March 20-21, 2019 - Munich, Germany
Please join us at Data Festival 2019 in Munich, Germany. HPCC Systems is a sponsor of the event and will have a speaker. Please stop by our both and learn more about HPCC Systems!
The conference will present the latest techniques and technologies for data management, data analysis, and visualization. It brings the community of users and data experts together, such a data scientists, data engineers, analysts, BI experts, software developers, software architects, scientists, researchers, and many more. The conference is aimed at all users and experts working with data.
The focus of the conference will be on Data Science & Machine Learning, Data Engineering & Architecture, Data Visualization & Analytics, Fast Data, Infrastructure & Databases as well as Agile Development, Blockchain, Data Ethics and Data Privacy.
Our speaker, Fabian Fier, will be presenting HPCC Systems - A Hidden Champion in Big Data Processing (EN + DE). In this workshop, we'll walk through the architecture of HPCC Systems, its capabilities and popular extensions (ML, visualization, connectors to other systems), and learn basics of its main query language ECL. Users of other big data systems such as Spark or Flink will feel familiar with the dataflow-oriented query language, the data types, and the available operators. However, the workshop is open to everyone interested in the topic without prior knowledge. Fabian will be speaking on March 20th at 1:45pm.
About our speaker:
Fabian Fier is a PhD Student at Humboldt-Universität zu Berlin. Fabian studied computer science and worked as a consultant for project management, process optimization, and related software solutions. He is currently writing his PhD thesis on set-similarity joins and search on Big Data. Set-similarity is useful for entity linkage, record deduplication, and plagiarism detection. He showed that existing distributed approaches on Hadoop/MapReduce are not scalable to large amounts of data. In his work, he creates a framework that mitigates the scalability problem by evenly distributing the compute load over the cluster and regarding system restrictions such as limited main memory. He shows the practical applicability of the framework by implementing it on HPCC Systems. He optimizes the local join execution by exploiting multicore computation and cache-aware memory access patterns.