Kafka is a ubiquitous component of a modern data platform. It has acted as the buffer, landing zone, and pipeline to integrate your data to drive analytics, or maybe surface after a few hops to a business service. More recently, though, it has become the backbone for new digital services with consumer-facing applications that process live off the stream. As such, Kafka is being adopted by dozens, (if not hundreds) of software and data engineering teams in your organization.

Analysis-ready data models are built using sequences of transformations. Here's an example using Fivetran’s data model for Salesforce.

Digital technology promises transformative results. Yet, it’s not uncommon to encounter potholes and speed bumps along the way. One area that frequently trips up businesses is putting data into action. It can be extraordinarily difficult to take advantage of the right data at exactly the right time — in real time — to drive decision-making. For SAP customers wanting to maximize the value of their data, Google Cloud offers a number of capabilities.

There is a common saying in the software development field: “Repetition is the root of all software evil.” The more you repeat yourself in code, the more likely you are to make mistakes or cause yourself issues down the line when you come to update things. The aim is to keep things DRY!

Amazon Redshift says it executes data operations ten times faster than other enterprise data warehouses because of a hardware-accelerated cache called Advanced Query Accelerator (AQUAD). It also claims three times better price-performance than other similar technologies. Statements like these are what make Redshift an attractive option for companies that want to push data into a warehouse for analytics.

API management has been around for over a decade. It is the process of designing, analyzing, documenting, and publishing APIs in a secure environment. A comprehensive API management solution can guarantee that the APIs you create are consumable and secure. However, how do you know if data mesh or API management is right for your business? The concept of data mesh was introduced a few years ago by Zhamak Dehghani on Martin Fowler’s bliki.

The value of healthy data is obvious. But how do you build that practice in your own business? The difference between people who live a healthy lifestyle and those who don’t isn’t whether they know how to be healthier — it’s whether or not they prioritize diet, sleep, and exercise in their daily life. The same is true for your data: if you don’t have the infrastructure that supports your customer 360 initiatives , those initiatives become moot.

While it is a little dated, one amusing example that has been the source of countless internet memes is the famous, “is this a chihuahua or a muffin?” classification problem. Figure 01: Is this a chihuahua or a muffin? In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin.

Automating your software tests can speed up testing and provide more reliable test results. For most software teams, automation is necessary to achieve adequate test coverage. If you’re just getting started with automated software testing, you may be overwhelmed by all the options and have a hard time figuring out which tools are right for your team.

TL;DR: Unity does give you the test reports but not in a format that is readable by CI/CD. We write a C# script that transforms the NUnit XML format to JUnit XML that Codemagic can read and use to present you with a report on how your tests went. A few weeks ago, I wrote an article about unit testing Unity projects in which I discussed why testing is so valuable for game devs and showed how to re-create a basic Pong game with some unit tests.