[PyData] Data Engineering Principles - Build frameworks not pipelines - Gatis Seja

PyData London Meetup #54 Tuesday, March 5, 2019 Data pipelines are necessary for the flow of information from its source to its consumers, typically data scientists, analysts and software developers. Managing data flow from many sources is a complex task where the maintenance cost limits scale of being able to build a large reliable data warehouse. This presentation proposes a number of applied data engineering principles that can be used to build robust easily manageable data pipelines and data products. Examples will be shown using Python on AWS. Sponsored & Hosted by Man AHL **** www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.