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@yourtwitterhandle | developer.confluent.io
What are the best practices to debug client applications
(producers/consumers in general but also Kafka Streams
applications)?
Starting soon…
STARTING SOOOOON..
Starting sooooon ..
Starting soon…
Starting soon…
@yourtwitterhandle | developer.confluent.io
What are the best practices to debug client applications
(producers/consumers in general but also Kafka Streams
applications)?
Starting soon…
STARTING SOOOOON..
Starting sooooon ..
Starting soon…
Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc.
Streaming Architecture
Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc.
Streaming Architecture
@yourtwitterhandle | developer.confluent.io
What are the best practices to debug client applications
(producers/consumers in general but also Kafka Streams
applications)?
Goal
Partners Tech Talks are webinars where subject matter experts from a Partner talk about a
specific use case or project. The goal of Tech Talks is to provide best practices and
applications insights, along with inspiration, and help you stay up to date about innovations
in confluent ecosystem.
@yourtwitterhandle | developer.confluent.io
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STARTING SOOOOON..
Starting sooooon ..
@yourtwitterhandle | developer.confluent.io
Starting soon…
STARTING SOOOOON..
Starting sooooon ..
Confluent Perspective Real Time Analytics
9
10
Business challenges Technical challenges
Increasingly complex application
environments and mounting pressures to
track and respond to every indicator and
issue.
Data latency and lack of end-to-end,
scalable observability for monitoring
behavior, performance, and health of
complex systems, applications, and
infrastructure.
Technology failures and security risks
that result in disruption to
customer-facing services and costly losses
for the business.
Higher operational costs due to more
troubleshooting time, bottlenecks, and
suboptimal performance requiring
additional resources/infrastructure.
INDUSTRY: ALL
11
Why Confluent
Stream
data everywhere, on premises and in every
major public cloud.
Connect
operational data like logs, metrics, and traces
from across your entire business including
on-prem, cloud, and hybrid environments.
Process
data streams to feed real-time analytics
applications that query and visualize critical
metrics at scale including latencies, error
rates, overall service health statuses, etc.
Govern
data to ensure quality, security, and
compliance while enabling teams to discover
and leverage existing data products.
Business impact
Enable early detection of system-wide issues
to prevent incidents and downtime.
Deliver proactive, faster responses to open
incidents for quicker resolution.
Gain the ability to deeply analyze all systems
and make more informed decisions.
INDUSTRY: ALL
Why?
Connect
Connect natively to Confluent
and develop scalable APIs in
minutes with SQL.
Share
Share as high-concurrency,
low-latency APIs with other
engineers so they can start
building.
Combine
Combine Kafka data with
other sources (Snowflake,
BigQuery, etc.) to build rich and
fast data products.
@yourtwitterhandle | developer.confluent.io
What are the best practices to debug client applications
(producers/consumers in general but also Kafka Streams
applications)?
Starting soon…
STARTING SOOOOON..
Starting sooooon ..
Starting soon…
Speed Wins.
Data Platforms must be fast.
Engineers must also be fast.
Tinybird + Confluent
From Confluent to APIs in minutes.
Unify streams, files,
tables, and more
Develop faster with SQL
and publish APIs
Empower others to build
data products
Real-time personalisation
User-facing analytics
Operational intelligence
Streams
Files
DB tables
Data sources Data products
API Endpoints
Milliseconds
Examples of user-facing analytics
Build differentiated features that delight users
In-product dashboards Real-time personalisation Fraud + anomaly detection
Capture data about user interactions within an application, send that data to an
analytics platform, and build metrics that are then served back to the user as
dashboards or features.
Status quo
Data
Warehouse
Data
Modeling
ETL
Low-Latency
Store
Backend
Data
app
Frontend
Confluent
Common data stack for analytics
New approach
Data
Warehouse
Data
Modeling
ETL
Low-Latency
Store
Backend
Data
app
Real-time data
platform
Frontend
The real-time way
Confluent
Example
User-facing Analytics Architecture
About Tinybird
We accelerate data
and engineering
teams
➔ Open-source ClickHouse at the core
➔ Serverless & fully managed
➔ Cloud-native
➔ Consumption based
➔ Unrivaled developer productivity
Customer
Implementations
"Real-time data is the new
standard, and we want to win.
The best way to deliver a
differentiated user experience is
with live, fresh data."
Damian Grech, Director of Engineering, Data Platform
FanDuel relies on
Tinybird for real-time
personalization and
observability
Processed per month.
273TB+
Requests per day.
Average query latency.
216K+
<50ms
Development time for
the first use case.
<3w
Retail operates in
real-time with
Tinybird
Rows read during
Black Friday
11.9T
Internal Users
+1000
➔ Real-time business intelligence
➔ Real-time inventory management
➔ Real-time personalization
➔ Real-time in-house Web Analytics
P95 latency
240ms
With numbers
Top 5 Global Fashion Retailer
Canva relies on
Tinybird to deliver
insights to their
users
Peak API
1250 rps
➔ Real-time ingestion and analysis of
web events
➔ Real-time User-facing insights about
users published videos
With numbers
Split calculates
the impact of A/B
tests in real-time
Avg. ingested from Kafka
per month (compressed).
220TB+
Requests per day.
From 30-min latency to
real-time.
2.5M+
1-3s
features in production
within 4 months of signing
7
IMAGE
Factorial built 12
new product
features in 6
months
Processed per month.
65TB+
Requests per month.
Average feature dev time
1m
2 weeks
Initial POC to Production
launch time
1 month
IMAGE
The Hotels Network
provides real-time
competitive insights
and personalized
booking experiences
➔ User-facing dashboards and real-time
personalization.
➔ Streaming join in ksqlDB.
➔ Ingested into Tinybird for historical
enrichment and publication via API
Endpoints with sub-second latency.
API requests per month
1B+
Processed per month
6PB
Demo
Working together
The ideal joint Confluent
+ Tinybird customer
Performance is table stakes. Tinybird +
Confluent enable engineering teams to develop
faster and ship more..
Are they trying to adapt their existing DW?
Implement a new database? Simplify their stack?
Tinybird + Confluent are better in the cloud.
Prioritise speed to market
Moving from batch to real-time DSP
Cloud native
hi@tinybird.co
Thank you.
Confluent Partners do more with Tinybird

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Speed Wins: From Kafka to APIs in Minutes

  • 1. @yourtwitterhandle | developer.confluent.io What are the best practices to debug client applications (producers/consumers in general but also Kafka Streams applications)? Starting soon… STARTING SOOOOON.. Starting sooooon .. Starting soon… Starting soon…
  • 2. @yourtwitterhandle | developer.confluent.io What are the best practices to debug client applications (producers/consumers in general but also Kafka Streams applications)? Starting soon… STARTING SOOOOON.. Starting sooooon .. Starting soon…
  • 3. Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc. Streaming Architecture
  • 4. Copyright 2021, Confluent, Inc. All rights reserved. This document may not be reproduced in any manner without the express written permission of Confluent, Inc. Streaming Architecture
  • 5. @yourtwitterhandle | developer.confluent.io What are the best practices to debug client applications (producers/consumers in general but also Kafka Streams applications)?
  • 6. Goal Partners Tech Talks are webinars where subject matter experts from a Partner talk about a specific use case or project. The goal of Tech Talks is to provide best practices and applications insights, along with inspiration, and help you stay up to date about innovations in confluent ecosystem.
  • 7. @yourtwitterhandle | developer.confluent.io Starting soon… STARTING SOOOOON.. Starting sooooon ..
  • 8. @yourtwitterhandle | developer.confluent.io Starting soon… STARTING SOOOOON.. Starting sooooon ..
  • 9. Confluent Perspective Real Time Analytics 9
  • 10. 10 Business challenges Technical challenges Increasingly complex application environments and mounting pressures to track and respond to every indicator and issue. Data latency and lack of end-to-end, scalable observability for monitoring behavior, performance, and health of complex systems, applications, and infrastructure. Technology failures and security risks that result in disruption to customer-facing services and costly losses for the business. Higher operational costs due to more troubleshooting time, bottlenecks, and suboptimal performance requiring additional resources/infrastructure. INDUSTRY: ALL
  • 11. 11 Why Confluent Stream data everywhere, on premises and in every major public cloud. Connect operational data like logs, metrics, and traces from across your entire business including on-prem, cloud, and hybrid environments. Process data streams to feed real-time analytics applications that query and visualize critical metrics at scale including latencies, error rates, overall service health statuses, etc. Govern data to ensure quality, security, and compliance while enabling teams to discover and leverage existing data products. Business impact Enable early detection of system-wide issues to prevent incidents and downtime. Deliver proactive, faster responses to open incidents for quicker resolution. Gain the ability to deeply analyze all systems and make more informed decisions. INDUSTRY: ALL
  • 12. Why? Connect Connect natively to Confluent and develop scalable APIs in minutes with SQL. Share Share as high-concurrency, low-latency APIs with other engineers so they can start building. Combine Combine Kafka data with other sources (Snowflake, BigQuery, etc.) to build rich and fast data products.
  • 13. @yourtwitterhandle | developer.confluent.io What are the best practices to debug client applications (producers/consumers in general but also Kafka Streams applications)? Starting soon… STARTING SOOOOON.. Starting sooooon .. Starting soon…
  • 14. Speed Wins. Data Platforms must be fast. Engineers must also be fast.
  • 15. Tinybird + Confluent From Confluent to APIs in minutes. Unify streams, files, tables, and more Develop faster with SQL and publish APIs Empower others to build data products Real-time personalisation User-facing analytics Operational intelligence Streams Files DB tables Data sources Data products API Endpoints Milliseconds
  • 16. Examples of user-facing analytics Build differentiated features that delight users In-product dashboards Real-time personalisation Fraud + anomaly detection Capture data about user interactions within an application, send that data to an analytics platform, and build metrics that are then served back to the user as dashboards or features.
  • 20. About Tinybird We accelerate data and engineering teams ➔ Open-source ClickHouse at the core ➔ Serverless & fully managed ➔ Cloud-native ➔ Consumption based ➔ Unrivaled developer productivity
  • 22. "Real-time data is the new standard, and we want to win. The best way to deliver a differentiated user experience is with live, fresh data." Damian Grech, Director of Engineering, Data Platform
  • 23. FanDuel relies on Tinybird for real-time personalization and observability Processed per month. 273TB+ Requests per day. Average query latency. 216K+ <50ms Development time for the first use case. <3w
  • 24. Retail operates in real-time with Tinybird Rows read during Black Friday 11.9T Internal Users +1000 ➔ Real-time business intelligence ➔ Real-time inventory management ➔ Real-time personalization ➔ Real-time in-house Web Analytics P95 latency 240ms With numbers Top 5 Global Fashion Retailer
  • 25. Canva relies on Tinybird to deliver insights to their users Peak API 1250 rps ➔ Real-time ingestion and analysis of web events ➔ Real-time User-facing insights about users published videos With numbers
  • 26. Split calculates the impact of A/B tests in real-time Avg. ingested from Kafka per month (compressed). 220TB+ Requests per day. From 30-min latency to real-time. 2.5M+ 1-3s features in production within 4 months of signing 7 IMAGE
  • 27. Factorial built 12 new product features in 6 months Processed per month. 65TB+ Requests per month. Average feature dev time 1m 2 weeks Initial POC to Production launch time 1 month IMAGE
  • 28. The Hotels Network provides real-time competitive insights and personalized booking experiences ➔ User-facing dashboards and real-time personalization. ➔ Streaming join in ksqlDB. ➔ Ingested into Tinybird for historical enrichment and publication via API Endpoints with sub-second latency. API requests per month 1B+ Processed per month 6PB
  • 29. Demo
  • 30. Working together The ideal joint Confluent + Tinybird customer Performance is table stakes. Tinybird + Confluent enable engineering teams to develop faster and ship more.. Are they trying to adapt their existing DW? Implement a new database? Simplify their stack? Tinybird + Confluent are better in the cloud. Prioritise speed to market Moving from batch to real-time DSP Cloud native
  • 32. Thank you. Confluent Partners do more with Tinybird