Data Architecture Deep Dive - Design & Implementation
Data Science
| Intermediate
- 12 videos | 35m 8s
- Includes Assessment
- Earns a Badge
This 11-video Skillsoft Aspire course explores the numerous types of data architecture that can be used when working with big data; how to implement strategies by using NoSQL (not only structured query language); CAP theorem (consistency, availability, and partition tolerance); and partitioning to improve performance. Learners examine the core activities essential for data architectures: data security, privacy, integrity, quality, regulatory compliances, and governance. You will learn different methods of partitioning, and the criteria for implementing data partitioning. Next, you will install and explore MongoDB, a cross-platform document-oriented database system, and learn to read and write optimizations in MongoDB. You will learn to identify various important components of hybrid data architecture, and adapting it to your data needs. You will learn how to implement DAG (Directed Acyclic Graph) by using the Elasticsearch search engine. You evaluate your needs to determine whether to implement batch processing or stream processing. This course also covers process implementation by using serverless and Lambda architecture. Finally, you will examine types of data risk when implementing data modeling and design.
WHAT YOU WILL LEARN
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describe data complexity management strategiesrecognize data modeling techniques and describe data modeling processeslist prominent distributed data models and their associative implementation benefitsdescribe data partitioning methods and data partitioning implementation criteriainstall MongoDB and implement data partitioning using MongoDBidentify important components of a hybrid data architecture
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demonstrate how to implement directed acyclic graphs using Elasticsearchdescribe CAP theorems and their implementation approachescompare the differences between batch and streaming datarecognize the read and write optimizations in MongoDBimplement serverless architecture with Lambda and data partitioning using MongoDB
IN THIS COURSE
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1.Course Overview1m 46sUP NEXT
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2.Data Complexity Management Strategies4m 23s
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3.Data Modeling Process2m 58s
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4.Distributed Data Management2m 40s
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5.Partitioning Methods and Criteria2m 23s
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6.MongoDB Partitioning5m 16s
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7.Hybrid Data Architectures2m 57s
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8.Implement Directed Acyclic Graph2m 52s
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9.CAP Theorem3m 7s
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10.Batch vs. Streaming2m 10s
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11.Read and Write Concerns2m 22s
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12.Exercise: Implement Serverless Architecture2m 13s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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