Druid @ branch

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The document summarizes Druid, an open source data analytics platform, and how it has enhanced the data platform for a company to enable better business decisions. Key features of Druid include sub-second aggregate queries, real-time analytics dashboards, and live queries for unique users. Druid has helped scale to several hundred terabytes of data with thousands of queries per second while supporting new analytics applications, ad hoc reporting, and exploratory analysis. Future plans include improving the query service and migrating components to technologies like Spark, Flink, Mesos and Docker.

Data & Analytics

Druid @ Branch
Enhancing the Data Platform for better Business
Decisions
● Sub Second aggregate queries
● Real time analytics dashboard
● Live queries for uniques
● Instant exploratory analytics
Technology powering the Data Platform
Performance & Scale Considerations
Opportunity for new Apps
Monitoring
Provisioning & deployment
Future Plan
Demo
Biswajit Das
Data Team
@biswajit @branch.io
Muwon Lum
Infra Team
@muwon @branch.io

Agenda
The Business Problem
Technology Gap
Data Platform Features
Performance and Scale
Opportunity for new Apps
Monitoring
Provisioning & deployment
Future Plan

The Business Problem
● Cannot perform live complex queries
● Lack of instant access to aggregate data
● Gathering unique impressions time consuming
● No single pane of glass to view all data
● Ad Hoc query requires pre-aggregation
Instant access to information at scale was a problem

Technology Gap
Key/Value Store (Aerospike)
● Pre-compute all permutations of possible user queries.
● Range scans on event data.
● Pre-computing all permutations of all ad-hoc queries can lead to a result sets that grow exponentially
with the number of columns of a data sets and can require hours of pre-processing time.

High Level Data Pipeline flow
SECOR
Tranquility
Parquet

Performance And Scale
● 25 node Production cluster (only Druid)
● Several hundred terabytes raw data indexed .
● Typical complex datasource with 30 dimension and 2 metrics
● Real time indexer with ~30k events per second to peak 50k
● Hourly bucketed data to support different timezones
● Sustained 2B + events day
● Thousands of queries per second for online dashboard applications
● Serving 11 million query every day

Opportunity for new Apps
● Druid helped us to support new analytics easily .
● Ad hoc reporting .
● Visualizing Data.
● Exploratory analytics .

Future Plan
● More robust Query Service .
● Migrate Hadoop indexer to Spark.
● Actively working to migrate streaming pipeline to Flink .
● Evaluating to move whole druid stack to Mesos/Docker .

Thank you
We are hiring : https://branch.io/careers

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Gen AI on Enterprise Cloud Apache NiFi Milvus Apache Kafka Apache Flink Cloudera Machine Learning Cloudera DataFlow https://medium.com/@tspann/building-a-milvus-connector-for-nifi-34372cb3c7fa https://www.meetup.com/futureofdata-princeton/events/300737266/ https://lu.ma/q7pcfyjn?source=post_page-----34372cb3c7fa--------------------------------&tk=TTyakY If you're interested in working with Generative AI on the cloud, this virtual workshop is for you. Tim Spann from Cloudera and Yujian Tang from Zilliz will cover how you can implement your own GenAI workflows on the cloud at enterprise scale. 9:00 - 9:05: Intro 9:05 - 9:15: What is Milvus 9:15 - 9:25: Cloudera Development Platform 9:25 - 10:00: Demo Location https://www.youtube.com/watch?v=IfWIzKsoHnA https://github.com/tspannhw/SpeakerProfile https://www.linkedin.com/in/yujiantang/

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Druid @ branch

1. Druid @ Branch Enhancing the Data Platform for better Business Decisions ● Sub Second aggregate queries ● Real time analytics dashboard ● Live queries for uniques ● Instant exploratory analytics Technology powering the Data Platform Performance & Scale Considerations Opportunity for new Apps Monitoring Provisioning & deployment Future Plan Demo Biswajit Das Data Team @biswajit @branch.io Muwon Lum Infra Team @muwon @branch.io

2. Agenda The Business Problem Technology Gap Data Platform Features Performance and Scale Opportunity for new Apps Monitoring Provisioning & deployment Future Plan

3. The Business Problem ● Cannot perform live complex queries ● Lack of instant access to aggregate data ● Gathering unique impressions time consuming ● No single pane of glass to view all data ● Ad Hoc query requires pre-aggregation Instant access to information at scale was a problem

4. Technology Gap Key/Value Store (Aerospike) ● Pre-compute all permutations of possible user queries. ● Range scans on event data. ● Pre-computing all permutations of all ad-hoc queries can lead to a result sets that grow exponentially with the number of columns of a data sets and can require hours of pre-processing time.

5. Druid to the rescue…….

6. High Level Data Pipeline flow SECOR Tranquility Parquet

7. Batch System

8. Query path

9. Performance And Scale ● 25 node Production cluster (only Druid) ● Several hundred terabytes raw data indexed . ● Typical complex datasource with 30 dimension and 2 metrics ● Real time indexer with ~30k events per second to peak 50k ● Hourly bucketed data to support different timezones ● Sustained 2B + events day ● Thousands of queries per second for online dashboard applications ● Serving 11 million query every day

10. Opportunity for new Apps ● Druid helped us to support new analytics easily . ● Ad hoc reporting . ● Visualizing Data. ● Exploratory analytics .

11.

12. Provisioning & Deployment SaltStack

13. Rolling Updates 1 2 3

14. Future Plan ● More robust Query Service . ● Migrate Hadoop indexer to Spark. ● Actively working to migrate streaming pipeline to Flink . ● Evaluating to move whole druid stack to Mesos/Docker .

15. Thank you We are hiring : https://branch.io/careers

Druid @ branch

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