Discover how Cloudflare leverages ClickHouse dictionaries to process hundreds of millions of rows per second for applications like web traffic analysis and customer dashboards in this London meetup report.

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London Meetup Report: How Cloudflare Processes Hundreds of Millions of Rows per Second with ClickHouse

Real-time insights can make or break a digital marketing campaign. As an automation marketing company based in Israel, [Ongage](https://www.ongage.com/) recognized this need. Revolutionizing the email marketing industry, they manage data for customers with hundreds of millions of contacts, executing campaigns on a huge scale across channels such as email and SMS.

However, Ongage faced a challenge. The data warehouse they used couldn't offer the real-time capabilities they were seeking. Processing around half a billion messages per day – from customer email feedback to user behavior metrics – required a robust solution. This blog post provides an in-depth look at how Ongage navigated this challenge and enhanced their analytical capabilities and operational efficiency by transitioning to ClickHouse.

## The Evolution of Ongage's Database Technology: From Limitations to Real-Time Analytics

In the early stages of Ongage's technology journey, the team found themselves working with a blend of MongoDB, MySQL, and Redshift. Each database served its unique purpose, but the absence of real-time capabilities soon became a clear bottleneck. According to Roman Raslin, VP of R&D at Ongage, “Our existing data warehouse lacked the real-time capabilities we needed. This was particularly important for our analytics and automation operations, where immediate event triggering and analysis were essential.” This prompted a clear need for evolution and the start of their search for a more efficient solution.

Ongage's prior analytics setup relied heavily on a large MySQL cluster, managed and operated by AWS RDS. As their data volumes increased, the limitations of MySQL started to surface. The performance sharply declined and the analytics page load times became too long. 

ClickHouse entered the picture, offering a significant leap in data processing real-time analytics capabilities. MySQL is still retained for handling transaction data that doesn’t grow significantly, and is used to enrich data in ClickHouse with details such as account and campaign names.

One of the key elements that eased this transition was ClickHouse's built-in MySQL engine. This feature enabled the Ongage team to extract data from MySQL and convert it into ClickHouse's native table format effortlessly. The MySQL engine was used not just to query data, but also to facilitate a smooth migration into ClickHouse.

## Why ClickHouse Overcame SingleStore in Ongage's Assessment

When selecting a data management solution, the team at Ongage didn't just go with the first option. They explored various solutions, SingleStore being a prime contender. During an in-depth evaluation, they stacked SingleStore against ClickHouse, considering storage costs and specific feature availability. 

From a cost perspective, ClickHouse presented a far more economical choice. SingleStore storage costs were six times more than those of ClickHouse Cloud for identical data volumes. Considering Ongage's data-intensive operations, this meant a considerable expense. 

In terms of feature offerings, ClickHouse offers a clear advantage that SingleStore lacks - the implementation of Materialized Views. This feature is a major timesaver as it facilitates aggregating a substantial number of events into a smaller, more manageable table. This, in turn, speeds up query execution considerably. 

Nevertheless, it's worth noting that SingleStore might be a more suitable fit for organizations prioritizing transactional capabilities. As Ongage specifically sought real-time analytics, this wasn't one of the factors for them. ClickHouse clearly emerged as the champion, considering the specific needs and plans Ongage had. 

## The Decision: Why ClickHouse Cloud?

Ongage's decision to use ClickHouse Cloud over the open-source self-managed option was influenced by several key factors.

Firstly, the operational efficiency of the cloud solution stood out. Raslin explained, "We spent so much work and money into managing the Mongo clusters and the Redshift clusters. We don't want to waste this time anymore." Despite their DevOps team being comfortable with self-hosting, they recognized the substantial time and resources they could conserve by opting for ClickHouse Cloud. The operational burden of managing MongoDB and Redshift clusters was already heavy, and bypassing such hassles was too appealing to pass up. 

Secondly, the value of the ClickHouse support team cannot be understated. They played a crucial role in Ongage's accelerated transformation, providing vital tips and clarifications that made the whole process smoother. This collaboration proved to be a winning formula - working directly with the developers of the product ensured that Ongage was using ClickHouse to its full potential. As Raslin put it, "For sure, the people who build the technology are best equipped to operate it."

Lastly, the financial aspect also weighed in favor of ClickHouse Cloud. During the transformation phase, the service offered substantial cost savings by scaling automatically based on use, ensuring that resources were used efficiently. It was the ideal balance of performance and cost-effectiveness for Ongage's needs.

## ClickHouse In Action: Seamless Transition and Enhancing Ongage's Use Cases

Ongage's transition to ClickHouse was smoother than expected due to its compatibility with MySQL, meaning most of their existing queries functioned without modifications. As Raslin highlighted “The query didn't change much from what we had before, given the similarities in table structures between MySQL & Redshift.” This compatibility greatly facilitated the integration, reducing potential disruptions.

Once Ongage had ClickHouse integrated into their system, two main use cases were identified.

The first use case revolved around campaign analytics. Ongage implemented ClickHouse with the current UI that their customers were already familiar with. This ensured a seamless transition where users noticed a marked increase in speed, without having to adjust to a new interface. Behind the scenes, a newly built microservice in front of ClickHouse was deployed, utilizing Node.js. 

![Analytics.jpg](/uploads/Analytics_ec486eef36.jpg)

The second use case focused on their automation system. Ongage designed a user-friendly drag and drop system that triggers specific actions based on user interactions. For example, if a user clicked on a specific campaign, the system will respond with actions such as sending an email or SMS after a set waiting period. It can also make checks for user segmentation before proceeding with an action. Crucially, each block of this drag and drop system has analytics attached, allowing for an overview of how it performed over a chosen time range.

Interestingly, despite the significant backend changes, there was no alteration to the UI for both use cases. 
![Marketing-automation-ongage.png](/uploads/Marketing_automation_ongage_5309175415.png)

The implementation of ClickHouse also brought real-time decision-making capabilities to Ongage. Previously, generating essential business reports was a time-consuming and sometimes troublesome task. "We used MySQL before, and sometimes the warehouse took two to three hours to generate a report, sometimes causing alerts in the system. It was a pain," said Raslin.

With ClickHouse, however, the situation changed dramatically. "When we tested how much time the same reports would take with ClickHouse, people were amazed. We ran the same data, and in a blink of an eye, we had the results," said Raslin. The rapid data access offered by ClickHouse not only impressed the team but also won over the company's directors. The newfound efficiency and speed has enabled them to make informed decisions faster than ever before.

## Ongage's Scalable Architecture: Leveraging ClickHouse for Real-Time Analytics

Ongage's architecture has evolved over time to cater to real-time data analytics and automation infrastructure needs. At the core of Ongage's architecture is ClickHouse.

1. Insert Raw Data and Enrichment: The first step in their architecture involves the transfer of raw data to S3. Using the S3 cluster function it is then immediately inserted into ClickHouse. Concurrently, around eight tables from MySQL are copied into ClickHouse using the MySQL engine to ensure that their data remains updated and enriched. This data is then transferred to a MergeTree table and becomes available with the exchange command. This process is streamlined thanks to ClickHouse's MySQL syntax compatibility.

2. Materialized Views and SummingMergeTree: The final step in their architecture involves the use of a materialized view triggered on the insertion of raw data. This materialized view runs multiple joins to enrich the data, which is then written to a SummingMergeTree table - the final destination from where consumers query. This step aggregates all the data together on different 'group by' parameters, enabling rapid analytical results. Depending on the use case, MySQL data is either append-only or refreshed completely.

Materialized Views and SummingMergeTree: The final step in their architecture involves the use of a materialized view triggered on the insertion of raw data. This materialized view runs multiple joins to enrich the data, which is then written to a SummingMergeTree table - the final destination from where consumers query. This step aggregates all the data together on different 'group by' parameters, enabling rapid analytical results. Depending on the use case, MySQL data is either append-only or refreshed completely.

![Events collector.png](/uploads/Events_collector_182f981f80.png)

## What's Next for Ongage and ClickHouse: The Road Ahead
Ongage began their journey with ClickHouse focused primarily on analytics and has since extended the usage to their automation project. The journey doesn't end here though; they are actively looking to migrate more of their processes to ClickHouse.

The first step in this continued transformation involves fully migrating some of their legacy pipelines. They plan to use Kafka to stream data, taking advantage of the [recent ClickPipes offering](https://clickhouse.com/blog/clickhouse-cloud-clickpipes-for-kafka-managed-ingestion-service).

Ongage plans to transfer more data from their existing Redshift clusters to ClickHouse. Their experiences with the real-time capabilities and cost-effectiveness of ClickHouse have shown them that the platform can manage many of the tasks that they currently use Redshift for.

## Conclusion

Ongage's transition to ClickHouse has provided impressive results in a remarkably short time. Not only has their system performance significantly improved, but their reporting and real-time analytics capabilities have also seen a substantial boost. The simplicity of query transition and the support from ClickHouse during the transformation process further eased their migration.

With the scalability of ClickHouse, handling data of their magnitude has become more streamlined. The use of materialized views and the strategic use of the MySQL engine improved the speed of data analysis, allowing better decision-making across the board.

Moreover, the cost-effectiveness of ClickHouse versus their previous systems ensured that Ongage not only gained a powerful data handling solution but also a sustainable one as data volumes invariably grow. As Ongage continues to leverage ClickHouse, the opportunities for further performance enhancements and cost savings are promising.

Learn more: [https://www.ongage.com/](https://www.ongage.com/)




Discover how Ongage, a leading marketing automation company, tackled the challenge of real-time email marketing analytics. Read about their strategic shift from traditional data warehouses to ClickHouse.

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Ongage's Strategic Shift to ClickHouse for Real-time Email Marketing

[MessageBird](https://messagebird.com/) is a cloud communications platform that connects businesses and customers through seamless, contextual conversations. Processing billions of messages, calls, and emails for 29k+ customers, MessageBird depends heavily on data-driven insights for efficient operations. ClickHouse, a robust analytical backend, has been integral in delivering these insights since 2017, supporting functions such as monitoring the delivery performance of SMS messages and powering customer-facing dashboards and APIs.

## Real-Time Analytics: Driving Customer-Facing Dashboards and APIs
At MessageBird, ClickHouse is integral to the company's [real-time analytics](https://clickhouse.com/resources/engineering/what-is-real-time-analytics) capabilities. The system delivers real-time data that directly powers its customer-facing dashboards and APIs. The ability of ClickHouse to support high-volume data ingestion and low response times means that customer queries are answered swiftly and accurately.

Understanding customer behavior and providing them with the information they need in real-time is crucial for MessageBird. Whether it's usage statistics, billing details, or performance metrics, the data powered by ClickHouse is immediately available to the end users, driving increased interaction and engagement.

As Javier Llorente, a Senior Data Engineer at MessageBird explained: "I think ClickHouse is really one of the best (solutions) when you have that intersection where you need to perform analytical queries over a huge data set, and you need to have a fast response within seconds. That is where I think ClickHouse has been a really good match for MessageBird." 

## Performance Monitoring: Guaranteeing Operational Excellence
Performance monitoring is critical for MessageBird. Metrics drawn from their ClickHouse-driven data infrastructure enable teams to track SMS delivery performance and promptly identify anomalies.

The versatility of ClickHouse plays a crucial role in supporting both internal and external use cases. Within MessageBird, the same ClickHouse cluster is used for a variety of functions. To ensure optimal performance, different policies and quotas are set based on the specific use case. "Although we use the same ClickHouse cluster for various functions, we have different policies and quotas in place depending on the use case. This allows us to control the timeout for queries and manage resource consumption effectively," said Llorente.

Their internal utilization of ClickHouse allows their teams to leverage the data in near real-time. As Dennis van der Vliet, Senior Engineering Manager at MessageBird, explains, "When a customer raises a concern about message delivery to a specific country, our tooling, powered by ClickHouse, becomes the go-to resource for understanding the situation."

## MessageBird's Cloud Transition with ClickHouse
MessageBird first adopted ClickHouse in 2017, transitioning from a custom cron setup on MySQL due to scalability and latency challenges. To further scale its services and manage growing data volumes, MessageBird migrated from a self-managed, on-premise solution to ClickHouse Cloud. This transition was driven by the need for a more scalable, resilient, and efficient data infrastructure, but was also significantly influenced by the cost factor. 

Migrating to ClickHouse Cloud brought significant benefits, including substantial cost savings from the shift from SSD storage to more economical S3 storage. Moreover, the use of [Projections](https://clickhouse.com/docs/en/sql-reference/statements/alter/projection) in ClickHouse became more cost-effective due to S3's low data storage costs.

## Cost Efficiency with ClickHouse Cloud
Since moving to ClickHouse Cloud MessageBird has experienced significant savings, with a reduction in costs of approximately 60-70%. This saving relates not only to the direct costs, such as self-hosted fees but also to the overhead costs associated with maintenance and infrastructure management.  In their experience, the migration to ClickHouse Cloud made considerable economic sense.

## ClickHouse Cloud Scalability and Performance Benefits
Besides cost savings, their shift to ClickHouse Cloud brought substantial scalability and performance improvements. There was a recent situation where increased data volumes led to concerns about slow performance. Before the cloud migration, such a situation would have escalated into a significant incident, owing to the complexities of scaling with their previous self-hosted setup. However, with ClickHouse Cloud, resolving the situation was straightforward. 

“Instead of escalating into an incident as it might have in our previous self-hosted setup, I simply fired up my computer, scaled up the services in the ClickHouse Cloud console and assured them performance should improve in about 20 minutes,” said van der Vliet. 

This example perfectly encapsulates how the cloud-based infrastructure enables MessageBird to rapidly respond to performance hiccups, ensuring service continuity.

## MessageBird's ClickHouse Cloud Architecture
![MessageBird Architecture.png](/uploads/Message_Bird_Architecture_8c3ab5570a.png)

MessageBird's architecture in ClickHouse Cloud integrates several modern cloud technologies. The data ingestion layer involves an ingestion service that moves data from Google Pub/Sub into ClickHouse using Apache Beam pipelines running on Google Cloud Dataflow. Data for lookup queries is stored in BigTable, which offers low latency and high throughput.
MessageBird leverages the capabilities of ClickHouse's CollapsingMergeTree table engine to ensure only the latest versions of rows are queried.

## The ClickHouse Advantage: Projections
[Projections](https://clickhouse.com/docs/en/sql-reference/statements/alter/projection) in ClickHouse offer the ability to speed up query performance by storing precalculated results for commonly used transformations or aggregate functions. This functionality significantly improves query performance, particularly for complex analytical queries that would otherwise require scanning large amounts of data or performing costly operations.
With the migration to ClickHouse Cloud, projections have become even more cost-effective for MessageBird. With the self-managed setup relying on SSDs, maintaining additional storage for projections could be costly due to the high cost per GB of storage. However, with the move to ClickHouse Cloud, and a shift to S3 storage, projections are now available at a much lower cost.

## Summary
ClickHouse has proven to be an essential tool for MessageBird, driving its real-time analytics and performance monitoring capabilities. By moving to ClickHouse Cloud, MessageBird has been able to easily handle high ingestion traffic, and save on costs. The use of ClickHouse's unique features like Projections and specific table engines aligns with the company's commitment to efficiency and innovation. As MessageBird continues its journey, ClickHouse stands as a key component of its robust, scalable, and cost-effective data infrastructure.

Learn more: [https://messagebird.com/](https://messagebird.com/)




Find out how MessageBird uses ClickHouse to manage billions of messages in real time. Dive into their journey and see how moving to ClickHouse Cloud led to powerful analytics with substantial cost savings.

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How MessageBird Uses ClickHouse to Monitor the Delivery of Billions of Messages

Discover how Helicone transformed their LLM monitoring with ClickHouse, processing 3 million requests daily and slashing query times from 100 seconds to milliseconds.

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SF Meetup Report: Helicone's Migration from Postgres to ClickHouse for Advanced LLM Monitoring

![Ahrefs_photo.png](/uploads/Ahrefs_photo_85bd1c256b.png)

On July 27th, 2023, Alibaba Cloud hosted the ClickHouse community meetup in Singapore. We had the pleasure of hearing from Yasunari (Yasu) Watanabe from Ahrefs, who shared their journey with ClickHouse on a massive scale.

Founded in 2010, [Ahrefs](https://ahrefs.com/) is renowned for processing immense volumes of web analytics data to provide valuable SEO metrics. They have the most active crawler in the industry, with the world’s largest index of live backlinks.

![Ahrefs1.png](/uploads/Ahrefs1_7df5bcab84.png)

## Early Data Storage Solutions ##
Yasu shared Ahrefs' journey over the years working with various data storage solutions. 
“We tried all the available solutions out there, including Cassandra and Hypertable, but none of them really met our requirements. So we ended up developing a custom solution that was optimized for crawling the web with limited resources,” Yasu explained.

As part of their own customer storage solution for the web crawler, they used the Quantcast File System (QFS), alongside Elasticsearch for other non-crawler tasks. While this combination served them well for some time, it soon revealed its limitations due to an inflexible query engine, the absence of advanced features, and scaling challenges. Yasu shared, “Times change, feature requirements become more complex, the size of the web is growing and our infrastructure keeps on scaling up, so we went looking for some alternative solutions”.

## Migration to ClickHouse ##
In 2019, Ahrefs discovered ClickHouse. They were initially drawn to the ClickHouse architecture which resembled their custom system. ClickHouse offered superior performance, a SQL interface, versatile I/O support, and a column-oriented approach. This made querying highly efficient for their growing datasets.

Currently, Ahrefs has embedded ClickHouse deeply into their system. They operate multiple ClickHouse clusters on their hardware, with the main cluster being geo-replicated for both redundancy and task-specific efficiency. Yasu shared the massive scale at which they operate, “We have multiple clusters deployed on our hardware with hundreds of hosts. Our main cluster is now geo-replicated, and we designate some replicas for read-heavy operations and others for write-heavy operations. Many of our tables are quite large, with trillions and trillions of rows, as well as tens of columns.”

![Ahrefs2.png](/uploads/Ahrefs2_99b947590b.png)

## Advanced Interactions with ClickHouse ##
Yasu revealed Ahrefs' strategies for advanced interaction with ClickHouse. To handle their large scale data insertions, Ahrefs uses a buffering technique, grouping data for fewer insert operations, which reduces subsequent merging tasks. Yasu explained "We also use extensive use of fetch and [attach commands to move parts efficiently](https://clickhouse.com/docs/en/sql-reference/statements/alter/partition) across different servers. This one, it's a really, really nice feature that’s handled by ClickHouse." 

![Ahrefs3.png](/uploads/Ahrefs3_6b163950fe.png)

## Internal Monitoring Tools ##
Ahrefs developed tools including the "Birdseye View Tool" for a complete overview of ClickHouse clusters and the "Query Analyzer" to understand and optimize query performance. Yasu hinted at the possibility of these tools being open-sourced, which would be a great contribution to the broader tech community.

![Ahrefs4.png](/uploads/Ahrefs4_e71ac490b3.png)
![Ahrefs5.png](/uploads/Ahrefs5_1a1adb6654.png)

## Mark Compression: Ahrefs' Contribution to ClickHouse ##
Discussing an upstream patch Ahrefs proposed to ClickHouse, Yasu explained the issue with marks, which help locate rows in compressed data files. Large-scale queries can strain the cache of these marks, affecting performance. Ahrefs' solution involved compressing these marks for efficiency. After using their solution for a year, Ahrefs discussed its potential with the ClickHouse team. The final accepted solution divided marks into blocks with a custom compression scheme, which eliminated the need for mutexes, reducing memory consumption by three to six times. 

![Ahrefs6.png](/uploads/Ahrefs6_6f39c3d04f.png)

## Summary ##
Ahrefs' switch to ClickHouse has brought significant improvements in data handling and performance. This transition has allowed them to manage massive data volumes more efficiently. Their innovations, like the mark compression solution, have enhanced query performance, saving memory and time. Yasu concluded “I would say that our decision to start using CickHouse in Ahrefs is not without its hurdles, but, overall it's been a great success. We're happy with the performance that is able to keep up with our usage demands. And we really appreciate the active feature development and bug fixes that go on in the regular monthly releases.”

Ahrefs continues to work on new features, promising further advancements in their collaboration with ClickHouse. 

## More Details ##
- This talk was given at the [ClickHouse Community Meetup](https://www.meetup.com/clickhouse-singapore-meetup-group/events/294428050/) in Singapore on July 27th, 2023
- The presentation materials are available [on GitHub](https://github.com/ClickHouse/clickhouse-presentations/tree/master/meetup80)


Learn how Ahrefs is able to handle trillions of rows efficiently with ClickHouse, their innovative solutions for data insertion, and their collaborative contributions to the broader ClickHouse community.

Singapore Meetup Report: How ClickHouse Powers Ahrefs, the World's Most Active Web Crawler

Balancing the increasing data volumes with the need to minimize environmental impact has always been a complex challenge in data engineering. However, [Modeo](https://modeo.ai/), a pioneering French data engineering firm, has managed to turn this challenge into an opportunity.

Recognized for its robust data engineering infrastructures, Modeo's services are trusted by industry leaders such as L'Oréal, Silvr, and ManoMano. Their latest solution, Stash, leverages the power of ClickHouse for its scalability and storage efficiency, to deliver sustainable data management.

Modeo's challenge was to manage growing data volumes while accurately measuring the real-time carbon emissions generated by data usage and storage. As a part of the Corporate Social Responsibility initiative focusing on climate change, Modeo's CTO, Ismael Goulani, expressed the need for a solution that would allow their customers to monitor and optimize their data platform's cost, carbon footprint, and usage. 

## Introducing Stash, Powered by ClickHouse
Modeo introduces [Stash](https://www.getstash.io/), a solution designed to synchronize metadata and logs from customers' data platforms into ClickHouse. Known for its low latency, high throughput, and efficient storage capabilities, ClickHouse empowers Stash to deliver an unparalleled service. “We believe that with more data-driven businesses, there will be an exponential growth in data consumers. Stash helps our customers manage this growth sustainably by providing insights into their data usage and cost, ultimately leading to better business outcomes and data adoption,” explains Goulani. 

![Stash_dashboard.png](/uploads/Stash_dashboard_df3e33fa3a.png)
_This screenshot displays the count of user activities on the platform. 'Activities' encompass various interactions such as running queries or opening dashboards. This monitoring allows users to track their usage patterns, the associated costs, and the resulting carbon footprint over time._

## Stash Architecture
Stash is integrated with ClickHouse Cloud. All logs and metadata are loaded first into object storage (Google cloud storage). It is then loaded and compressed into ClickHouse for efficient storage.

![Stash_arch_1.png](/uploads/Stash_arch_1_401077c5cd.png)
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## Why ClickHouse? The Ideal Data Warehouse Solution
Modeo needed a system that could quickly retrieve data and store it efficiently. ClickHouse fits the bill with its unique structure that allows for fast data access and excellent data compression for efficient storage. These key features made ClickHouse the top choice for Modeo's demanding requirements.

When asked why Modeo selected ClickHouse for Stash, Goulani said, "We evaluated several data warehouse solutions for scalability, real-time analytics, and customer satisfaction. ClickHouse outperformed the competition on all these parameters." 

Goulani further emphasized, "ClickHouse's high scalability, its ability to handle massive volumes of data, and its efficient storage make it an ideal solution for us. Our customers particularly appreciate the low latency and real-time analytics capabilities that ClickHouse provides." 

In addition to these benefits, ClickHouse shines in other aspects that were essential to their selection process. Its cost-efficiency is a major advantage, allowing Modeo to maintain a robust data warehouse solution without inflating their operational expenses. It also offers comprehensive SQL support, which enables seamless integration with existing tools and facilitates easy manipulation of data for their developers. Furthermore, its intuitive design and user-friendly interface contribute significantly to ease of use, reducing the learning curve for new team members and fostering efficient workflows. With ClickHouse, Modeo found a solution that not only meets their immediate needs but also offers adaptability for future demands.

## Seeing Results: Cost Reduction and Improved Data Adoption
The implementation of ClickHouse has brought significant benefits for Modeo. "We’ve seen a significant reduction in costs, improved data adoption, and enhanced business outcomes for our customers since introducing ClickHouse," Goulani reports. "We're in the process of rolling out Stash as a beta, and we eagerly anticipate sharing more impact data as more customers start using it." 

## ClickHouse for Sustainable and Efficient Data Management
ClickHouse has transformed data management for Modeo. As Goulani concludes, "With ClickHouse, we're not just managing data; we're doing so sustainably and efficiently, which leads to better business outcomes and data adoption." 

As the demand for data and access monitoring surges across industries, Modeo plans to maximize ClickHouse's capabilities through their Stash platform. A key focus lies in leveraging its real-time processing power for a range of emerging applications, particularly in security. With ClickHouse, Modeo can build sophisticated systems that promptly detect unauthorized data assets access (like tables, databases, etc), enhancing the platform's defense mechanisms and fostering customer trust. This integration positions Modeo to augment its services, delivering an increasingly secure and user-friendly platform.

## Learn more

Visit: [https://www.getstash.io/](https://www.getstash.io/)

Visit: [https://modeo.ai/](https://modeo.ai/)



Learn how Stash, powered by ClickHouse Cloud, helps businesses balance data volumes while minimizing environmental impact. ClickHouse's scalability and storage efficiency provide valuable insights into data usage, cost, and carbon footprint.

Elissa Weve

Blog posts by Elissa Weve

London Meetup Report: How Cloudflare Processes Hundreds of Millions of Rows per Second with ClickHouse

Ongage's Strategic Shift to ClickHouse for Real-time Email Marketing

How MessageBird Uses ClickHouse to Monitor the Delivery of Billions of Messages

SF Meetup Report: Helicone's Migration from Postgres to ClickHouse for Advanced LLM Monitoring

Singapore Meetup Report: How ClickHouse Powers Ahrefs, the World's Most Active Web Crawler

Driving Sustainable Data Management with ClickHouse: Introducing Stash by Modeo