Looking Back: Expanding the Data Analytics Universe
Here’s one of the most memorable quotes I have heard from a customer here in Asia: “Every time they tell me it’s ‘not in the universe’, I feel like mine is collapsing.”
Here’s one of the most memorable quotes I have heard from a customer here in Asia: “Every time they tell me it’s ‘not in the universe’, I feel like mine is collapsing.”
Fully managed ELT, DataOps and more trends that will change the way we use data this year.
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.
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.