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Introduction

Google Cloud Data Studio is a tool for transforming data into useful reports and data dashboards. As of now, Google Data Studio has 22 inbuilt Google Connectors and 571 different Partner connectors which help in connecting data from BigQuery, Google Ads, Google Sheets, Cloud Spanner, Facebook Ads Data, Adobe Analytics, and many more. 

Once the data is imported, reports and dashboards can be created by a simple drag and drop and using various filter options. Google Cloud Data Studio is out of the Google Cloud Platform, which is why it is completely free. 

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Securing applications is not the easiest thing to do. An application has many components: server-side logic, client-side logic, data storage, data transportation, API, and more. With all these components to secure, building a secure application can seem really daunting.

Thankfully, most real-life vulnerabilities share the same root causes. And by studying these common vulnerability types, why they happen, and how to spot them, you can learn to prevent them and secure your application.

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Designing for user experiences is what all designers do. UX is often thought of as the preserve of app or web designers; however, even a print designer laying out a magazine anticipates reader reaction to the scale of type, the placement of adverts, and the art direction of successive stories.

Because all designers design user experiences, the role of UX Designer has come to mean someone focused on creating a product or service utilizing research and testing to guide decision-making.

To research and test anything, you need metrics: a baseline and a target against which to measure. No one set of metrics is suitable for all projects, but because UX tends to be for financial profit, the Pirate Metrics Framework — Acquisition, Activation, Retention, Referral, Revenue — is a good starting place.

You might seek out very different metrics in some cases. For instance, a museum might measure the success of its education program based on how many students go on to study paleontology. However, those types of metrics are notoriously difficult to quantify. Excepting a few niche cases, successful UX increases user productivity, decreases errors, reduces the cost of support, and increases sales.

So if it’s as easy as counting dollars, why does UX go bad?

UX vs. Design Principles

To understand what UX is, you need to understand what UX is not.

One of the most straightforward design principles to understand is hierarchy: bigger is more important, i.e., a heading is visually stronger than a sub-heading, a sub-heading is visually stronger than the body text.

Design principles stem from one thing: human-centered design. At the most basic level, bigger is more important because the bigger a saber-toothed tiger appears, the more likely it intends to eat me.

The evolution of human beings is so slow that had a smartphone existed at the time, a neanderthal would have been able to tap a button with the same level of precision as me. Prehistoric man shares the same minimum button size as modern man: 48 x 48px. Design principles don’t change, don’t require research, and don’t need verifying with tests.

On the other hand, a neanderthal would not have understood a smartphone, let alone an app. You only need to step back by a single generation to find perfectly intelligent people baffled by a commonly employed design pattern.

Unlike design principles, user experience is a house built on sand. When the sand shifts, the walls crack. The bricks are still solid, but the rain gets in.

Because effective UX is temporary, so is the ROI.

Technology Breaks UX

Technology unfolds at a rapid pace. As technology develops, the user experience defined by that technology changes.

The classic example is the mobile revolution, but technological change does not necessarily mean hardware. One of the most significant shifts in UXD (User Experience Design) in my career has been the popularisation of AJAX — the process of using JavaScript to load new data without refreshing the page. This seamlessness has been around since the early 2000s, but it’s only in the last ten years, as the code to achieve it has simplified, that it’s been widely used.

Jakob’s Law states that users spend most of their time on other sites and, as a result, prefer your site to function like other sites by following familiar design patterns.

Even if your UX is rigorously tested and optimized, when other sites and services carry out their own research, they are testing against the background of younger technology, and the “other sites” Jakob Nielsen refers to begin to change. As a result, the UX of your site is gradually eroded.

The consequence of continual technological change is that user research is constantly invalidated. The UX of an app, site, or service begins to degrade as soon as it is created.

User-Experience Lifecycle

Human beings have two deep-seated motivations: survival and procreation. The most important, survival, depends on discovery — new food sources, new routes through dangerous territory, new ways to skin a mammoth. We are biologically programmed to seek out the new.

A typical user passes through three phases of a relationship with a site, app, or service: discovery > comfort > boredom. Churn, or drop-off, tends to occur in the discovery phase (if the comfort phase is too slow in developing) or the boredom phase. The sweet spot is the comfort phase. That’s the part of the business-customer relationship in which the customer requires minimal support and is least likely to drop off.

The most effective form of UX — meaning the one that satisfies most metrics — rapidly moves a user from discovery to comfort and then continually eases the user back to the start of the comfort phase without tipping back into discovery.

This can be achieved with numerous micro-discoveries, tiny chunks of new experience, from simple functionality tweaks to style revisions.

Summary

All UXD, regardless of the quality, level of investment, and skill of the practitioner, begins to degrade the moment it is created.

Design principles like simplicity are good indicators of successful UID (User Interface Design) and are timeless; comprehensive design systems, brand assets, and content offer good ROI.

The most effective UX is broadly familiar and continually refreshed in small ways, allowing users to enjoy the comfort of the familiar while also experiencing the excitement of discovery again and again.

 

Featured image uses photos by Wolfgang Hasselmann & Shainee Fernando.

Source

The post When UX Goes Bad (and How to Fix It) first appeared on Webdesigner Depot.

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It’s not unfair to say that our online data is caught in a tug-of-war between continually updated security controls and hackers that relentlessly find new, inventive ways of breaching those controls. 

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When Document Generation API launched a few months ago, we included a Microsoft Word add-in to make it simpler for folks to design their Word templates for use within the API. To use the add-in, you needed to provide data in JSON format, either pasted in or uploaded via an existing file:

This worked perfectly fine if you had your data ready to go, but that wouldn’t always be possible, especially if you’re starting a new project and need to start prototyping quickly. Luckily, our latest update adds a few features to simplify this. Let’s take a quick look at what’s changed. Note — for folks who’ve already installed the Word add-in, it should update automatically for you. Suppose you haven’t installed this add-in yet; head over to our documentation for instructions on how to do it. 

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One of the fascinating aspects of Adobe Document Generation is how incredibly flexible it is. One aspect of the API that can enhance the final result is the ability to include images in your template. In a typical use case, you would provide a static image defined in your data used with the API. In this blog post, I will demonstrate a more advanced example — dynamically generating images, in our case, charts, on the fly.

The Basics

Before we get into a more advanced demo, let’s quickly cover the basics. (My coworker has an intense look into Document Generation and images you should check out.) As our docs describe, using a dynamic image in your Word template requires a few steps.

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Amazon Web Services (AWS) is the biggest cloud platform in the world, with over 200 features. In this article, we break down 10 AWS services that support at least some SQL syntax, talk about their use cases, and give examples of how to write queries.

Service Description SQL Support Use Case
RDS Postgres, MySQL, etc. Full Small-medium web apps
Aurora Serverless databases Full Serverless apps
Redshift Data warehouse Full OLAP, Petabytes of data, analytics
DynamoDB NoSQL database Some – PartiSQL Ecommerce, building fast
Keyspaces Managed Cassandra (key value) Some – CQL Messaging
Neptune Graph database Some – openCypher Social networks
Timestream Time series database Partial IOT, Logging
Quantum Ledger Cryptographically verified transactions Some – PartiSQL Finance
Athena Ad-hoc queries on S3 Some – CTAS Historical data
Babelfish MSFT SQL Server on Aurora Full .NET

The table above shows how SQL support varies between the services. A graph database cannot be queried in the same way as a classic relational database, and various subsets of SQL, like PartiQL, have emerged to fit these models. In fact, even within standard SQL, there are many SQL dialects for different companies like Oracle and Microsoft.

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With microservices architecture becoming the de facto standard for web applications now, effective debugging and anomaly detection calls for a system that is observable — which means, the internal state of an application can be inferred by observing and tracking the metrics, traces, and logs.

Observability is all about data exposure and easy access to information required to find issues when the communications fail, internal events do not occur as expected or events occur when they shouldn’t. Here, you’ll learn and know about different microservices monitoring tools and how to monitor microservices. Let’s take a look!

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For decades, developers have struggled with optimizing persistence layer implementation in terms of storing business data, retrieving relevant data quickly, and — most importantly — simplifying data transaction logic regardless of programming languages.

Fortunately, this challenge triggered the invention of Java ecosystems in which developers can implement the Java Persistence API (JPA). For instance, Hibernate Object-Relational Mapper (ORM) with Panache is the standard framework for JPA implementation in the Java ecosystem.

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