Normally, images based on Alpine or BusyBox are

It’s easy to dismiss the concept of over-optimism all together just because it’s common sense that no developer who ever struggled to meet a deadline will be optimistic when setting deadlines. Now if project management is not coming from an engineering background and they set deadlines without knowing what they are doing, that’s a whole different issue that is outside the scope of this article.
Our preferred way to expose analytics data to the business is through a Dimensional Kimball model. We call this our presentation, reporting, or front-end DWH layer. We use those terms interchangeably.
The DIMENSIONAL KIMBALL model is perfect for organizing data around business entities and enforcing conformed dimensions. Therefore, whenever we talk about entities such as products, customers, fulfillment centers, hubs, and electric vehicles, we always use the same master data in the respective dimension. Our fact tables span from low-level transactional or event data to highly-aggregated multidimensional cubes. Regardless of the aggregation level, we maintain the KPIs’ logic in a single place and allow for a root cause analysis.

By changing the Dockerfile to the following one, the final size of our image is 22.4 MB. If we run the container, we can see that the web works without problems (Picture 7).

With multi-stage build, we can use multiple base images in the Dockerfile and copy artifacts, configuration files, etc. from one stage to another, so that we can discard what we don’t need.

Marketing efforts can be wasted, clients can be dissatisfied, stressed developers can write poor quality code to meet deadlines and compromise product reliability, and ultimately, projects can outright get canceled.Just like a functioning library needs a classification system, a usable and intuitive Data Warehouse needs data models. In fact, data modeling is one of the most critical, yet often overlooked Data Engineering skills. It allows us to create and maintain order amidst the chaos.Now we have a Dockerfile with two stages: in the first one we compile the project and in the second one we deploy the application on the web server. However, a Node container is not the best option to serve web pages (HTML, CSS and JavaScript files, images, etc.), the best option would be to use a server like Nginx or Apache. In this case I will use Nginx.By changing the Dockerfile to the following one, the final size of our image is 22.4 MB. If we run the container, we can see that the web works without problems (Picture 7).This problem is especially prevalent in software engineering, but other engineering disciplines are also known to suffer from the same downfall. So while this article focuses on software engineering, it also applies to other disciplines, to an extent.

The applied logic lives in the same space as the README files and comments, providing context for decisions. It also makes it easy to identify the most knowledgeable Data Engineer in any given area.
Some also attribute bad time estimation to the Dunning-Kruger effect, however, if inexperience or overestimating one’s ability is behind underestimating time then definitely more experience should alleviate the issue, right? The biggest companies out there with almost infinite resources still have a shockingly high rate of missing deadlines, so that hypothesis is debunked. Not to mention, we have all experienced this ourselves. More experience barely helps when it comes to time estimates.
Whether you are a junior, senior, project manager, or a top-level manager with 20 years of experience, software project time estimation never becomes easy. No one no matter how experienced or genius they are can claim to know for sure the exact time a software project would take.

By changing the Dockerfile to the following one, the final size of our image is 91.5 MB. Keep in mind that the image from the previous stage (lines 1–4) is not automatically deleted, Docker keeps it in cache to run faster if we use the same stage in another build, so it has to be deleted manually .In this example, what we need to deploy the React application is the compiled code; we don’t need the source files, nor the node_modules directory, nor the package.json, etc.A library’s classification system makes sure that readers can find the book they want in a logical place, and with all the pages intact. Similarly, a data model provides a map to the Business Analysts and Data Scientists, so they can find exactly what they need — and be confident that it’s correct.
Our Data Warehouse consists of two layers: front-end and back-end. Further, those are implemented by multiple schemas, split based on data modeling techniques and function.
We architected our DWH with schemas to depict data in different data modeling techniques, and we have a strict separation between back-end and front-end reporting DWH layers. The back-end schemas are only used by the Data Engineers and Data Scientists. Simultaneously, the front-end is an API also available to Business Analysts and Developers depending on their role. The Data Vault is the primary modeling technique for the back-end DWH while Dimensional Kimbal for the front-end.

Normally, images based on Alpine or BusyBox are

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