Category: Data Management & Engineering

Do believe the hype!

Many companies are migrating their data to the cloud. Cisco forecasts that by 2021, 94% of all workloads and compute instances will be processed by cloud data centers.

But Gartner’s 2018 hype cycle did have cloud migration falling into the “trough of disillusionment.” This could be because migrating data to the cloud isn’t easy. Organizations encounter numerous issues when trying to take advantage of the cloud’s benefits.

Organizations need to plan properly before undertaking such an endeavor. Let’s look at what you can do to minimize the issues that can plague a cloud data migration project.

Continue reading “What Is Cloud Data Migration? A Detailed Guide”

What is data modeling in software engineering? Let me explain.

Every company has a lot of data in its databases. But don’t you think managing massive chunks of data can create confusion? We know for a fact that data becomes obsolete after some time if it’s unorganized. After that, it doesn’t matter how relevant it was. To clarify, without organization, data isn’t of much use. Moreover, you can’t use it to its full potential. In fact, messed-up data makes it tough to store, retrieve, and capture it efficiently.

Data modeling is a method that helps you avoid the cons that come with poorly designed data. It’s like a map that helps organize information for better use. In this post, we’re going to take a detailed look at data modeling and why it’s important. We’ll also check out the types of data models and the pros and cons. This post will also cover the steps of data modeling in detail. So, let’s dive right in!

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If you’re like most data scientists, scoping projects probably isn’t your favorite part of your job. Scoping projects can often feel like a mix between tedious busywork meant to placate executives and wildly guessing. Chances are, you’ve had projects in the past totally miss their scoping requirements, only to see no negative side effects. Feeling like your work is meaningless, tedious and baseless is a recipe for frustration, no matter your profession. For data scientists, who are used to measuring things to determine their efficiency, it’s excruciating.

Fortunately, it’s possible to get better at scoping your projects. Project scoping is never going to go away, and despite what it might feel like, it’s not meaningless. Project scoping helps decision-makers determine how to prioritize projects for an organization. Doing it well means both that the most important projects receive the attention they need, and also that you’re more likely to be successful when you embark on a new project. In this post about how to scope a data engineering project, we’ll walk through a detailed guide on what you need to understand.

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