Javier Ramirez

In this talk, we share our journey in making QuestDB, an Apache 2.0-licensed open-source time-series database, a significantly faster analytical database. Over the course of just one year, we achieved query performance gains of up to 6x by implementing specialised data structures, SIMD-based optimisations, scalable aggregation algorithms, and parallel execution pipelines.

Teams of all shapes and sizes benefit from near real-time analytics. In this session, I will present a project template that can serve as the foundation to build one such high performing system, powered by Apache Kafka, QuestDB, Grafana OSS, and Jupyter Notebook.

Teams of all shapes and sizes benefit from near real-time analytics . In this session, I will present a project template that can serve as the foundation to build one such high performing system , powered by Apache Kafka , QuestDB , Grafana OSS , and Jupyter Notebook .

Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data.

Time series data pipelines tend to prioritise speed and freshness over completeness and integrity. In such scenarios, it is very common to ingest duplicate data, which may be fine for many analytical use cases, but is very inconvenient for others. There are many open source databases built...

When doing real-time analytics, you not only want your database to ingest as quickly as possible, but also to have your data available for (fast) querying as soon as possible. In this session I will show you the technical decisions we made when building QuestDB, an open source time-series database, and how we can achieve over a million row writes per second without blocking or slowing down the…

Data used to be a batch thing, but more and more we get unbounded streams of data, fast or slow, that we need to process and analyse in near real time. In this talk I'll show you how you can use Apache Flink and QuestDB to build reliable streaming data pipelines that can grow as much as you need.

Time-series data can be found anywhere, from DevOps metrics to gaming applications and IoT devices. Traditional operational or analytical databases do not fit the time-series model and can be complex to manage and difficult to scale.