The growth of a code base is unpredictable. To account for this uncertainty, we call on everything we've learnt in the last x years about how to scale an application effectively: adopting naming conventions, creating file and folder structures, using the latest patterns, and producing sensible abstractions. These actions add up over time. Eventually, you'll mentally high-five your past self for having taken the time to do them ✋.

Transforming your data is an essential part of ELT. Learn how Fivetran’s Wizard for dbt Core™ and BigQuery can help.

In the competitive market of ETL solutions, platforms like Hevo, Fivetran and Integrate.io are amongst the top contenders. While they all are ETL/ELT platforms, each of them has their own unique set of features to offer. The best ETL tool for your business is the one that is best aligned to your requirements. So how do you decide which tool meets your business needs?

As we mentioned in the previous article, at NodeSource, we are dedicated to observability in our day-to-day, and we know that a great way to extend our reach and interoperability is to include the Opentelemetry framework as a standard in our development flows; because in the end our vision is to achieve high-performance software, and it is what we want to accompany the journey of developers in their Node.js base applications.

Plenty of development life cycles are involved in a software project, so selecting the correct one becomes difficult. Software Development Models should be selected wisely by looking at the budget, team size, project criticality, and criticality of the product. Choosing the suitable model will improve the efficiency of your IT projects and manage risks associated with the software development lifecycle.

The difference between consuming data and actioning it often comes down to one thing: effective data visualization. Case in point? The John Snow’s famous cholera map. In 1854, John Snow (no, not that one) mapped cholera cases during an outbreak in London. Snow’s simple map uncovered a brand new pattern in the data—the cases all clustered around a shared water pump.

Conceptually, a paradigm is a system of concepts and practices that reflect the current state of our understanding of the field. In general, a programming paradigm refers to a style, way, or classification of programming. Programming languages are used in order to solve problems. A paradigm's difficulty varies according to the language. Paradigms can be used in several programming languages, but a strategy or methodology must be followed.

We’ve all heard about the significant WhatsApp breakdowns that have happened in the recent past, during which the app was unavailable for the public for an hour. However, from a technical standpoint, WhatsApp returned in less than an hour. What would have enabled the engineers at WhatsApp to quickly restore the services? Technically speaking, the team experienced an extremely stressful production failure because of this.

Developing high-quality apps involves pressure to make tradeoffs on speed, quality, and features to meet deadlines for release. This tension between speed and quality comes to a head with QA: you need a functional product but can’t afford weeks of turnaround time. You can’t skip QA: the true cost of software bugs – the direct cost of mitigating the defects and the indirect cost of decreased consumer trust – is extraordinary.

Big Data started as a replacement for data warehouses. The Big Data vendors are loath to mention this fact today. But if you were around in the early days of Big Data, one of the central topics discussed was — if you have Big Data do you need a data warehouse? From a marketing standpoint, Big Data was sold as a replacement for a data warehouse. With Big Data, you were free from all that messy stuff that data warehouse architects were doing.