SRE Republic Düsseldorf: SRE Republic x Sipgate

Earlier this month Third Republic held the fourth in the series of SRE Republic meetups. And after three successful meetups in Berlin and Munich, this time we came to Düsseldorf and to host at the fantastic Sipgate HQ.

Once again we had a great line-up of three incredible speakers and industry experts in the world of SRE and Cloud technology – Daniel Kleuser, Cloud and Platform Engineer at trivago, Florian Zink, SRE Team Lead at Talend and Thomas Peitz, DevOps Engineer at Invision AG. 

Cloud and platform engineering at trivago

Veteran Cloud and Platform Engineer, Daniel Kleuser, started the evening of insightful talks with his presentation about working in independent teams at trivago. He provided us with some really interesting insights all about the world of SRE – from implementation to team work. Below is a list of the key points that Daniel made on the evening:

•   Communication is key. Be transparent, give context, share information easily           accessible within your company. This also creates visibility on your work.
•   Don’t underestimate the importance of documentation, especially if you’re               collaborating across teams.
•   It can take time to establish a DevOps culture. Don’t force people, rather show        them the advantages and let them evolve on the mindset in their own pace.
•   Upfront alignment is important in a world of independent teams to lower the           risk of bad surprises, i.e. if you suddenly have to collaborate with others on a           project.

Educating yourself and your company

Following Daniel, we had the talented Florian Zink, SRE Team Lead at Talend. Florian’s talk provided attendees with top tips on implementing SRE and how to work with your business on the process. His presentation was incredibly insightful, here are just some of the key takeaways that came out of Florian’s talk:

•   Read the Google SRE book to understand what SRE is and why Google created         the term
•   Identify topics that could help you
•   Involve people early in the implementation process, to get support from your  .      colleagues
•   Be prepared to spend quite some time on the implementation and follow up           tasks

Florian ended his talk by explaining to attendees the importance of bearing in mind that SRE approach requires a certain mindset. Titles, roles and labels are meaningless if the company does not embrace the way of thinking that is required.

Have fun with it

For the final talk of the evening, we heard from Thomas Peitz, who has been working as a Cloud Engineer for Invision AG since 2013. The running theme throughout Thomas’ talk was to encourage a team culture and to have fun during work. Something he suggested our attendees ask themselves was whether “tech really sparks joy?”. 

Thomas ended on a positive note, requesting that everyone take care of themselves and the team they work with. He discussed the importance of encouraging a culture of failure, learning from mistakes quickly and not dwelling on them. 

Thank you

We would like to thank our fantastic speakers, Daniel, Florian and Thomas for providing our attendees with a hugely insightful and interesting evening. We’d also like to thank Sipgate for helping us host our first event in Düsseldorf and of course everyone who attended for contributing to such a successful evening.  We hope to see you all at our next event for more networking and interesting conversations around the world of SRE.

If you would like to join our growing SRE Republic community and to attend future events sign up to our meetup page for details of future events here.

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Software Developers
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No1 software engineers

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React.js
Node.js
AWS
JavaScript
React
Native
PostgreSQL
Ruby on Rails
Java
Angular
Golang
PHP

React.js
Node.js
AWS
JavaScript
React
Native
PostgreSQL
Ruby on Rails
Java
Angular
Golang
PHP

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Data Scientists
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Data Architects
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BI Developers
Data Analysts
Data Quality Engineers
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Sample of Platforms & Tools

Cloud DW: Snowflake, BigQuery, Redshift ETL/ELT Tools: Airflow, dbt, Fivetran, Talend Big Data Frameworks: Spark, Hadoop, Kafka BI & Visualization: Tableau, Power BI, Looker ML Libraries: TensorFlow, PyTorch, Scikit MLOps Platforms: MLflow, Kubeflow, W&B Data Governance: Collibra, Alation Data Observability: Monte Carlo, Databand Data Lakes: Lake Formation, Delta Lake SQL/NoSQL DBs: PostgreSQL, MongoDB Real-time Analytics DBs: Apache Pinot, Druid Gen

Cloud DW: Snowflake, BigQuery, Redshift ETL/ELT Tools: Airflow, dbt, Fivetran, Talend Big Data Frameworks: Spark, Hadoop, Kafka BI & Visualization: Tableau, Power BI, Looker ML Libraries: TensorFlow, PyTorch, Scikit MLOps Platforms: MLflow, Kubeflow, W&B Data Governance: Collibra, Alation Data Observability: Monte Carlo, Databand Data Lakes: Lake Formation, Delta Lake SQL/NoSQL DBs: PostgreSQL, MongoDB Real-time Analytics DBs: Apache Pinot, Druid Gen

Sample of Skills Delivered

Programming in Python, R, and SQL
Python
Data Modeling Statistical
Analysis ETL/ELT
Pipeline Development
Database Design & Administration
Machine Learning Fundamentals
Cloud Data Warehousing
Data Ethics & Compliance
Data Security
Data Visualization & Storytelling
Real-Time Data Streaming

Programming in Python, R, and SQL
Python
Data Modeling Statistical
Analysis ETL/ELT
Pipeline Development
Database Design & Administration
Machine Learning Fundamentals
Cloud Data Warehousing
Data Ethics & Compliance
Data Security
Data Visualization & Storytelling
Real-Time Data Streaming

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LLMs: Open AI, Anthropic, Llama, Gemini ML Platforms: SageMaker, Vertex AI, Azure ML ML Libraries: scikit-learn, XGBoost, LightGBM DL Frameworks: TensorFlow, PyTorch, Keras LLM Frameworks: LangChain, LlamaIndex Vector Databases: Pinecone, Weaviate, Milvus Data Science Notebooks: Jupyter, Colab, Kaggle Data Visualization Libraries: Matplotlib, Seaborn NLP Libraries: Hugging Face Transformers CV Libraries: OpenCV, Pillow, Albumentations MLOps Tools: MLflow, Kubeflow, W&B

LLMs: Open AI, Anthropic, Llama, Gemini ML Platforms: SageMaker, Vertex AI, Azure ML ML Libraries: scikit-learn, XGBoost, LightGBM DL Frameworks: TensorFlow, PyTorch, Keras LLM Frameworks: LangChain, LlamaIndex Vector Databases: Pinecone, Weaviate, Milvus Data Science Notebooks: Jupyter, Colab, Kaggle Data Visualization Libraries: Matplotlib, Seaborn NLP Libraries: Hugging Face Transformers CV Libraries: OpenCV, Pillow, Albumentations MLOps Tools: MLflow, Kubeflow, W&B

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Generative AI Development
Machine Learning Algorithms
Deep Learning Architectures
NLP and NLG
Computer Vision
Data & Feature Engineering
MLOps & Model Deployment
Statistical Analysis & Hypothesis Testing
Python and R Programming Expertise
Model Evaluation & Performance Tuning
AI Governance & Compliance
Autonomous AI Agent Engineering
Building Agentic AI Applications

Generative AI Development
Machine Learning Algorithms
Deep Learning Architectures
NLP and NLG
Computer Vision
Data & Feature Engineering
MLOps & Model Deployment
Statistical Analysis & Hypothesis Testing
Python and R Programming Expertise
Model Evaluation & Performance Tuning
AI Governance & Compliance
Autonomous AI Agent Engineering
Building Agentic AI Applications