PyAmsterdam meetup @ KPN offices
================================

Hello everyone! This is our 2nd meetup of the year and we want to
thanks KPN for hosting us one more time!


Schedule
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| 18:00 | Doors open! let's share some food and drinks thanks to our host.                                   |
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| 19:00 | First talk: "Interesting takeaways from book 'Test Driven Development With Python'" -- Rok Klancar |
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| 19:30 | Second talk: "Graph-based stream processing in Python" -- Katarina Šupe                            |
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| 20:15 | Lightning talks?                                                                                   |
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| 20:30 | Networking                                                                                         |
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| 21:00 | Closing time                                                                                       |
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Interesting takeaways from book 'Test Driven Development With Python'
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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 =)


Abstract
~~~~~~~~

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.

Twitter: https://twitter.com/supe_katarina Linkedin:
https://www.linkedin.com/in/katarina-supe/ Medium:
https://medium.com/@supe.katarina


Abstract
~~~~~~~~

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.


Links
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