Gang Tao
Timeplus, CTO
Vancouver, Canada
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Gang has over 20 years experience in software development, and is a recognized expert of AI, BI, Big Data and Data Visualization. He was the principal engineer of Splunk's ML and Data platform. He's also a die-hard fan of AC Milan.
Area of Expertise
Topics
Query Your Streaming Data on Kafka using SQL: Why, How, and What
Streaming data is rapidly becoming a key component in modern applications, and Apache Kafka has emerged as a popular and powerful platform for managing and processing these data streams. However, as the volume and complexity of streaming data continue to grow, it becomes increasingly critical to have efficient and effective ways of querying and analyzing this data.
This is where query engines like Apache Flink, Trino, Timeplus, Materialize, and ksqlDB come in. These powerful tools offer flexible and scalable ways of processing and analyzing streaming data in real-time, enabling users to extract valuable insights from their data streams.
In this talk, we will introduce the audience to the world of querying streaming data on Apache Kafka with SQL, compare and contrast the features and capabilities of each of these tools, and provide an in-depth analysis of their respective Pros and Cons. We will also discuss the best practices and scenarios where each tool is most effective.
Edge AI and IoT Anomaly Detection Simplified with Real Time SQL
In this session, we will explore how to build a robust IoT anomaly detection solution by combining the power of local AI and Timeplus’ real-time SQL engine. By leveraging Ollama's AI capabilities, integrated directly at the edge, this approach eliminates the need for cloud infrastructure, providing a scalable, low-latency solution.
Attendees will learn how Timeplus enables seamless integration of real-time and historical data as context for AI-driven applications, all within a simple SQL-based environment. This session is ideal for developers looking to simplify LLM application development, reduce operational complexity, and deploy powerful AI solutions with minimal components.
Key takeaways:
How to provide Real-time and historical context for IoT anomaly detection
Simple SQL-based interface for building LLM-powered AI applications
Operational efficiency with edge computing and minimized deployment complexities
Unveiling the Power of Real-Time Streaming Data Visualization
As the prevalence of streaming data continues to rise, its significance in contemporary data analytics cannot be overstated. This tech talk explores the critical role of visualizing streaming data, emphasizing its pivotal impact on instantaneous awareness, anomaly detection, and dynamic decision-making.
Key Points:
1. Instantaneous Awareness:
Streaming data visualizations offer immediate insights, empowering quick responses to emerging trends, issues, and opportunities.
2. Quick Detection of Anomalies:
Visualizations provide a swift identification of irregular patterns in streaming data, enhancing users' ability to detect anomalies promptly and take timely actions.
3. Dynamic Decision-Making:
In scenarios requiring on-the-fly decisions, visualizations offer dynamic representations, facilitating quick and informed decision-making without exhaustive analysis of raw data streams.
In this session, we will showcase the creation of a real-time streaming data visualization using open-source tools, including Proton (an open-source streaming database) and AntV G2 (an open-source data visualization library). The demonstration will address key challenges, such as making charts dynamic, supporting interactions on live data, managing time series with a moving time scale and axis, and implementing replay functionality to review historical data.
Join us to unravel the potential of real-time streaming data visualization and learn how to overcome challenges in building dynamic and interactive visualizations with open-source tools.
Securing Tomorrow: Real-Time Fraud Detection with Apache Kafka, Streaming Databases
The pervasive threat of online fraud significantly impacts businesses, necessitating a robust end-to-end strategy for the detection and prevention of new account fraud, account takeovers, and suspicious payment transactions. Crucial to the success of a fraud detection and prevention system is the capability to identify fraud in close proximity to its occurrence. It is imperative that the system not only detects fraud effectively but also promptly alerts end-users, empowering them to take immediate action and mitigate further abuse.
To tackle these challenges, the adoption of real-time machine learning becomes essential. However, this introduces a set of hurdles, including ensuring the freshness of features in real-time, maintaining feature consistency during training and inference, backfilling historical data for feature regeneration, ensuring point-in-time correctness, and managing the complexity of streaming systems.
In this session, we will illustrate a solution that leverages apache kafka and streaming database to construct a real-time feature pipeline effectively addressing these challenges. Key highlights include:
Building a low-latency streaming feature pipeline utilizing streaming SQL.
Providing a consistent feature pipeline for both training and serving purposes.
Establishing a simplified, single-box system that is easy to manage and operate.
Join us to explore a practical demonstration of how this real-time feature pipeline can revolutionize fraud detection and prevention in the ever-evolving landscape of online security.
Query Live Data Using Open Source SQL Engines
Streaming data is rapidly becoming a key component in modern applications, and Apache Kafka, Redpanda and Apache Pulsar have emerged as a popular and powerful platform for managing and processing these data streams. However, as the volume and complexity of streaming data continue to grow, it becomes increasingly critical to have efficient and effective ways of querying and analyzing this data.
This is where query engines like Apache Flink, ksqlDB, Trino, Timeplus Proton, RisingWave, Materialize, etc come in. These powerful tools offer flexible and scalable ways of processing and analyzing streaming data in real-time, enabling users to extract valuable insights from their data streams.
In this talk, we will introduce the audience to the world of querying streaming data on Apache Kafka with SQL, compare and contrast the features and capabilities of each of these tools, and provide an in-depth analysis of their respective Pros and Cons. We will also discuss the best practices and scenarios where each tool is most effective.
In conclusion, query engines like Apache Flink, Trino, ksqlDB, Proton, RisingWave are useful tools in processing and analyzing streaming data on Kafka or other message buses. With their ability to extract valuable insights from real-time data streams, these tools are a valuable asset for modern data-driven applications.
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