Memgraph can natively ingest streaming data from sources like Apache Kafka, Apache Pulsar, RedPanda. You can use graph traversal and graph algorithms to analyze this data in real-time.
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.
Nikola is an experienced Account Executive that loves working with people on innovative and groundbreaking projects. His passion for helping people make an impact lead him to the Memgraph team. Data is the new oil and Memgraph is on a journey to transform the way companies interact and use data. His interest include business development, graph based technologies, data and machine learning.
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.
