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Version 1.0
Anirudh Ramanathan
Rockset
Twitter: foxish_
Operational Analytics on Event Streams in Kafka

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● Product Team @ Rockset
● Ex Google Cloud
● K8s Contributor & Release Team
(with a focus on Controllers,
Operators)
● Apache Spark committer
Me Rockset
● Search and analytics engine
● Enables real-time applications and
operational analytics
About

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Overview
● Streaming Data
● Kafka & Analytics
● Operational Analytics with Rockset
● Under the Hood
● Live Demo: building a microservice to analyze streaming event data
● Q&A
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Why streaming data?
Event data
● Advertising
● Financial Transactions
● Web clicks
● Online Gaming Interactions
● IoT - sensor data
● Travel Bookings
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Between systems
● Data enrichment & transformation
● Moving data between sources and sinks
● Event driven architectures

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Kafka and streaming data
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Source: https://kafka.apache.org/intro
● Apache Kafka is a distributed streaming
platform.
● Widely used for…
○ building systems that transform or
react to streams of data
○ building pipelines that reliably get
data between systems

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Why move streaming data?
When operating on streaming data, there are some common patterns that emerge:
● Trigger downstream events, alerts, etc based on certain conditions
● Write into data lakes & warehouses for archival
● Write into datastores for analytics
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Analytics on Streaming Data
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Operational Analytics
Considerations for an operational analytics engine
● Data Latency - How up-to-date is my database?
● Query - Can I express complex queries, JOINs, etc?
● Query Latency - How fast are my individual queries?
● Query Throughput - Can I serve many users and requests?
● Retention - Lifecycle management for incoming events
● Operations - Maintenance, capacity planning and scaling
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Rockset for Operational Analytics
● Low latency indexing and querying
● Full featured SQL
● Continuous Ingest
● Time-based data retention
● REST API and Client SDKs
● Low operational overhead - availability, replicas, sharding handled under the
hood
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● Component of Apache Kafka
● Framework for connecting Kafka with
external systems such as databases,
key-value stores, search indexes, and
file systems.
● Solves schema management, fault
tolerance, parallelism, scaling, delivery
semantics and monitoring in a
consistent way for sources and sinks.
Kafka Connect
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Source:
https://www.confluent.io/blog/announcing-kafka-connect-building-larg
e-scale-low-latency-data-pipelines/

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Under the Hood
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Converged Indexes
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● Multiple indexes are built and maintained behind the scenes
● Fast analytical queries + fast search queries
● SQL Optimizer picks between columnar store or search index
● All indexes stored on write-optimized RocksDB layer
SELECT *
FROM search_logs
WHERE keyword = ‘kafka’
AND locale = ‘en’
Search index
SELECT keyword, count(*)
FROM search_logs
GROUP BY keyword
ORDER BY count(*) DESC
Columnar store

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Smart Schemas
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● Type information stored with values,
not “columns”
● Strongly typed queries on dynamically
typed fields
● No explicit schema definition needed
● Designed for nested semi-structured
data

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Disaggregated, Cloud-Native Architecture
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● Ingest, query, and storage tiers grow and shrink automatically
● Hierarchical storage (RAM, SSDs, Object Storage)
● Uses hardware elasticity in the cloud with node autoscaling
● K8s custom controllers scale up the data and compute tiers with pod autoscaling

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Disaggregated, Cloud-Native Architecture
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Live Demo: building a microservice
to analyze streaming event data
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Building a Stateful Microservice
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Ingest
No schema definition
Continuous sync of new
data from data sources
Query Build
Full SQL across both
structured and
semi-structured data
Fast query serving at
scale to power your
applications and
dashboards

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Start Building Now: rockset.com

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Thank You
Anirudh Ramanathan anirudh@rockset.com
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