GitHub Actions are a powerful way to add automation to any source code repository. When you take that power and connect it with InfluxDB, you get an amazing combination that allows you to automate data generation, manage GitOps workflows, and a whole lot more. This post will highlight some of the interesting ways to use InfluxDB and GitHub Actions.
Whether you’re an analyst or line-of-business user, Yellowfin Stories makes it easy to create and share a data-led narrative that provides your peers with insightful context, inspires action, and fosters a data-driven culture.
Data transformations are the backbone of smooth-running data operations. Transformations are used in data replication between databases, data migration from cloud to on-premise, and data cleaning (aggregations, outlier removal, deduplication …) aka all the good stuff that goes into extracting insights from data. But as any data professional can attest, transformation can also be a painful bottleneck. Think scripts that run for an entire day and finish just before the next scheduled job.
Change Data Capture (CDC) helps your business stay competitive by keeping data up-to-date
“40% of all enterprise workloads will be deployed in CIPS [cloud infrastructure and platform services] by 2023, up from only 20% in 2020.”.As the cloud permeates every aspect of business, decision-makers must make critical choices regarding infrastructure at every turn. Their answers will ultimately determine if every part of an organization is empowered to move forward in a cohesive way to reach business outcomes.
Modern data and analytics leaders know that every business user is different. No two marketers or finance managers will use data in exactly the same way because no two share the same contextual view or understanding of the business. Their challenges are as nuanced as they are complex. And they need insights tailored to their specific needs if they are to be successful at solving business problems with data. Unfortunately, traditional BI tools treat everyone like carbon copies.
In our previous blog, we talked about the four paths to Cloudera Data Platform. If you haven’t read that yet, we invite you to take a moment and run through the scenarios in that blog. The four strategies will be relevant throughout the rest of this discussion. Today, we’ll discuss an example of how you might make this decision for a cluster using a “round of elimination” process based on our decision workflow.
Customers wanting to drive self-service analytics as part of creating a data-driven organization will often ask, “Can we achieve self service analytics, when our work force has low data literacy?” Or they might say they are not ready for self-service analytics, incorrectly thinking they need first to improve data literacy. But the two are inextricably linked. I liken it to teaching a child to read without giving them any books on which to build their skills.
