Hello everyone! This is our 2nd meetup of the year and we want to thanks KPN for hosting us one more time!
|18:00||Doors open! let's share some food and drinks thanks to our host.|
|19:00||First talk: "Interesting takeaways from book 'Test Driven Development With Python'" -- Rok Klancar|
|19:30||Second talk: "Graph-based stream processing in Python" -- Katarina Šupe|
Interesting takeaways from book 'Test Driven Development With Python'
About Rok Klancar
Started programming via the web developer path. Now use mainly Python for programming with Django web framework. Coming from Slovenia, I enjoy interesting meetup events and talks =)
Test Driven Development was introduced to Rok via the official Django tutorial.
It seemed like a must-have skill, to be a competent and competitive programmer in the industry. Python developers are in luck, because someone had written a book on TDD with Python. As a beginner, he picked it up, and went from cover to cover.
Then, he did it again. You may ask yourself:
"Can I give this book to someone else to make their development practices better?"
"What can I learn from it?"
"Should I invest time into this?"
Well, the awesome thing is, that the book is available for free at https://www.obeythetestinggoat.com/
But who is the Testing Goat and why would you want to obey it? 🐐
Come and listen, where Rok will turn this mega 500 pager into a bite-sized talk.
Graph-based stream processing in Python
About Katarina Šupe
Developer Relations Engineer, Memgraph Katarina Šupe is a Developer Relations Engineer at Memgraph. 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.
The understanding of complex relationships and interdependencies between different data points is crucial to many decision-making processes.
Graph analytics have found their way into every major industry, from marketing and financial services to transportation. Fraud detection, recommendation engines, and process optimization are some of the use cases where real-time decisions are mission-critical, and the underlying domain can be easily modeled as a graph.
By ingesting data with Apache Kafka and applying graph-based stream processing in real-time, you can perform near-instantaneous graph analytics on vast amounts of data. When it comes to complex networks, it’s often necessary to perform graph algorithms such as calculating the PageRank, identifying communities, traversing relationships, etc.
While solutions such as ksqlDB or Apache Spark are useful for processing relational data, Memgraph is an open-source streaming platform that can be used to analyze graph-based data models. Graph analytics can provide insights into complex networks that would otherwise require resource-intensive computations. It is also much simpler to store streaming data in the form of graphs, as the graph model doesn't rely on predefined and rigid tables. When connecting a Kafka data stream to Memgraph, you only need to create a transformation module that will map incoming messages to the property graph model. This data can then be traversed and analyzed using the Cypher query language without having to implement custom algorithms or relying on development-heavy solutions. MAGE (Memgraph Advanced Graph Extensions) is a graph library that works well with Kafka-powered systems and contains graph algorithms meant for analyzing streaming data. Besides stream processing, you can also utilize standard graph algorithms from the MAGE library to explore the stored data. We are going to showcase the Twitter Network Analysis demo, a web application with a Python Flask backend that uses Memgraph to ingest real-time data scraped from Twitter. You will learn how to use an Object Graph Mapper (OGM) in Python to connect to a graph database and perform stream processing on data streamed with Apache Kafka.