Presto Con 2021 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 talk, we will explore how to get the best of both worlds using Apache Pinot and Presto: 1. How people do analytics today to trade-off Latency and Flexibility: Comparison over analytics on raw data vs pre-join/pre-cube dataset. 2. Introduce Apache Pinot as a column store for fast real-time data analytics and Presto Pinot Connector to cover the entire landscape. 3. Deep dive into Presto Pinot Connector to see how the connector does predicate and aggregation push down. 4. Benchmark results for Presto Pinot connector.

1. Real-time Analytics with
Presto and Apache Pinot
Xiang Fu
Sept. 24, 2020

2. User Facing Applications Business Facing Metrics
Apache Pinot
Anomaly Detection
- Ingestion: Millions of events/sec
- Workload: Thousands of queries/sec
- Performance: Millisecond

3. Fact Table
Dimension Table Pre-Join Pre-Aggregation Pre-Cube
Presto Pinot
Latency
Flexibility
low
high
low
high
Latency vs Flexibility

4. SPEED
FLEXIBILITY
Presto + Pinot

5. Presto + Pinot
SPEED
FLEXIBILITY

6. Thank you
- Getting Started
https://tinyurl.com/prestoPinotTutorial
- Pinot Slack Channel
https://tinyurl.com/pinotSlackChannel
Contributors: Devesh Agrawal, Dharak
Kharod, Haibo Wang, James Sun, Venki
Korukanti, Xiang Fu, Zhenxiao Luo