Mats Rydberg - Databricks

Mats Rydberg

Software Engineer, Neo4j

Mats has worked with Neo4j for more than four years with a focus on graph query language design and implementation. Mats is leading the development of the Cypher for Apache Spark (CAPS) project, now called Morpheus, which has been accepted as a Spark 3.0 major feature under the name of Spark Graph and will bring the leading graph query language Cypher to Apache Spark. Mats holds a Master’s degree in Computer Science specialized on graph algorithms.

UPCOMING SESSIONS

PAST SESSIONS

Graph Features in Spark 3.0: Integrating Graph Querying and Algorithms in Spark Graph—continuesSummit Europe 2019

Spark 3.0 introduces a new module: Spark Graph. Spark Graph adds the popular query language Cypher, its accompanying Property Graph Model and Graph Algorithms to the data science toolbox. Graphs have a plethora of useful applications in recommendation, fraud detection and research. The tutorial aims to help understanding when graphs should be used and how Spark Graph can be used to extend analytical workflows. In this tutorial we will explore the concepts and motivations behind graph querying and graph algorithms, the components of the new Spark Graph module and their APIs, and how those APIs allow you to successfully write your own graph applications and integrate them in your data science workflows.

The tutorial is a mixture of presentation, code examples, and notebooks. We will demonstrate how to write an end-to-end Graph application that operates on different kinds of input data. We will show how Spark Graph interacts with Spark SQL and openCypher Morpheus, a Spark Graph extension that allows you to easily manage multiple graphs and provides built-in Property Graph Data Sources for the Neo4j graph database as well as Cypher language extensions. At the end of the tutorial, attendees will have a good understanding of when to apply graphs in their data science workflows, how to bring Spark Graph into an existing Spark workflow and how to make best use of the new APIs.

This tutorial will be both lead by the presenters and also hands-on interactive session. The tutorial material will be made available during the presentation.

Graph Features in Spark 3.0: Integrating Graph Querying and Algorithms in Spark GraphSummit Europe 2019

Spark 3.0 introduces a new module: Spark Graph. Spark Graph adds the popular query language Cypher, its accompanying Property Graph Model and Graph Algorithms to the data science toolbox. Graphs have a plethora of useful applications in recommendation, fraud detection and research. The tutorial aims to help understanding when graphs should be used and how Spark Graph can be used to extend analytical workflows. In this tutorial we will explore the concepts and motivations behind graph querying and graph algorithms, the components of the new Spark Graph module and their APIs, and how those APIs allow you to successfully write your own graph applications and integrate them in your data science workflows.

The tutorial is a mixture of presentation, code examples, and notebooks. We will demonstrate how to write an end-to-end Graph application that operates on different kinds of input data. We will show how Spark Graph interacts with Spark SQL and openCypher Morpheus, a Spark Graph extension that allows you to easily manage multiple graphs and provides built-in Property Graph Data Sources for the Neo4j graph database as well as Cypher language extensions.

At the end of the tutorial, attendees will have a good understanding of when to apply graphs in their data science workflows, how to bring Spark Graph into an existing Spark workflow and how to make best use of the new APIs. This tutorial will be both lead by the presenters and also hands-on interactive session. The tutorial material will be made available during the presentation.

Neo4j Morpheus: Interweaving Documents, Tables and and Graph Data in SparkSummit Europe 2018

Fuse graph, document and relational data from transactional and analytic data sources, into a property graph "bird's eye view". The property graph data model is Chen's "entity relationship" model, without clutter. Use "ASCII Art" visual property graph schemas to define "graph data lifts", mapping from data lake, RDBMS, RDF or graph data cloud services into Spark. Graphs in Spark draw on multiple data sources. Leverage the Cypher query language to combine, split, and project graphs in Spark memory. Graph data is "woven" in Spark without altering or copying the original source. The results of graph workloads can be written back into HDFS or other file systems. Graphs can be read from, stored and merged into a Neo4j transactional database. And tabular datasets can be extracted from graphs. Data scientists and engineers load, wrangle and analyze mixed model data through Morpheus transformations. Enterprises use graphs to catalogue their disparate data assets and processes. They store graph datasets in the data lake. In a world of concern about data protection, see how graph data lifts allow tailored, canonical data views to be realized, in Spark, without remodeling and moving data. Morpheus combines SparkSQL and Cypher queries, and table/graph functions.Choose the right language for the job: eliminate cumbersome multi-joins for connected-data traversals by using super-concise Cypher patterns for sub-graph detection and graph projection; use the power of table projection, grouping, aggregation in SparkSQL, all in one application. Feel free to "dismantle your graph": expose your graph nodes or relationships as dataframes, or as Hive tables. Key Takeaways Graph technology meets Big Data and Spark Analytics Property graphs: the superset data model Graph, relational and document data, interwoven Lift, split, combine, and create new graphs, from any data source Get your data fit to exploit graph compute, without losing any of your existing tools undefined undefined undefined undefined undefined Session hashtag: #SAISDD9