Mary Grygleski

In this session we will briefly describe Apache Pulsar and Jakarta JMS. We will see how Apache Pulsar concepts map to the Jakarta Messaging Specifications. You will also see how to connect a Jakarta EE application to Pulsar just by dropping a Resource Adapter in your application server and basically zero code changes.

Real-Time Data Analytics is becoming very popular these days because of the important values and insights that it is bringing to the business. It also relies on super-fast data ingestion and that is all possible by leveraging on a powerful event-driven streaming platform. Two very important Open Source Apache projects have risen to serve such a grand purpose: Apache Pulsar for event streaming and Apache Pinot for real-time analytics. We take a look at how both of these projects can integrate very well together and achieve the blazingly fast results that we desire and show you a use case that we've built.

When you hear "decision maker", it's natural to think, "C-suite", or "executive". But these days, we're all decision-makers. Restaurant owners, bloggers, big box shoppers, diners - we all have important decisions to make and need instant actionable insights. In order to provide these insights to end-users like us, businesses need access to fast, fresh analytics.

In this session we will learn how to build our own real-time analytics application on top of a streaming data source using Apache Pulsar, Apache Pinot, and Streamlit. Pulsar is a distributed, open source pub-sub messaging and streaming platform for real-time workloads, Pinot is an OLAP database designed for ultra low latency analytics, and Streamlit is a Python based tool that makes it super easy to build data based apps.

After introducing each of these tools, we’ll stream data into Pulsar using its Python client, ingest that data into a Pinot real-time table, and write some basic queries using Pinot’s Python SDK. Once we've done that, we’ll bring everything together with an auto refreshing Streamlit dashboard so that we can see changes to the data as they happen. There will be lots of graphs and other visualisations!

This session is aimed at application developers and data engineers who want to quickly make sense of streaming data.

A quick study on several popular MQTT brokers!

We will learn about the benefits of MQTT, which has become the de facto standard for IoT messaging. The MQTT protocol has in fact undergone a major transformation from its early days as a protocol for handling data transmission in oil pipelines via satellite, to now being able to manage devices operating in the current internet and cloud native environments that are often constrained. We'll take a look at some of the popular implementations of the MQTT broker, such as the Java-based HiveMQ and the C/C++ Eclipse Mosquitto.

IoT applications run on IoT devices and can be created to be specific to almost every industry and vertical, from small devices to large ones, including healthcare, industrial automation, smart homes and buildings, automotive, and wearable technology. The possibilities are limitless. Increasingly, IoT applications are using AI and machine learning to add intelligence to devices. Among all of the variables in the IoT ecosystem, one common theme is the need to be able to handle the constrained operating environment, such as unreliable network connectivity, limited bandwidth, low battery power, and so on. We will take a look into the MQTT protocol, how it has evolved from its early days which was intended for the connection of oil pipelines via satellite, to now the ever-increasing demand in IoT and M2M applications, to how this protocol will evolve to meet the modern needs especially in the current cloud computing era. We will study a few outstanding MQTT libraries that are available in the market, such as the Java-based HiveMQ, and open source libraries such as Eclipse Mosquitto and Apache licensed VerneMQ.