Dashboards plays a vital role to derive insights from data. When we say dashboard, two things will always come to my mind. Is it real time dashboard or historic reports? Based on the business requirements people choose which type of dashboard they want. Some will prefer historic reports because there requirement might be like daily or weekly reports. Some would say they need real time dashboards because they might need to actions immediately based on real time data.

But when we say real time dashboards again two things will come to my mind. Is it for monitoring or is it for live dashboards. As per my understanding, monitoring will give details about what is happening currently. Peoples in support will need this type of dashboard, because they will be monitoring systems to check whether any issues in happening now. But live dashboards will give details about what has happened till now. Examples like wordpress/blogspot stats

In the remaining post, I am gonna explain about a technology stack which could be helpful for live dashboards.

When people hear live dashboards immediately people will think about ELK (Elasticsearch, Logstash, Kibana). We can achieve both monitoring and live dashboard using ELK. Eventhough it is completely streaming and near real time, but with my exposure to ELK, I always prefer ELK stack for monitoring because of the scalability requirement of Elasticsearch for volume of data and the way kibana updates every interval. If rarely we want to see historic reports in kibana then it is fine but storing historic information for a long time ELK is not a good choice. I always prefer some other technology or hadoop ecosystem for live dashboards.

Recent days I came to know about a new technology called TICK stack which I feel can meet both the requirements – monitoring and live dashboard. I have already heard about this and I have mentioned this in my earlier post but when explored more I feel it is a good choice for real time data. I have not worked in this stack but I guess this can be used for both monitoring and live dashboards. Because TICK stack is mainly helpful for time series data. So handling data in real time is not an issue. At the same time as per scalability features of InfluxDB, TICK stack looks promising for live dashboards.

TICK

T elegraf collects time-series data from a variety of sources. This mainly serves the purpose of data ingestion.

elegraf collects time-series data from a variety of sources. This mainly serves the purpose of data ingestion. I nfluxDB stores time-series data. This is the core component of the stack like Elasticsearch in ELK. Today different database technologies are available like nosql, newsql, time series databases. Time series databases are much preferred in recent days to address the volume of data.

nfluxDB stores time-series data. This is the core component of the stack like Elasticsearch in ELK. Today different database technologies are available like nosql, newsql, time series databases. Time series databases are much preferred in recent days to address the volume of data. C hronograf visualizes and graphs the time-series data. This is similar to Kibana

hronograf visualizes and graphs the time-series data. This is similar to Kibana Kapacitor is a data processing framework for both streaming and batch data. It also provides alerting and detects anomalies in time-series data.