It’s the type of nightmare that leaves developers in a cold sweat. Imagine waking up to a message from your team that simply says, “We lost a cluster,” but it’s not a dream at all. InfluxDB Cloud runs on Kubernetes, a cloud application orchestration platform. We use an automated Continuous Delivery (CD) system to deploy code and configuration changes to production. On a typical workday, the engineering team delivers between 5-15 different changes to production.

A time series is a sequence of data points (observations) arranged chronologically and spaced equally in time. Some notable examples of time series data are stock prices, a record of annual rainfall, or the number of customers using a bike sharing app daily. Time series data exhibits certain patterns, such as the highs and lows of hotel prices depending on season.

This article was published in The New Stack. For most developers, software development means there is an API for almost everything, hardware is provisioned via the cloud and the core focus is on building only the features most crucial to your business. Of course, all these integrations and modern distributed architectures create their own set of problems. Having full insight into your application has become even more important and is now commonly known as observability.

SAN FRANCISCO, June 15, 2022 – InfluxData, creator of the leading time series platform InfluxDB, today announced Edge Data Replication, a new capability for centralized business insights in widely distributed environments. Edge Data Replication enables developers to collect, store and analyze high-precision time series data in InfluxDB at the edge, while replicating all or subsets of this data into InfluxDB Cloud.

There are technical and business reasons to have a time series data presence both at the edge and in the cloud – InfluxDB has always played a key role in both contexts. Today, we’re announcing Edge Data Replication, a new feature that combines these two deployment strategies. With this announcement, InfluxData begins a greater initiative to accommodate both edge and cloud data workloads in one unified solution.

Using technology to help businesses thrive is always a thrill, but it doesn’t compare to the sense of accomplishment on both a personal and organizational level when you see your tech used to positively impact humanity. It’s one thing to function in a support role for these initiatives, but it’s also important to acknowledge the businesses at the vanguard, building their primary mission around positive human and global impact.

The U.S. Army Combat Capabilities Development Command (DEVCOM) Army Research Laboratory (ARL) faces unique challenges in working with data to develop new technologies. It needs the ability to seamlessly analyze data both in the field and in the lab. Connectivity in the field can also be very unpredictable. Without a database that can handle intermittent connectivity, the systems become inefficient and waste time and money.

Time series databases like InfluxDB are databases that specialize in handling time series data, which is data that is indexed by time. Unlike traditional databases, time series databases are optimized for reading and writing data with less performance consideration for updating or deleting data. Due to the time-dependent nature of time series data, time series databases are handy for application monitoring.

Hulu, the entertainment streaming platform, needed a solution to scale up its internal application and infrastructure monitoring platform as it grew beyond 1 million metrics per second. The solution it created combines two open source tools— InfluxDB, a time series database, and Kafka, an event-streaming platform. It’s not just global enterprises like Hulu that have access to world-class tools and infrastructure to achieve their business goals.

A common debate in software development focuses on whether to use already-available tools or services, which offer better developer productivity, or stick with lower-level tools or custom-built solutions, which offer more control and potentially better performance and flexibility. This can be boiled down to the decision of whether to build or buy. These two approaches are at the root of many current tech industry ideological conflicts.