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A process to build RESTful APIs that solve users’ needs with simplicity, reliability, and performance. Every piece of software exists to solve a real-world problem. Directly or indirectly. Most web APIs are consumed by client applications running on PCs, mobile devices, etc., which in turn are used by humans. Despite being consumed directly by machines, APIs are made to satisfy the needs of human beings, so designing them should follow a user-centered process, but often it doesn’t.
Not too long ago I had written about creating an API with Node.js and Express that accepted image uploads and manipulated the images to be Android compliant before returning them in a ZIP archive. This article was titled, Create an Android Launcher Icon Generator RESTful API with Node.js, and Jimp , and it was a great example of creating APIs that that did most of their work in memory.
Today, I'm happy to announce that the AWS EU (Paris) Region, our 18th technology infrastructure Region globally, is now generally available for use by customers worldwide. With this launch, AWS now provides 49 Availability Zones, with another 12 Availability Zones and four Regions in Bahrain, Hong Kong, Sweden, and a second AWS GovCloud (US) Region expected to come online by early 2019.
The Mars Climate Orbiter was a robotic space probe manufactured by Lockheed Martin and launched by NASA’s JPL on December 11, 1998. The purpose of this probe was to study the Mars climate, atmosphere, and surface changes and to act as the communications relay in the Mars Surveyor '98 program for Mars Polar Lander. The total cost of this mission was $327.6 million.
At SpeedCurve, we're fond of the phrase "a joyous user experience". Creating this joy requires delivering what users want as quickly as possible. It's important that the critical content is downloaded and rendered before users get frustrated. Network metrics have been around for decades, but rendering metrics are newer. Speed Index. Start Render. Time to First Interactive.
Kubernetes has emerged as go to container orchestration platform for data engineering teams. Kubernetes has a massive community support and momentum behind it. In 2018, a widespread adaptation of Kubernetes for big data processing is anitcipated. Organisations are already using Kubernetes for a variety of workloads [1] [2] and data workloads are up next.
Learn how to properly design RESTful APIs communication with clients, accounting for request structure, authentication, and caching. This series of articles shows you how to derive an easy-to-use, robust, efficient API to serve users on the web or on mobile devices. We are using the principles of RESTful architecture over HTTP. In the first piece, we started from a list of specs for a simple bike rental service, defining URLs and the HTTP methods to serve the app.
My last talk for 2017 was at AWS re:Invent, on "How Netflix Tunes EC2 Instances for Performance," an updated version of my [2014] talk. There was so much demand for it this year that I had three overflow rooms streaming it, and people still couldn't get in. (I shouldn't let this go to my head, as there were 42,000 attendees at re:Invent looking for something to see!
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My last talk for 2017 was at AWS re:Invent, on "How Netflix Tunes EC2 Instances for Performance," an updated version of my [2014] talk. There was so much demand for it this year that I had three overflow rooms streaming it, and people still couldn't get in. (I shouldn't let this go to my head, as there were 42,000 attendees at re:Invent looking for something to see!
This article titled " Daten müssen strategischer Teil des Geschäfts werden " appeared in German last week in the "IT und Datenproduktion" column of Wirtschaftwoche. How companies can use ideas from mass production to create business with data. Strategically, IT doesn't matter. That was the provocative thesis of a much-talked-about article from 2003 in the Harvard Business Review by the US publicist Nicolas Carr.
Regardless of what project management software you use, be it JIRA, Asana, Trello, or whatnot, one thing is true for all of them: adding new tasks is really easy. Finishing things requires actually doing the work or coming up with an extremely compelling reason to delete a task. The notion of tasks being easy to create and hard to remove will naturally result in many tasks piling up.
NativeScript is great. There is an easy setup process (at least for Windows, I unfortunately don’t have a Mac so I can’t speak on the MacOS side of things) that provides an executable that helps you through all the steps of setup. Unfortunately, like any automated setup process, things can go awry and stop your excitement on starting on your native apps with some errors that have little suggestion on how to move forward.
At re:Invent 2017, Amazon Web Services (AWS) announced Amazon Time Sync Service which provides a highly accurate and reliable global time reference. This service is immediately available in all AWS regions. In many ways, Amazon Time Sync Service is truly inspired by Google' TrueTime. TrueTime is a global reference clock with a bounded non-zero error.
A simple framework for implementing message-based, user-initiated CRUD operations. A message-based microservices architecture offers many advantages, making solutions easier to scale and expand with new services. The asynchronous nature of interservice interactions inherent to this architecture, however, poses challenges for user-initiated actions such as create-read-update-delete (CRUD) requests on an object.
My last talk for 2017 was at AWS re:Invent, on "How Netflix Tunes EC2 Instances for Performance," an updated version of my [2014] talk. There was so much demand for it this year that I had three overflow rooms streaming it, and people still couldn't get in. (I shouldn't let this go to my head, as there were 42,000 attendees at re:Invent looking for something to see!
Today, I am happy to announce the general availability of AWS China (Ningxia) Region, operated by Ningxia Western Cloud Data Technology Co. Ltd. (NWCD). This is our 17th Region globally, and the second in China. To comply with China's legal and regulatory requirements, AWS has formed a strategic technology collaboration with NWCD to operate and provide services from the AWS China (Ningxia) Region.
Load testing is a form of non-functional testing that is carried out to understand the behaviour of an application under a specific user load. A load test is done by simulating the real time user load on the application under test, and this determines how the application responds when multiple users hit the server simultaneously and it reveals the application’s response time, CPU and memory usage, network and bandwidth usage.
Many developers have one of two opinions about unit tests: they believe 100% of the code has to be covered by unit tests, or they believe 100% of the code has to be covered by unit tests but they have reasons for not writing any. It isn’t common to find people who dislike the idea of unit tests. The main cost of having unit tests is spending time to write them.
ETL refers to extract, transform, load and it is generally used for data warehousing and data integration. ETL is a product of the relational database era and it has not evolved much in last decade. With the arrival of new cloud-native tools and platform, ETL is becoming obsolete. There are several emerging data trends that will define the future of ETL in 2018.
For stack scalability, elasticity at the business logic layer should be matched with elasticity at the caching layer. Ever increasing numbers of people are using mobile and web apps and the average time consumed using them continues to grow. This use comes with expectations of real-time response, even during peak access times. ? Modern, cloud-native applications have to stand up to all this application demand.
This marks the third year that I’ve been producing content for The Polyglot Developer and all of its channels. It’s been a long year and I’ve accomplished a lot, some of which was different than previous years. Below you’ll find my statistics that may help you if you’re planning on creating a blog or developing a course. It will give you an idea of costs and what to expect.
It quickly became clear that the problem was not to explain why the market was in decline. it was to explain why the market had ever been so large in the first place. — John Kay, Merry Christmas, whether or not you celebrate it with a sherry Managers become interested in innovation when their company’s fortunes start to wane. Innovation is a hoped-for remedy to arrest the decline, spark new growth, and convince nervous investors that management is up to the task.
I’ve been gradually installing some IoT automation at home over the last few months, and was looking for a lighting solution to fit a fairly complex situation. It’s a large open plan area with several connected zones, kitchen, patio, dining area, living area, with lots of light bulbs and far too many light switches. Upgrading all the bulbs to something like Philips Hue didn’t look like the right way to go, there are too many bulbs so it would be expensive, and we’d need to keep the light switche
Regardless of what project management software you use, be it JIRA, Asana, Trello, or whatnot, one thing is true for all of them: adding new tasks is really easy. Finishing things requires actually doing the work or coming up with an extremely compelling reason to delete a task. The notion of tasks being easy to create and hard to remove will naturally result in many tasks piling up.
Bob Ward and I worked with our SQL Server Tool developers (thanks David) to enable ‘Q uick XE Trace ’ capabilities. The feature is available in the latest SQL Server Management Studio (SSMS) release. Despite the deprecation of SQL Profiler several years ago, as well as various documents and blogs pointing out the older trace facilities shortcomings and performance impact on the SQL Server, SQL Profiler is still a top choice of SQL Server Developers and DBAs.
I was recently working on a Functions as a Service (FaaS) project using AWS Lambda and Node.js. However, I was running into an issue where my package dependencies found in my node_modules directory were for the wrong platform once deployed to Lambda. This is not the first time I experienced a problem like this. I knew the issue straight away because I encountered the same thing when trying to use a node_modules directory generated on Mac from a Windows computer.
On this blog from very early on, we have advocated the concept of service mesh. In fact, our post a sidecar for your service mesh is one of the most viewed posts this year. When Buoyant announced the Conduit - their next-generation lightweight service mesh for Kubernetes - we were really thrilled. Buoyant is also the creator of Linkerd which is one of the most widely used service mesh currently available to the microservices community.
This is the latest edition of our newsletter. To receive the email version please subscribe here. Hello everyone, and welcome to the last Serverless Insights of 2017! Before we get going on the content, we want to say thanks to all of our clients, partners, and friends that we’ve worked and interacted with this year. As we enter January, Symphonia enters its second year of business.
Many developers have one of two opinions about unit tests: they believe 100% of the code has to be covered by unit tests, or they believe 100% of the code has to be covered by unit tests but they have reasons for not writing any. It isn’t common to find people who dislike the idea of unit tests. The main cost of having unit tests is spending time to write them.
SQL Server 2019 CTP2. SQL Server 2019 adds broad support for distributed transactions (both MSDTC/OLE-TX and XA) to SQL Server on Linux. SQL Server 2019 on Linux enables the MSDTC service in SQLPAL providing distributed transaction capabilities on Linux alongside our SQL Server on Windows counterpart, supporting MSDTC/OLE-TX and XA transaction types.
CSPs are in a critical period of transformation right now. Omnipresent connectivity is driving new disruptive business models which are further driving up demands on the networks. Virtualization of appliances and systems is seen as a necessary step to add the agility to meet these increasing and evolving service demands. As vendors and CSPs are faced with building these virtualized systems, it’s imperative to look at the software engineering methodologies that the IT industry has successfully ap
When it comes to serverless solutions, there are many options available. If you’re a fan of the Microsoft cloud, you could create Azure Functions. If you’ve been keeping up, I wrote a tutorial called, Take a Node.js with Express API Serverless Using AWS Lambda , which used the Amazon Web Services cloud. Another solution is Apache OpenWhisk , a solution available on IBM’s Bluemix cloud.
Data integration generally requires in-depth domain knowledge, a strong understanding of data schemas and underlying relationships. This can be time-consuming and bit challenging if you are dealing with hundreds of data sources and thousands of event types (see my recent article on ELT architecture ). Various data integration solution providers are trying to capitalize on this gap by offering various machine learning based features to overcome these challenges.
The most important new text box of re:Invent? Lambda is a wonderful platform. However, it is still in its early years, and has a number of limitations and rough edges. One of those edges?—?that of the ability to overwhelm downstream systems with unfettered load?—?got a lot smoother at re:Invent 2017. The problems In Learning Lambda Part 9 , I described Lambda’s scaling behavior?
This year, I’ve been lucky enough to write two articles for two great “advent calendar” style sites. I try to write for the Performance Calendar every year around this time and this year I’ve written about progressive image loading using Intersection Observer and SQIP. In this article, I dive into an image loading technique known as LQIP and walk through a step by step process of getting this up and running.
Cheap storage and on-demand compute in the cloud coupled with the emergence of new big data frameworks and tools are forcing us to rethink the whole ETL and data warehousing architecture. There is a strong argument for ELT i.e. extract, load, and transform model. Classic ETL. In classic Extract, transform, and load (ETL) model, we store entities in their corresponding application databases i.e. as rows in the relational tables.
Docker is a great way to package and deploy web applications. Applications that have been containerized can easily be created, destroyed, or even moved between servers, as long as the servers are using the Docker runtime. Not too long ago I wrote about creating a RESTful API that could process images and generate Android compliant launcher icons. This article was titled, Create an Android Launcher Icon Generator RESTful API with Node.js, and Jimp and it was powered by Express Framework.
Can machine learning-based data structures i.e. learned data structures replace traditional data structures? This is a question recently asked and explored by a team of Google researchers led by Jeff Dean with a major focus on database indexes. Jeff is a Google Senior Fellow in the Google Brain team and widely known as a pioneer in artificial intelligence (AI) and deep learning community.
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