All speakers

Timo Walther

Principal Software Engineer @ Confluent, PMC @ Apache Flink, Confluent

Timo Walther is a long-term member of the management committee and among the top committers in the Apache Flink project. He studied Computer Science at TU Berlin and participated in the Database Group. Timo worked as a software engineer at Data Artisans and lead of the SQL team at Ververica. He was a Co-Founder of Immerok which was acquired by Confluent in 2023. In Flink, he is working on various topics in the Table & SQL ecosystem to make stream processing accessible for everyone.

Timo Walther

Sessions / 2023 / 1 talk

  • CDC Stream Processing with Apache Flink

    An instant world requires instant decisions at scale. This includes the ability to digest and react to changes in real-time. Thus, event logs such as Apache Kafka can be found in almost every architecture, while databases and similar systems still provide the foundation. Change Data Capture (CDC) has become popular for propagating changes. Nevertheless, integrating all these systems, which often have slightly different semantics, can be a challenge. In this talk, we highlight what it means for Apache Flink to be a general data processor that acts as a data integration hub. Looking under the hood, we demonstrate Flink's SQL engine as a changelog processor that ships with an ecosystem tailored to processing CDC data and maintaining materialized views. We will discuss the semantics of different data sources and how to perform joins or stream enrichment between them. This talk illustrates how Flink can be used with systems such as Kafka (for upsert logging), Debezium, JDBC, and others.

Sessions / 2019 / 1 talk

  • Stream Processing Beyond Streaming Data

    Stream processing is becoming something like a ""grand unifying paradigm"" for data processing. Outgrowing its original space of real-time data processing, stream processing is becoming a technology that offers new approaches to data processing (including batch processing), real-time applications, and even distributed transactions. We will take a look at these developments from the view of Apache Flink and present some of the major efforts in the Flink community to build a unified stream processor data processing and data-driven applications. Flink already powers many of the world's most demanding stream processing applications. We present the approach of Flink's next generation streaming runtime that also offers a state-of-the-art batch processing experience and performance. A new Machine Learning library, built on top of a unique new API supports many algorithms to train dynamically across static and real-time data. Finally, we look at new building blocks stream processing offers for data-driven applications that open a new direction to solve application consistency. With use cases from different users, we show how companies apply this broader streaming paradigm in practice.

Sessions / 2018 / 1 talk

  • Flink SQL in Action

    SQL is the lingua franca of data processing, and everybody working with data knows SQL. Apache Flink provides SQL support for querying and processing batch and streaming data. Flink's SQL support powers large-scale production systems at Alibaba, Huawei, and Uber. Based on Flink SQL, these companies have built systems for their internal users as well as publicly offered services for paying customers. In my talk I will show how to leverage the simplicity and power of SQL on Flink. I’ll explain why unified batch and stream processing is important and what it means to run SQL queries on streams of data. Once we’ve covered the basics, I will spend the remainder of the talk demonstrating the capabilities of Flink SQL. We will explore different use cases that Flink SQL was designed for by running queries on Flink’s SQL shell. In particular, I will demonstrate the unified batch and streaming engine by running the same query on batch and streaming data and show how to build a real-time dashboard that is powered by a streaming SQL query, which continuously updates an external result table.

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