Access to data is critical for SaaS companies to understand the state of their applications, and how that state affects customer experience. However, most companies use multiple applications, all of which generate their own independent data. This leads to data silos, or a group of raw data that is accessible to one stakeholder or department and not another.

Data is essential to all JavaScript applications. It determine what we see, hear and interact with when we open the app, and it must be stored properly to guarantee a smooth user experience. Specifically, the data must be a stored in a way that facilitates retrieval, manipulation and is efficient in storage usage. need data to represent information. One of the most important prominent data structures in JavaScript is known as an Array.

Companies who develop software products generate massive quantities of product performance and user engagement data that can be analyzed to support decision-making about everything from feature planning and UX design to sales, marketing, and customer support.

Learn how we utilize the Fivetran Log Connector to maintain our own Fivetran demo account.

As fraud continues to grow in prevalence, SecOps teams are increasingly investing in fraud prevention capabilities to protect themselves and their customers. One approach that’s proved reliable is the use of log analytics and telemetry data for fraud prevention. By collecting and analyzing data from various sources, including server logs, network traffic, and user behavior, enterprise SecOps teams can identify patterns and anomalies in real time that may indicate fraudulent activity.

Artificial intelligence (AI) is now well and truly mainstream. Once the preserve of futurists and doom-mongers muttering about job losses, it has become a global watercooler topic thanks to the rise of ChatGPT, which promises to transform the way the entire world goes to work. But AI has been a vital tool in the developer’s armoury for a while. It has given us new ways to optimize workflow and focus on those high-level tasks that will be forever human.

The flood of software innovation over the past 20 years would not have been possible without Agile working. The concept of releasing fast, taking feedback and building back better has birthed the iPhone, social networks and the Cloud. The world would be a slower, duller place without it. But we can’t do Agile unless we can get real insights from our user’s screens, and here, the developer tools published by Safari, Chrome and other browsers are crucial.

Once, your users may have forgiven a bug in your app. Today, they likely won’t. Today’s consumers, many of them Gen-Zers who’ve been using gadgets since they learned their hands, expect a mobile experience that’s swift, seamless and secure. And with page speeds increasing all the time, lags and snags are no longer acceptable. Which means our apps need to glitch-free right out of the gate.

What’s the right way to manage growing volumes of enterprise data, while providing the consistency, data quality and governance required for analytics at scale? Is centralizing data management in a data lake the right approach? Or is a distributed data mesh architecture right for your organization? When it comes down to it, most organizations seeking these solutions are looking for a way to analyze data without having to move or transform it via complex extract, transform and load (ETL) pipelines.

As enteprise networks grow larger and more complex, IT teams are increasingly dependent on the enhanced network visibility and monitoring capabilities provided by log analytics solutions. Log analytics gives enterprise Engineering, DevOps, and SecOps teams the ability to efficiently troubleshoot cloud services and infrastructure, monitor the security posture of enterprise IT assets, and measure application performance throughout the application lifecycle or DevOps release pipeline.