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E-raamat: Mastering Snowflake Solutions: Supporting Analytics and Data Sharing

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 27-Feb-2022
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484280294
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Mastering Snowflake Solutions: Supporting Analytics and Data SharingSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 27-Feb-2022
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484280294
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Design for large-scale, high-performance queries using Snowflake’s query processing engine to empower data consumers with timely, comprehensive, and secure access to data. This book also helps you protect your most valuable data assets using built-in security features such as end-to-end encryption for data at rest and in transit. It demonstrates key features in Snowflake and shows how to exploit those features to deliver a personalized experience to your customers. It also shows how to ingest the high volumes of both structured and unstructured data that are needed for game-changing business intelligence analysis. 

Mastering Snowflake Solutions starts with a refresher on Snowflake’s unique architecture before getting into the advanced concepts that make Snowflake the market-leading product it is today. Progressing through each chapter, you will learn how to leverage storage, query processing, cloning, data sharing, and continuous data protection features. This approach allows for greater operational agility in responding to the needs of modern enterprises, for example in supporting agile development techniques via database cloning. The practical examples and in-depth background on theory in this book help you unleash the power of Snowflake in building a high-performance system with little to no administrative overhead. Your result from reading will be a deep understanding of Snowflake that enables taking full advantage of Snowflake’s architecture to deliver value analytics insight to your business. 


What You Will Learn
  • Optimize performance and costs associated with your use of the Snowflake data platform
  • Enable data security to help in complying with consumer privacy regulations such as CCPA and GDPR
  • Share data securely both inside your organization and with external partners
  • Gain visibility to each interaction with your customers using continuous data feeds from Snowpipe
  • Break down data silos to gain complete visibility your business-critical processes
  • Transform customer experience and product quality through real-time analytics

Who This Book Is for

Data engineers, scientists, and architects who have had some exposure to the Snowflake data platform or bring some experience from working with another relational database. This book is for those beginning to struggle with new challenges as their Snowflake environment begins to mature, becoming more complex with ever increasing amounts of data, users, and requirements. New problems require a new approach and this book aims to arm you with the practical knowledge required to take advantage of Snowflake’s unique architecture to get the results you need.   


Intermediate user level
About the Author xi
About the Technical Reviewer xiii
Acknowledgments xv
Introduction xvii
Chapter 1 Snowflake Architecture
1(18)
Technology and Data Are Inseparable
1(1)
Unlocking Business Value
1(1)
Business Agility Is More Important Than Ever
2(1)
All Hail the Cloud!
3(1)
Decisions, Decisions, Decisions!
4(1)
Snowflake Architecture
4(2)
Database Storage
6(1)
Micro Partitions
6(2)
What Is the Benefit of Micro Partitioning?
8(1)
Partitioning in the Pre-Snowflake World
9(1)
Data Clustering
9(1)
Virtual Warehouses
9(1)
Caching
10(1)
Result Cache
11(1)
Local Disk Cache
11(1)
Configuring Virtual Warehouses
12(1)
Number of Clusters
12(2)
Scaling Policy
14(1)
Auto Suspend
15(1)
Query Processing
15(1)
Cloud Services
16(1)
Authentication
16(1)
Infrastructure Management
16(1)
Metadata Management
16(1)
Query Parsing and Execution
17(1)
Access Control
17(1)
Summary
18(1)
Chapter 2 Data Movement
19(34)
Stages
19(1)
External Stages
20(1)
External Tables and Data Lakes
20(2)
Internal Stages
22(1)
File Formats
22(2)
The COPY INTO Command
24(1)
COPY INTO Syntax
25(1)
Transformations
26(1)
Data Loading Considerations
27(1)
File Preparation
27(1)
Semistructured Data
28(1)
Dedicated Virtual Warehouses
29(1)
Partitioning Staged Data
30(1)
Loading Data
31(1)
Loading Using the Web UI
31(1)
Unloading Data from Snowflake
32(1)
Bulk vs. Continuous Loading
32(1)
Continuous Data Loads Using Snowpipe
33(1)
Streams and Tasks
34(1)
Change Tracking Using Streams
35(1)
Stream Metadata Columns
36(1)
Tasks
36(3)
Bringing It All Together
39(1)
The Example Scenario
39(1)
Steps
40(12)
Summary
52(1)
Chapter 3 Cloning
53(14)
A Word on Performance Testing
53(1)
Testing with Data
54(1)
Forget the Past!
55(1)
Sensitive Data
56(1)
Why Clone an Object?
57(1)
Working with Clones
58(1)
Which Objects Can Be Cloned?
59(1)
Clone Permissions
60(2)
Bringing It All Together
62(1)
The Example Scenario
63(1)
Steps
63(2)
Summary
65(2)
Chapter 4 Managing Security and Access Control
67(32)
Roles
67(2)
Role Hierarchy
69(1)
Inheritance
70(2)
Objects
72(1)
Extending the Role Hierarchy
72(3)
User and Application Authentication
75(1)
Multi-Factor Authentication
75(5)
MFA Caching
80(1)
Security Assertion Markup Language
80(1)
OAuth
81(1)
Key Pair Authentication
82(1)
Storage Integration
83(1)
Network Policies
83(1)
Option 1 Native Network Security
84(1)
Option 2 Network Policies
84(1)
Option 3 Cloud Service Provider Capabilities
85(1)
Handling PII Data
85(1)
Separately Storing PII Data
86(1)
Removing Data in Bulk
86(1)
Auditing
87(1)
Controlling Access to PII Data
87(6)
Row Access Policies
93(1)
Example Scenario
93(1)
Steps
93(4)
Advanced Snowflake Security Features
97(1)
Future Grants
97(1)
Managed Access Schemas
97(1)
Summary
98(1)
Chapter 5 Protecting Data in Snowflake
99(14)
Data Encryption
100(1)
Encryption Key Management
101(1)
Customer Managed Keys
102(2)
Time Travel
104(1)
Data Retention Periods
104(1)
Querying Historical Data
105(1)
Dropping and Undropping Historical Data
106(1)
Fail-safe
107(1)
Underlying Storage Concepts
107(1)
Temporary and Transient Tables
108(1)
Bringing It All Together
108(3)
Summary
111(2)
Chapter 6 Business Continuity and Disaster Recovery
113(18)
Regions and Availability Zones
114(1)
Data Replication, Failover, and Fallback
114(1)
Primary and Secondary Databases
115(1)
Promoting Databases
116(1)
Client Redirect
116(1)
Business Continuity
117(1)
Process Flow
117(4)
Monitoring Replication Progress
121(1)
Reconciling the Process
122(1)
Data Loss
123(1)
Bringing It All Together
123(1)
The Example Scenario
124(1)
Steps
125(4)
Summary
129(2)
Chapter 7 Data Sharing and the Data Cloud
131(18)
The Data Cloud
132(3)
Data Sharing
135(1)
The Data Marketplace
136(1)
Providers and Consumers
137(1)
What Is a Share?
137(3)
Reader Accounts
140(1)
Using a Dedicated Database for Data Sharing
140(1)
Data Clean Rooms
141(1)
Bringing It All Together
142(1)
The Example Scenario
142(6)
Summary
148(1)
Chapter 8 Programming
149(18)
Creating New Tables
149(1)
Create Table Like
149(1)
Create Table as Select
150(1)
Create Table Clone
150(1)
Copy Grants
151(1)
Stored Procedures
152(2)
User-Defined Functions
154(1)
Scalar Functions
154(2)
Table Functions
156(1)
SQL Variables
156(2)
Transactions
158(1)
Transactions Within Stored Procedures
159(2)
Locking and Deadlocks
161(1)
Transaction Tips
161(1)
Bringing It All Together
162(1)
The Example Scenario
162(1)
Steps
162(4)
Summary
166(1)
Chapter 9 Advanced Performance Tuning
167(34)
Designing Tables for High Performance
167(1)
Data Clustering
168(4)
Designing High-Performance Queries
172(1)
Optimizing Queries
172(6)
Materialized Views
178(2)
Search Optimization Service
180(3)
Optimizing Warehouse Utilization
183(3)
Warehouse Utilization Patterns
186(6)
Leveraging Caching
192(1)
Monitoring Resources and Account Usage
193(1)
Resource Monitors
193(2)
Query History
195(2)
Useful References
197(1)
Summary
198(3)
Chapter 10 Developing Applications in Snowflake
201(20)
Introduction to SnowSQL
201(2)
Versions and Updates
203(1)
Config File
204(1)
Authentication
205(1)
Using SnowSQL
206(4)
Data Engineering
210(1)
Java User-Defined Functions
210(1)
Snowpark
211(2)
Combining Snowpark and UDFs
213(2)
Connectors
215(1)
Snowflake Connector for Python
215(1)
Querying Data
216(1)
Snowflake Connector for Kafka
217(1)
A Solution Architecture Example
217(2)
Summary
219(2)
Index 221
Adam Morton is a senior data and analytics professional with almost two decades of experience. He has architected, designed, and led the implementation of numerous data warehouse and business intelligence solutions. Adam has extensive experience and certifications across several data analytics platforms ranging from Microsoft SQL Server, Teradata, and Hortonworks, to modern cloud-based tools such as AWS Redshift, Google Big Query, and Snowflake.  Having successfully combined his experience with traditional technologies with his knowledge of modern platforms, Adam has accumulated substantial practical expertise in data warehousing and analytics in Snowflake, which he has captured and distilled into this book. Today, Adam runs his own data and analytics consultancy which focuses on helping companies solve problems with data, along with designing and executing modern data strategies to deliver tangible business value. Adam currently lives in Sydney, Australia and is the proud recipient of a Global Talent Visa.
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