As digital transformation reaches more industries, the number of data points generated is growing exponentially. As such, data integration strategies to collect such large volumes of data from different sources in varying formats and structures are now a primary concern for data engineering teams. Traditional approaches to data integration, which have largely focused on curating highly structured data into data warehouses, struggle to deal with the volume and heterogeneity of new data sets.
Time series data present an additional layer of complexity. By nature, the value of each time series data point diminishes over time as the granularity of the data loses relevance as it gets stale. So it is crucial for teams to carefully plan data integration strategies into time series databases (TSDBs) to ensure that the analysis reflects the trends and situation in near real-time.
In this article, we’ll examine some of the most popular approaches to data integration for TSDBs:
