A data warehouse serves the purpose of providing low latency queries for high volumes of data. A data warehouse is often part of a data pipeline, which moves data through different areas of infrastructure in order to build applications such as machine learning models, dashboards, and reports. Modern data pipelines are often associated with the term “ELT” or Extract, Load, Transform. In the “ELT” w
Materialize: Streaming SQL on Timely Data with Arjun Narayan and Frank McSherry Distributed stream processing frameworks are used to rapidly ingest and aggregate large volumes of incoming data. These frameworks often require the application developer to write imperative logic describing how that data should be processed. For example, a high volume of clickstream data that is getting buffered to Ka
LinkedIn has become a staple for the modern professional, whether it’s used for searching for a new job, reading industry news, or keeping up with professional connections. As a rapidly growing platform that serves more than 675 million users today, LinkedIn is a company that can boast of having one of the largest user bases in the world. How these users interact with the site and react to recomme
Originally published May 24, 2018 When a user takes a ride on Uber, the app on the user’s phone is communicating with Uber’s backend infrastructure, which is writing to a database that maintains the state of that user’s activity. This database is known as a transactional database or “OLTP” (online transaction processing). Every active user and driver and UberEATS restaurant is writing data to the
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