Operations | Monitoring | ITSM | DevOps | Cloud

Welcome to our latest benchmark figures for Aiven for PostgreSQL on AWS, GCP and MS Azure.

It’s never been more important for government agencies to embrace innovation than over the past 18 months. Citizens are relying on our government more than ever to provide critical services during the global pandemic — from developing COVID-19 vaccines at warp speed to extending benefits to the record number of unemployed Americans. The pandemic not only made government services more critical, it also challenged agencies’ ability to deliver those services.

Data is the new oil. It’s a phrase we’ve heard a lot in recent years, and it’s not hard to understand why. We’re generating more data every day than ever before, and companies are scrambling to find ways to store that information without running out of space.

ZE PowerGroup Inc. is a British Columbia-based software company. It offers ZEMA, an award-winning data management, analytics, and integration platform. Although ZEMA was created in-house, the developers at ZE were never successful at measuring the performance of the application during the initial years. They tried a few third-party tools, but measuring the actual application performance continued to be a dilemma until they evaluated ManageEngine’s Applications Manager.

To make life easier for database admins and simplify DevOps, Aiven now offers a Kubernetes Operator for Aiven for PostgreSQL and Aiven for Apache Kafka services. Find out how you can use it to manage your whole service stack.

This article was written by Cameron Pavey, a full-stack dev living and working in Melbourne. Scroll below for this picture and bio. As a developer, it is likely that you will eventually run into a situation where a traditional relational database’s document stores don’t quite cut it. If you need to store points of data over time, you’ll likely need a time series database.

Data application developers using Snowflake as the data warehouse and who are new to Kubernetes, spinning up a single cluster on their laptop and deploying their first application can seem deceptively simple. As they start deploying data-driven applications using microservices and Kubernetes in production, the difficulty increases exponentially. It quickly throws the developer into a kind of configuration hellscape that drives productivity down for many data engineering teams.

Aggregations are a powerful tool when processing large amounts of time series data. In fact, most of the time you’re going to care more about the min, max, mean, count or last values of your dataset than you will about the raw values you’re collecting. Knowing this, InfluxDB and the Flux language make it as easy as possible to run these aggregations, whenever and wherever you need to, and sometimes that leads people to running them in ways that aren’t as efficient as they could be.

You may have read or heard about IDC’s recent Global DataSphere Forecast, 2021-2025, which predicts that global data creation and replication will experience a compound annual growth rate (CAGR) of 23% over the forecast period, leaping to 181 zettabytes in 2025. That’s up from 64.2 zettabytes of data in 2020 which, in turn, is a tenfold increase from the 6.5 zettabytes in 2012.