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We can test a React Native application using Cypress end-to-end test runner while running it in the web moPersonal experience led author to share some of the things he has learned hoping that it might help other developers in a similar position. By Sam Walpole.

Every person is different, and every person’s circumstances are different. Therefore the only meaningful measure of success is against yourself. Are you a better developer than you were 6 months ago? Yes, so that sounds like success to me.

The article is split into these sections:

• Stop comparing yourself to others
• Give yourself a break
• Focus on now

We liked: … the truth is that looking at these people and having such high standards of success for yourself is only going to leave you feeling inadequate and not good enough. Nice one!

We can test a React Native application using Cypress end-to-end test runner while running it in the web mode using Expo. By Gleb Bahmutov.

If your company is evaluating using the React Native technology for developing its native mobile application, you are probably wondering how to write and run the tests. The React Native testing docs really only focus on unit testing using Jest running in Node. You cannot see what the application is doing, and if something goes wrong, good luck debugging it.

Most of the sections in this post are covered by short videos in author’s Testing React Native Application Using Cypress video playlist. Some of the videos are embedded in the relevant sections of the post:

• The initial application
• The first Cypress test
• Continuous integration
• Network control
• Test the loading indicator
• Stubbing network call
• Code coverage
• Resources
• Adding expo to react-native projects
• Expo camera
• Code examples

… and much more. You will also get link to further learning resources in the article. Good job!

Learn how to analyze time-series data through RedisTimeSeries with Apache Kafka in this practical walkthrough. RedisTimeSeries is a Redis module that brings native time-series data structure to Redis. By Abhishek Gupta.

Generally speaking, time-series data is (relatively) simple. Having said that, we need to factor in other characteristics as well: Data velocity - e.g. Think hundreds of metrics from thousands of devices per second and volume (big data): Think data accumulation over months (even years)

Thus, databases such as RedisTimeSeries are just a part of the overall solution. You also need to think about how to collect (ingest), process, and send all your data to RedisTimeSeries. What you really need is a scalable data pipeline that can act as a buffer to decouple producers and consumers. That’s where Apache Kafka comes in! In addition to the core broker, it has a rich ecosystem of components, including Kafka Connect (which is a part of the solution architecture presented in this blog post), client libraries in multiple languages, Kafka Streams, Mirror Maker, etc.

The article also deals with:

• Scenario: Device monitoring
• Solution architecture
• Set up the infrastructure components
• Setup local services
• Deploy the device data processor application
• Start simulated device data generator
• Delete resources
• What about long term data retention?

Your time-series data volumes can only move one way—up! It’s critical for your solution to be scalable. Very nice!

Data without context is meaningless. We want every dossier, page, and visualization to tell a story. By Keng Fu Chu.

A great story usually progresses towards a central message, and so should your dossier. In this article, you will learn how to incorporate storytelling to deliver insights to your end users, as well as how to use dossier features to build a narrative dossier.

Further in the article:

• Creating stunning infographic-style dossiers with vertical scrolling pages
• Freeform Layout - Blending data into your design, not the other way around
• Responsive group and form-factor show/hide — Tailoring the mobile consumption experience
• Enable authors to create information - rich dossiers with panel stack
• Create data-oriented design with Visualization Gallery, Rich Text, Shapes, and more

Unlike data visualization, storytelling with data enables authors to offer a more narrative, emotional, and holistic view of their information. It has never been easier for analysts, designers, or data scientists to engage in data storytelling. Recommended for anybody interested in the UX and UI.

A very popular stack for building responsive web apps is Tailwind CSS and Next.js by Vercel. By Max Lynch.

Tailwind, a utility-first CSS framework that replaces the need to write custom class names or even any CSS at all in many cases, makes it easy to design responsive web apps through small CSS building blocks and a flexible design foundation.

Next.js, a React framework for building high performance React apps, is one of the leading environments for building production React apps on the web.

The article then describes:

• The stack visualized
• Mobile UI and native runtime
• Introducing the Next.js + Tailwind CSS + Ionic Framework + Capacitor Starter
• Deploying to iOS and Android

If you’ve been interested in building mobile apps using popular web dev projects like Next.js or Tailwind, hopefully this starter provides inspiration and a solid foundation for building your next app using web technologies. Good read!

The benefits of multi-model serving where latency matters. By By Alejandro Lince and Steven Ross.

Machine Learning (ML) inference, defined as the process of deploying a trained model and serving live queries with it, is an essential component of many deployed ML systems and is often a significant portion of their total cost. Costs can grow even more uncontrollably when considering hardware accelerators such as GPUs.

Many modern user-focused applications critically depend on ML to substantially improve the user experience (by providing recommendations or filling in text, for example). Accelerators such as GPUs allow for even more complex models to still run with reasonable latencies, but come at a cost.

The article then deals with:

• Benefits of multi-model serving
• Analyzing single versus multi-model serving latency
• Costs Versus Latency

Multi-model serving enables lower cost while maintaining high availability and acceptable latency, by better using the RAM capacity of large VMs. While it is common and simple to deploy only one model per server, instead load a large number of models on a large VM that offers low latency, which should offer acceptable latency at a lower cost. These cost savings also apply to serving on accelerators such as GPUs. Good read!

Choosing an AWS region is the first decision you have to make when you set up your AWS components. You can’t do anything in the AWS Management Console, SDK or CLI without choosing a region. Most AWS customers choose one based on proximity to themselves or to their end users, which sounds like a sensible thing to do. Price calculations using AWS Price List API (2021-05-26). By Ernesto Marquez.

However, proximity alone is not enough. There are a lot of other factors to consider when choosing a region. Cost varies by region - choose the wrong one and you could end up paying a lot more. Author uses the AWS Price List API a lot. He uses it to programmatically calculate the cost of every configuration he makes. For some regions, it’s not difficult to find a 30% or 70% price difference compared to the cheapest AWS region.

Source: https://www.concurrencylabs.com/blog/choose-your-aws-region-wisely/

It is also worth noting that:

• Reserved EC2 Instance Cost and Savings vary by region
• Regions have different latencies and data transfer speeds
• Consider the type of resource that is most important to you (i.e. compute, data transfer, latency, storage, etc.)
• Not all services are available in all regions (some take a VERY long time before they are)
• Not all regions have the same number of Availability Zones
• All data transfer between AWS regions costs the same, except for one region (Ohio)

You would pay $0.02 per GB of data transferred to any region in the world, except Ohio. For Ohio, you would pay $0.01 per GB.

As you can see, there are a lot of factors to consider when choosing the right AWS region. Not everything is about proximity to your end users. Choosing your AWS region wisely can save you the pain of overspending thousands of dollars and potentially hindering the growth of your applications. For detailed charts and breakdown of costs follow the link to the full article. Excellent!

According to a 2020 study, over half of the companies that use the public-cloud said that doing so had improved their ability to meet their business goals. By James Harvey, EMEA CTO at Cisco AppDynamics.

Another recent study found that shifting business applications to the cloud helped boost revenue by 16% and productivity by 19%. Little wonder, that 93% of enterprises today already have a multi-cloud strategy in place and 73% plan to optimise their existing use of the cloud in the future. The advent of sophisticated, scalable cloud architectures — native and hybrid — has enabled organisations to take full advantage of modern cloud infrastructures and microservices architecture to boost productivity and increase cost savings.

Despite these advantages, moving to the cloud still has its headaches for some organisations. Often, in highly complex - cross-platform and multi-site — deployments, it can be hard for IT managers and CIOs to demonstrate the gains from migrating to the cloud.

What’s needed, is a way to monitor, configure and optimise the organisation’s entire technology landscape, through a single lens:

• Scale, adapt and grow with the cloud
• The impact of the pandemic on cloud strategies
• The power of observability and monitoring

Observability is important here because it provides the raw, granular data necessary to gain an in-depth understanding of complex and highly distributed systems. Perhaps even more importantly, businesses are able to identify how well the infrastructure supports the applications, identifying any bottlenecks and load issues, in real-time. Insightful!

Reading a lot of great books improves our knowledge, judgment and mental models. But many people rarely engage with the content of their books. By Thomas Oppong.

When you aim to read hundreds of books a year with no regard for absorption, you probably won’t get all the knowledge you need from the books. To improve your retention rate, you have to slow down and think deeply about the new ideas.

Successful reading requires metacognition.

When you are metacognitive, you are aware of your own thought processes and think about your thinking. It’s a vital skill for learning and retaining new knowledge.

When you apply metacognition to reading:

• You make time to analyze the content and reflect on what you are reading.
• You ask critical, challenging, and analytical questions whilst reading.
• You make time to figure out what you already knew before reading and what you want to improve.
• You’ve thought about what to do to retain more of what you read
• You plan on applying some of the ideas in the book in your life. Successful readers use metacognition to understand what they want from books. They also use it to improve their reading experience.

Most great books require an investment of 5–10 hours to absorb the content thoroughly. When you plan of spending a lot of your time on good books, it’s essential to make sure you are getting the most knowledge out of them without wasting time.

We can learn to pay attention, concentrate, devote ourselves to authors. We can slow down so we can hear the voice of texts, feel the movement of sentences, experience the pleasure of words. Deliberate reading means you take your books seriously. It’s one of the best ways to acquire and retain new knowledge. Good read!

Improving your workforce performance is probably one of the biggest priorities for all executives and business owners. When your team’s productivity is up, everything else is expected to be just as high in performance. By Thomas Jones.

One way to improve workforce performance is by using digital transformation as your primary means of driving this transition. Hence, here’s how you can use digital transformation to improve workforce performance:

• Define realistic and clear goals
• Align your strategy with your goals
• Create an environment of transparency
• Provide access to relevant information
• Give opportunities for decision-making
• Be flexible and adapt to employee needs

To sum up, using digital transformation to improve your team’s workforce performance is probably one of the best things you can do for the short-term and long-term success of your business. Good read!