Operations | Monitoring | ITSM | DevOps | Cloud

Big data analytics help organizations use data to explore both new and improvement opportunities. Whichever cloud data platform you choose, there are two data storage technologies you will want to understand. Data warehouses and data lakes are the two dominant data solutions commonly used for defining how an organization stores, queries, analyzes, and reports on big data. This post will define what a data warehouse and data lake are, how they work, and their differences.

Since the initial release of InfluxDB OSS 2.0 in November 2020, more than 10% of the community has successfully upgraded, and the pace of the upgrades continues at a steady rate. We have released a number of maintenance releases to address defects, expand platform coverage, and enhance the update experience based on feedback.

One of the things we needed to either adopt or build for InfluxDB IOx is a database catalog. If you haven’t heard us talk about it yet, InfluxDB IOx (pronounced eye-ox) is the new in-memory columnar database that uses object storage for persistence. We’re building it as the future core of InfluxDB. A database catalog usually contains the definitions of a database’s structure like schema and indexes.

In this post we’ll explore the concepts of data lake, data hub and data lab. There are many opinions and interpretations of these concepts, and they are broadly comparable. In fact, many might say they’re synonymous and we’re just splitting hairs. But let’s look again carefully. We can discern some subtle trends in the way people are doing things, and find distinctions in these expressions.

This article will show how we kept cardinality under control with a few tweaks in the Telegraf configuration. If you’re not yet familiar with it, Telegraf is the native and open-source plugin-driver metrics collection agent of InfluxDB. As you may know, cardinality is the combination of measurements, tags, sets, fields, and values in a time-series database, and having high cardinality can be a challenge.

Today’s telecom engineers are expected to handle, manage, optimize, monitor and troubleshoot multi-technology and multi-vendor networks, in a competitive and unforgiving market with minimal time to resolution and high costs for errors. With the ongoing growth in operational complexities, effectively managing radio networks, current and legacy core networks, services, and transport and IT operations is becoming a radical challenge.

Have you ever heard anyone saying: “Our data is great, we’ve never had any data quality issues“? Ensuring data quality is hard. The magnitude of the problem makes us believe that we need some really big actions to make any improvements. But the reality shows, often the simplest and most intuitive solutions can be incredibly impactful. In this article, we’ll look at one idea to improve the process around data quality, and make it more rewarding and actionable.

As companies accelerate their digital transformation, technology innovations are now a critical component of any business strategy. Industry leaders are spending more money on technology than their counterparts, prioritizing growth and customers. CEOs now see CIOs and tech leaders as their primary partners in driving business innovation.

Today’s business demands that IT transform from a cost center to an innovation engine. To foster a business mindset, IT leaders need full visibility into operations so they can achieve desired business outcomes. At ServiceNow, we run IT like a business to deliver value quickly, improve productivity, and create great user experiences. The CIO Dashboard is one of the capabilities our IT leadership uses daily to advance business outcomes that help us scale.