Agile Query-Driven Data Modeling for NoSQL

Agile Query-Driven Data Modeling for NoSQL

.MP4, AVC, 1000 kbps, 1280x720 | English, AAC, 128 kbps, 2 Ch | 1.5 hours | 637 MB

Instructor: Pascal Desmarets
Recorded live at Data Modeling Zone

Data modeling is under pressure to demonstrate its relevance in a world of Agile Development and NoSQL. Now, a query-driven approach completely redefines the field of data modeling.

NoSQL technology is almost mainstream now. And with self-managed agile development teams embracing the flexibility of JSON in NoSQL document databases, the field of data modeling is too often strictly associated with relational databases, and hence viewed as too rigid and cumbersome.

The data modeling actors, roles, lifecycle, processes, and tools are rapidly changing in that context, and the entire field is re-inventing itself to remain relevant and demonstrate its contribution. As it turns out, data modeling becomes even more important when the guardrails provided by normalization have been taken down.

At the same time, unstructured and polymorphic Big Data is creating challenges both in terms of data governance (in the context of privacy regulation - GDPR and PII) and the ability of enterprises to leverage the mountains of information accumulated. Data modeling is now used in production on a daily basis to help discover how data has been stored, in order to help query it effectively, and keep it compliant.

In this session, you will learn a completely new approach to data modeling, to leverage the power and flexibility of MongoDB, AWS DynamoDB, Couchbase, Azure Cosmos DB, Elasticsearch, Cassandra, HBase, Redis, Google Firebase, MarkLogic, and other NOSQL and multi-model databases. You will also learn how data modeling helps enterprises migrate from RDBMS to NoSQL.

There are three clips in this series:

Why NoSQL? The drivers behind NoSQL are discussed, and each type of database (Relational, Column, Key-Value, Document, and Graph) are introduced.
Document Databases. Learn all about document databases, and how they can be modeled. Understand the differences between embedding and referencing and the tradeoffs with using document databases versus relational.
Graph Databases. Learn all about graph databases, including how to model graph databases and graph database use cases.


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