Tag: Best Practices

In the dynamic realm of software development and deployment, Docker has emerged as a cornerstone technology, revolutionizing the way developers package, distribute, and manage applications. Docker simplifies the process of handling applications by containerizing them, ensuring consistency across various computing environments. A critical aspect of Docker that often puzzles many is Docker networking. It’s an essential feature, enabling containers to communicate with each other and the outside world. This ultimate guide aims to demystify Docker networking, offering you tips, tricks, and best practices to leverage Docker networking effectively.

Understanding Docker Networking Basics

Docker networking allows containers to communicate with each other and with other networks. Docker provides several network drivers, each serving different use cases:

First of all, what’s a code review? Basically, it’s a review of written code performed by other team members. The result of the code review is feedback on the completed task: change requests and comments or green light for further testing and release.

Even though the task itself seems pretty straightforward, there are many things to take into account and a number of different ways of performing code review, and there is no unified guide that would work for every company or project.

Local storage has seen a notable reduction in demand over the past few years. With inexpensive internet packages, users can explore the cloud-enabled infrastructure to stream all their files.

As the demand for cloud-based apps surged, more entrepreneurs switched to cloud models for their applications. However, there is a key concern involved with the cloud. It is cloud application security.

Technical debt refers to the accumulation of suboptimal or inefficient code, design, or infrastructure in software development projects. It occurs when shortcuts or quick fixes are implemented to meet immediate deadlines, sacrificing long-term quality and maintainability. Just like financial debt, technical debt can accumulate interest over time and hinder productivity and innovation. Managing technical debt is crucial to the long-term success of software development projects. Without proper attention and mitigation strategies, technical debt can lead to increased maintenance costs, decreased development speed, and reduced software reliability.

Types of Technical Debt

There are different types of technical debt that software development teams can accumulate. Some common types include:

Understanding Data Virtualization

Data-driven decision-making stands as a key strategy for numerous companies globally. For decision-making to be effective, data must be provided to users promptly. Companies utilize ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) approaches to develop analytics layers from various data sources, aiding users in making informed decisions. However, both these paradigms face challenges in producing datasets on time for consumer use due to the involvement of multiple processes and tools.

Many companies find it challenging to establish a unified view from diverse data sources. With the daily increase in data sources and consumers, technology vendors focus on zero ETL as a solution. Data virtualization can be employed to avoid unnecessary ETL processes and data replication.

In my previous article ‘Data Validation to Improve Data Quality’, I shared the importance of data quality and a checklist of validation rules to achieve it. Those validation rules alone may not guarantee the best data quality. In this article, we focus on the best practices to employ while building the data pipelines to ensure data quality. 

1. Idempotency

A data pipeline should be built in such a way that, when it is run multiple times, the data should not be duplicated. Also, when a failure happens and it is resolved and run again, there should not be a data loss or improper alterations. Most pipelines are automated and run on a fixed schedule. By capturing the logs of previous successful runs such as the parameters passed (date range), record inserted/modified/deleted count, timespan of the run, etc., the next run parameters can be set relative to the previous successful run. For example, if a pipeline runs every hour and a failover happens at 2 pm, the next run should capture the data from 1 pm automatically and the timeframe should not be incremented until the current run is successful.

Enterprises should guard the data as their deepest secret, as it fuels their lasting impact in the digital spectrum. In pursuing the same, synthetic data is a weapon that emulates actual data and enables many data functions without revealing the PII. Even though its utility is below real-time data, it is still equally valuable in many use cases. 

For example, Deloitte generated 80% of training data from an ML model using synthetic data feeds. 

If you’re into monitoring, Prometheus is probably an essential part of your stack. Thanks to its expressive query language (PromQL), scalability, and configurable data format, it remains one of the most popular tools for data collection. 

Paired with Prometheus exporters, the tool can adapt to a variety of surroundings, which is one of its strongest points. With the help of exporters, Prometheus can provide insightful data and effectively monitor a variety of services, including databases, web servers, and custom-made applications.

Globally, it’s estimated that $3.4Tn would be spent annually by organizations on digital transformation initiatives, with cloud, AI, big data, cybersecurity, and robotics as core transformative technologies. It doesn’t matter which technologies you plan to ride on; Software development is always going to be an integral part of all your digital transformation initiatives. 

By the way, could you guess what’s common between software development, meal preparation, manufacturing a car, building a house, and composing music? 

When creating applications that store and analyze large amounts of data, such as time series, log data, or event-storing ones, developing a good and future-proof data model can be a difficult task. Determining the most appropriate data types to store the information depends on various factors, including the required precision of float-point values, the content of the values (such as text), compressibility, and query speed.

Choosing the right data types in PostgreSQL can significantly impact your database's performance and efficiency. In this article, we'll explore best practices for selecting data types that align with PostgreSQL's recommendations. Whether you're designing tables for a new project or optimizing an existing database, these insights will help you make informed decisions.

Microservices architecture has revolutionized modern software development, offering unparalleled agility, scalability, and maintainability. However, effectively implementing microservices necessitates a deep understanding of best practices to harness their full potential while avoiding common pitfalls. In this comprehensive guide, we will delve into the key best practices for microservices, providing detailed insights into each aspect.

1. Defining the "Micro" in Microservices

Single Responsibility Principle (SRP)

Best Practice: Microservices should adhere to the Single Responsibility Principle (SRP), having a well-defined scope of responsibility that encapsulates all tasks relevant to a specific business domain.

Previously, I delved into the realm of integrating React Hook Form with Redux, exploring ways to harness the power of these two essential tools for building dynamic forms in React applications. However, my journey didn't end there. In the process of working on that project, I found myself immersed in the intricacies of developing complex form-based applications. This deep dive into form development unveiled a wealth of repeating patterns, invaluable best practices, and insights that significantly influenced my approach to coding, decision-making, and architectural design—especially when tackling large-scale form-based applications.

In this follow-up exploration, I'm excited to share the culmination of my experiences and discoveries. We'll dive into a set of best practices that have proven to be invaluable when dealing with the challenges of developing extensive form-based apps using React Hook Form, and we'll emphasize the added benefits of incorporating TypeScript for enhanced type safety and developer productivity. Whether you're embarking on a new form-based project or looking to optimize an existing one, these practices will undoubtedly pave the way for more efficient development and a smoother user experience. So, let's journey into the world of form-based app development and explore the best practices that can transform your approach and outcomes.

We live in a data-driven world, and SQL (Structured Query Language) is essential for managing data. SQL is used to manipulate and interact with data in a relational database. It's used by businesses of all sizes, from startups to large corporations, to extract insights from their data and make informed decisions.

But writing SQL code is not just about knowing the language — it's also about using best practices for coding and development. Poorly written SQL code can lead to performance issues, security vulnerabilities, and difficult-to-maintain databases. On the other hand, well-written code can make your database faster, more secure, and easier to manage.

This article presents a discussion on the best strategies and practices that should be adopted when developing and deploying applications for the cloud — to make your application more stable, scalable, and secure. When developing applications for the cloud, you should have your design, development, and deployment strategy in place. First off, is your application cloud-ready or cloud-centric? While in the former case, your application can be deployed to the cloud (either a public or a private cloud), the latter is a cloud-native application that leverages the cloud principles (multi-tenancy, elastic scaling, etc.).

Cloud computing has many business benefits. These include:

Kubernetes, the de-facto standard for container orchestration, supports two deployment options: imperative and declarative.

Because they are more conducive to automation, declarative deployments are typically considered better than imperative. A declarative paradigm involves: