Gabor Szarnyas

DuckDB has traditionally been seen as a last-mile analytics powerhouse, the fastest way to run a SQL query on your laptop. But DuckDB offers more than just fast SQL, of course; it supports full database semantics and ACID transactions, behaving like a fully fledged, in-process OLAP database.

Data management systems have gone through significant changes in the last 10 years, driven by user demands, novel techniques and improvements in hardware. These have far-reaching implications on how systems are deployed and used in practice. In this talk, I will focus on three key aspects of...

Neo4j had its first stable release in 2010 and soon became one of the defining NoSQL systems, establishing "graph databases" as a new category. These systems represent data as nodes and edges, allowing for intuitive querying and visualization, as well as performance benefits on certain types of queries (e.g.

Data management systems and data scientists have a troubled relationship: Common systems such as Postgres or Spark are hard to set up and maintain, hard to transfer data into and out of, and hard to integrate into Python workflows.

DuckDB is an analytical database management system. It runs in-process, which makes its configuration trivial and eliminates any overhead between the client application and the database.

We motivate and present an open-source benchmark suite for graph processing, created and maintained by the Linked Data Benchmark Council (LDBC). We first define common graph workloads and the pitfalls of benchmarking systems that support them, then explain our guiding principles that allow for conducting meaningful benchmarks.

In this talk, we describe the LDBC Social Network Benchmark's two workloads: the Interactive workload for transactional graph database systems and the Business Intelligence workload for analytical graph data systems.

There is increasing interest to apply graph analytical techniques to a wide array of problems, many operating on large-scale graphs with billions of edges. While graph algorithms and their complexity is textbook material, efficient implementation of such algorithms is still a major challenge due to a number of reasons.

Introduction Graph analysis workloads present resource-intensive computations that require a large amount of memory and CPU time. Consequently, there an abundance of graph processing tools which build on distributed data processing frameworks, including Spark GraphX , Flink Gelly and Giraph (which runs on Hadoop).