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Time series data differs from “normal” data in an interesting way. The essential characteristic is that the data’s primary point of reference is a timestamp showing at which point in time a sample of data was measured. Time series databases like InfluxDB are helpful for situations that involve this kind of data.

You’d probably struggle to find a big data practitioner who’s never heard of Apache Spark or used big data with Spark. We’d even go so far as to say it’s near impossible—and that’s for good reason. Spark is well known because it’s fast, reliable, and capable. Let’s dive into why that is, answer some common questions surrounding Spark computing, how to easily use it to achieve success, and more.

InfluxDB Cloud wins Best Use of the Cloud in the Internet of Things category SAN FRANCISCO, February 8, 2022 – InfluxData, creator of the leading time series platform InfluxDB, today announced InfluxDB Cloud was named a winner in the 2021-22 Cloud Awards category for Best Use of the Cloud in the Internet of Things. Now in its 10th year, the Cloud Awards recognize innovation and excellence in cloud computing among startups and global enterprises.

This article was written by Nicolas Bohorquez. Scroll below for the author’s picture and bio. Time series databases, like InfluxDB, index data by time. They are very efficient at recording constant streams of data, like server metrics, application monitoring data, sensor reports, and any data containing a timestamp. Data in a time series database is always written with the most recent data values but with the previous values not updated.

Aiven for Apache Kafka® Migrating from one database technology (such as PostgreSQL®) to another (such as MySQL) using batch has a variety of problems. Find out how to use Apache Kafka® to perform streaming updates, and keep the target continually in sync with the source.