What is the Web?

The World Wide Web (commonly referred to as the Web) is a global information system that enables users to access and share a vast array of digital resources through interconnected documents and applications hosted on web servers worldwide. Unlike the broader Internet — which is the underlying network infrastructure connecting devices globally — the Web is a service built on top of this network infrastructure, primarily accessed via web browsers.

Invented in 1989 by Sir Tim Berners-Lee, the Web introduced key concepts such as Uniform Resource Locators (URLs), Hypertext Transfer Protocol (HTTP), and Hypertext Markup Language (HTML). These foundational technologies standardized how information is addressed, requested, delivered, and displayed on the Internet, enabling seamless, user-friendly access to multimedia content, interactive applications, and online services.

In essence, the Web transformed the Internet from a niche network of academic computers into a mass communication platform, powering everything from simple static webpages to complex interactive applications used daily by billions.

Major Use Cases of the Web

Over the past three decades, the Web has evolved into an indispensable platform underpinning a broad spectrum of human activity. Some of the major use cases include:

1. Information Sharing and Publishing

The Web is the primary platform for publishing and consuming information. Websites, blogs, wikis, news portals, and online encyclopedias like Wikipedia deliver vast repositories of knowledge accessible from anywhere on the planet.

2. E-commerce and Online Shopping

The Web revolutionized commerce by enabling companies to sell goods and services online. E-commerce platforms such as Amazon, Alibaba, and Etsy provide storefronts, secure payment processing, and personalized shopping experiences to millions.

3. Social Networking and Communication

Web-based social platforms (Facebook, Twitter, LinkedIn) and messaging services foster social connections, content sharing, and real-time communication across global audiences.

4. Education and E-Learning

Distance learning and massive open online courses (MOOCs) have made education accessible worldwide. Platforms like Coursera, Khan Academy, and edX provide interactive courses and certifications online.

5. Entertainment and Multimedia Streaming

The Web enables streaming of videos, music, podcasts, and games. Services like YouTube, Netflix, Spotify, and Twitch deliver multimedia entertainment directly to users’ devices.

6. Cloud Computing and SaaS Applications

The Web hosts cloud services and Software as a Service (SaaS) platforms like Google Workspace, Microsoft 365, and Salesforce, allowing users to collaborate and perform complex tasks without installing software locally.

7. Government and Public Services

Governments use the Web to provide information, enable online voting, issue documents, and offer services such as tax filing and social benefits.

8. Internet of Things (IoT) and Web of Things

Increasingly, devices—from smart home gadgets to industrial sensors—connect to the Web to transmit data, enabling remote monitoring and control through web interfaces and APIs.

How the Web Works Along with Architecture

The Web’s architecture is a layered client-server model built on standardized protocols and technologies enabling resource sharing, dynamic content delivery, and interactivity.

1. Clients (Web Browsers and User Agents)

• The client is typically a web browser (Chrome, Firefox, Safari, Edge) that acts as the user’s gateway to the Web.
• Browsers interpret markup languages (HTML), style sheets (CSS), and scripts (JavaScript), rendering them into visually accessible, interactive webpages.
• Browsers manage network connections, security policies, caching, and user input handling.

2. Servers

• Web servers host websites, applications, and resources.
• They receive HTTP requests, process them (often involving application servers, databases), and respond with the requested resources.
• Examples include Apache HTTP Server, Nginx, Microsoft IIS, and cloud-hosted servers (AWS, Azure).

3. Protocols

• HTTP/HTTPS (Hypertext Transfer Protocol Secure):
  The foundation for communication between clients and servers, with HTTPS adding encryption via TLS/SSL for privacy and security.
• TCP/IP:
  Underlying Internet protocols ensuring reliable data transmission.
• DNS (Domain Name System):
  Resolves domain names (example.com) to IP addresses necessary to locate servers.

4. Core Web Technologies

• HTML:
  Defines the structure and content of webpages.
• CSS:
  Controls the presentation and layout.
• JavaScript:
  Provides dynamic, interactive behavior client-side.
• WebAssembly:
  Enables near-native performance code execution in browsers.
• Web APIs:
  Browser-provided interfaces that allow access to device hardware, storage, geolocation, media playback, etc.

5. Resource Loading and Rendering Pipeline

• Request Phase:
  User navigates or clicks a link, prompting the browser to request resources.
• Response Phase:
  Server returns HTML, CSS, JavaScript, images, and data.
• Parsing and Rendering:
  Browser parses HTML to build the Document Object Model (DOM), CSS to construct the CSS Object Model (CSSOM), and JavaScript executes scripts, potentially modifying the DOM dynamically.
• Layout and Painting:
  The browser calculates element positions, sizes, and styles, then paints pixels to the screen.
• Interactivity:
  JavaScript event handlers respond to user inputs, network events, timers, etc.

6. Modern Web Application Architecture

• Single Page Applications (SPA):
  Utilize client-side routing and dynamic content loading to avoid full page reloads (frameworks: React, Angular, Vue).
• Server-Side Rendering (SSR):
  Generates HTML on the server for faster initial load and SEO.
• Progressive Web Apps (PWA):
  Leverage service workers and caching for offline capability and native-like experiences.

Basic Workflow of the Web

Understanding the Web from the perspective of a typical user or developer reveals the following workflow:

1. User Requests Resource:
   By entering a URL or clicking a hyperlink, a browser initiates an HTTP request.
2. DNS Resolution:
   The browser queries DNS servers to find the IP address of the requested domain.
3. TCP Connection Setup:
   Establishes a TCP/IP connection with the server.
4. HTTP Request Sent:
   The browser sends an HTTP GET or POST request with headers specifying resource type, language, cookies, etc.
5. Server Processing:
   The server processes the request, possibly querying databases, authenticating users, or running business logic.
6. Response Delivery:
   The server sends back HTTP response codes (e.g., 200 OK, 404 Not Found) along with content.
7. Content Rendering:
   Browser downloads and parses HTML, CSS, JS, renders the page, and executes scripts.
8. User Interaction and Further Requests:
   User interactions trigger further HTTP requests via form submissions or AJAX/fetch calls for dynamic content updates.

Step-by-Step Getting Started Guide for the Web

Here’s how you can start exploring and creating for the Web:

Step 1: Set Up Your Environment

• Install a modern web browser (Chrome, Firefox, Safari, Edge).
• Choose a text editor or IDE (VS Code, Sublime Text, Atom).

Step 2: Create Your First HTML File

Write a simple HTML document:

<!DOCTYPE html>
<html lang="en">
<head>
  <meta charset="UTF-8">
  <title>My First Webpage</title>
</head>
<body>
  <h1>Hello, World!</h1>
  <p>This is my first webpage.</p>
</body>
</html>

Save as index.html.

Step 3: Open the HTML File in Your Browser

Double-click index.html or open it through your browser’s file open dialog.

Step 4: Add Styling with CSS

Create styles.css:

body {
  font-family: Arial, sans-serif;
  background-color: #eef2f5;
  margin: 40px;
}
h1 {
  color: #2a9df4;
}

Link it in your HTML <head>:

<link rel="stylesheet" href="styles.css">

Refresh the browser to see styles applied.

Step 5: Add Interactivity with JavaScript

In your HTML, add:

<button id="alertBtn">Click me</button>

<script>
  document.getElementById('alertBtn').addEventListener('click', () => {
    alert('You clicked the button!');
  });
</script>

Click the button to see a popup alert.

Step 6: Explore Advanced Concepts

• Learn HTTP methods, status codes.
• Experiment with APIs, JSON, AJAX.
• Explore frameworks (React, Vue, Angular).
• Use developer tools in your browser for debugging.

Step 7: Host Your Website

• Use platforms like GitHub Pages, Netlify, or cloud services (AWS, Google Cloud).
• Register a domain name.
• Upload your files and make your website accessible globally.

Cloud Storage and the Backup Misconception

Cloud storage has become so ubiquitous and so reliable that a dangerous assumption has crept into how teams think about data protection: that storing data in the cloud is backing it up. It is not. Understanding the difference between storage redundancy and a genuine backup strategy is one of the most important disciplines in operational engineering.

What Cloud Storage Actually Provides

At its core, cloud storage is still hard disks in a rack somewhere — typically many racks in purpose-built, physically nondescript data centres distributed around the world. What makes cloud storage impressive is the reliability engineering layered on top of that hardware:

• Hardware redundancy: Data is spread across multiple physical drives using techniques such as erasure coding or replication, so a single drive failure causes no data loss.
• Multi-site replication: Many providers allow or automatically arrange for data to be stored in more than one geographic region simultaneously, providing strong resistance to localised disasters — fires, floods, power outages.
• High availability: The operational burden of keeping storage online shifts from the user to the provider, along with their staffing, monitoring, and hardware replacement cycles.

This is genuinely valuable. For most workloads, cloud object storage or block storage is far more reliable than a single on-premises server. But reliability against hardware failure and geographic disasters is not the same thing as protection against data loss caused by human error, software bugs, or account-level events.

Cloud Storage Is to Backup What RAID Is to Backup

The analogy that best frames this problem is RAID. A RAID array protects against individual disk failures; it does not protect you from accidentally deleting a critical directory, from ransomware encrypting your data, or from a bug that silently corrupts files over weeks. RAID is not backup — a principle the storage industry has repeated for decades.

Cloud storage occupies the same category. Multi-site cloud replication protects against hardware failure and data-centre-level disasters. It does not protect against:

• Accidental deletion — a mistyped command, a runaway script, or a UI mistake replicates instantly to all redundant copies.
• Account compromise or closure — a lost password, a billing lapse, or a provider-side account termination can make data inaccessible at the system level, regardless of how many redundant copies exist within that system.
• Logical corruption — if corrupt data is written, all replicas store the corrupt version.
• Ransomware — encrypted files sync faithfully to every replica and every region.
• Provider-side errors — though rare, cloud providers have suffered data loss incidents. A provider’s internal backup is designed to recover the provider’s infrastructure, not necessarily to give individual users point-in-time recovery on demand.

The Single Point of Failure Hidden in Plain Sight

No matter how many physical locations hold your data, everything passes through one logical control plane: the cloud account, the API credentials, and the provider’s access management system. Lose access at that level — through a credential incident, an inadvertently closed account, or a provider dispute — and all the geographic redundancy in the world does not help you retrieve your data.

This is a single point of failure at the access layer, not the storage layer. Genuine backup strategy requires a copy of your data that is reachable through a different mechanism, ideally controlled by a different set of credentials and, ideally, stored with a different provider or on a different media type.

What Cloud Providers Actually Back Up — And for Whom

Most major cloud providers do maintain their own backup and snapshot infrastructure. However, that infrastructure exists primarily to recover the provider’s platform, not to provide users with self-service point-in-time recovery. If a data centre suffers a catastrophic event, the provider’s backup may restore service — but possibly to a state from hours or days earlier, and on the provider’s schedule, not yours.

Some providers offer user-facing backup features — versioning, soft-delete windows, cross-region replication, or snapshot schedules — either as default behaviour or as paid add-ons. These features genuinely do improve your protection posture and are worth enabling. But before treating them as your backup strategy, ask specific questions:

• How far back does versioning or snapshot history go?
• Is that retention period guaranteed in the SLA, or is it best-effort?
• How quickly can a large restore be completed, and at what cost?
• Does accidental deletion of the bucket or container also delete the version history?
• Is there an additional fee to retrieve backup data?

If the answers to those questions are satisfactory and documented in your service agreement, cloud versioning or snapshots can legitimately function as one layer of backup. The key phrase is one layer.

The 3-2-1 Rule Still Applies in the Cloud Era

The long-established 3-2-1 backup rule remains the most practical starting point for any data protection policy:

• 3 copies of your data
• 2 different storage media or platforms
• 1 copy stored off-site (or off-account)

Applying this in a cloud-native context might look like: primary data in one cloud provider’s object storage, automated snapshots or exports to a second cloud provider or to on-premises storage, and a periodic cold copy to tape or to an air-gapped system for long-term retention. The exact combination depends on your recovery time objectives (RTO) and recovery point objectives (RPO), your budget, and your compliance requirements.

Practical Recommendations for Engineering Teams

• Enable versioning and soft-delete on every storage bucket or blob container from day one. These are cheap and provide a safety net for the most common data loss scenarios.
• Automate cross-account or cross-provider exports for any data that would be genuinely catastrophic to lose. Do not rely on manual processes.
• Test restores regularly. A backup you have never restored is a backup you cannot trust. Schedule restore drills and document the results.
• Treat cloud account credentials as critical infrastructure. Use MFA, audit access logs, and have a documented process for credential rotation and account recovery.
• Read the SLA carefully before choosing a cloud storage tier. Understand the durability guarantees, the support response times for data loss incidents, and what — if any — compensation is offered if the provider fails to meet those guarantees.
• Document your RPO and RTO before designing your backup architecture, not after. Your backup strategy should be derived from those targets.

Summary

Cloud storage is one of the most reliable ways to store data at scale. Its redundancy, geographic distribution, and operational guarantees are genuinely impressive compared with most on-premises alternatives. But reliability is not the same as recoverability. A single logical system — however redundant its physical infrastructure — remains a single system. Accidental deletion, account-level failures, logical corruption, and ransomware all bypass physical redundancy entirely.

RAID is not backup. Cloud storage is not backup. Keeping data in both a RAID array and cloud storage adds one layer of protection, but a proper backup strategy requires independent copies, independently accessible, tested and verified on a regular schedule. The old adage has not changed; the technology around it has.

What Is a Cloud Computing System?

A cloud computing system is a group of servers interconnected to function as a unified platform. This platform has two primary components:

• Processing Nodes — virtualized servers that handle computation workloads.
• Storage Area Networks (SANs) — physically separated, dedicated storage infrastructure where persistent data resides.

The term cloud itself refers to this clustered, virtualized model. Rather than tying a workload to one physical machine, resources are drawn from a shared pool, enabling greater flexibility and resilience.

What Does Cloud Hosting Mean?

Cloud hosting means that internet hosting services — such as virtual servers or web hosting — are produced and delivered by a cluster of virtualized servers working together as a system. Understanding this requires distinguishing two related concepts:

• Virtual Machine (VM) / Virtual Server — a virtualized computing instance running on top of a single physical server.
• Cloud Server — a virtualized computing instance running on top of a clustered computing system, drawing resources from multiple physical nodes.

Cloud hosting therefore means your web or application workload is served from a cloud computing system rather than a single physical machine. This distinction matters for resilience: if one physical node fails, the workload can continue on another node in the cluster.

Types of Cloud Services

Cloud deployments are typically categorized into three models, each suited to different operational requirements:

Public Cloud

Public clouds are open to the internet and used to deliver IT services to external users or customers. Resources are shared across multiple tenants, though logically isolated. Public clouds are cost-effective for variable workloads because you pay only for what you consume.

Private Cloud

Private clouds are isolated computing environments not accessible over the public internet. They are built for the internal workflows of a specific organization, providing greater control over security, compliance, and resource allocation.

Hybrid Cloud

Hybrid clouds connect private and public cloud environments, enabling workloads, data, and operations to move between them. Organizations use hybrid architectures to keep sensitive workloads on private infrastructure while leveraging the scalability of public clouds for demand spikes or less sensitive services.

Key Advantages of Cloud Infrastructure

Resource Scalability

One of the most significant benefits of cloud computing over standalone physical servers is on-demand scalability. A cloud server can receive additional processor cores, memory, or storage at any time — typically without downtime — because resources are drawn from the shared pool rather than physically installed into a single machine.

High Availability (HA)

High Availability is a capability that automatically restarts a cloud server if it stops delivering services due to an operating system failure or an outage on the underlying physical host. HA reduces unplanned downtime by ensuring that a failed instance is quickly restored, often without manual intervention. It is one of the foundational resilience features of cloud platforms.

Automated Fault Tolerance (AFT)

Automated Fault Tolerance goes a step further than High Availability. Where HA reduces outage duration by restarting a failed instance, AFT eliminates service outages by maintaining a live shadow (failover) instance that is continuously synchronized with the primary cloud server.

In the event of an OS failure or hardware outage, the failover instance takes over automatically, delivering zero downtime and preventing data loss. The distinction is important for applications with strict uptime SLAs:

• High Availability → minimizes downtime (seconds to minutes to recover).
• Automated Fault Tolerance → eliminates perceived downtime (instant, transparent failover).

Cloud Virtualization Technologies

Cloud providers build their infrastructure on top of virtualization platforms. VMware is one widely adopted example in the enterprise space, offering tools for compute, storage, and network virtualization. Other major hypervisors and virtualization stacks used across the industry include KVM (used by OpenStack and many Linux-based clouds), Microsoft Hyper-V, and Xen. The choice of underlying virtualization technology affects the feature set, performance characteristics, and management capabilities available to cloud users.

Summary

Cloud services describe computing and hosting delivered from a clustered, virtualized infrastructure over the internet. Key concepts to understand are:

• Cloud Server vs. Virtual Machine — cluster-backed vs. single-host virtualization.
• Public, Private, and Hybrid deployment models — chosen based on security, cost, and operational needs.
• Scalability — add resources on demand without hardware changes.
• High Availability — automatic restart after failure.
• Automated Fault Tolerance — zero-downtime failover via a live shadow instance.

Understanding these distinctions helps engineers and architects select the right cloud model and features for their availability and performance requirements.

Why Cloud and DevOps Go Hand in Hand

Cloud computing and DevOps are two sides of the same coin. DevOps aims to reduce friction between development and operations so that software changes reach production faster and more reliably. Cloud platforms amplify this by providing on-demand infrastructure provisioning, elastic scaling, and managed services that would otherwise require significant manual work.

Despite the natural synergy, many teams still struggle when combining the two. The most common obstacles are not purely technical—they stem from cultural misalignment, unclear ownership, and poorly defined processes. The five practices below address both dimensions.

1. Train Staff on Both DevOps and Cloud Computing

Most DevOps adoption failures have a people problem at their root, not a tools problem. When engineers and managers do not share a common understanding of DevOps principles—continuous integration, continuous delivery, shared responsibility for reliability—the process breaks down regardless of which cloud platform you use.

Invest in structured training that covers both DevOps methodologies and the specific cloud services your teams use. Training alone is rarely sufficient; pair it with mentoring, hands-on projects, and clear explanations of why specific practices matter. When engineers understand the business rationale behind a practice, adoption follows more naturally than when it is imposed as a mandate.

• Schedule recurring knowledge-sharing sessions (brown bags, internal wikis, post-mortems).
• Identify internal champions who can mentor peers and model good practices.
• Use cloud provider training programs (most major providers offer free foundational courses) to accelerate cloud-specific literacy.

2. Integrate Security from the Start (Shift Left)

Security is one of the most commonly neglected aspects of cloud DevOps, and it is also one of the most consequential. Cloud environments introduce security models that differ significantly from traditional on-premises setups. Identity and access management, secrets handling, network policies, and shared-responsibility boundaries all require deliberate design choices.

Rather than treating security as an audit gate at the end of a release cycle, embed it throughout the pipeline:

• Static analysis and dependency scanning in every pull request.
• Secrets management using dedicated vaults (such as HashiCorp Vault or your cloud provider’s native secrets service) rather than environment variables or config files checked into source control.
• Infrastructure-as-code security scanning to catch misconfigured storage buckets, overly permissive IAM roles, or open security groups before they reach production.
• Automated compliance checks integrated into your CI/CD pipeline.

If your organization’s cloud footprint is significant, consider designating a cloud security engineer or establishing a dedicated security-as-code practice to own these controls across all applications.

3. Choose Flexible, Cloud-Agnostic DevOps Tooling Where Practical

It is tempting to rely exclusively on the native DevOps tooling bundled with a single cloud provider. This approach can accelerate early adoption, but it can also create tight coupling that makes migrations, multi-cloud strategies, or negotiating contract renewals significantly harder.

Where feasible, prefer tools that are portable across environments. CI/CD platforms, container orchestration, monitoring solutions, and infrastructure-as-code frameworks that work across providers give you flexibility without sacrificing capability. Evaluate your toolchain against these questions:

• Can this tool deploy to a second cloud or on-premises environment if needed?
• Does the tool’s configuration live in source control as code, or is it locked inside a proprietary console?
• Is there a viable migration path if the vendor changes pricing or deprecates the service?

Cloud-agnosticism is a spectrum, not a binary choice. Even teams committed to a single provider benefit from keeping deployment manifests and pipeline definitions in a portable format.

4. Establish Governance and Resource Management Early

Cloud platforms make it easy to spin up new services—so easy that organizations frequently accumulate sprawling collections of resources with no clear owner, undocumented dependencies, and runaway costs. This problem tends to emerge during the first year of serious cloud DevOps adoption, when the pace of provisioning outpaces the maturity of governance processes.

Avoid this by establishing governance policies before the sprawl starts:

• Tagging conventions: enforce resource tags (owner, environment, cost center, application) through policy-as-code so every resource is attributable.
• Cost visibility: set up budget alerts and review cloud spend in your regular engineering review cadence, not just in finance.
• Service catalog: define approved infrastructure patterns teams can self-serve, reducing the likelihood of insecure or non-standard resources being created ad hoc.
• Deprecation workflows: build a process for decommissioning unused resources. What gets provisioned easily should be cleaned up easily.

5. Automate Performance Testing in the Pipeline

Performance issues are notorious for remaining hidden until an application reaches production under real load. By that point, users have already experienced the problem, and the reputational and operational cost is high. Automated performance testing, run as part of your CI/CD pipeline, moves performance regressions into the development phase where they are far cheaper to fix.

Practical approaches include:

• Load testing on every release candidate using tools that simulate realistic traffic patterns against a staging environment that mirrors production in configuration.
• Performance budgets: define acceptable latency and throughput thresholds and fail the pipeline automatically if they are breached.
• Continuous profiling in production to capture baselines and detect gradual degradation between releases.
• Chaos engineering experiments to validate that the system behaves gracefully under failure conditions—not just under normal load.

Putting It All Together

No single practice here is sufficient on its own. The organizations that get the most value from cloud DevOps treat it as an ongoing discipline rather than a one-time migration project. Staff capability, security posture, toolchain flexibility, governance, and quality assurance all need to mature in parallel.

Start by auditing where your team currently stands against each of these five areas, identify the highest-risk gaps, and address them incrementally. Continuous improvement—the same principle that underpins DevOps itself—applies equally to the way you practice DevOps in the cloud.

With Snap’s IPO reportedly imminent and the giant Consumer Electronics Show in Las Vegas now wrapped up, many in the tech world have been focused on innovations in consumer technology lately. But I think the coming year also will be marked by lesser-noticed tectonic shifts in enterprise-IT: Trends related to cloud computing, big data, and even basic computer chips, which are getting a makeover thanks to the new demands placed on them by the deluge of data and analytics now swamping many organizations.

These trends may not be as sexy as chat apps or talking robots. But here are my predictions for seven B2B, enterprise-IT trends to watch in 2017 — trends that will drive billions in corporate IT spending and could eventually create billions in market value.

1. Cloud computing moves from the “core” into the “fog.”

Amazon’s meteoric ascent to the top of the hot cloud-computing market was driven by its ability to cheaply and effectively move core IT functions — including basic computing, networking, and storage — out of company datacenters into the public “cloud.” With cloud computing, customers pay as they go for computing power, much as homeowners pay utilities for the electricity they consume each month. In 2017, however, I predict we’ll see the rise of “fog”, or “edge” computing, through which data thrown off by billions of smart, Web-enabled devices at the edge of computer networks (where they actually touch people) drives interesting applications. Think of self-driving Teslas, Nest-controlled smart homes, or robots powering smart manufacturing and oil-drilling. The migration of $300 billion in datacenter spend to the core cloud is clearly a big deal, but the emergence of edge clouds driving new revenues in industries previously untouched by advanced cloud technologies may be a significantly larger opportunity, and I expect to see early signs of this in 2017.

2. Microsoft Azure and Google Cloud — better late than never!

Despite stealing all the headlines, Amazon Web Services’ (AWS’s) $13 billion cloud business is barely scratching the surface of the total market for IT datacenter spending. In 2016 we witnessed a watershed moment when #1 enterprise player VMware essentially threw in the towel and agreed to partner, instead of compete, with AWS. (The deal will allow customers to use their existing VMware software to run some computing in Amazon’s cloud.) The deal will actually encourage cautious CIOs to go all-in with public-private cloud combos as opposed to continuing to funnel IT spending into datacenters. This should help Microsoft Azure, another big cloud-computing player, since the company already has a big enterprise presence and a cloud program that CIOs can tap into. Clouds run by Google and China’s Alibaba could similarly benefit. I think by 2017, we’ll see a sort of oligopoly of four or five big cloud providers, similar to the market for US wireless carriers.

3. Data gone wild.

Corporations today are awash in data, from the billions of sensors proliferating in smart homes and buildings to President-elect Trump tweeting on matters big and small. Organizations have changed their attitude by making data the core of their digital transformations rather than simply a by-product of running operations. I believe 2017 could be the year of the “Chief Data Officer” as more and more organizations create C-level positions to manage this crucial information and figure out how to use it to their competitive advantage. A recent Forrester survey of more than 3,000 executives focused on data and analytics found that 45 percent of the respondents’ companies already have a CDO, up from only a handful two years ago. As the office of the CDO grows in prominence, 2017 will witness increased adoption of critical “data middleware” – governance, cataloging, prep, and discovery software – that rides on top of multiple clouds and databases and feeds into a plethora of analytics tools.

4. The second coming of silicon.

The exponential growth in corporate data, deeper analytics on complex image and voice data, and the expansion of edge computing are all stressing IT infrastructure to the extreme. Large increases in performance and decreases in cost cannot be accomplished by simple software-tuning, nor has Moore’s Law kept up with the gains needed for new applications. As semiconductor behemoths ranging from Broadcom, Avago, Altera, and others are busy merging and cutting costs, AWS, Google, and others have taken matters into their own hands in terms of semiconductor innovation — as have a few startups like Innovium, Barefoot, and Fungible. While funding silicon companies is not for the faint of heart, because of the high costs, the companies that succeed in raising cash and delivering building blocks for this secular cloud/application shift will be rewarded handily, and some will becomes the next generation silicon powerhouses.

5. Cybersecurity becomes key component of international relations.

We thought it was bad when the North Koreans hacked Sony to try to stop the release of a lighthearted movie about Kim Jong-Il. In 2016, if you believe the CIA and the FBI, we saw the Russians hack DNC emails to try to disrupt the American presidential election. Clearly, cybersecurity is only becoming more critical to organizations as they try to guard against increasingly common and more sophisticated attacks, from state and non-state actors. This will drive increasing investment in security startups by venture capitalists. This year, as a result of the Russia/US election episode, I think we’ll also see cyber-warfare become a bigger issue in international relations and driving more geopolitical tension, which will further highlight cybersecurity as an area big and small companies need to be concerned about, driving more spending on security tools.

6. High-end immigration headaches.

On the topic of politics: One big headache for tech companies in 2017 could be increasing restrictions on immigration if Trump carries through on his campaign promises. These policies, if applied to high-skilled workers, threaten to worsen the existing dearth of US tech talent in areas like data science and software development/operations. That said, the rise of free, “open source” software and new workplace-collaboration tools mean that it’s easier for people to work together on tech projects from different locations, including overseas. 2017 will witness a larger number of companies deploying these types of highly distributed workforces and leveraging freely available, open-source building blocks contributed by developers worldwide. An unintended, but welcome, side effect may be a cooling of the housing and rental market in the San Francisco Bay Area!

7. Cashing in.

The moribund IPO market showed signs of life in late 2016, with 21 technology companies ultimately debuting on US exchanges last year. I predict we’ll see more IPO activity this year, both with high-profile, consumer “unicorn” tech companies like Uber and Airbnb and wonkier enterprise names. Those enterprise companies could include open-source juggernauts like data-management company Cloudera, database providers MongoDB and Datastax, and data-analytics company Elastic. (While serious, I don’t think the recent news about the cyberattacks targeting MongoDB databases will have lasting long-term consequences.) Open source, to me, represents the de-facto delivery method of enterprise computing, and I think Wall Street will start to look beyond first-generation, open-source companies to newer models that can work.