Data Science helps companies build models about sales, demand, finance, customer information and what not. R is widely used among data scientists for analysis and model building. But most R users have used it only for research and prototyping, not in actual production analytics code.
- How to deploy R models in a production environment? Pros and cons of these methods.
- Comparing R with other options for data science work.
From tools and technology to people and requirements, what's different in the data engineering space? App development is traditional now. All enterprises want to become data-guided. Data lake is good start yet the know-hows and do-hows are so many. There's much anew to be learnt and also unlearnt. Everyone has their version of a big data stack, keeps continuously re-inventing.
Sharing experiences from building a data lake in the retail domain, I will be talking about - what is this vast new space of data engineering,
- why it is critical to think in terms of data rather than features and
- how important it is to understand these technologies and create a data lake that is usable and insightful to business
This talk gives a brief overview of the Spark Architecture along with the common issues that are encountered while running spark jobs and how to go about avoiding them. It will also cover the best practices one should adopt to maximize performance and ensure efficient memory utilization.
Networking & Dinner
Geek Night is a monthly event to promote sharing of technical knowledge and increase collaboration between geeks in Chennai. It is organized by a passionate group of programmers and sponsored by ThoughtWorks.
It happens on the Last Thursday of every month, unless that's a public holiday or any other unavoidable cause, like an Alien invasion.
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