talk-data.com

Prometheus has become the go-to standard for metrics-based monitoring, but as environments grow in complexity and scale, teams often find themselves hitting its operational limits, especially around cardinality and long-term storage. This talk explores how VictoriaMetrics builds on Prometheus fundamentals to offer a more scalable and efficient alternative for teams managing high-ingestion workloads and demanding retention needs, without abandoning the familiar Prometheus ecosystem. I’ll dive into how VictoriaMetrics supports Prometheus-compatible scrape configurations and exporters, allowing seamless integration with existing workflows. The session will showcase practical strategies for setting up and tuning scrape jobs, managing cardinality through label analysis and relabeling, and using VictoriaMetrics’ UI and tools to gain insight into metric usage patterns. This talk is tailored for advanced users eager to push the boundaries of Prometheus-based observability, demonstrating how the core philosophy of Prometheus can be extended and elevated through the integration of high-performance systems like VictoriaMetrics.

At Criteo, we’ve relied on automatic aggregations for years. “Automatic aggregation” is the name we give to a system of recording rules that matches most metrics and removes certain dimensions, such as the instance emitting the metric, to reduce the cardinality (i.e., the number of metrics) and thus makes queries faster. What started as a workaround has become a key part of how we ensure backend stability and reliability at scale, with hundreds of millions of active metrics, all without requiring users to write a single recording rule. It also significantly reduces the cost of metrics storage. Internally, we call this approach zero-effort Observability, as most teams don’t have to write/maintain recording rules. In this talk, Raphael will explain how our approach to automatic aggregations has evolved over time and how we’ve adapted it to fit naturally into our Prometheus-based stack. He will share the different implementations we’ve tried, the lessons we’ve learned, and how our latest version takes advantage of recent improvements in Prometheus (new type label).

SRE gets many customer tickets, some of which are answered in the many go links we have on our page that no one will read. RAG trains an LLm on our codebase, internal documentation, forums, issues queries, etc. These contextual resources help the customer get better answers to their questions faster, freeing up time on both the customer, dev, and SRE side. Additionally, this helps train our team more efficiently as well.

Apache Kafka is the simplest possible reliable, horizontally scalable low-latency storage system for commodity hardware. This is increasingly making it the backbone of analytic data collection stacks and event-bus like architectures. Critical systems like this require very reliable operations. Kafka is both stateful and distributed, so it has traditional sysadmin kind of problems and those that require pretty deep expertise. We will discuss the problems with CPU and disk capacity management as well as defining availability SLOs for a distributed stateful system. We will also show some of the ways in which the Google Cloud Managed Service for Apache Kafka and lenses.io helps in solving these problems in a demo.

How we choose a metrics solution for our hybrid cloud platform and how we scale it. As enterprises grow, observability platforms must keep pace with increasing data volumes, stricter reliability requirements, and the need for faster insights. At Deutsche Bank, these challenges became especially critical within the IB (Investment Banking) Hybrid Cloud platform, where one monitoring environment needed to be migrated and optimized without disruption. The talk will walk through the migration process, technical decisions, and lessons learned, highlighting both the benefits and challenges encountered along the way. It will cover practical strategies for seamless migration between monitoring systems and best practices for running VictoriaMetrics at scale.

In today’s fast-paced financial landscape, data-driven decision-making is no longer optional, it’s essential. This session explores how Databricks empowers finance teams to accelerate intelligence. We’ll dive into how unified data platforms and lakehouse architecture streamline data ingestion and processing, enabling faster insights and smarter automation.

Explores token design fundamentals and why some tokens succeed while others fail.

Overview of token listings on exchanges and who controls the process.

Session on liquidity provision and market-making strategies.

Session on how crypto startups raise capital through structured processes.