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Text analytics on LinkedIn Talent Insights using Apache Pinot

· One min read
LinkedIn
LinkedIn Engineering Team

LinkedIn Talent Insights (LTI) is a platform that helps organizations understand the external labor market and their internal workforce, and enables the long term success of their employees. Users of LTI have the flexibility to construct searches using the various facets of the LinkedIn Economic Graph (skills, titles, location, company, etc.).

Read More at https://engineering.linkedin.com/blog/2021/text-analytics-on-linkedin-talent-insights-using-apache-pinot

Text analytics on LinkedIn Talent Insights using Apache Pinot

Introduction to Geospatial Queries in Apache Pinot

· One min read
Kenny Bastani
Kenny Bastani

Geospatial data has been widely used across the industry, spanning multiple verticals, such as ride-sharing and delivery, transportation infrastructure, defense and intel, public health. Deriving insights from timely and accurate geospatial data could enable mission-critical use cases in the organizations and fuel a vibrant marketplace across the industry. In the design document for this new Pinot feature, we discuss the challenges of analyzing geospatial at scale and propose the geospatial support in Pinot.

Read More at https://medium.com/apache-pinot-developer-blog/introduction-to-geospatial-queries-in-apache-pinot-b63e2362e2a9

Introduction to Geospatial Queries in Apache Pinot

Automating Merchant Live Monitoring with Real-Time Analytics - Charon

· One min read
Uber
Uber Data Team

At Uber, live monitoring and automation of Ops is critical to preserve marketplace health, maintain reliability, and gain efficiency in markets. By the virtue of the word “live”, this monitoring needs to show what is happening now, with prompt access to fresh data, and the ability to recommend appropriate actions based on that data. Uber’s data platform provides the self-serve tools which empower the Ops teams to build their own live monitoring tools, and support their regional teams by building rich solutions.

For this project, the requirement was to provide merchant level monitoring and handle the edge cases which remain unaddressed by the sophisticated internal marketplace management tools. We used a variety of Uber’s real-time data platform components to build a tool called Charon to reduce impact of poor marketplace reliability on the merchants.

Read More at https://eng.uber.com/charon/

Operating Apache Pinot at Uber Scale

Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches

· One min read
LinkedIn
LinkedIn Engineering Team

The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.

Read More at https://engineering.linkedin.com/blog/2021/pinot-and-theta-sketches

From Lambda to Lambda-less Lessons learned

Introduction to Upserts in Apache Pinot

· One min read
Kenny Bastani
Kenny Bastani

Since the 0.6.0 release of Apache Pinot, a new feature was made available for stream ingestion that allows you to upsert events from an immutable log. Typically, upsert is a term used to describe inserting a record into a database if it does not already exist or update it if it does exist. In Apache Pinot’s case, upsert isn’t precisely the same concept, and I wanted to write this blog post to explain why it’s exciting and how you can start using it.

Read More at https://medium.com/apache-pinot-developer-blog/introduction-to-upserts-in-apache-pinot-987c12149d93

Introduction to Upserts in Apache Pinot

Real-time Analytics with Presto and Apache Pinot

· One min read
PinotDev
Pinot Editorial Team

In this world, most analytics products either focus on ad-hoc analytics, which requires query flexibility without guaranteed latency, or low latency analytics with limited query capability. In this blog, we will explore how to get the best of both worlds using Apache Pinot and Presto.

Read Part 1 at https://medium.com/apache-pinot-developer-blog/real-time-analytics-with-presto-and-apache-pinot-part-i-cc672caea307

Read Part 2 at https://medium.com/apache-pinot-developer-blog/real-time-analytics-with-presto-and-apache-pinot-part-ii-3d09ff937713

Real-time Analytics with Presto and Apache Pinot

Change Data Analysis with Debezium and Apache Pinot

· One min read
Kenny Bastani
Kenny Bastani

In this blog post, we’re going to explore an exciting new world of real-time analytics based on combining the popular CDC tool, Debezium, with the real-time OLAP datastore, Apache Pinot.

Read More at https://medium.com/apache-pinot-developer-blog/change-data-analysis-with-debezium-and-apache-pinot-b4093dc178a7

Change Data Analysis with Debezium and Apache Pinot

Operating Apache Pinot at Uber Scale

· One min read
Uber
Uber Data Team

Uber has a complex marketplace consisting of riders, drivers, eaters, restaurants and so on. Operating that marketplace at a global scale requires real-time intelligence and decision making. For instance, identifying delayed Uber Eats orders or abandoned carts helps to enable our community operations team to take corrective action. Having a real-time dashboard of different events such as consumer demand, driver availability, or trips happening in a city is crucial for day-to-day operation, incident triaging, and financial intelligence.

Read More at https://eng.uber.com/operating-apache-pinot/

Operating Apache Pinot at Uber Scale