Implementing Oracle Database-as-a-Service for Cloud-Like Agility

Market leaders in every industry are beginning to see the power of real-time big data integration and how connecting it can enable them to re-imagine their markets, customers, products, and business models.

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.

As part of its platform, GoodData provides a fault-tolerant, high performance and scalable system for data integration. While built for large-scale analytic applications, it is a metadata-driven, modular system that can start small and grow with your business. In this session, Cameron demonstrates how to set up and schedule regular data extraction from SQL databases and other sources. He also covers some of the issues requiring attention in data extraction such as data merging and incremental loads. A future session will cover transformations and data enrichment along with data distribution.

With data coming from so many different sources nowadays (both old and new, both internal and external), it is inevitable that data will arrive in many different structures, schema, and formats, with other variables for latency, concurrency, and requirements for storage and processing. When data types are extremely diverse and combined, we now call it “hybrid data.” This usually drives users to deploy many types of databases and different platforms to capture, store, process, and analyze the data, which in turn results in hybrid data management architectures.