An expert interview on real-time analytics, converged data architectures, and enterprise-grade operational security
Across many areas of modern business, decisions need to be made in seconds — or even fractions of a second. In financial fraud prevention, risk analysis must be completed before a transaction is approved. SRE teams need immediate insight into massive volumes of logs during infrastructure incidents, while e-commerce platforms rely on fresh data to personalize services during an active user session.
In these scenarios, delays in accessing information can translate directly into measurable financial and operational losses.
We spoke with a Linux Polska database platform expert about the challenges of modern data architectures, the capabilities of the ClickHouse® analytical engine, and what it takes to build a stable, enterprise-grade analytics environment.
Table of contents:
- Why do traditional data warehouses struggle with operational analytics?
- What gives ClickHouse its performance advantage?
- What measurable business benefits can ClickHouse deliver?
- What are the challenges of running an open-source analytics engine in production?
- How do EDB Postgres® AI for ClickHouse and Linux Polska address these requirements?
- Expert perspective: combining real-time analytics with enterprise support
Why do traditional data warehouses struggle with operational analytics?
Expert: Traditional data warehouse environments were designed primarily for historical analysis, reporting, and the processing of large datasets in predefined cycles. Modern data warehouses increasingly use columnar technologies and can execute complex analytical queries very efficiently.
The challenge arises when large scale must be combined with very fresh data, high query concurrency, and consistently low response times.
Traditional ETL (Extract, Transform, Load) pipelines can introduce another limitation. In these architectures, data is periodically transferred from production systems into an analytical layer. With batch-based ETL processing, latency may range from several minutes to many hours.
When the business requires near-real-time analytics — and when data is increasingly consumed by AI-based solutions — the time between an event occurring and the corresponding data becoming available for analysis becomes a key architectural consideration.
What gives ClickHouse its performance advantage?
Expert: ClickHouse® is a column-oriented OLAP engine designed to execute analytical queries across very large datasets.
A row-oriented database may need to read multiple attributes of a record even when an analytical query only requires a subset of them. ClickHouse, by contrast, typically reads only the columns required to execute the query.
Combined with data compression, parallel processing, and an appropriate data organization strategy, this provides three key benefits:
- Very low query latency: properly designed aggregation queries can execute in under a second, even across datasets containing billions of records.
- Highly efficient data compression: storing values of the same type together in columns enables high compression ratios. Actual results depend on the nature of the data, cardinality, sorting strategy, and the codecs used.
- Scalability and performance: ClickHouse is designed to process large streams of incoming data while supporting many concurrent analytical queries.
Key ClickHouse® capabilities
- analytics across very large datasets, with response times often measured in fractions of a second,
- efficient compression of columnar data,
- support for high volumes of incoming events,
- horizontal scalability,
- Converged Analytics, including access to PostgreSQL data and integration with modern data architectures and formats such as Apache Iceberg®.
What measurable business benefits can ClickHouse deliver?
Expert: Publicly documented deployments show that ClickHouse is being used across observability, cybersecurity, and large-scale analytical environments.
Observability and log analytics: Tesla uses ClickHouse as part of an observability platform processing telemetry data at very large scale. In scalability testing, the solution handled more than a quadrillion rows. Character.AI uses ClickHouse for log analytics and reports an approximately 50% reduction in logging infrastructure costs, while reducing search times from minutes to seconds.
Cybersecurity: Cisco Talos uses ClickHouse to analyze cyber threat data. According to a published case study, migrating the environment resulted in an approximately 75% reduction in total infrastructure cost of ownership.
Analytics cost optimization: Changan Ford uses ClickHouse in an analytics environment supporting marketing operations. The architectural change reduced annual compute and storage infrastructure costs by more than 40%.
Financial services: real-time analytics architectures can support risk assessment, anomaly detection, customer behavior analysis, and other use cases that require large event streams to be processed with very low latency.
Examples of documented ClickHouse® deployments
| Company / Organization | Sector / Area | Use Case | Measurable Business Outcome |
| Cisco Talos | Cybersecurity | Threat Intelligence / Log Analytics | approx. 75% reduction in TCO following a change in the infrastructure model |
| Tesla | Automotive / IoT | Observability and telemetry | very large data scale; more than a quadrillion rows handled in scalability tests |
| Character.AI | AI / SRE | Observability and log analytics | approx. 50% lower logging costs; search times reduced from minutes to seconds |
| Changan Ford | Automotive / Marketing | Data analytics | more than 40% reduction in annual infrastructure costs |
What are the challenges of running an open-source analytics engine in production?
Expert: Open-source software offers considerable technological freedom. In enterprise environments, however, access to the software alone does not address every operational requirement.
Three areas are particularly important.
- Enterprise support and SLA: organizations using an analytics platform for critical business processes may require formal 24/7 technical support, defined service levels, and stringent incident response times.
- Security and vulnerability management: organizations need a process for monitoring vulnerabilities, assessing their impact, and delivering and deploying verified security fixes.
- Integration without creating new data silos: the analytics layer needs to work with existing data sources, including transactional databases such as PostgreSQL, while minimizing unnecessary data duplication and avoiding overly complex ETL integration pipelines.
For large organizations, therefore, selecting the right analytics engine is only part of the challenge. How that engine is deployed, operated, secured, supported, and integrated with the wider data environment is equally important.
How do EDB Postgres® AI for ClickHouse and Linux Polska address these requirements?
Expert: EnterpriseDB extends the PostgreSQL ecosystem with a supported version of the open-source ClickHouse engine. EDB provides signed and maintained software packages together with technical support aligned with the selected service plan, including support for security issues and vulnerabilities.
Linux Polska complements this technology layer with local consulting and engineering services — from requirements analysis and architecture design to installation, integration, and ongoing operational support.
An important element of the EDB architecture is the concept of Converged Analytics, which brings together the transactional world of PostgreSQL and specialized analytical engines.
One component of this approach is the use of the open Apache Iceberg® format. Within the EDB architecture, PostgreSQL data can be made available for analytics through an Iceberg-based layer accessible to ClickHouse.
This approach can reduce the need for traditional batch ETL pipelines and limit the number of separate data copies maintained solely for analytical purposes.
ClickHouse can also work alongside other components of the EDB data platform, including WarehousePG, creating an architecture in which the right engine can be used for the right type of workload.
The solution can also be deployed in an organization’s own data center or private cloud. This gives organizations greater control over where and how their data is processed — an important consideration for regulated enterprises and organizations with stringent architecture, security, and data governance requirements.
The available per-core licensing model also makes infrastructure costs more predictable in environments with a known or planned scale.
Expert perspective: combining real-time analytics with enterprise support
Combining the high-performance ClickHouse® analytical engine with the PostgreSQL ecosystem and technical support from EDB and Linux Polska enables organizations to build modern analytics environments without abandoning open technologies.
The value of this architecture goes beyond the performance of individual queries. Equally important are the ability to process very large volumes of continuously arriving data, horizontal scalability, integration with PostgreSQL-based environments, and the support and security capabilities required for enterprise use.
ClickHouse can therefore serve as a specialized analytical engine for organizations that need access to fresh data with very low response times — from observability and cybersecurity to customer behavior analysis, operational analytics, and AI-driven solutions.
