For instance, a client’s contractor can mistakenly remove the integration on Android. We identified 3 stakeholders that could break the system: In effect, we are monitoring the 3Vs: volume, velocity, and variety, but we are blind to the additional 2Vs: veracity and value.īefore digging into solutions, we needed to understand how incidents could surface. Also, one client could have all the transactions transformed from dollar to euros, the DevOps metrics couldn’t care less. Because 4000 clients are sending events, a single client going down cannot be spotted in these DevOps metrics. We use standard DevOps monitoring tools to monitor this pipeline: request rate, network usage, message number, throughput, etc… We are able to detect if the data does not flow correctly, but we don’t have a fine-grained view on the data. Our clients look at their experiment results in a reporting UI.Įvent collection architecture (simplified) Background Our clients send events, we transform these events, then we write them in a data warehouse. The figure below shows a simplified architecture of our data collection system. This blog describes our journey to build new monitoring tools and the solutions we found to improve data quality operations. We understood we had to catch data quality issues proactively. Also, debugging was unnecessarily difficult: finding the root cause of a problem gets harder when the problem gets older. A data problem could go unnoticed for weeks. After a few checks, the ticket would end in the data team’s hands. If still in doubt, he would open a support ticket.
If in doubt, he would compare the numbers with another analytics tool. He would wait some weeks, then look at the results. Over the last few years, we realized we were spending too much time on data quality questions. At AB Tasty, we do hear “Is this number correct?” a lot. Without data quality insurance, our clients could end up making catastrophic business decisions. Based on the figures we give, our clients make changes impacting millions of end-users. AB Tasty provides solutions to create online experiments and optimize user experiences. Is this number correct? If you work with analytics, you hear this question a lot. Data Quality: Timeseries Anomaly Detection at Scale with Thirdeye
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