Case StudyPICNIC.APP

Picnic Modernizes Data Architecture withConfluent for Streaming Analytics

Picnic is Europe’s fastest-growing online-only supermarket, currently operating in France,
the Netherlands, and Germany. The company relies heavily on data-driven decisions toprovide the lowest-price guarantee to its customers. Picnic processes more than 300 million
unique events per week from customer applications (i.e., shopping applications) and internal
systems to power predictive analytics in their data warehouse.
Headquarters
Amsterdam-Duivendrecht, North
Holland, Netherlands
Industry
Retail/Online Grocery
Challenge
Address legacy tech-related
operational overhead and scalability
issues to allow for better customer
behavior analytics and improve
internal processes.
Solution
Confluent Cloud to save time andmoney by reducing operational
overhead and allowing for real-timeprocessing and easy scalability ofevent data.
Results• Reduced infrastructure costs
by 40%• Simplified, future-proof dataarchitecture• Improved infrastructure
monitoring for better SLAs
and system health• Elimination of data loss

Picnic’s blazing-fast growth, attributable to consumer demand, industry recognition, and a
successful series D funding, underpinned the need to seek out a better performing and morereliable data streaming platform, as well as improved streaming analytics capabilities.
That’s when Picnic turned to Confluent.

The Challenge
Two quickly growing data pipelines
Picnic has two primary data pipelines. The first one is for their customer-facing application,
which collects data on user behavior related to things like product engagement and checkout.
Picnic analyzes this data to determine how to improve product recommendations andenhance the application UI.
The second data pipeline processes data for internal backend systems, which help with data
warehouse, payment processing, product availability status, and much more. It enables Picnic
to plan procurement, optimize warehouse operations, and ensure customers have a positive
experience by delivering the right products in a timely manner.
As Picnic grew, they ran into issues using AWS Kinesis for these data pipelines, specifically:• An inability to store data for more than one week. Replayability and recoverability are
key for Picnic to quickly fix malformed events or pipeline failures and also to accomplish
complex stream processing.
• Picnic began to require vast tooling around its data pipelines to enable seamless data
streaming to other systems without having to create customer services from scratch.
• Last but not least, Picnic needed exactly-once semantics for certain use cases such as
dynamic business rules evaluation.

“Confluent provides exactly what we dreamed of: an ecosystem of tools to source andsink data from data streams. It’s provided us not only with great data pipeline agility
and flexibility but also a highly simplified infrastructure that’s allowed us to reduce costs.”— dima kalashnikov, technical lead

© 2022 Confluent, Inc. | Confluent.io1

Case Study | Picnic

Technical Solution

Business Results

Easy scaling and data flexibility

Simplified architecture, saved money

Picnic felt that the time for a change had come and sought asolution that could address all of the above issues at once. Their
search led them to Confluent Cloud and its Apache Kafka©
foundation.

Reduced infrastructure cost “Our projected cost-savings improvedby 40% and optimizing costs is one of the biggest impactsConfluent has made at Picnic,” Dima Kalashnikov, Technical Leadat Picnic said.

They redesigned the architecture of the data pipelines, which led
to a dramatic simplification of their internal-services pipelines,
and more efficient processing of customer application data.

Simplified IT architecture “The internal-services pipeline shrankfrom five managed services to only one and the number ofmaintained services went down by 40% across all managedservices,” Kalashnikov said.

Very quickly, they experienced the following benefits by moving
to Confluent:• They can store data in Confluent for as long as they want, since
Confluent’s Infinite Storage doesn’t have limitations on volume
or retention periods.
• Confluent offers a rich ecosystem of pre-built, fully managed
connectors that are ready to use out-of-the-box and eliminatedthe operational burden of maintaining them
• Exactly-once semantics is finally possible thanks to Kafka andConfluent’s fully managed connectors.
Previously, all data from RabbitMQ was sent into a single Kinesis
stream, which put the burden of event separation on the data
warehouse and made scalability more challenging.
Now, Picnic uses connectors for RabbitMQ to forward data from
RabbitMQ into Confluent Cloud Kafka topics, after which theyuse fully managed sink connectors to load data seamlessly into
Snowflake and Amazon S3 for further analysis by data scienceteams. They also use Confluent’s Data Preview feature on fully
managed connectors to help iterate and test a connector’s output
prior to writing to production.
This new setup has led to greater scalability and establishedclear streams of homogenous data. It has also benefited fromConfluent’s managed connectors, which enabled Picnic to moreeasily monitor APIs for solutions like Prometheus.

Improved infrastructure monitoring for better SLAs
Per Kalashnikov: “Thanks to the way Confluent has simplified ourinfrastructure, it’s allowed us to improve our SLAs on data delivery
and also prevent us from having any data loss, which is a major
SLA for us.”

What’s Next?
Moving forward, Picnic plans to leverage ksqlDB for new
streaming analytics use cases like powering real-time reporting
dashboards, for streaming ETL, and to enable things like real-time
recommendations based on user behavior.
Picnic also wants to improve data delivery to and from its
automated fulfillment center and create a self-service platform
for data delivery via automated deployment systems for
Confluent-based pipelines. Ultimately, the goal of this project isto deliver data from an automated fulfillment center to a data
warehouse so analysts are able to analyze it in conjunction withother available data.

Learn More About Picnic
https://picnic.app/de/
https://blog.picnic.nl/“Our machine learning developers and data scientists can now start to think about and work on in-stream data analysis andreal-time models. We now have a vast and thriving ecosystem of Confluent enterprise-grade tooling that allows us to expand ouranalytics platform to the places where it would have been too much of a burden in the past.”— dima kalashnikov, technical lead

© 2022 Confluent, Inc. | Confluent.io2