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E-raamat: Informed Company: How to Build Modern Agile Data Stacks that Drive Winning Insights

  • Formaat: PDF+DRM
  • Ilmumisaeg: 22-Oct-2021
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119748021
  • Formaat - PDF+DRM
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 22-Oct-2021
  • Kirjastus: John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781119748021

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"In their work at Chartio, Fowler and David get to meet many people who work with data every day. One of their favorite questions to ask them is, "Where did you learn everything you know about data?" Surprisingly, most people tell them they're completelyself-taught and have "just figured it out". As a follow-up, they ask what sources they've relied on, and the answers are all over the map. Mostly they'll cite Google, StackOverflow, blogs, and sometimes these books: Agile Data Warehouse Design by Lawrence Corr (2011) or The Data Warehouse Toolkit by Ralph Kimball (originally published in 2004 with a 3rd edition update in 2013). These books were very good for their time, and became classics. But in the timeframe of data, they're ancient. Both were writtenbefore Redshift and the gains of the cloud C-Store warehouse. Back then, data was at a totally different scale, had very different costs, was used with totally different products, and was handled by people with very different training--primarily just at enterprise companies. It has gotten to the point where pointing people to these books can do more harm than good. Over the years,Fowler and David have had the incredible opportunity to work with many data teams, architectures, tools, and platforms, and they've built up a body of knowledge around what works--and what doesn't--when it comes to data. They've been sharing this knowledge with customers, and waiting for someone to publish a book on these maturing modern data best practices.They got a bit impatient and earlier this year,theye gathered their notes and combined knowledge and started writing the definitive new data book themselves"--

Learn how to manage a modern data stack and get the most out of data in your organization!

Thanks to the emergence of new technologies and the explosion of data in recent years, we need new practices for managing and getting value out of data. In the modern, data driven competitive landscape the "best guess" approach—reading blog posts here and there and patching together data practices without any real visibility—is no longer going to hack it. The Informed Company provides definitive direction on how best to leverage the modern data stack, including cloud computing, columnar storage, cloud ETL tools, and cloud BI tools. You'll learn how to work with Agile methods and set up processes that's right for your company to use your data as a key weapon for your success . . . You'll discover best practices for every stage, from querying production databases at a small startup all the way to setting up data marts for different business lines of an enterprise.

In their work at Chartio, authors Fowler and David have learned that most businesspeople are almost completely self-taught when it comes to data. If they are using resources, those resources are outdated, so they're missing out on the latest cloud technologies and advances in data analytics. This book will firm up your understanding of data and bring you into the present with knowledge around what works and what doesn't.

  • Discover the data stack strategies that are working for today's successful small, medium, and enterprise companies
  • Learn the different Agile stages of data organization, and the right one for your team
  • Learn how to maintain Data Lakes and Data Warehouses for effective, accessible data storage
  • Gain the knowledge you need to architect Data Warehouses and Data Marts
  • Understand your business's level of data sophistication and the steps you can take to get to "level up" your data

The Informed Company is the definitive data book for anyone who wants to work faster and more nimbly, armed with actionable decision-making data.

About This Book xiii
Foreword xxi
Introduction xxv
Stage 1 Source (aka Siloed Data)
1(22)
Chapter 1 Starting with Source Data
3(8)
Common Options for Analyzing Source Data
4(7)
Chapter 2 The Need to Replicate Source Data
11(4)
Replicate Sources
12(2)
Create Read-Only Access
14(1)
Chapter 3 Source Data Best Practices
15(8)
Keep a Complexity Wiki Page
15(1)
Snippet Dictionary
16(1)
Use a Bl Product
17(1)
Double Check Results
18(1)
Keep Short Dashboards
19(1)
Design Before Building
20(3)
Stage 2 Data Lake (aka Data Combined)
23(46)
Chapter 4 Why Build a Data Lake?
25(8)
What Is a Data Lake?
26(1)
Reasons to Build a Data Lake Summarized
27(6)
Chapter 5 Choosing an Engine for the Data Lake
33(12)
Modern Columnar Warehouse Engines
35(3)
Modern Warehouse Engine Products
38(3)
Database Engines
41(1)
Recommendation
42(3)
Chapter 6 Extract and Load (EL) Data
45(10)
ETL versus ELT
46(2)
EL/ETL Vendors
48(1)
Extract Options
49(2)
Load Options
51(1)
Multiple Schemas
52(1)
Other Extract and Load Routes
53(2)
Chapter 7 Data Lake Security
55(4)
Access in Central Place
56(1)
Permission Tiers
57(2)
Chapter 8 Data Lake Maintenance
59(10)
Why SQL?
60(1)
Data Sources
61(3)
Performance
64(4)
Upgrade Snippets to Views
68(1)
Stage 3 Data Warehouse (aka the Single Source of Truth)
69(92)
Chapter 9 The Power of Layers and Views
75(8)
Make Readable Views
77(1)
Layer Views on Views
78(3)
Start with a Single View
81(2)
Chapter 10 Staging Schemas
83(28)
Orient to the Schemas
84(1)
Pick a Table and Clean It
85(13)
Other Staging Modeling Considerations
98(8)
Building on Top of Staging Schemas
106(5)
Chapter 11 Model Data with dbt
111(8)
Version Control
111(1)
Modularity and Reusability
112(1)
Package Management
112(1)
Organizing Files
113(1)
Macros
113(1)
Incremental Tables
114(1)
Testing
115(4)
Chapter 12 Deploy Modeling Code
119(4)
Branch Using Version Control Software
119(1)
Commit Message
120(1)
Test Locally
120(1)
Code Review
121(1)
Schedule Runs
122(1)
Chapter 13 Implementing the Data Warehouse
123(12)
Manage Dependencies
124(2)
Combine Tables Within Schemas
126(2)
Combine Tables Across Schemas
128(2)
Keep the Grain Consistent
130(1)
Create Business Metrics
131(2)
Keeping Accurate History
133(2)
Chapter 14 Managing Data Access
135(8)
How to Secure Sensitive Data in the Data Warehouse
137(3)
How to Secure Sensitive Data in a BI Tool
140(3)
Chapter 15 Maintaining the Source of Truth
143(18)
Track New Metrics
144(3)
Deprecate Old Metrics
147(2)
Deprecate Old Schemas
149(1)
Resolve Conflicting Numbers
150(1)
Handling Ongoing Requests and Ongoing Feedback
151(1)
Updating Modeling Code
152(1)
Manage Access
153(3)
Tuning to Optimize
156(1)
Code Review All Modeling
157(1)
Maintenance Checklist
158(3)
Stage 4 Data Marts (aka Data Democratized)
161(50)
Chapter 16 Data Mart Implementation
167(4)
Views on the Data Warehouse
167(1)
Segment Tables
168(1)
Access Update
169(2)
Chapter 17 Data Mart Maintenance
171(6)
Educate Team
172(1)
Identifies Issues
172(4)
Identify New Needs
176(1)
Help Track Success
176(1)
Chapter 18 Modern versus Traditional Data Stacks: What's Changed?
177(4)
What's Changed?
177(4)
Chapter 19 Row - versus Column-Oriented Database
181(10)
Row-Oriented Databases
182(2)
Column-Oriented Databases
184(6)
Summary
190(1)
Chapter 20 Style Guide Example
191(8)
Simplify
192(2)
Clean
194(1)
Naming Conventions
195(2)
Share It
197(2)
Chapter 21 Building an SST Example
199(12)
First Attempt--Same Tables with Prefixes
199(6)
Second Attempt--Operational Schema (Source Agnostic)
205(2)
Third Attempt--Application Separate, Other Sources Smashed
207(2)
Less Planning, More Implementing
209(2)
Acknowledgments and Contributions 211(2)
Index 213
MATT DAVID is the Product Marketing Manager for Platform Data at Atlassian. He formerly worked at Chartio as the Head of Data and before that at Udacity as Product Lead for the School of Data Science.

DAVE FOWLER is Head of Analytics and Visualization at Atlassian and Founder of Chartio. He has worked in business intelligence for over ten years. His professional focus is on enabling anyone and everyone to explore and understand their data.