The speaker begins by introducing the topic of stream processing and its relevance in various domains such as finance, IoT, sports, hospitals, and machine learning. They emphasize the importance of combining real-time and historical data to provide context and explain that scaling stream processing can be challenging. The speaker then introduces their company, Hadoopcast, and its open-source platform for stream processing. They explain the architecture of the platform, which includes stream processing engines and fast data stores. The speaker discusses the importance of storing data close to the compute and highlights the platform's partition awareness and linear scaling capabilities. They also mention different tools and platforms available in the stream processing domain. The speaker then moves on to a technical demonstration where they show how to analyze traces using the Hadoopcast platform. They explain the process of storing data in the cloud using a map structure and demonstrate how to perform stream processing using SQL or Java. They also discuss the option of using a journal map to track changes in real time. The speaker concludes by summarizing the key takeaways and best practices for stream processing, including storing logs in a data platform, considering the format and security of the data, and being mindful of scalability and performance. They invite the audience to attend their upcoming conference and offer further discussions. The talk ends with a Q&A session.