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Deep Analysis of Russian Twitter Trolls

· One min read
Kenny Bastani
Kenny Bastani

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.

Read More at https://towardsdatascience.com/a-deep-analysis-of-russian-trolls-with-apache-pinot-and-superset-590c8c4d1843

Deep Analysis of Russian Twitter Trolls

Leverage Plugins to Ingest Parquet Files from S3 in Pinot

· One min read
PinotDev
Pinot Editorial Team

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 -

  • pinot-batch-ingestion-spark
  • pinot-s3
  • pinot-parquet

Read more at https://medium.com/apache-pinot-developer-blog/leverage-plugins-to-ingest-parquet-files-from-s3-in-pinot-decb12e4d09d

Leverage Plugins to Ingest Parquet Files from S3 in Pinot

Monitoring Apache Pinot with JMX, Prometheus and Grafana

· One min read
PinotDev
Pinot Editorial Team

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?

Read more at https://medium.com/apache-pinot-developer-blog/monitoring-apache-pinot-99034050c1a5

Monitoring Apache Pinot with JMX, Prometheus and Grafana

Achieving 99th percentile latency SLA using Apache Pinot

· One min read
PinotDev
Pinot Editorial Team

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.

Read more at https://medium.com/apache-pinot-developer-blog/achieving-99th-percentile-latency-sla-using-apache-pinot-2ba4ce1d9eff

Achieving 99th percentile latency SLA using Apache Pinot

Utilize UDFs to Supercharge Queries in Apache Pinot

· One min read
PinotDev
Pinot Editorial Team

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.

Read more at https://medium.com/apache-pinot-developer-blog/utilize-udfs-to-supercharge-queries-in-apache-pinot-e488a0f164f1

Utilize UDFs to Supercharge Queries in Apache Pinot

Building a culture around metrics and anomaly detection

· One min read
Kenny Bastani
Kenny Bastani

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.

Read More at https://medium.com/apache-pinot-developer-blog/building-a-culture-around-metrics-and-anomaly-detection-da740960fcc2

Building a culture around metrics and anomaly detection

Moving developers up the stack with Apache Pinot

· One min read
Kenny Bastani
Kenny Bastani

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.

Read More at https://medium.com/apache-pinot-developer-blog/moving-developers-up-the-stack-with-apache-pinot-29d36717a3f4

Moving developers up the stack with Apache Pinot

Monitoring business performance data with ThirdEye smart alerts

· One min read
LinkedIn
LinkedIn Engineering Team

Explain how ThirdEye smart alerts and automated dashboards helped the LinkedIn Premium business operations team monitor key metrics—such as new free trial signups—for the timely detection of outliers in business performance data.

Read More at https://engineering.linkedin.com/blog/2020/monitoring-business-performance-data-with-thirdeye-smart-alerts

Monitoring business performance data with ThirdEye smart alerts