Author: Hiren Dhaduk

The software industry has discovered effective solutions to its development-oriented problems in DevOps, CI/CD, and containers. Although not mandatory to use all three together, they often complement and rely on each other.

DevOps promotes collaboration between development and IT teams, while CI/CD simplifies the software delivery process for quicker outcomes. Containerization combines an application with its dependencies to establish consistent development and deployment environments.

In order to efficiently handle and analyze data for decision-making purposes, data warehousing has become a crucial aspect of modern enterprises. Tools used for data warehousing are essential for efficient data management and analysis. 

Software developers may now easily extract, process, and load data from disparate sources into a data warehouse for subsequent analysis and reporting with the help of these technologies. In this article, we'll take a look at the importance of data warehouse tools, highlight some of the most widely used ones in the development community, and also see a use case for these tools.

In today's world, UI/UX is essential for any digital product, whether a website or a mobile application. It plays a crucial role in shaping users' interaction with the application and their overall experience while using it. Given the rapidly evolving technology landscape, it has become imperative to keep up with the latest trends and modernize UI/UX applications to stay ahead of the competition.

The UI/UX application modernization process is centered around enhancing the existing applications or replacing them altogether to deliver a superior user experience. However, the task of modernization is not without its obstacles. This article will explore the hurdles encountered during UI/UX application modernization and present effective strategies for overcoming them.

In the beginning, IT teams utilized Application Performance Monitoring (APM) and Network Performance Monitoring (NPM) as tools to oversee and diagnose problems at the application and infrastructure levels. However, with the advent of contemporary development practices, the introduction of numerous distributed components made it difficult for APM and NPM solutions to offer complete visibility across the entire system.

As a result, observability emerged as the logical successor to APM and NPM, owing to its capacity to provide comprehensive visibility within a distributed IT system. Through observability, businesses can take proactive measures to resolve production-level issues.

Deployment is the day when the software is finally released to the world. 

Yet, as Stackify CEO Matt Watson said, organizations lack confidence in deployment. One of the greatest strengths of agile is the ability to deploy rapidly. However, moving too fast and without following the right processes, one can witness problems like downtime, errors, and poor user experience.  

As more and more companies move to the cloud, it’s becoming essential to keep track of their resource usage to ensure cost-effectiveness. Amazon Web Services (AWS) is a leading platform among cloud providers, but its extensive range of services can pose a challenge when monitoring resource consumption efficiently. This article delves into the significance of tracking AWS resource utilization for cost optimization and offers practical tips on accomplishing this.

What Is AWS Resource Utilization?

As an AWS professional, it’s essential to understand the concept of AWS resource utilization. Essentially, it refers to the computing resources that your website or application consumes on the AWS platform. These resources may include CPU, memory, disk I/O, and network usage, among others. Fortunately, AWS offers several tools you can utilize to monitor your resource utilization. These tools include Amazon CloudWatch, AWS Trusted Advisor, and AWS Cost Explorer. By leveraging these services, you can keep track of your resource consumption and optimize your AWS usage for maximum efficiency.

Cases Where ETL Still Bosses Over ELT

In the past few years, ELT has become very popular, mostly because more people are using big data and cloud-based solutions. ELT does have some benefits, but it may not be the best choice for all uses.

In some situations, ETL may be a better and more efficient choice. In this article, we’ll look at the situations where ETL is still better than ELT. We will talk about situations where ETL is the best choice because of the complexity of the data transformations, the size of the data, and the need for data quality and consistency. By the end of this article, you’ll have a better idea of when ETL is better than ELT for integrating data.

Application modernization has become a hot topic in recent years as organizations strive to improve their systems and stay ahead of the competition. From improved user experience to reduced costs and increased efficiency, there are many reasons companies consider modernizing their legacy systems.

So, should you consider this investment? Let’s find out!

Cloud cost optimization refers to the process of analyzing resources to identify areas where unused or wasted resources can be optimized to reduce expenses. This involves a variety of tasks, including resource analysis, identification, monitoring, and management of instances. However, the process of optimizing cloud costs doesn’t end with resource monitoring and management. It’s important to take a holistic view of cloud costs for every phase of the software development lifecycle (SDLC). Achieving cost optimization at each stage requires careful monitoring and visualization of data.

Cloud cost optimization process is dynamic and requires continuous assessment of cloud resources, pricing, and changing business requirements. Here is how developers can apply cloud cost optimization to different phases of SDLC.

With constant digital evolution, the sources of streaming data are rising, such as IoT, networked devices, online activities of various kinds, server log files, and so on. And with every industry becoming reliant on this data to unlock data-driven business insights, streaming processing systems power everything from real-time fraud detection, stock trading platforms, and sentiment analysis from social media feeds to multiplayer games and GPS tracking.

However, streaming data is generated at very high velocities by many data sources. Thus, it can be challenging to build robust stream processing solutions. This article describes how stream processing works and the fundamental building blocks of its architecture.

What Is Sentiment Analysis?

In just 4 years, a whopping 6 billion users — that’s half of the world’s population — will be active on social media. And if you’re curious to know the time we spend on social media, it is a jaw-dropping 147 minutes daily.

Any place where people spend so much time of their day is important from a business perspective. Many businesses realize this and invest heavily in analyzing data from social media. In most cases, businesses are concerned about the sentiments on social media regarding their brand. It helps them gain insights into the kind of sentiments that social media users have regarding their brand.

Testing is a best-case scenario to validate the system's correctness. But, it doesn't predict the failure cases that may occur in production. Experienced engineering teams would tell you that production environments are not uniform and full of exciting deviations.

The fun fact is – testing in production helps you test the code changes on live user traffic, catch the bugs early, and deliver a robust solution that increases customer satisfaction. But, it doesn't help you detect the root cause of the failure.

Data fuels insight-driven business decisions for enterprises today; be it planning, forecasting market trends, predictive analytics, data science, machine learning, or business intelligence (BI).

But to be fully useful, data must be:

Companies use software to run their business in today’s digital world. With the increased use of microservices, containers, and cloud-based technologies, traditional methods of monitoring and solving problems are no longer enough. That’s where observability comes in.

Observability and monitoring are often confusing. While monitoring refers to regular observation and recording of activities taking place within a project, observability watches and understands how a system performs and behaves in real time. Leveraging observability allows developers to better understand the system and quickly resolve any potential issues.

Legacy modernization aims to meet the organization's current business needs by enhancing business agility with new functionality and appealing features, strengthening customer service, and increasing efficiency.

However, modernizing a legacy system is not child's play, as nearly 74% of enterprises fail to complete legacy modernization processes due to a disconnect between technical and leadership teams.

In 2017, Amazon faced an unfortunate four-hour outage, causing S&P 500 companies to lose $150 million. What was the cause? An erroneous command removed more servers than intended, causing system downtime. Although downtime is not an alien topic for developers, its impact can be deeply felt. Gartner estimates the average cost of downtime to be as high as $5600 per minute, which may build up to over $300k per hour. These are definitely scary numbers for any organization.

But there's no need to panic just yet!