English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 2h 39m | 376 MB
Online activities, mobile devices, and Internet of Things (IoT) sensors are generating immense volumes of data. Much of that data requires relational database capabilities, such as consistent reads/writes and complex transaction processing. In this course, get a holistic overview of the essential elements of designing and implementing highly scalable and available relational databases. Instructor Dan Sullivan helps developers and data modelers grasp essential architecture concepts and design patterns to ensure their databases can scale to the needs of their business. Dan goes over key requirements related to both specific functions and nonfunctional requirements, such as availability. He shows how to use your requirements to create data architectures and data models. Plus, he examines the problems of data ingestion at scale, describes design patterns to support a variety of ingestion patterns, discusses how to design for scalable querying, and more.
Table of Contents
1 Challenges to scaling relational databases
2 What you should know
3 Business requirements for database scalability
4 Identify use cases for data
5 Identify security and compliance requirements
6 Estimate data growth
7 Challenge Identify business requirements in a scenario
8 Solution Identify business requirements in a scenario
9 Choose a datastore SQL, NoSQL, or analytical
10 Identify schemas
11 Identify key entities
12 High-level physical design
13 Challenge Revised database architecture
14 Solution Revised database architecture
15 Human-scale and machine-scale data
16 Different data ingestion strategies
17 Designing scalable user interfaces
18 Message queues to buffer ingested data
19 Data modeling for scale Event sourcing
20 Distributing workload Command Query Response Separation (CQSR)
21 Challenge Services and APIs for a scable user interface
22 Solution Services and APIs for a scable user interface
23 Transactional vs. analytical queries
24 Indexing for transactional queries
25 Materialized views for transactional queries
26 Using read replicas to improve query performance
27 Understanding write-ahead logging
28 Denormalizing for analytical queries
29 Aggregation and sampling for analytical queries
30 Challenge Optimize a data model for an analytical queries
31 Solution Optimize a data model for an analytical queries
32 Monitoring relational databases
33 Reducing latency with caching
34 Partitioning for scalability
35 High-availability architectures
36 Data lifecycle management
37 Challenge Understanding database DevOps
38 Solution Understanding database DevOps
Resolve the captcha to access the links!