Schedule: Streaming and IoT sessions

Arun Kejariwal, Karthik Ramasamy, and Anurag Khandelwal walk you through the landscape of streaming systems and examine the inception and growth of the serverless paradigm. You'll take a deep dive into Apache Pulsar, which provides native serverless support in the form of Pulsar functions and get a bird’s-eye view of the application domains where you can leverage Pulsar functions. Read more.

There are too many edge devices and agents, and you need to control and manage them. Purnima Reddy Kuchikulla, Timothy Spann, Abdelkrim Hadjidj, and Andre Araujo walk you through handling the difficulty in collecting real-time data and the trouble with updating a specific set of agents with edge applications. Get your hands dirty with CEM, which addresses these challenges with ease. Read more.

Kafka is omnipresent and the backbone of streaming analytics applications and data lakes. The challenge is understanding what's going on overall in the Kafka cluster, including performance, issues, and message flows. Purnima Reddy Kuchikulla and Dan Chaffelson walk you through a hands-on experience to visualize the entire Kafka environment end-to-end and simplify Kafka operations via SMM. Read more.

Trill has been open-sourced, making the streaming engine behind services like the Bing Ads platform available for all to use and extend. James Terwilliger, Badrish Chandramouli, and Jonathan Goldstein dive into the history of and insights from streaming data at Microsoft. They demonstrate how its API can power complex application logic and the performance that gives the engine its name. Read more.

Krishna Maheshwari provides an overview of the major features and enhancements in the HBase 2.0 release, upcoming releases, and the future of HBase. You'll be able to ask her questions at the end. Apache HBase 2.0 comes packed with a lot of new functionalities: off-heap read paths, multitier bucket cache, new finite state machine-based assignment manager, etc. Read more.

Stavros Kontopoulos and Debasish Ghosh explore online machine learning algorithm choices for streaming applications, especially those with resource-constrained use cases like IoT and personalization. They dive into Hoeffding Adaptive Trees, classic sketch data structures, and drift detection algorithms from implementation to production deployment, describing the pros and cons of each of them. Read more.

Narvar provides next-generation posttransaction experience for over 500 retailers. Karthik Ramasamy and Anand Madhavan take you on the journey of how Narvar moved away from using a slew of technologies for their platform and consolidated its use cases using Apache Pulsar. Read more.

Stephan Ewen details how stream processing is becoming a "grand unifying paradigm" for data processing and the newest developments in Apache Flink to support this trend: new cross-batch-streaming machine learning algorithms, state-of-the-art batch performance, and new building blocks for data-driven applications and application consistency. Read more.

Jonghyok Lee Chon Yong Lee discuss T-CORE, SK Telecom’s monitoring and service analytics platform, which collects system and application data from several thousand servers and applications and provides a 3D visualization of the real-time status of the whole network. Join in to hear lessons learned during development. Read more.