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.).
Blog gives an overview of our use of Apache Pinot to solve some of biggest challenges around Data Analytics in Large Retail Chain
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.
The history behind Russian disinformation is a dense and continuously evolving subject. The world’s best research hasn’t seemed to hit the mainstream yet, which made this an excellent opportunity to see if I could use some open source tooling to surface new analytical evidence.
In this blog post, I’ll show you how to use Apache Pinot and Superset to analyze 3 million tweets by the Internet Research Agency (IRA) open-sourced by FiveThirtyEight.
One of the primary advantages of using Pinot is its pluggable architecture. The plugins make it easy to add support for any third-party system which can be an execution framework, a filesystem, or input format.
In this tutorial, we will use three such plugins to easily ingest data and push it to our Pinot cluster. The plugins we will be using are -
I may be kicking open doors here, but a simple question has always helped me start from somewhere. When it comes to investigating degraded user experience caused by latency, can I observe high resource usage on all or some nodes of the system?
In this article, we talk about how users can build critical site-facing analytical applications requiring high throughput and strict p99th query latency SLA using Apache Pinot.
Apache Pinot is a realtime distributed OLAP datastore that can answer hundreds of thousands of queries with millisecond latencies. You can head over to https://pinot.apache.org/ to get started with Apache Pinot.
While using any database, we can come across a scenario where a function required for the query is not supported out of the box. In such time, we have to resort to raising a pull request for a new function or finding a tedious workaround.
Scalar Functions that allow users to write and add their functions as a plugin.
Anomaly detection is a very broad term. Usually it means that you want to see if things are running as usual. This could go from your business metrics down to the lowest level of how your systems are running. Anomaly detection is an entire process. It’s not just a tool that you get out of the box that measures time series data. Similar to DevOps, anomaly detection is a culture of different roles engaging in a process that combines tooling with human analysis.
Once upon a time, an internet company named LinkedIn faced the challenge of having petabytes of connected data with no way to analyze it in real-time. As this was a problem that was the first of its kind, there was only one solution. The company put together a talented team of engineers and tasked them with building the right tool for the job. Today, that tool goes by the name of Apache Pinot.
In this blog post, we’ll show you how Pinot and Kafka can be used together to ingest, query, and visualize event streams sourced from the public GitHub API. For the step-by-step instructions, please visit our documentation, which will guide you through the specifics of running this example in your development environment.
ThirdEye is a comprehensive platform for real-time monitoring of metrics that covers a wide variety of use-cases. LinkedIn relies on ThirdEye to monitor site performance, track member growth, understand adoption of new features, flag sustained attempts to circumvent system security, and many other areas