DynamoDB is a fast and performant NoSQL database for creating highly available web applications. This article explores how to interact with DynamoDB in Python using the Boto3 library and covers in-depth concepts such as concurrency, leader model, throttling, and more.

Imagine releasing a product or application with poor quality. The application is unfeasible to use and navigate from one menu to another. By the time we realize that the features are not generating value for intended customers, it may be too late to make any modifications, and it will be exorbitant to fix them. In such situations, Dogfooding testing helps prioritize features.

Apache Spark has become one of the most popular open-source data processing platforms in the enterprise ecosystem, especially thanks to its ability to handle both batch and real-time data processing on large amounts of data.

On-premises software accounted for 58% of the market share for tech deployment in 2020 and is projected to grow 30% by 2030. While cloud services will likely overtake this deployment method in the years to come, your business might not be able to afford to switch to the cloud anytime soon, especially if predictions about an impending recession come true.

An array is a set of items kept close to one another in memory. With just an offset added to a base value, it is possible to determine the position of each element when storing multiple items of the same type together. In Python, an " array " module is used to manage arrays. Although lists can be used like arrays, users cannot specify the kind of elements that can be stored there. Therefore, when using the array module to create arrays, all array components ought to be of the same sort.

JSON is among the most popular data exchange formats, while Go is mainly used for software development. In Go, the encoding/json package provides built-in support for encoding and decoding JSON data. With this package, Go programs can easily read and write JSON data to communicate with external systems, such as web services or databases, or to exchange data between different components of the same program.

When building APIs for the first time, it is important to understand error status codes and how they impact the overall performance of the API. Error status codes are essential for ensuring that the API is functioning correctly, communicating errors and warnings to the user or system, and providing a seamless experience. It is important to understand the different types of error status codes, what they mean, and how to interpret them so that your API will function properly.

Imagine you open a shopping website, browse around and see some products of your liking. After placing them in the cart, you're all ready to check out, only to discover that your favorite items are nowhere to be found on the list. This is one of many examples of a software bug that could be caught using integration testing. There are multiple testing stages that software goes through before release, the two most popular being unit and integration testing.

Data has historically been in the hands of a select few in most businesses - until recently. Business intelligence (BI) solutions have evolved dramatically in the last few years to not only be more sophisticated, but simpler and more accessible for regular professionals to use analytics tools and get the insights they need to make decisions.

Overview With the increasing use and discussion surrounding ChatGPT and its applications, I decided to test out what it says about important skillsets for data engineers. I conducted a search about both soft and hard skills and here is what it came up with. I have added a lot of commentary to each of the 13 skills identified.