Machine data: how to handle it better?
The rise of IoT and smart infrastructure has led to the generation of massive amounts of complex data. Traditional solutions struggle to cope with this shift, leading to a decrease in performance and an increase in cost. In this session, I will talk about time-series data, machine data, the challenges of working with this kind of data, ingestion of this data using data from NYC cabs and running real time queries to visualise the data and gather insights. By the end of this session, you will be able to set up a highly scalable data pipeline for complex time series data with real time query performance.
Outline/Structure of the Talk
High level outline of topics that will be covered in this presentation:
1. Growth of IoT and Sensor Data
2. Time-series data
3. Challenges that are posed by large volumes of time-series data
4. Showcasing and overcoming the problem: A case-study
5. Demo time: Geospatial queries on machine data, 2017 NYC cab data and visualisation on Grafana
By the end of this session, we will be able to set up a highly scalable data pipeline for complex time series data with real time query performance.
Developers, Managers, IoT Enthusiasts
Prerequisites for Attendees
Some knowledge of databases, data pipelines and containers will help the audiences to follow along and make the most of this talk.
schedule Submitted 4 months ago
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