Apple will hold its 29th Worldwide Developers Conference (WWDC) June 4-8 at its new home, the McEnery Convention Center in San Jose. Here’s what to expect.
What is Apple saying?
Short answer: Not much. Some Apple watchers may get a little pleasure unpacking the company’s statement issued when it announced the event:
“Every year, WWDC provides an opportunity for millions of developers to learn more about how to create new experiences across Apple’s platforms for iPhone, iPad, Apple Watch, Apple TV, Mac and HomePod,” it said.
“A broad range of robust developer APIs – including SiriKit, HomeKit, HealthKit, GymKit, MusicKit, ResearchKit and Core ML – give developers new ways to help users take command of everything from their health and homes, to how they get around, shop and learn.”
Make of that what you will.
Operating system enhancements
Earlier this year, we heard Apple plans to change its approach to software. The new approach involves moving away from attention-seeking improvements in favor of making sure what is introduced works.
That focus on quality control will be welcome, but once you look at it, it seems hard not to see the AR-like city map in Apple’s publicity shot (above). Might this mean useful improvements in Maps and some revelations around what the company has been doing with those ubiquitous Apple Maps cars the last few years?
Whatever else, it seems reasonable to expect improvements in ARKit and better Continuity-style features designed to bind the company’s growing list of platforms. We’ll be given a look at the next iterations of iOS 12, watchOS 5, and tvOS 12.
What’s coming in iOS 12?
iOS speculation currently favors:
- New Animoji for the iPhone X
- Animoji integration into FaceTime
- A new Stocks app (Why? I humbly request a new Mail app instead)
- A better version of Do Not Disturb
- Improved parental controls
- Stability enhancements.
Health will also be a big deal. How does Apple intend to proliferate the new heath records features within iPhones?
What to expect in macOS 10.14
The big speculation this year is around Apple’s plans to make it possible to enable a new breeds of app that will run on iPhones, iPads, and Macs. Some of Apple’s own iOS apps will also be upgraded to run on all these platforms, Home being one that people mention a lot.
It also seems reasonable to anticipate improvements in FaceTIme, such as Animoji support. Apple’s focus on imaging makes it reasonable to predict new filters, masks and AI-support within Photos, while Safari will likely experience fundamental improvements around collaboration and video conferencing through the browser. It would be wise to anticipate enhancements to Metal and Mac graphics APIs with a view to creating better AR experiences. This will likely extend to some cutting-edge demos from third-party partners in the graphics, gaming, design, and AR space.
What’s coming in watchOS 5?
Apple continues to identify and refine what you can do with Apple Watch.
The company’s core message for the product remains health improvements. With that in mind, it’s reasonable to predict new sensors, support for additional Workouts and enhancements in the health-focused predictive intelligence you already find in the product.
Sleep tracking seems a certainty, given Apple’s 2017 acquisition of Beddit. An EKG heart monitor and glucose sensor are also frequently discussed.
What would make me happy? A third-party watch face store.
I suspect Apple may also want to talk a little about its work with health insurance firms in which you can get free or subsidized watches so long as you maintain fitness.
What’s coming in tvOS?
Apple’s entertainment industry chief, Eddy Cue, has told us a little about Apple’s plans for television and original content production.
Can we expect more news on this at WWDC? A little, perhaps, but I think the company will use the event to try to interest developers in new APIs that can be used to supplement existing content: sports scores and interactive commentary, for example.
Cue has hinted that new technologies for interaction and notification that he’s plotting for sports TV will also apply to original content. Apple wants to create platform advantage, I imagine.
What can we expect for Siri?
SiriKit is becoming an integral user interface element. Smart money at the moment features things such as Siri integration in Photos and improvements to the Do Not Disturb feature. It also seems probable that we’ll see the varieties of third-party apps that are compatible with Siri extended. I also imagine we may see enhanced Siri integration around use of Workflow, enabling voice control for more complex apps.
What about the iPad Pro?
Apple will certainly want to improve its iPad software to make that platform an even more effective notebook replacement.
When it comes to hardware, various sources predict new models (11-inch and 12.5-inch iPad Pro models) will (like iPhone X) be equipped with Face ID, narrower bezels and much faster processors. And a redesigned Apple Pencil, perhaps one that catches what you are writing in a note when you write on any surface.
What’s new in Mac?
Apple isn’t expected to introduce a new Mac Pro until later in the year, but I wouldn’t be at all surprised if the company gave developers a glimpse at the new machine.
Apple knows developers need powerful Macs to build the software we all use, and as it accelerates its plans for AR on its platforms, the company really has no choice but to ensure it offers the world’s best computers to build those experiences on. Think of this as a (modular) iMac Pro on steroids. I expect the performance benchmarks to set new standards for workstation-class desktops.
Beyond the desktop, there’s plenty of speculation concerning a new model MacBook (Air?) at a lower price and equipped with a Retina Display. Might we also see a new model MacBook Pro?
What about the home?
There is some speculation Apple intends to release a smaller HomePod unit this year. I have little to add to that, though I would be interested to see a waterproof model equipped with a rechargeable battery for use in kitchens, bathrooms, and outside (or in car).
Will we really have to wait until later this year for AirPlay 2? Or will that feature (along with stereo HomePod pairing) appear in the next couple of weeks at Apple’s next anticipated spring event?
What about Apple Pay?
It seems logical to predict international roll-out of Apple Pay’s personal payments feature.
How to watch the show
WWDC tickets will go on sale March 22 at 10 a.m. PT via the event website. Demand will be high, so successful applicants will be selected at random from March 23 – only a lucky few will be given the chance to spend $1,599 on their ticket.
If you’re not at WWDC, Apple usually streams the main event keynote online at apple.com, as well as via the WWDC app on iPhones, iPads, and the Apple TV. The keynote usually circulates online soon after the show via all the usual streaming video websites.
Predicting what Apple does is not an exact science. I’ve deliberately avoided speculation I am uncertain about. Some improvements may never appear, while others may not appear until another time. With this caveat in mind, what do you expect to see at WWDC 2018?
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Kubernetes has become a standard way—many would say the standard way — to deploy containerized applications at scale. But if Kubernetes helps us to tame sprawling and complex container deployments, what’s available to help us tame Kubernetes? It too can be complex, messy, and difficult to manage.
As Kubernetes grows and evolves, it is likely that some of its excesses will be tamed from within. But some people aren’t waiting around for Kubernetes to get any easier to work with, and have rolled their own solutions to many common problems with Kubernetes in production.
Here we highlight 10 projects that simplify Kubernetes in various ways, from easing command-line interactions, to simplifying application deployment syntax, to integrating with AWS, to providing a window into multiple clusters.
Bitnami Cabin: Kubernetes dashboard for iOS and Android
No modern web application or service should be without some kind of mobile interface. Cabin provides Kubernetes admins with a version of the Kubernetes dashboard that is accessible from an iOS or Android smartphone. Many of the functions available in the full Kubernetes dashboard can be launched from Cabin, including Helm charts, scaling deployments, reading pod logs, and accessing web-based apps hosted by Kubernetes.
Kedge: Concise Kubernetes deployment definitions
The most common complaint about Kubernetes is how complex and verbose its manifests, or application definitions, are. They’re a pain to write and a pain to maintain, so it’s little wonder folks turn to third-party tools for relief. Kedge offers a simpler, more concise syntax. You provide the simple version of the Kubernetes definition file to Kedge, and Kedge expands it into its full-blown Kubernetes counterpart. Unlike Koki Short (see below), Kedge doesn’t use a modular syntax for its declaration files; it just boils down application definitions to common shortcuts.
Koki Short: Manageable Kubernetes manifests
Koki Short — like Kedge above—is a project to improve the way application definitions, or manifests, work in Kubernetes. Like Kedge, Short provides an abbreviated syntax for describing Kubernetes pods that can be translated into the full-blown syntax and back again. Unlike Kedge, Short is also modular, meaning details from one Short declaration can be re-used in others, so that many pods with common elements can be defined succinctly.
Kube-ps1: Smart Kubernetes command prompt
No, Kube-ps1 isn’t a first-gen Sony PlayStation emulator for Kubernetes (although that would be rather nifty). It’s a simple addition to Bash that displays the current Kubernetes context and namespace in the prompt. Kube-shell includes this along with a great many other things, but if all you want is the smarter prompt, Kube-ps1 provides it with little overhead.
Kube-prompt: Interactive Kubernetes client
Another minimal but useful modification to the Kubernetes CLI, Kube-prompt allows you to enter what amounts to an interactive command session with the Kubernetes client. Kube-prompt spares you from having to type
kubectl to prefix every command, and provides autocomplete with contextual information for each command.
Kube-shell: Shell for the Kubernetes CLI
The Kubernetes command line is powerful, but like any command line app it can be tedious to pick through its options. Kube-shell wraps the standard Kubernetes command line in an integrated shell that provides auto-completion and auto-suggestion of common commands, including suggestions provided by the Kubernetes server (e.g., for the names of services). It also gives you a more robust command history function, a
vi-style editing mode, and running context information for user, namespace, cluster, and other installation-specific details.
Kubernetes Ingress Controller for AWS
Kubernetes provides external load balancing and network services to a cluster through a service called Ingress. Amazon Web Services provides load balancing functionality, but doesn’t automatically couple these services to Kubernetes’ facilities for same. The Kubernetes Ingress Controller for AWS closes that gap. The Ingress Controller manages AWS resources for each Ingress object in a cluster automatically, creating load balancers for new ingress resources and deleting load balancers for removed ones, drawing on AWS CloudFormation to ensure the consistent state of the cluster. It also auto-manages other elements used in the cluster like SSL certificates and EC2 Auto Scaling Groups.
Kube-ops-view: Dashboard for multiple Kubernetes clusters
Kubernetes has a useful dashboard for general-purpose monitoring, but the Kubernetes community is experimenting with other ways to present data usefully to the Kubernetes admin. Kube-ops-view is one such experiment; it provides a broad at-a-glance view of multiple Kubernetes clusters, rendered graphically, so one can see at a glance the CPU and memory usage and status of pods across a cluster. It doesn’t allow you to invoke any commands, though; it’s strictly for visualization. But the visualizations it provides are striking and efficient, born for a wall monitor in your operations center.
Stern: Log tailing for Kubernetes
That’s “stern” as in a ship’s stern, not as in a disciplinarian attitude. Stern lets you produce color-coded output (as per the
tail command) from pods and containers in Kubernetes. It’s a quick way to pipe all the output from these resources into a single stream that can be read at a glance, and provides you with an at-a-glance way (the color coding) to distinguish the streams.
Teresa: A simple PaaS on Kubernetes
Teresa is an application deployment system that runs as a simple PaaS on Kubernetes. Users, organized into teams, can deploy and manage applications that belong to them. This makes it a little easier for people who are trusted with a given application to work with it, without having to deal with Kubernetes directly.
Over the past few years, there has been a subtle but significant shift in the way that data is structured in databases. Whereas yesterday’s databases were typically limited to storing data in rows and tables, today’s modern databases often make use of nested data structures.
In this article, we will take a deeper dive into the nature of nested data structures, how they are represented in different databases, and the benefits and challenges of using nested data structures. Finally, we’ll propose an approach that addresses the challenge of marrying the traditional world of business intelligence with the modern world of nested data.
What is nested data?
Let’s start with a little introduction to dimensional modeling, using a website visit as an example. There are measures of the visit that exist at the visit level, such as the number of visits and the length of the visit. There are also attributes of the visit that only exist at the visit level, such as the user’s IP address, browser type, and OS. There are also page views that occur as part of each visit, each with their own measures, for example the number of page views and the time on page. And there are page view specific attributes, such as page name, page category, and page URL.
In the traditional world of data mart or data warehouse design, a common approach to creating a model to support the analysis of this web data might be to create something that looks like the following (simplified) data model.AtScale
This type of “dimensional” modeling addresses a few challenges that occur when building models for business intelligence. First, it reduces the number of rows containing “duplicate” data: The visit fact doesn’t need to contain all of the page level details, meaning queries against the visit fact will perform better. Second, a dimensional model allows the use of a single key column (for example Browser_OS_Key) in the fact instead of multiple columns for each detail around browser and OS (versions, device, etc). This reduces the storage cost associated with the model. Third, this type of model can also improve query performance, especially for commonly used values (list of browsers, page categories, etc).
However, there are also challenges with this type of model. First, this approach is at odds with the “natural” form of web log data (and with machine and log data in general). Typically this type of data may be written to disk in the manner at which it occurs. For example, website logs may contain a record that looks like:
TimeStamp, VisitID, Referrer,UserAgentString, UserIP, CookieID, PageURL, PageName
The process of turning this data into the dimensional model shown above can be expensive and time consuming. Additionally, the traditional dimensional data approach can lead to expensive queries, especially when those queries require joins in a distributed computing environment. Large joins—for example, joining billions of page view records with millions of user records—can be time consuming, and will perform worse than simple filter, scan, and aggregation operations on a single table.
The past decade has seen several market shifts that have resulted in the emergence of a new approach to dealing with the data types and challenges described above. Specifically:
- Vastly reduced storage costs: There has been a “race to zero” for storage costs. As a result, companies are no longer as intensely focused on reducing the cost of data warehouse and data mart models through key-based dimensional “denormalization” as they once were.
- Adoption of columnar storage: As more and more databases and big data platforms support columnar storage, the read-time benefits provided by denormalization and dimensional models has also been reduced.
- Expanded adoption of distributed databases: In the world of big data, distributed processing architectures (on premises MPP databases like Greenplum, cloud databases like Redshift and BigQuery, Hadoop-based SQL engines like Hive and Impala) have become the rule, and not the exception.
- An explosion of machine data: Sensor data, log data, and other machine data are increasingly the focus of analytical workloads, leading to an explosion in overall data volumes.
Nesting data within database records
As a result of the factors listed above, a new approach to storing and querying log data and machine-generated data in relational databases has emerged. While different databases have different implementations of this new approach, the general concept is the same, which is to support a single table where information is “nested” within records. Using the same example above, a nested model for a web session might look like the following conceptual diagram.
Because this nested approach stores all data as a set of discrete column elements in a single table, it addresses the challenges inherent in the traditional dimensional approach, and achieves the following objectives:
- Reducing the bottlenecks and performance issues that result from doing large-scale joins.
- Taking advantage of columnar data formats for all data elements.
- Optimizing query performance for scans and aggregations at the expense of optimizing for storage costs.
- Reducing the amount of pre-processing required to query data in its “natural” form.
For more information on how this capability is supported in a number of modern big data platforms, check out the following documents:
Support for nested data in these platforms makes modeling and storage decisions easier and also improves query performance. Nevertheless, there remain a number of challenges related to using this type of data in traditional analysis and BI scenarios. In the remainder of this article, we will discuss these challenges and the opportunities that exist when it comes to supporting BI and analytics workloads on these data structures.
For the purpose of this exercise, we will dive into how AtScale and BigQuery can be used together to analyze large-scale nested data sets.
Analyzing nested data in Google BigQuery
Google BigQuery is a modern, serverless, cloud-based data warehouse that has been architected to handle modern big data challenges, including working with nested data sets. While you can learn more about BigQuery’s nested data support here, we’ll run through a quick example using a sample Google Analytics (web analytics) data set.
Google Analytics schema for BigQuery.
Let’s take a look at how BigQuery is able to store and query data that matches the session and page view example discussed above. With BigQuery’s support for nested data structures, it is possible to define a “nested” table structure with the schema shown at left.
This schema uses a “dot” notation approach to specify nested fields within the same table structure. For example, for a single record with one visitNumber, the totals.<field> record contains the aggregate values for this record. The hits.<field> records can contain multiple values within a single visitNumber record. This means that a single record can be represented as show below. Note how there are multiple hits with different page paths nested within the same visit record.
When it comes to query performance and simplicity, this structure has some very nice advantages. For example, it’s very easy to write a query that extracts the “landing” page for a visit, as show below:
SELECT visitorId, visitId, totals.visits, hits.page.pagePath
Note that this query is able to reference the nested data by using the “dot” notation approach, and then no joins are required to combine page view level information with session level information. The other benefit of this structure is that query times are significantly faster, and there is no need to join together data from a session fact table and data from a page view fact table. In our internal testing of data structures with nested records that were able to avoid joins, we have seen data load time improvements ranging from 5X to 50X faster, and data read times of 2X to 80X faster. Clearly, nested data structures can yield huge benefits in a big data environment.
However, when it comes to using traditional data visualization and analysis tools to access this type of data structure, things get a little more complicated. Let’s take a look at what happens when you connect a tool like Tableau directly to nested data sets in Google BigQuery. In this first query, we will look at two “record level” values: the total page views for the visit and the browser used for the visit. Note that for the Chrome browser type there are 162 page views. On the lower left of the screen you can see that, in total, there were 249 page views for the visits being analyzed.
Let’s now dig a little deeper, and add “page path” to the colors shelf of the visualization. This should allow the user to break down the 162 Chrome visits to the specific pages (and associated page views) that were part of each visit.
Note that in this updated visualization, there are now almost 1800 page views for the Chrome browser, and a total of 2,262 page views for the visits being analyzed. Clearly this is the wrong result!
What’s the cause of this error? In the nested data set, the page views measure for the visit is recorded at the visit level. However, for each visit there are multiple nested page path rows. As a result, when Tableau asks for the page view value for each nested page path record, there is duplicate counting of the visit level page view for each page path, resulting in overstated page view results.
This issue is a classic “multi-fact” model challenge that is traditionally resolved in business intelligence platforms by adding support for multilevel models (you can read more about this concept in this multilevel metrics blog). The screenshot below shows an AtScale multilevel model that has been constructed on the nested Google Analytics data set.
As you can see in this model, there are two “fact tables” that have been projected on top of the single Google Analytic nested data set (using our Query Data Set, or QDS, functionality). There are also a number of shared dimensions between the sessions and page facts, as well as some dimensions that are only relevant to the page fact (including page path). In the preview panel, you can see that a Page Views measure has been clearly defined. Let’s redo the same Tableau analysis, but this time connecting to the AtScale virtual model.
You can see in this visualization that we get the same session level results as before: 162 Chrome page views, and a total of 249 page views for all of the visits being analyzed. Now, let’s add page path to the analysis.
Note that in this scenario, the number of page views for Chrome has remained at 162, despite the fact that we are now looking at the nested page level records within the visit records. This is because the multi-level model has included the appropriate logic to avoid the duplicate counting of the page view records within the visit. The result is that Tableau users can seamlessly analyze nested Google Analytics data sets without needing to think about how the underlying data is stored, and benefitting from the great self-service data visualization capabilities of Tableau.
Old BI, new tricks
If you are an analytics or business intelligence practitioner, it behooves you to be aware of the increasing presence of nested data sets in modern data platforms, along with the benefits and challenges introduced by these types of data structures. While using nested data may make modeling and storage decisions easier and also improve query performance, there remain a number of challenges related to using this type of data in traditional analysis and BI scenarios.
With the increasing adoption of modern, cloud-based data platforms and the proliferation of machine-generated data, it’s clear that the ability to analyze nested data structures must be part of any business intelligence architecture. In this article, we introduced the concept of nested data, discussed the advantages, and highlighted some of the challenges in analyzing this type of data. And we illustrated one approach that allows organizations to take advantage of nested data support along with the ability to use the tools of choice for analysts and BI consumers.
Josh Klahr is vice president of product management at AtScale.
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Want to know the lifespan of an Apple device? This quick calculation tells you
When a company falls 24 places in a reputation survey, you know something is wrong. But what?
It’s not just Apple that suffered. Google also fell twenty places, from 8th to 28th place. Retaining the top spot once again is Amazon.
OK, so what’s going on at Apple? Well, a few things spring to mind.
First, the methodology of the poll. Here is what Reuters had to say:
“The poll, conducted since 1999, surveyed 25,800 U.S. adults from Dec. 11 to Jan. 12 on the reputations of the ‘most visible’ corporate brands.”
So, reputation translates into visibility, which in turn translates into what companies spring to people’s minds.
John Gerzema, CEO of the Harris Poll, offered Reuters a possible explanation for why Apple and Google tanked so hard:
“Google and Apple, at this moment, are sort of in valleys. We’re not quite to self-driving cars yet. We’re not yet seeing all the things in artificial intelligence they’re going to do.”
That’s an interesting idea, but it’s not one that I can agree with. After all, many of those adults will have bought new Apple hardware (Apple sells a lot of iPhones at the tail end of every year), or may have been polled on their iPhones or Macs. And the last time I looked, plenty of people are still using Google services every day.
I think the reason for the drop is more to do with the lack of buzz and maybe that Apple as a brand is being overshadowed by the iPhone. “Apple iPhone” has given way to just ‘iPhone.” The same could be said for Google services. The company has taken second place to the products.
There was no end of hype in the run-up to the unveiling of the iPhone X in September of last year, but I was surprised just how quickly that buzz faded away into a dull hum by the launch date. Sticker shock, and the gap between the launch of the iPhone 8 and the iPhone X, no doubt threw a dampener on things. While there’s no doubt that the iPhone X has sold well, we didn’t get any of the usually boasts from the Apple PR team, which we would have expected if it broke records.
Just over the last few weeks of the year, we saw both macOS and iOS hit by several high profile bugs. And what’s worse is that the fixes that Apple pushed out — in a rushed manner — themselves caused problems.
And this is just a selection of the bugs that users have had to contend with over the past few months. I’ve written at length about how it feels like the quality of software coming out of Apple has deteriorated significantly in recent years.
Now don’t get me wrong, bugs happen. There’s no such thing as perfect code, and sometimes high-profile security vulnerabilities can result in patches being pushed out that are not as well tested as they could be. But bugs, especially high-visibility ones that get a lot of press coverage, are going to put a ding in any company’s reputation.
Here’s what I wrote back in December of 2017:
“Apple owes a lot of its current success to its dedicated fanbase, the people who would respond to Windows or Android issues with ‘you should buy Apple, because that stuff just works.’ Shattering that illusion for those people won’t be good in the long term, which is why I think Apple needs to take a long, hard look at itself in the run up to 2018 and work out what’s been going wrong and come up with ways to prevent problems from happening in the future.”
Apple, maybe the time to take that long, hard look at yourself is here.
A wrong-number text had doubly heartwarming results last week. It all started when a woman named Syd texted a picture of herself in a dress, apparently seeking advice—but not to the person she meant to send it to. Instead of just ignoring it or telling her she had the…
ORLANDO, Fla. – The wide variety of collaboration and communications tools now available to businesses can help connect employees in different locations, but can also result in fragmented and siloed conversations.
With that in mind, a new breed of team collaboration chat apps has emerged in recent years that, in many cases, incorporate video and voice call functionality alongside text-based messaging. That means enterprise organizations can begin to consolidate some of their existing communication tools into a single platform deployed across the business, according to a panel of CIOs at the Enterprise Connect conference here in Orlando.
Jason Kasch, CIO at Structural Group, a Maryland-based engineering and construction firm, said his company had previously used a range of applications to connect staffers – everything from Microsoft’s Yammer and Oracle Social Network to group text messaging platform GroupMe and Slack. By moving to RingCentral’s Glip, the company found it easier for employees to start conversations with the right people in the organization.
“We had a bunch of different products where people were collaborating and the one piece that was missing was a central contact point,” Kasch explained. “If everyone was on a different platform, they didn’t have the ability to single point click to start a video conference or a conversation immediately.”
Kasch said deploying the Glip team chat app helped the company replace its various collaboration platforms and made it easier to connect colleagues in different locations.
“When we collapsed all of those onto one platform we didn’t do it consciously saying, ‘Hey, this was a problem,’ as we didn’t realize it was a problem,” he said. “But the minute we launched RingCentral and gave everyone in the company access to Glip, everyone started migrating all of their conversations to this platform simply so that so they could converse with everybody else in the company.”
John Herbert, CIO at 21st Century Fox, said that relying on numerous collaboration tools can create more headaches for companies rather than solve them. “Fragmented conversations and fragmented tools actually create a bigger problem,” he said.
The media corporation has rolled out Slack as a core collaboration hub for 25,000 employees across 90 countries, uses Zoom’s video conferencing across the company, turned to Okta for identity management and relies on Quip for content collaboration. “Once we committed to Slack and Zoom it just changed everything, because then everybody is on that same platform with that experience for some consistency.”
Other firms are moving in a similar direction. Mott MacDonald, a 16,000-strong engineering consultancy headquartered in the UK, has gone “all-in” on Microsoft’s Office365 applications. It uses Skype for Business to connect staff as well as Yammer and Lync. “For us, it is still a bit fragmented,” said the firm’s CIO, Ronald Sattan.
Sattan welcomed Microsoft’s decision to consolidate Skype for Business into Teams, which will help to “bring those conversations together into one place.
“The convergence of all that into one thing is going to be massive for us,” he said.
Michael Sherwood, director of technology and innovation for the City of Las Vegas, said that supporting a range of tools is the reality – for now. While he would like to have a single, central collaboration application, “most likely we will have a fragmented world,” he said.
“We allow employees to have an Android or an iPhone, we will continue to allow a Microsoft path and a Cisco Spark/WebEx path,” he said. “All of our vendors use different platforms and internally we will allow the same type of provision.
“Hopefully, at some point we can get to the utopia of having one platform: from a maintenance standpoint it would be a lot more progressive.”
(Screenshot: Jason Cipriani/ZDNet)
Developers can now register for a chance to attend Apple’s 2018 Worldwide Developers Conference (WWDC).
The event will take place June 4 to June 8 in San Jose, Calif., at the McEnery Convention Center.
Apple typically uses the opening keynote of WWDC to announce software updates to the company’s various platforms such as iOS, macOS, watchOS, and tvOS.
Occasionally the company also uses the event to announce hardware products. Rumors have already begun circulating that Apple will announce a new MacBook at this year’s event.
The registration window for developers is open until March 22 at 10am PST. Apple will then randomly select developers who can then purchase a ticket for $1,599.
On the surface, Google’s Android P release — in its current, unfinished form — isn’t exactly what you’d call “exciting.”
Yes, the first developer preview of Android P has plenty of fresh functional touches (including a new native system for editing screenshots on the fly — who woulda thunk?!). And yes, it has its fair share of visual refinements, too.
But the bulk of Android P’s biggest features so far have revolved around under-the-hood improvements — things like support for a newer Wi-Fi protocol that’ll improve indoor location pinpointing, a more advanced system of image processing and compression for developers to utilize, and a more intelligent system for data management that’ll let apps prefetch data only when network conditions are optimal.
Android P also gives apps expanded access to Google’s neural networks system for advanced types of machine learning, creates a more effective system for the universal autofill process introduced in Oreo, and provides substantial improvements to the underlying systems that allow apps to operate. Apps on Android P should use less memory, be more power efficient, and be faster-loading than what we see now.
Then there’s all the system-level privacy and security stuff, which is a story in and of itself. Among other things, Android P will bring about more controlled access to your device’s camera, mic, and sensors; better encryption for backup data; more privacy with network connections; stronger protection from unsecure traffic; better protection of your unique device identifier; and the advent of user-facing warnings that’ll help you avoid using apps that ignore the latest (and thus most advanced and secure) systems for interacting with your data.
Like I said, it’s not the most sensational set of features — and Google is almost certainly saving the more attention-grabbing, marquee elements for a future pre-release update. What’s interesting about this approach, though, is that it highlights something that often gets overlooked amidst an operating system’s more exciting features: the fact that an Android upgrade is about much more than what we see on the surface.
Yadda, yadda, yadda — right? I know; I’m not exactly preaching rocket science here. But there’s a reason this is important to point out and discuss.
Android P and the OS update puzzle
Just like clockwork, every time we start talking about Android upgrades and the fact that most manufacturers are doing an embarrassingly awful job at delivering software to their devices (you’ve seen these charts, right?), the puzzlingly-allegiant-to-this-or-that-company crowd shows up with the same lines of curious defense:
“Well, yeah, maybe Company X does take forever to get upgrades out. But people who buy its phones don’t care about upgrades, anyway!”
“Company X’s phones had features like split-screen mode long before Android did, so what’s the difference if we get the latest update now or nine months late?”
And, of course, the always popular:
“So what? Android upgrades don’t really matter, anyway.”
To all of these statements, my dear discerning readers, I say the following: pshaw.
Let’s start at the beginning: Yes, it’s true that most of the general phone-buying population doesn’t care about upgrades. But there’s a difference between ignorance — not knowing why something is impactful or how it matters — and actual informed indifference. For the vast majority of smartphone owners, the former is the factor at play.
Next: Yes, the front-facing features of an OS update may not always be new or relevant to every Android make and model. And absolutely, Google has worked hard to make OS updates less all-important by deconstructing Android and pulling as many pieces as possible out of the operating system so they can be updated instantly and universally, bit by bit, all throughout the year. That’s enormously significant.
But to say any of that suggests OS upgrades themselves simply don’t matter anymore is glossing over a critical piece of the puzzle — specifically, all of the beneath-the-surface stuff we were just talking about a second ago. No matter how much Google may deconstruct the operating system, certain foundational elements tied to performance, security, and privacy can only be addressed in the OS itself.
Despite all the mitigating factors, in other words, the core OS absolutely still matters — and so, too, do the updates that maintain and improve it.
P is for perspective
Android P certainly isn’t the first time any of this has been true. Most recently, Oreo introduced numerous important changes related to privacy and security — things that, again, aren’t the most titillating topics to talk about but are immensely impactful to a phone’s day-to-day operation (arguably more so than any buzzworthy feature addition). It also introduced the first form of native autofill functionality for password managers and a powerful system for opting out of specific types of notifications without muting an app entirely.
The difference with Oreo was that those elements came about alongside the update’s more attention-grabbing features — and consequently, they got buried beneath the easier-to-focus-on front-facing items. With Android P in its current state, the under-the-hood stuff has the stage to itself for now, so there’s no flashy headlining item monopolizing our attention and stealing the spotlight.
At the end of the day, it’s all about perspective. Between standalone app updates and monthly security updates, Android has evolved to a point where OS updates are no longer the pivotal, wildly transformative entities they once were. And that’s without a doubt a good thing.
But if you’re deluding yourself into thinking upgrades don’t matter at all anymore — or that companies treating them as afterthoughts and not getting around to providing them until well over half a year after their release is somehow acceptable — you’re turning a blind eye to an important part of the mobile tech picture.
To a certain degree, deciding what device is right for you is always going to be a bit of a balancing act. For some people, other factors — the presence of expandable storage, support for wireless charging, or whatever the case may be — may genuinely prove to be higher priorities than timely and reliable post-sales software support. That’s what choice is all about, and there’s nothing wrong with making that type of informed decision.
Writing off upgrades entirely, however, or pretending they have no significance is the equivalent of putting your head in the sand. And unless you’re an ostrich, that sure doesn’t seem like a smart thing to do.*
* Android Intelligence is fully supportive of the ostrich community and in no way discriminates against ostriches or other flightless birds. If you are an ostrich, please don’t kick or peck me. I love ostriches. Let me repeat: I LOVE OSTRICHES. This message has been approved by JR Raphael and the Council For Not Getting Pecked In The Noggin.
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Global spending on mobile hardware, software and services will rise this year by 3.2% to more than $1.6 trillion, pushed by developments such as augmented reality (AR), artificial intelligence (A.I.) and 5G, according to a new report from IDC.
“Overall, businesses will invest more in new devices and apps that enable A.I. and AR use cases for the business,” Phil Hochmuth, IDG’s program director of Enterprise Mobility, said via email. “On AR, businesses will spend on new mobile devices [and] hardware to help support enterprise use cases, such as medical AR, real-time data/schematic integration in industrial scenarios, etc.”
Spending on AR headsets and smart glasses will also be a part of the spending.
And, when 5G finally becomes a reality, “there will be big device upgrade waves to take advantage of increased bandwidth, security and functionality of 5G,” Hochmuth said.
With its launch of iOS 11 last year, Apple introduced native augmented reality (AR) through its ARKit SDK.
Allowing businesses to leverage AR with devices they have already bought and deployed – namely iPhones and iPads – will only add to AR’s growth in the enterprise.
Various industries have already seen AR’s possibilities through the Windows HoloLens ecosystem and early on with ARKit brands, such as Ikea and Kohl’s, said Carolina Milanesi, an analyst with Creative Strategies. Both retailers use ARKit to allow consumers to fit furniture or other items in their home before purchasing them.
Augmented reality also enables assembly line workers, utility field workers and remote employees to call up schematics and get guidance from home offices via video and chat services.
Among the many areas that will see increased spending: mobility services, which will account for nearly 60% of mobile-related spending during the 2016-2021 period forecast by IDC, according to IDC’s Worldwide Semiannual Mobility Spending Guide. IDC expects mobility services spending to pass $1 trillion in 2021. That category will be dominated by telecom investments in mobile connectivity; telecoms are expected to account for more than 90% of the money spent.
The U.S. and China will lead the way in mobility spending, each accounting for around 20% of the market share. Western Europe and Asia/Pacific (excluding China and Japan) will be the next largest regions in terms of mobility spending, and will see the fastest growth rates.
Consumers will shell out 70% of total mobility spending, with more than $1 trillion a year going to mobile connectivity services and the purchase of smartphones through 2021. That spending is forecast to slow considerably starting next year when annual growth rates dip below 1%, contributing to a five-year CAGR of 1.6%.
While a smaller part of the spending pie, enterprise mobility services will account for a vastly greater percentage of spending, with a five-year combined annual growth rate of 15%. That spending will focus on planning, implementation, operation, and maintenance and support of mobile strategies, apps and devices or the consumption of services through a mobile device.
Managed mobility and managed workspace, which combine devices, applications and identity, will be big drivers of the enterprise mobility services over the next three years, according to Hochmuth.
In addition, the new “Device-as-a-Service” procurement/management model where mobile devices and laptops are used as a service, instead of being bought outright, will also drive spending.
Device-as-a-Service will allow for more flexible upgrades, including new hardware features and models, as well as device provisioning that mirror staff levels, “i.e. not having to buy a bunch of new hardware if hiring is up, or being stuck with unused hardware that must be sold if a large layoff happens,” Hochmuth said.
Within the enterprise market, professional services is expected to lead all other industries in spending on mobility solutions with $45 billion; that will be followed by banking ($43 billion), discrete manufacturing ($38 billion) and retail ($32 billion).
“In all four cases, a majority of the spending will go to mobile connectivity services and devices, primarily smartphones and notebook PCs,” the report said. “Enterprise mobility services will also be a significant spending category as these industries implement and execute their mobile strategies.”
Banks and manufacturers are expected to invest more than $1 billion in mobile enterprise apps and mobile app development platforms this year. Again, the professional services industry will experience the fastest spending growth over the next five three years with a 7% CAGR, followed by the telecommunications and utilities industries, each with 6.9% CAGR.
“Even smaller-scale mobility solutions are expanding from their initial single-function footprint to empower and enable workers across the enterprise,” Jessica Goepfert, program vice president of IDC’s customer Insights & Analysis, said in a statement.
In transportation, for example, airplane pilots may have been the initial target users of mobile devices and applications. But mobile devices are now also widely used by customer service agents, baggage handlers, mechanics and other transportation employees.
“So long as organizations strive to gain efficiencies and deliver a superior customer experience, we expect to see continued interest, adoption, spending, and growth of mobility solutions,” Goepfert said.
From a company size perspective:
- Large and very large businesses – those with 500-999 employees and 1000-plus employees, respectively – will account for nearly $190 billion in mobility spending in 2018, with that figure rising to nearly $230 billion in 2021.
- Medium-sized businesses – from 100 to 499 employees – will surpass $90 billion in 2021.
- The small business sector – companies with 10 to 99 employees – will grow account for more than $100 billion in 2021.
- And the small office market – firms with up to nine employees – will invest $78 billion in mobility solutions in 2021.
Despite being the smallest tech category, software will see strong spending growth with 14.7% CAGR over the five-year forecast. Mobile enterprise applications will be the largest segment of mobile software spending, growing to $7.1 billion in 2021.
Businesses are also expected to increase their development efforts with mobile application development platforms seeing a five-year CAGR of 19.5%. All four software segments, including mobile enterprise security and enterprise mobility management, are forecast to deliver double-digit five-year CAGRs.