Memgraph lets you stream data from Apache Kafka, build in-memory dynamic graphs, run graph algorithms and deploy real-time applications with ease.
Her journey there started with a summer internship, and her mathematics and computer science background was a perfect match to work in Memgraph. She enjoys contributing to different areas and exploring new real-time data visualization technologies. She sees the graph world as a future of data analytics due to the variety of algorithms used for stream processing.
His passion for mathematics and graph theory inspired him to become part of the Memgraph team and start contributing to the field of graph analytics. Besides graph-based technologies, he is also interested in streaming platforms, stream processing, and event-driven development.
Dominik participated in Microsoft’s Imagine Cup and was one of the 4 teams that won it in 2011, meeting Bill Gates in the process. He is passionate about building distributed systems, highly concurrent and lock-free algorithms, and data structures. In his spare time, he loves airplanes and making cocktails.
P.S. He loves graphs!
Implementation language: C/C++
Storage engine architecture: In-Memory
On-disk persistance: Yes
ACID compliant: Yes
Streaming connectors: Apache Kafka, Apache Pulsar, Redpanda
Query language: Cypher
Licence: Open source
High-availability replication: Business source License
Custom Cypher procedures: Python, C/C++, Rust
Hosted Cloud service: Memgraph Cloud
Download and start Memgraph (on Windows, Linux or Mac).
Define a transform module (Python, C, C++ or Rust) that maps messages to Cypher queries.
Create a stream in Memgraph tha connects to a Kafka topic...
Start the stream and process the incoming data.
