Peeling the onion: How data abstraction helps building big data applications
Data abstractions have many benefits: They keep application code free from the details of how data is stored; they make applications portable between different environments and storage engines; they provide data access patterns that are reusable across applications; and they allow injection of enterprise-grade production capabilities, such as security and data lineage, at a platform level. This talk will illustrate the various levels of data abstractions with examples taken from the Cask Data Application Platform (CDAP).
Big Data and Analytics in the Cloud
Cloud is a key component of the future of big data. In this talk, we’ll discuss current and future considerations for deploying big data within the cloud, including major public cloud vendors, the on-prem vs. cloud tradeoffs, and the advantages of portability and hybrid models. Ryan will present example use cases for AWS and Azure.
Designing Modern Data Pipelines with Apache Kafka
Modern data pipelines are real-time, flexible, reliable and scalable. Over years of building these pipelines, we recognized several design patterns required for implementing them successfully. In this session, we will introduce these patterns, show how to implement them using Apache Kafka and explain the benefits of building data architectures with Kafka at their core.