prose renderings.[8] Mr. Reynold A. Nicholson has thus established a right to pronounce judgment on the merits of Jeláleddín, and we now listen to him with deference, and no longer with astonishment, when he deliberately characterises him as 'the greatest Mystical Poet of any Age.' As the object of this Introduction is only to determine, in some measure, the literary interest of the Lyrical Poetry—the Díván, as it is technically called—of Jeláleddín, space need not be taken up by narrating again what is traditionally known of his Life, and it is the less necessary as excellent accounts are now easily accessible. Sir James W. Redhouse gives in somewhat abridged translation El Eflākī's interesting narrative, with its romantic wreath of legend, and its quaint anecdotes and racy sayings. Mr. Nicholson furnishes an excellent summary. Professor Hermann Ethé's notice in the Encycl. Brit. has been already referred to, and reference may also be made to his Morgenländische Studien, and his popular Lecture in the Virchow-Holtzendorff Series, 1888, on 'The Mystical, Didactic, and Lyrical Poetry, and the later Literature of the Persians,' with its fine characterization, which we would fain have quoted. Rosen translates into German the Biographical Sketches of Devletshah and Jāmi.
- 65. Professor E. G.Browne's recent 'Literary History of Persia,' which carries the subject down to A.D. 1000, and is undoubtedly so far the best History of Persian Literature yet produced, contains appreciative references to Jeláleddín, with a masterly account of the Sufi Mysticism; and we look forward with much interest to a comprehensive and judicial summing up of the great Mystic Poet, by this high authority upon the whole subject.[9] II. The interest of the writer in Jeláleddín has been from the first, and all through, philosophical and theological rather than specially historical or textual. This interest was awakened in him by Hegel. In early student days, when to him as to so many then, the Hegelian Philosophy was the all in all of his thought, he was startled by the unwonted enthusiasm with which the great thinker at the climax of his severest exposition, paused to pay a warm tribute to 'the excellent Jeláleddín,' when he came into view in the light of the Supreme Idea of his own System.[10] This passage in Hegel, seems always to have impressed the students of his own writings, and it has been frequently referred to both by his German and English expounders. The greatest speculative Thinker of the Nineteenth Century, seems to have felt a deep satisfaction in recognising the affinity of the greatest speculative Poet of the East to his own deepest thought, while at the same time carefully distinguishing the clearer and higher form of his own conception. Nay more, although parsimonious to the utmost of his space and words, in this, the most condensed and compacted Text Book of Philosophy written in any European language since Aristotle, the stern German Dialectician in a comparatively long Foot Note, says he 'cannot refrain' from quoting several passages from the Poet, in order that the reader may get a clearer knowledge of his ideas; and he quotes them from Rückert's Versions, to give, at the same time, some specimens of 'the marvellous Art of the translation.' The Reader who is not acquainted with German will find Hegel's words accurately translated by the late Dr. W. Wallace, who also gives an English version of the