This session describes how to significantly simplify network programmability using APIs generated from YANG data models. Model-driven APIs allow the network progra …

Prometheus is great when it comes to monitoring and alerting. But the long term storage opportunities are comparatively weak compared to related time series databases (missing data distribution, shard …

Streaming telemetry data enables network operators to bring network monitoring out of the 20th century and into the rich space of big data analytics. Having spent …

FINRA ingests over 50 billion records of stock market trading data daily into multipetabyte databases. Janaki Parameswaran and Kishore Ramachandran explain how FINRA technology integrates data feeds from disparate systems to provide analytics and visuals for regulating equities, options, and fixed-income markets.

Whether we're talking about spam emails, merging records, or investigating clusters, there are many times when having a measure of how alike things are makes them easier to work with (e.g., with unstructured data that isn't incorporated into your data models). Melissa Santos offers a practical approach to creating a distance metric and validating with business owners that it provides value.

Jeff Carpenter describes how data modeling can be a key enabler of microservice architectures for transactional and analytics systems, including service identification, schema design, and event streaming.

Kaushik Deka and Phil Jarymiszyn discuss the benefits of a Spark-based feature store, a library of reusable features that allows data scientists to solve business problems across the enterprise. Kaushik and Phil outline three challenges they facedsemantic data integration within a data lake, high-performance feature engineering, and metadata governanceand explain how they overcame them.

Git has quickly become an incredibly popular version control system, but how does it actually work? It's very different from a centralized version control system, and understanding how it models history allows you to understand how to use it. Edward Thomson explains how Git actually works under the hood, the fundamentals of a repository, and how this makes Git fast and flexible.

Python is so consistent that we can often infer the behavior of new objects by assuming they work like the built-ins. The Python data model is the foundation of this consistent behavior. Luciano Ramalho explores the construction of Pythonic objects: classes that feel "natural" to a Python programmer and leverage some of the best language features by implementing key protocols of the data model.

Thomas Cozzolino highlights the growing importance of the developer experience, API-first thinking, common data models, and enterprise ecosystems.

Roopa Tangirala details Netflix's migration from Oracle to Cassandra, covering the problems encountered, what worked and what didn't, and lessons learned along the way.

Even though each of us is only 1 of 7 billion, we all want to feel special. Product personalization is transforming the Internet experience to be all about me (and you too!). Yao Morin explores how to create customer-centric experiences through data science and software engineering, using Intuit TurboTax as a case study.