Update 2024-09-03-pinot-for-low-latency-offline-table-analytics.mdx

data/blog/2024-09-03-pinot-for-low-latency-offline-table-analytics.mdx

@@ -2,7 +2,8 @@
 title: 'Pinot for Low-Latency Offline Table Analytics'
 date: 2024-09-03
 authors: ['sultana', 'balci', 'uber']
-summary: Unais Siddiqui is a Senior Software Engineer at Xische & Co., a hybrid boutique consulting company based in Dubai. For a recent project, Unais needed to employ real-time analytics.
+summary: Apache Pinot™ is a real-time OLAP database capable of ingesting data from streams like Apache Kafka® and offline data sources like Apache Hive™. At Uber, Pinot has proven to be really versatile in handling a wide spectrum of use cases: from real-time use cases with over one million writes per second, 100+ QPS, and <500 ms latency, to use cases which require low-latency analytics on offline data. Pinot tables fall in three broad categories: real-time, offline and hybrid. Real-time tables support ingesting data from streams like Kafka, offline tables allow uploading pre-built “segments” via Pinot Controller’s HTTP APIs, and hybrid tables have both real-time and offline parts. Hybrid tables allow a single logical table (same name and schema) to ingest data from real-time streams as well as batch sources. This article shares how Uber uses Pinot’s offline tables to serve 100+ low-latency analytics use cases spanning all lines of businesses.
+
 tags:
     [
         Pinot,