Modern Real-Time Data Stack: Emerging Cloud Architectures for Streaming Data Analytics
In this talk, we’ll discuss core components and architectural best practices for the modern real-time data stack.
According to a global survey of engineering leaders by Confluent Cloud, more than 80% of organizations describe streaming data as key to building responsive business practices and interactive user experiences. Yet, implementing real-time analytics including personalization, logistics tracking and predictive analytics continues to be a challenge for many engineering teams. One reason is that teams continue to adopt batch frameworks within their streaming architectures, costing them on data latency and compute.
In this talk, we’ll discuss core components and architectural best practices for the modern real-time data stack including:
- The key components of the modern real-time data stack including event and CDC streams for ingestion, real-time ELT, real-time analytics database, data APIs and visualization layers
- Requirements of the stack including its ability to handle data torrents, out of order events, schema changes and message delivery guarantees
- The modern elements of the stack including support for SQL, cloud-native technologies, low latency, low data ops and affordability
This tech talk is sponsored by Intel and Rockset. Rockset achieves 84% faster performance with Intel Xeon Scalable processors for real-time analytics in the cloud.
About the Speaker
Shruti Bhat leads product management and marketing at Rockset. Prior to Rockset, Shruti led Product Management for Oracle Cloud, with a focus on AI, IoT and Blockchain. Previously, Shruti was VP Marketing at Ravello Systems, where she drove the start-up's rapid growth from pre-launch to hundreds of customers and a successful acquisition. Prior to that, she was responsible for launching VMware's vSAN and has led engineering teams at HP and IBM. Shruti has a bachelor's in computer science engineering and an MBA from UCLA Anderson.