January 14, 2020

143 words 1 min read

Build your own data lake with AWS Glue and Amazon Athena (sponsored by Amazon Web Services)

Build your own data lake with AWS Glue and Amazon Athena (sponsored by Amazon Web Services)

Damon Cortesi demonstrates how to use AWS Glue and Amazon Athena to implement an end-to-end pipeline.
Talk Title Build your own data lake with AWS Glue and Amazon Athena (sponsored by Amazon Web Services)
Speakers Damon Cortesi (Amazon Web Services)
Conference Strata Data Conference
Conf Tag Making Data Work
Location London, United Kingdom
Date April 30-May 2, 2019
URL Talk Page
Slides Talk Slides
Video

From ingesting raw data to optimizing your production dataset, building a data lake is a complex process that can take months and requires expertise across several domains. Damon Cortesi demonstrates how to use AWS Glue and Amazon Athena to implement an end-to-end pipeline. Join in to learn how Damon used this technique to build his own personal data lake collecting data across native AWS service logs and external third-party services. This session is sponsored by Amazon Web Services.

data lake dataset pipeline aws
comments powered by Disqus
Executive Briefing: AWS technology trendsData lakes and analytics

Executive Briefing: AWS technology trendsData lakes and analytics

January 12, 2020

Nikki Rouda shares key trends in data lakes and analytics and explains how they shape the services offered by AWS. Specific topics include the rise of machine-generated data and semistructured and unstructured data as dominant sources of new data, the move toward serverless, SPI-centric computing, and the growing need for local access to data from users around the world.
From BI to big data; Or, There and back again

From BI to big data; Or, There and back again

January 10, 2020

Francesco Mucio shares the basic tools he and his team had to learn (or relearn) moving from the coziness of their database to the big world of Spark, cloud, distributed systems, and continuous applications. It was an unexpected journey that ended exactly where it started: with an SQL query.
From legacy to cloud: An end-to-end data integration journey

From legacy to cloud: An end-to-end data integration journey

January 10, 2020

Max Schultze details Zalondo's end-to-end data integration platform to serve analytical use cases and machine learning throughout the company, covering raw data collection, standardized data preparation (binary conversion, partitioning, etc.), user-driven analytics, and machine learning.
Herding elephants: Seamless data access in a multicluster clouds

Herding elephants: Seamless data access in a multicluster clouds

January 10, 2020

Travel platform Expedia Group likes to give its data teams flexibility and autonomy to work with different technologies. However, this approach generates challenges that cannot be solved by existing tools. Pradeep Bhadani and Elliot West explain how the company built a unified virtual data lake on top of its many heterogeneous and distributed data platforms.
Spark NLP in action: How Indeed applies NLP to standardize rsum content at scale

Spark NLP in action: How Indeed applies NLP to standardize rsum content at scale

January 6, 2020

Alexander Thomas and Alexis Yelton demonstrate how to use Spark NLP and Apache Spark to standardize semistructured text, illustrated by Indeed's standardization process for rsum content.
Lightning Talk: Automated AI Model Deployment within ONAP

Lightning Talk: Automated AI Model Deployment within ONAP

January 5, 2020

AI will enable more intelligent automation, able to constantly adapt to field reality and specificities.Through this talk, a prospective vision of the management of an Al model learning, deployment an …