Workshop: Beyond Relational:Applying Big Data Cloud Patterns
Categorization and category leaders for database system types, i.e. RDBMS, NoSQL, Hadoop, and then within NoSQL, K/V, Wide-column, Document and Graph
Realistic paths to move from on premise to migrating and migrated database projects
Tradeoffs between big relational and non-relational databases
When to use Hadoop - and when not to use it and why
Cloud database products mapped to value for solution type
- Some experience with database design and development
- About 50-60% of the time will be devoted to paper-based exercises and working in groups and pairs, the rest will be lecture
In this full-day workshop, you will learn applied big data solution patterns. most often, but not always using the public cloud.We'll cover Amazon Web Services and Google Cloud Platform, and work with in small groups to design solutions for common scenarios.
We'll discuss database categories, products and understand actual cost to use one or more database types in your solution(s). Using combinations of Big Relational (for hot, cold or cold data), and one or more types from the NoSQL category (Key-value, Wide-column, Document or Graph) and sometimes even along with NewSQL, we'll design implementable solutions. We'll also cover what types of problems are best suited to the Hadoop ecosystem (including use of Spark and the associated libraries).
We'll also look at product maturity (particularly surrounding cloud products such as AWS' Aurora, Athena and Google's Big Query and Big Table). Also we'll examine widely used partner products, such as ETL or visualization tools.
Finally, we'll look at data load-testing patterns and security practices, including considerations around connecting between different vendor clouds.
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