A selection of the top articles and videos from the last week on SystemsDigest.com. Don't forget to check back regularly for daily updates from around the globe.
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Answering questions about your performance and progress is easier with strategic reporting. Learn how to create great strategic reports and when to use them.
The Structured Query Language (SQL) is a Relational Database Management System (RDBMS) that is pronounced like the word "sequel." It was the first simple way to store and retrieve many sorts of data on computer systems, and it was invented in 1974. Since then, the language has grown in popularity, and it is still used in many content management systems (CMS) today, such as WordPress. The issue of the now-famous SQL injection surfaced shortly after the language gained popularity in the scientific research community. Many activities in systems like WordPress employ SQL queries to retrieve and save data. These SQL queries were created by developers and are not intended to be flexible.
Today, there are 1.88 billion websites on the internet and the number is rapidly increasing. There are 3.8 billion people in the world that own smartphones today – which amounts to 48.16% of the world population. The internet traffic from mobile (as compared to that from desktops) has also been increasing, and now stands at 56%. The point I am trying to make is that if you have a website – it can be accessed from a web browser or a mobile browser. Or if you have a mobile application – it could be installed and used on a wide variety of mobile devices.
Business process automation shifted into high gear during the COVID-19 crisis, making technologies such as low-code platforms, AI, and robotic process automation (RPA) critical success factors for any organization in the decade ahead. The same is true for the insurance industry where many companies are leaning into hyperautomation to streamline operations and take friction out of the customer journey as well. But pivoting from paper to digital isn’t easy. Nine out of 10 insurance companies say they’re struggling to develop the technology infrastructure they need because of legacy software and gnarly IT systems according to McKinsey.
So many announcements, so many surprises and a seemingly never-ending list of impactful customer stories! After two and a half days packed with fun and learning, we bid farewell to Kong Summit for another year. If you were one of the almost 5,000 people that registered, you know exactly what we are talking about. Here is a recap of some of the most exciting parts of Kong Summit 2021.
Following up on an earlier blog post where we discussed what is product lifecycle management for semiconductors, in this blog we will delve deeper into the challenges that PLM presents for a semiconductor design environment. Although PLM tools have seen some success in industries such as defense, automobile, aerospace, and others with large design teams and well-established methodologies, the adoption rate in the semiconductor space has been slow.
Consider for a moment, if you will, plastic patio furniture. Plastic Fantastic is a global manufacturer with several factories, warehouses, and plenty of stores. One can only imagine the sheer amount of data resulting from sales, production, suppliers, and finances. Everything that happens, from purchase and onward, to these chairs, tables, and cupboards in all corners of the world is measured.
Today, according to w3c, the most frequently used browsers are Chrome, Edge, Firefox, Safari & Opera. It is always advisable to run cross-browser test cases at least on these. However, the process of analyzing and selecting the thousands of device, OS, and browser combinations requires time, expertise, and effort too. Additionally, deciding which cross-browser test cases are ideal for automation requires proper analysis and expertise. Let us explore a few of such cases in this article. Find some helpful tips for cross-browser testing here.
Let’s start this post with a situation. An organization working in the logistic business has started to gather positive reviews and millions of users are now opting for their services through their mobile app. In other words, the organization is scaling very fast. Even though they were managing quite well with manual testers and in-house test automation setup, they know this arrangement cannot work for long. They are smart people. They also know that automation testing is gaining popularity and it is one of the strategies to get hold of ROI faster. Hence, they start searching for a tool that can act as a bridge between efficient mobile testing and ROI.
Apple and Google’s dominance over mobile app stores has never seemed less certain, and there are many signs that new payment methods and alternative app stores might become the norm. They aren’t exactly new, though. What are alternative app stores? What opportunities do they provide? And why aren’t developers publishing there en masse, today?
JavaScript is an awesome language to create fantastic client-side apps. You can use it to drive interactive elements and create engaging single-page applications to woo your customers. More importantly, most developers are familiar with JavaScript and can provide you top expertise for your project. Most of the JavaScript developers at hiring platforms like Reintech are adept at crafting innovative solutions for your unique business problems. However, JavaScript programmers need reliable tools to develop efficiently. These resources can help software engineers to write less code and develop faster. In addition, programmers can get rid of repetitive tasks and save their effort.
Kubernetes continues to lead the container orchestration charge. In fact, according to the latest CNCF survey, 83% of respondents said they were using Kubernetes in production. Kubernetes provides you with key features such as self-healing capabilities, automated rollouts and rollbacks, automated scheduling, scaling, and infrastructure abstraction. This provides a truly extensible, highly available and infrastructure-agnostic environment to deploy all your modern microservices-based applications.
2.5 quintillion bytes of data are produced every day, and those numbers are continually increasing. With such astronomical volumes of data, businesses have to understand and interpret data faster than ever before. However, data transfers must occur for businesses with millions of data entry points to properly store and interpret their data. Whether the business decides to transfer data to data warehouses such as Amazon Redshift or Snowflake, data transport is critical to any successful analytics strategy. Before your business can securely transfer data, there are many security measures and safeguards you must take.
We’re seeing a massive shift in how companies build their software. More and more, companies are building—or are rapidly transitioning—their applications to a microservice architecture. The monolithic application is giving way to the rise of microservices. With an application segmented into dozens (or hundreds!) of microservices, monitoring and consolidated logging become imperative. At any given moment, one of your microservices could fail or throw an error or begin hogging resources. You need to monitor for this so that you can respond quickly and appropriately. In addition, your ability to troubleshoot errors and understand system behavior depends heavily on the existence and effectiveness of your logging tool.
Database-driven realtime architectures are becoming more and more common as evidenced by key backers and widespread use of software like Firebase and Supabase. The two key priorities for an app following database-driven realtime messaging are long-term storage and change data capture (CDC) updates from the database. In this two part article series, we’ll take a detailed look at a fully serverless chat app where you can edit previously published messages. The chat app uses the Ably Postgres connector to achieve this, the details of which you’ll see throughout the article.
Who are my top sellers? What kinds of deals have the highest close rate? How have our sales opportunities changed over time? As a sales leader, these are just some of the questions you ask yourself every day to keep your team on track. And the answers are in your data. Every form fill, cold call, and MQL is another data point you can use to assess the health of your sales organization. But with so much of your data locked up in Salesforce, it can be difficult to drill down and explore trends or anomalies in a meaningful way. And honestly, who has time for complex data extractions or pivot tables when you’ve got a quota to crush?
Reverse ETL and customer data platforms (CDPs) are two big data trends that have been receiving a great deal of attention. While both CDPs and reverse ETL can help you make smarter data-driven decisions, there are also several crucial points of distinction. In this article, we’ll answer the question: what’s the difference between reverse ETL and a customer data platform?
When I joined Sisu over a year and half ago, I thought I was joining a data analytics company. And in some ways, I was. Our founder and CEO, Peter Bailis, started Sisu based on the insight that with the emergence of cloud data infrastructure – and the resulting exponential growth in the complexity of cloud data – the analytics layer needed a fundamental rewrite. Peter’s insight about the need for a foundational shift in conventional analytics is arguably even more relevant now than it was when he started Sisu in 2018. But after spending the last few years immersed in how businesses leverage their data today and the biggest challenges they face, we’ve reached an even sharper realization about what’s missing in the current data ecosystem.
We talk about risk like it’s a bad thing. But all innovation involves risk. Which isn’t necessarily bad. Not if the consequences of being wrong about it don’t pose an existential threat. But businesses everywhere are struggling to cope with doomsday scenarios such as climate change, cyber smash-and-grabs, the global COVID-19 crisis, and more.
Embedded system software developers have a difficult job. For one, the work itself is challenging — embedded systems must withstand numerous updates to their environment to function properly. Plus, when something goes wrong in development, those higher up the chain tend to look at developers rather than the tools the team is using. You can’t solve new problems with old tools, and they are often at the root of a team’s mistakes or delayed releases. With the right toolchain, teams can overcome challenges in developing embedded system software and maximize productivity.
It’s official (well, according to the gospel that is Stack Overflow): Svelte is the world’s favorite web development framework. According to a snapshot of over 80,000 devs carried out in May, this relatively new user interface tool is now more popular than established behemoths like React.js, Django and Vue.js. Programmers love its open-source convenience, its code-efficiency and its super-smart compilation, which generates an optimised JavaScript code based on your manual submissions. The fact that it’s free is a lovely little bonus, too.
Unless you’ve hidden under a rock for the past decade, you can’t have failed to notice that data in today’s enterprise is very much alive. It’s always moving, constantly changing, and we’re continually using it to create new business value. However, while data fluidity and visibility have blossomed, the opportunity to use that data to drive business actions seems to have withered in comparison.
In a recent webinar by TDWI, 45% of analysts reported that “every day seems to be a different fire drill.” No surprise to anyone in the industry. As much as analysts need to be focused on more strategic tasks, their skills are frequently deployed to answer basic questions. Greater self-service capabilities for end-users would no doubt alleviate these fire drills, but this is not yet a reality for the majority of companies. Take for instance the fact that self-service analytics (the blue bar in the graphic below) has ranked as the biggest priority — ahead of automation or ever-popular machine learning — for the last three years in a row.
Payments testing mimics each step of the payment process to verify that connections, transactions, and paths for communications are working. The best payments testing strategies include a thorough range of tests, from functional to performance to location. Need a place to start? Follow this checklist when developing a payment gateway testing strategy. A good strategy should detail exactly what information is needed, common flags for problems, and the markers of a successful payment gateway. Found a bug in your payment funnel? Working through this checklist can aid in understanding the elements of a payment testing system and break down each test to find the problem — and the solution.
Without payments gateway testing, you risk cart abandonment, over or under-payments, penalties, and increased customer support costs. These risks occur when web or mobile app payment transactions run into issues. To avoid these issues, online retailers should create test cases, conduct periodic checks, and update their processes to ensure the system’s integrity. From functional testing to usability testing, there are many ways to test payment processing. These five common payment gateway testing use cases illustrate the complexity of testing financial transactions across many payment platforms and devices.
In financial services, data has always been viewed as a strategic asset. To manage this data, organizations have invested heavily over several years and across a number of technology generations in the underlying data infrastructure. This approach has left a large data technology legacy along with silos of data linked to specific infrastructure and applications.
Apart from being a synonym to a sandal, a ruby flip-flop refers to a system that comprises two alternating (on/off) states that one can switch between. This can be pretty helpful when you want to loop through arrays and capture contiguous subsets that lie between specific bounds (you’ll understand more of what I mean as you read along). However, not many programming languages have leveraged a mechanism like this. Let’s dive in and learn more about what this operator is, how it works, its different types, what it offers, and where it makes sense to use it. Here’s an outline of what we’ll be covering so you can easily navigate or skip ahead in the guide –
Production debugging in its current form is a relatively new area of technology that aims to make it easier for developers to solve problems in their code. More often than not, we don’t have all the information we need to solve those hard to reproduce bugs. This leads to long hours of debugging, adding more log lines, and creating separate reproduction environments to try to isolate and reproduce problems. The objective of production debugging solutions is to take much of the pain out of these situations by giving developers direct access to their code-level data in live applications whenever they need it.
Star schemas and snowflake schemas are the two predominant types of data warehouse schemas. A data warehouse schema refers to the shape your data takes - how you structure your tables and their mutual relationships within a database or data warehouse. Since the primary purpose of a data warehouse (and other Online Analytical Processing (OLAP) databases) is to provide a centralized view of all the enterprise data for analytics, data warehouse schemas help us achieve superior analytic results. How do schemas help analytics? And what are the differences and trade-offs between star and snowflake schemas?
One of the most substantial big data workloads over the past fifteen years has been in the domain of telecom network analytics. Where does it stand today? What are its current challenges and opportunities? In a sense, there have been three phases of network analytics: the first was an appliance based monitoring phase; the second was an open-source expansion phase; and the third – that we are in right now – is a hybrid-data-cloud and governance phase. Let’s examine how we got here.
Apache Hive and Apache HBase are incredible tools for Big Data. While there is some overlap in their functions, both Apache Hive and Apache HBase have unique qualities that make them better suited to specific tasks. Some key differences include: Ultimately, comparing Apache Hive to Apache HBase is like comparing apples to oranges or Google to Facebook. While the two entities are similar, they don't provide users with the same functionality. However, despite their differences, both Apache Hive and Apache HBase are fantastic tools to use when working with Big Data. Read on to discover more about Apache Hive, Apache HBase and how their various functionalities can improve your business when it comes to working with big data.
Data breaches exposed 18.8 billion records in the first half of 2021. The ability to transfer information between systems is fundamental to data integration. Given these numbers, transferring that information securely is even more essential. Whether transferring information internally or externally, using a secure method to share information is important to prevent the information from being compromised. Here, we’ll discuss Secure File Transfer Protocol or SFTP, how do I access SFTP server, and what you need to make it all work.
