Feb 22, 2022

InfluxDB: time series data analysis made simple

Alexis Leibbrandt

When it comes to data analysis, there are several aspects to take into consideration in the creation of a high-performance solution. This article contains everything you need to know to do this, using the ETL process applied to an example of InfluxDB and the akenza platform.

When it comes to data analysis, there are several aspects you have to consider to create a high-performance solution. Most importantly, you need to avoid working on your production database because:

  • An application-oriented database is built and optimized for transaction processing, but data analysis is entirely another job.

  • Data analysis requires complex queries with a vast number of tuples involved, affecting your application’s performance.

  • An abstraction layer to consolidate raw data is needed to avoid using complex queries.

The process responsible for bringing the data from the sources that run your business into an analysis tool is commonly known as ETL: Extract Transform Load.

Source https://www.stitchdata.com/etl...

In a standard configuration, ETL requires different stages and, most likely, different systems to transform your database from transactional into time-series.

Transactional database vs time-series database

A transactional database is commonly used to manage, well, transactions. It is the most common type of database and it is optimized to enable data updates to modify the content of its tables and manage relationships between them.

In a transactional database, tables are linked by relations. Therefore, every transaction can affect one or more tables simultaneously.

On the other hand, time-series databases are built for time-series analysis. This implies that once the data is written into a table, it will not be changed anymore. In a typical IoT use case, opening a table will display a list of identical tuples where the only data that changes are the time and the value you are measuring.

Dashboard vs analysis

In a standard IoT use case, you will have two choices once you have all your data available:

  • Visualize data on a dashboard

  • Gain insights from the data through data analysis

Most of the time, a dashboard where you can visualize the current status of your system may be enough. It allows you to get an immediate feeling about the current situation and react to changes if needed.

The combination of akenza and InfluxDB makes it easy to build up such a visualization dashboard. With the connectivity and device management of akenza and its built-in output connector to InfluxDB, you can get started in no time; check out our step-by-step tutorial on the topic.

But if you want to manipulate your data and generate real business value by defining KPIs, aggregations, etc., here is where InfluxDB makes the difference.

The process of data mining is integrated into InfluxDB thanks to Flux: a standalone data scripting and query language that increases productivity and code reuse.

With Flux, you can perform the entire ETL process into InfluxDB with all the advantages of an integrated architecture that scales with your needs.

IoT is all about data: data-driven processes and decision-making should be the final goal of every IoT project. The integration of akenza and InfluxDB makes these kinds of solutions feasible and cost-effective, generating a positive ROI for your business.

Take the chance to try it by yourself by subscribing to a 30-day free trial here, and profit from a free account on InfluxDB here. Making your POC running couldn't be easier, faster and cheaper.

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