How to Handle NoSQL with a Relational Database

It can be difficult to scale out relational databases and provide more schema flexibility, thus the rise of NoSQL. However, you shouldn’t have to sacrifice data integrity and transactions in order to scale out on commodity hardware and support semi-structured data. By using an RDBMS with built-in sharding and JSON support, you don’t have to. You get the scalability and flexibility of a NoSQL database along with the consistency and reliability of a relational database – and the ability to mix and match relational and JSON data. In this webinar, we’ll explain how MariaDB Platform can be deployed as a NoSQL database by using the Spider storage engine and built-in SQL functions for JSON. In addition, we’ll discuss how you can access relational data as JSON documents, and how to enforce data integrity if a relational data model is extended with JSON
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Spotlight

OTHER ON-DEMAND WEBINARS

Why is a Central Analytics Team Vital to a Self-Service Data Culture?

When your teams are building products for competitive markets and demanding customers, the right insights can make all the difference. Join us for this month’s Coffee Chat webinar to learn how you help your whole organization make better decisions, take faster action—and deliver the kind of experiences that drive revenue, loyalty, and lifetime value.
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How does AXA Belgium Replicate its operational data into its Data Lake?

To preview your data, you must first have access to it. This operation can be difficult when distributed into different systems and silos which is why many companies choose to bring them together in a Data Lake. However, without governance or stewardship, these data lakes are rapidly turning into swamps, veritable brakes on innovation.
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Analyze Data Faster with an Open Source Columnar Database

MariaDB

If you are looking for the scalability and performance needed to support interactive, ad hoc analytics on billions of rows – and with SQL – this latest Data Science Central webinar will show you how to combine distributed, columnar storage and parallel query processing with powerful aggregate functions to deliver faster time to insight using modern, on-demand analytics, as well as how to leverage the power of Kafka and Spark connectors to plug into existing data pipelines. We will discuss the architectural overview of columnar databases, share real-world use cases and give a live demonstration.
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Modernizing Metadata

tdwi.org

Metadata is more relevant than ever. It continues to be a powerful enabler for high-value data-driven business activities across operations, analytics, and compliance. However, to maintain this relevance, metadata management must deal with the increasing complexity of today’s business use cases and hybrid data environments. Metadata management is notoriously manual which makes it slow in development and error-prone in maintenance. Metadata management needs better tool automation. Metadata management is typically siloed due to users managing metadata per tool or platform. Metadata management needs a unified solution that is suited to sharing, reuse, governance, and comprehensive views of distributed data.
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