Introduction

As part of a project of mutual interest between OVHcloud and Sopra Steria, the technical teams of each company joined forces to design the deployment of a Landing Zone. Initially, the project was designed and adapted to the needs of the public sector, to be later expanded to other markets and use cases.

Why a Landing Zone?

Compliance and regulatory requirements

The starting point of the project was very specific: from day one, the Canadian government required a cloud environment that meets very strict sovereignty and security standards. Without a ready-to-use solution, each public client had to spend weeks, if not months, setting up their architecture, drafting procedures, and validating each component with the authorities. This created a bottleneck when projects were initiated.

“The lack of a process to deploy a Landing Zone that was compliant with the requirements of the Canadian public sector was a real roadblock.”

The Landing Zone was designed as a deployment that complies with a regulatory framework: the created infrastructure automatically meets the segmentation, encryption, logging, and access control requirements imposed by authorities. Clients no longer have to worry about how to prove that their environment is compliant; the compliance report is generated simultaneously with the deployment. In fact, this is the whole idea behind the solution.

Accelerating cloud adoption

Once the compliance issue is resolved, the Landing Zone proves to be a true adoption accelerator for all types of clients: startups, SMEs, large accounts, and, of course, governments.

• Automation of best practices: the tools automatically deploy private networks, subnets, firewalls, service accounts, and IAM policies.
• Standardisation: each environment follows the same architectural model, which facilitates maintenance, monitoring, and upgrades.
• Time-to-market: where a manual deployment could take several weeks of work (reading documentation, manually creating resources, compliance testing), the Landing Zone allows for an operational (and compliant) environment in less than an hour.

“In half an hour, you have something ready to use: 30 to 40% of security requirements are already automatically deployed.”

The concrete benefits for technical teams

Time saving and reproducibility

At the core of automating a Landing Zone is Infrastructure as Code (IaC). Terraform (or its fork OpenTofu) orchestrates all OVHcloud services.
Thanks to this model:

• a single script can be executed multiple times, across different accounts or regions, without changing the outcome
• human errors that occur when manually creating resources (typographical errors, configuration oversights, incorrect role assignments) are nearly eliminated
• teams move from tedious configuration to deployment validation, freeing up several days of work for each project

Governance and access management

In addition to the infrastructure, the Landing Zone incorporates a governance model: roles, policies, and safeguards are preconfigured, simplifying access management and revoking rights when someone leaves the company. This layer of abstraction addresses one of the main challenges for IT departments: visibility and control over cloud resources.

Modularity and adaptability to different profiles

The code has been designed to be modular. Three basic profiles are available: “small business”, “medium” and “government”. Each profile activates a tailored set of services in terms of cost, scalability, and compliance requirements. 

The same code base can be extended: if a client from the financial sector requires an HSM encryption module or a certified payment gateway, it is simply a matter of adding the corresponding module and rerunning the script. This flexibility allows you to reuse the same foundation for very different projects, while ensuring compliance and performance guarantees.

A solution that makes a difference

Landing Zone is more than just scripts that create networks and accounts.

It is a comprehensive set of services that covers the entire lifecycle of a cloud project: from strategic reflection to production deployment, and then to daily management administered by the client.

This dual expertise provides coverage for the entire lifecycle: strategy → deployment → operation.

Conclusion

The Landing Zone deployed on OVHcloud infrastructures addresses two major challenges: compliance  for many regulated sectors (particularly public, finance and health) and speed of adoption  for all cloud customers. By automating part of the security requirements, offering ready-to-use governance, and remaining highly modular, it frees up technical teams to focus on their business value.

Are you responsible for a cloud project and looking to reduce your production timelines while ensuring compliance? Contact OVHcloud to find out how a Landing Zone can become the foundation of your digital transformation.

Scaling a business is never linear. Growth brings new possibilities but also new pressures. The real test lies in staying agile while building strong technical foundations that let innovation flourish.

Across industries, ambitious teams are scaling faster and smarter with cloud infrastructures that adapt to them, not the other way around.

In this blog, two OVHcloud experts – Emma Dennard, Managing Director Corporate UK, the Netherlands, the Nordics & Canada; and Sylvie Houlière Mayca, Managing Director Corporate France Belux & MEA – share insights and examples of the scale-ups reshaping challenges into tangible success.

Whether you’re an AI innovator or digital pioneer, these are ways in which businesses can triumph when openness, scalability and transparency meet technical creativity.

Turning intelligence into impact

Emma Dennard

For many companies building AI, the biggest barrier isn’t imagination. It’s integration. Swedish scale-up Hopsworks knows that better than most. Born from a university research project, its platform helps developers speed up everything from image recognition to fraud detection and LLM training.

When Hopsworks first met OVHcloud, the team was frustrated by hyperscale tools that didn’t play well together and complex cloud pricing. What the company needed was freedom. Freedom to innovate fast, stay open and manage growth on their terms.

By joining the OVHcloud Startup Program, Hopsworks migrated its serverless offering to OVHcloud Public Cloud, using S3-compatible Object Storage and open-source frameworks to stay agile as AI evolves. The result? 62% savings on cloud costs, freeing capital for new strategic projects.

More than that, Hopsworks avoided the lock-in that can slow innovation. With OVHcloud’s predictable pricing, European Sovereignty and open standards, the company gained the flexibility to keep adapting.

Read more about how Hopsworks scales AI innovation without compromise.

Raising the AI game

Emma Dennard

Before the world was talking about generative AI, Leetify was already using machine learning to help gamers improve. Its AI coaching platform analyzes player performance in Counter-Strike: Global Offensive, giving instant feedback after each match.

That requires serious bandwidth. In 2022, Leetify was processing over two million matches every month, moving around 70TB of data in real time. Any lag, outage or DDoS attack could mean lost players. And lost trust.

By combining OVHcloud Dedicated Servers with Public Cloud, Leetify gained both reliability and flexibility: 99.99% uptime, unlimited unmetered traffic and the ability to scale instantly during peak tournaments.

This resulted in around 50% lower costs and uninterrupted performance for players worldwide. For a fast-moving team pioneering AI in gaming, that kind of stability means savings, but it also means freedom to innovate, experiment and win.

See more on how Leetify stays competitive with OVHcloud reliability.

Reinventing reliability

Sylvie Houlière Mayca

In an age of disruption, few names say trust quite like Ravensburger. The German company has been sparking creativity for over a century through games, books and puzzles that millions of families know by heart. But even tradition had to transform.

As Ravensburger’s global business grew, so did its digital footprint. The brand’s go-to-market strategy combines physical retail with a thriving e-commerce platform. Both have to perform seamlessly, all the time.

At peak moments like Christmas, or lockdowns introduced during the COVID-19 pandemic, traffic to the online store could surge overnight. The company’s on-premises infrastructure simply couldn’t keep up. The challenge was clear, deliver the same premium experience for every customer, at any hour, without interruption.

Ravensburger migrated its online business to OVHcloud Hosted Private Cloud (HPC), gaining an infrastructure built for elasticity, high availability and international reach. With compute, storage and networking resources tuned for performance, demand spikes are handled automatically.

Now, with its e-commerce foundations secured, Ravensburger’s next chapter is all about intelligence. This means using AI to better understand customer profiles and match products to children and families worldwide.

Learn more about how Ravensburger combines resilience and growth with OVHcloud.

Cultivating sustainable growth

Sylvie Houlière Mayca

Agriculture may not always be the first industry associated with AI, yet Colombian scale-up Manglar is showing how technology can meaningfully enhance and support this vital sector. Focused on agrotech innovation, the company uses drone imagery and machine learning to monitor crops, detect issues early and improve yield sustainably.

When Manglar first partnered with OVHcloud, the team needed a way to balance rapid growth with predictable costs and performance. What began as a search for efficient storage evolved into a full-scale infrastructure collaboration. By integrating OVHcloud GPU instances for training and inference, and storage servers for data processing, Manglar now runs high-performance image analysis that delivers real-time insights to farmers across Colombia, Mexico and Brazil. But the company isn’t stopping there, with plans to expand to the US and Argentina next.

For a business working at the intersection of technology and the environment, reliability and governance are everything. OVHcloud’s transparent pricing, open architecture and dedicated local support have helped Manglar optimize resources, maintain investor confidence and grow responsibly – both technically and financially. 

Behind the success is a team of builders who blend data science with purpose. These engineers have fine-tuned inference models to deliver faster, more accurate predictions, while leadership focuses on scaling impact on economic and environmental levels. It’s a model for how innovation can help drive sustainability, not strain it. 

Next steps for Manglar include the full migration of its ML and inference workloads to OVHcloud, requiring H100 or H200 GPU availability. This will allow for more efficient scaling while improving processing speed. Part of the OVHcloud difference is the ability to turn infrastructure into a growth engine while keeping governance and impact front of mind.

Scale smarter – 6 inspiring use cases

1. Rebuilding for agility

Webmecanik transformed its monolithic infrastructure into a distributed platform to better support growing demand. By adopting OVHcloud’s Kubernetes solution, the company gained scalability, enhanced performance and improved reliability. Webmecanik’s commitment to customer-centric automation means it delivers marketing solutions that can adapt to client needs dynamically.

Discover how Webmecanik scaled up for success, here.

2. Mastering momentum

Programmatic advertising requires speed, precision and reliability. Nexx360 leveraged OVHcloud’s Kubernetes infrastructure to scale its advertising platform globally, maintaining high performance even during spikes in ad delivery. The team’s innovative use of automation in campaign optimisation makes them stand out as builders transforming digital marketing operations.

Learn how Nexx360 stays flexible and cost-efficient in the cloud, here.

3. Enhancing user experience

CUX.io, a platform for UX automation, required tailored hosting to manage unique workloads and deliver consistent user experiences. OVHcloud provided customised cloud solutions that ensured stability and performance. CUX.io’s dedication to measuring every interaction to optimize UX demonstrates the creative thinking behind technical scaling.

See how CUX.io optimized user experience across multiple platforms, here.

4. Setting new boundaries

Floatplane’s hybrid cloud approach combines bare-metal servers with Managed Kubernetes, enabling rapid scaling while maintaining low latency for its video streaming service. What differentiates Floatplane is its focus on community-driven content delivery, ensuring that both creators and viewers benefit from a seamless experience, even as the platform grows.

Learn how Floatplane keeps viewers engaged, here.

5. Leading with clarity

Lota.cloud aims to become a leader in FinOps and opted for OVHcloud’s managed services to streamline operations while ensuring regulatory compliance. By embracing scalable cloud solutions, Lota.cloud can support complex financial processes without sacrificing speed or flexibility. The company’s dedication to transparent, data-driven decision-making highlights the careful planning to builders navigating high-stakes sectors.

Learn how Lota.cloud scaled its logistics platform effortlessly, here.

6. Growing responsibly

As a leading provider of healthcare analytics, IAMDS relies on OVHcloud to manage sensitive patient data securely while scaling services across multiple regions. By combining secure infrastructure with flexible cloud solutions, IAMDS ensures compliance with strict regulations while enabling real-time analytics that drive better patient outcomes.

Find out how IAMDS delivers secure, scalable digital solutions, here.

Driving growth and impact with confidence

Companies thrive when their infrastructure is flexible, reliable, transparent and cost-predictable. Whether powering AI platforms, enabling global e-commerce, or supporting high-performance gaming, OVHcloud equips teams to focus on what matters most: innovation, growth and tangible results.

The right cloud partner provides the technology you need. That much is clear. But it also lays the foundation for strategic decision-making, international expansion and long-term success. With robust infrastructure, open standards and responsive support, companies can scale with confidence and absorb peak demand while builders invest energy into advancing their vision rather than managing technical hurdles.

Explore how OVHcloud can help your business scale with confidence here.

Building a real-time data pipeline used to mean months of infrastructure work. Here is what the modern stack looks like in 2026.

Your database is doing its job. It records every transaction, every event, every state change your application produces. The data is reliable, consistent, and safely stored.

But a database is a starting point, not a destination. The question is what happens after the write. Does that data stay in storage, answering application queries, while the insights it could generate stay locked in place? Or does it flow downstream, in real time, to the systems built to make sense of it?

Building that downstream pipeline used to be a serious infrastructure project. Kafka clusters to provision and tune, ZooKeeper ensembles to manage, connectors to configure, sinks to wire up, and a schema registry to operate. A team could spend weeks on the scaffolding before the first event reached an analytics engine.

In 2026, the pipeline architecture has not changed. But the operational weight has. Managed services handle provisioning, replication, failover, and upgrades. What used to take a dedicated platform team now takes an afternoon. This article walks through what a complete real-time data pipeline looks like, how each layer connects, and what it takes to build one without building the infrastructure underneath it.

The four layers

A modern real-time data pipeline has four layers. Each has a distinct job, and none of them are optional if you want data to move continuously from storage to insight.

Storage

Where your data originates. A relational database like PostgreSQL handles transactions, enforces consistency, and serves your application. It is your source of truth. Every insert, update, and delete is recorded with precision. That write log is also the starting point for everything downstream.

Streaming

How data moves. Apache Kafka captures changes from your database the moment they happen and distributes them to downstream consumers as events. Every row-level modification becomes a structured message that other systems can act on in real time. Kafka’s core design principle is decoupling: producers write events without knowing who will consume them, and consumers read events without touching the source system. This means you can add new downstream use cases – a new analytics engine, a new alerting system, a new data warehouse – without modifying the database or the application above it.

Analytics

Where data becomes answers. Two distinct engines cover the two main post-storage use cases. ClickHouse handles OLAP workloads: fast columnar queries, real-time aggregations, time-series analysis, and dashboards over millions of rows per second. OpenSearch handles full-text search, log analytics, and observability: complex queries across weeks of event data, anomaly detection, distributed tracing, and alert rules over live event streams. Both engines are designed for reads at scale, not for transactional consistency: this is exactly the trade-off you want after Kafka.

Visualisation

How the answers reach people. Grafana connects to both ClickHouse and OpenSearch, pulling from live data to power dashboards, alert panels, and operational monitors. It is the layer that makes the pipeline visible across the organisation – to the product team checking feature adoption, to the security team watching for anomalies, and the platform team tracking infrastructure health.

Two paths through the same architecture

The topology is consistent regardless of your use case: source database, Kafka as the streaming backbone, one or both analytics engines, and Grafana on top. How you build it depends on what your team needs first.

The real-time analytics path

PostgreSQL + Kafka + ClickHouse + Grafana is the right starting point when the primary need is fast, flexible querying. Product analytics, business intelligence, time-series reporting, funnel analysis, A/B test results in real time: these all call for ClickHouse. It ingests directly from Kafka topics, materialises views that dashboards query in milliseconds, and handles aggregations over hundreds of millions of rows without breaking a sweat. The SaaS company that needs to see feature adoption as it happens, the fintech tracking transaction volumes by the minute, the e-commerce platform running live inventory analytics – these all follow this same path.

The search and observability path

PostgreSQL + Kafka + OpenSearch + Grafana is the right starting point when the primary need is full-text search, log aggregation, or system-wide observability. OpenSearch indexes events as they arrive, enabling complex searches across months of structured and semi-structured data with sub-second response times. The security team correlating events across distributed services, the platform team centralising logs from dozens of microservices, the SRE team building alerts on top of live event streams – these all follow this path.

How the data moves

The connection between your database and Kafka starts with change data capture. CDC is the mechanism that reads your database’s internal write log and turns each modification into a stream of structured events.

In PostgreSQL, this works through logical replication. The write-ahead log records every change made to the database, at the row level. With logical replication enabled, a connector can read that log and emit each change as a structured event with the full before and after state of the row. Kafka Connect is the integration layer that runs these connectors. Debezium, configured as a source connector within Kafka Connect, reads the PostgreSQL WAL and publishes each change to a dedicated Kafka topic. From there, sink connectors route events to ClickHouse, OpenSearch, or both.

One technical development worth understanding for anyone building on Kafka in 2026: Kafka 4.0 removes ZooKeeper entirely. ZooKeeper was the external service Kafka relied on to manage cluster metadata, leader election, and coordination. It was a separate system to deploy, configure, monitor, and upgrade alongside the Kafka brokers. Kafka 4.0 replaces it with KRaft, Kafka’s native Raft-based consensus protocol. Cluster metadata is now managed internally by Kafka itself. The result is a single, self-contained system with fewer components, fewer failure modes, and faster recovery. For anyone who operated a Kafka cluster under the ZooKeeper model, this is a material simplification. For anyone running Kafka as a managed service, the transition is mostly invisible: what you get is a faster, more resilient cluster with one less operational surface.

OpenSearch 3.0 is also a meaningful release for anyone building observability pipelines. The upgrade to Apache Lucene 10 delivers up to 60 per cent lower search latency, with the largest gains on vector search, KNN, and neural search workloads. Star-tree indexing reduces query work for heavy aggregations by up to 100 times. And OpenSearch 3.0 adds native MCP protocol support, which means it integrates directly with AI agents and LLM-based tooling. For teams building observability pipelines that feed into AI-driven incident investigation or alerting workflows, this is a capability that was unavailable even twelve months ago.

Why managed matters

The first thing most teams underestimate about this stack is not the initial setup: it is the ongoing operational surface.

On Kafka, the harder work starts after launch. Partition leader rebalancing when a broker restarts under load. KRaft controller quorum recovery after a node failure. Consumer group offset management when a sink connector falls behind and you need to replay events without duplicating records downstream. JVM heap tuning as throughput grows. Connector worker restart policies that do not lose in-flight events. On OpenSearch, add shard allocation decisions during cluster scaling, index lifecycle management policies to control storage costs as event volumes grow, and JVM tuning for the ML nodes that power vector and neural search. On ClickHouse, merge tree settings and partition pruning strategy matter from day one: getting these wrong early means rewriting table schemas under load.

Managed services absorb this operational surface. Rolling upgrades run without downtime because the service handles leader migration before taking a node offline. Connector workers are monitored and restarted automatically. Failover is handled at the infrastructure level across availability zones. Index lifecycle policies are configurable through the console rather than through manually applied configuration files. Analytics engineers already spend up to 40 per cent of their time on infrastructure maintenance rather than delivering insights. The managed services model moves that complexity to a layer you do not have to own.

For teams in Europe, there is a second dimension. Hyperscaler deployments in EU regions run on infrastructure governed by U.S. law, which means the legal framework around who can access your data, and under what circumstances, is not straightforward. Running on OVHcloud means the infrastructure is European, operational control is European, and the jurisdiction governing data access is unambiguous. For teams in regulated industries, or for any team that fields GDPR compliance questions from customers or auditors, that is a material difference from a cloud region that happens to be located in Europe.

Pricing is the third dimension. Hyperscaler bills can be opaque: data transfer costs between services, storage billed separately from compute, egress fees that compound as event volumes grow. OVHcloud pricing includes IOPS, traffic, and backups. You see the cost before you provision. There are no surprises when your Kafka throughput increases.

Building it on OVHcloud

OVHcloud runs all four layers as managed services, deployed and monitored from a single console.

Provision Managed Kafka, pick your region, and your cluster is ready in minutes. Kafka 4.0 with KRaft means no ZooKeeper to configure or monitor. Add Managed Kafka Connect and configure your PostgreSQL source connector, with optional Debezium CDC for full row-level change capture. Then provision Managed ClickHouse and add a sink connector to start routing events from your Kafka topics into ClickHouse tables, or provision Managed OpenSearch and route events there instead. Add Managed Grafana to connect to both engines and begin building dashboards on live data.

The full stack is five managed services on one platform: PostgreSQL, Kafka, ClickHouse or OpenSearch (or both), and Grafana. One console for provisioning and monitoring. One bill. One support team that understands the full pipeline, not just individual components in isolation.

Start with one layer

You do not have to build the whole pipeline on day one. The most common starting point is Kafka: get your data flowing before you decide where it is going. Once events are moving through Kafka topics, adding ClickHouse or OpenSearch is a connector configuration, not a re-architecture.

The pipeline you build this way is modular by design. Each layer adds independent value. The team that starts with Kafka and adds ClickHouse six months later has not wasted anything in between. The Kafka layer was already doing its job. The pipeline grew without disrupting what was already working.

The infrastructure is managed. The data stays in your hands. The pricing is transparent. The pipeline you need already exists on one stack.

Explore the OVHcloud managed data pipeline

Most managed database users stop at storage. Here’s how to complete the pipeline with streaming and analytics, all from one console.

You picked a managed database for the right reasons. No patching, no replication headaches, no 3 a.m. pages about a failed backup. Your PostgreSQL, MySQL or MongoDB cluster runs, stores, and serves. It does exactly what you asked.

But here is the thing: storing data and understanding data are two different jobs. And right now, roughly nine out of ten managed database customers at OVHcloud stop at storage. They run rock-solid operational databases yet never connect them to a streaming or analytics layer. The data sits there, doing its job, answering application queries, serving the API, while the insights it could generate stay locked inside. No streaming. No real-time analytics. No search across logs or events. Just storage.

That is the missing half.

The gap between storage and insight

Most teams hit this wall at some point. The application database handles transactions, serves the API, keeps the frontend alive. Then someone asks a question the database was never designed to answer. “What are customers doing right now?” “Which features are driving retention this week?” “Can we detect anomalies before they become incidents?”

The reflex is to run analytical queries directly on the production database. It works, briefly, until those queries start competing with the application for resources. Response times creep up, the ops team starts throttling reports, and the data team ends up with a spreadsheet export and a frustrated expression.

The real problem is not the database. It is the absence of everything after it: a streaming layer to move data in real time, and an analytical engine purpose-built for fast, flexible queries. Without those two pieces, the pipeline stops at storage.

What a complete pipeline looks like

A modern data pipeline has three layers, each doing what it does best.

Storage

What you already have. Your managed PostgreSQL, MySQL, or MongoDB handles transactions, enforces consistency, and serves your application. It is your source of truth.

Streaming

The bridge. Apache Kafka captures changes from your database the moment they happen and distributes them to downstream consumers. For teams that need a full change data capture, Debezium can be configured through Kafka Connect, a process we will cover later. Instead of batch exports or nightly ETL jobs, your data flows continuously. Every insert, update, and delete becomes an event that other systems can act on in real time. Kafka is not just a transport layer: it decouples your producers from your consumers, which means you can add new downstream use cases without touching anything upstream.

Analytics

Where data becomes answers. ClickHouse processes millions of rows per second for OLAP workloads: dashboards, aggregations, time-series analysis. OpenSearch handles full-text search, log analytics, and observability. Together, they cover the two big post-storage use cases: structured analytics and unstructured search.

Each layer is independent but connected. Your production database stays lean because it is not fielding analytical queries. The analytics engines are optimised for reads at scale, not for transactional consistency, which is exactly the trade-off you want.

This architecture scales in stages. You do not have to build the entire pipeline on day one. Start with Kafka to get your data flowing, then add ClickHouse or OpenSearch when the use case demands it. Each layer adds value without disrupting what came before. That modularity matters, because most teams do not need everything at once. They need the next piece.

How the pieces connect on OVHcloud

OVHcloud offers all three layers as managed services, deployed and operated from the same console.

Start with your existing managed database. Connect Managed Kafka through the OVHcloud console in a few clicks, then add Managed Kafka Connect as the integration layer. Kafka Connect handles the mechanics of pulling change events out of your database and pushing them into Kafka topics. From there, you route events to Managed ClickHouse for analytical queries, Managed OpenSearch for search and observability, or both.

For teams that want to go further, Debezium can be configured on top of Kafka Connect to implement full change data capture (CDC). This means every row-level change in your database is captured as a structured event, preserving the complete history of modifications. Debezium runs as a connector within Kafka Connect, so the infrastructure is already in place.

What you get at the end is a complete pipeline: source of truth, real-time streaming, and fast analytics. All managed. One console for provisioning and monitoring. One bill. One support team that understands the full stack.

When your database, streaming layer, and analytics engines are scattered across different providers, debugging a broken pipeline means opening three dashboards, contacting three support teams, and reconciling three billing cycles. With OVHcloud, the whole chain lives in one place.

What this looks like in practice

Product analytics at a SaaS scaleup

A B2B SaaS company needs to understand how customers use their product in real time. Their managed PostgreSQL database records every user action, but running analytical queries on it directly means competing with the application for resources. Whenever the data team runs a report, response times start spiking.

They add Managed Kafka to capture database change events as they happen, then configure Kafka Connect to route them to Managed ClickHouse. ClickHouse ingests the stream and pre-aggregates it into the materialised views their dashboards need. The product team now sees feature adoption, session lengths, and funnel conversions updated in seconds, not hours, without any additional load on the production database.

New analytical use cases – such as cohort analysis or A/B test reporting – are added as new Kafka consumers without any changes to the application or the source database. The implemented architecture enables the pipeline to grow without touching what already works.

Observability at a cybersecurity scaleup

A cybersecurity company analyses millions of events per day across its platform. Detecting anomalies means searching across weeks of structured and semi-structured data with sub-second response times. Their PostgreSQL operational database stores event metadata reliably, but it was never designed for full-text search at this volume.

Managed Kafka streams event data as it is written to the database, routing it to Managed OpenSearch. OpenSearch indexes everything in real time. The security team can now run complex searches across months of data in milliseconds, set alerts on anomaly patterns, and correlate events across distributed services from a single dashboard.

No separate log management vendor. No data egress fees between services. And because the data never leaves OVHcloud infrastructure, there is no question about where it sits or who can access it.

Why it matters that it is managed (and European)

You chose a managed database because you did not want to babysit infrastructure. The same logic applies to streaming and analytics. Self-hosting Kafka is notoriously complex: broker management, partition rebalancing, schema registries, monitoring, upgrades. ClickHouse and OpenSearch have their own operational weight.

Analytics engineers already spend up to 40% of their time maintaining infrastructure[1] instead of delivering insights. Managed services flip that ratio. With OVHcloud, rolling upgrades run with zero downtime. Automated failover covers availability zones. End-to-end encryption, ISO 27001 and SOC 2 compliance, and a 99.99% SLA are included, not extras.

And because this is OVHcloud, your data remains your data. No extraterritorial exposure, no ambiguity about which jurisdiction governs your data. For teams operating under GDPR or working in regulated industries, this is not a nice-to-have. It is a requirement.

Pricing is transparent and predictable. No data-transfer surcharges between services, no opaque consumption-based billing that spikes when your analytics workload grows. IOPS, traffic and backups are included.

There is also the question of open-source compatibility. Every engine in the OVHcloud managed pipeline runs the real open-source project: Apache Kafka, ClickHouse, OpenSearch. No proprietary forks, no API incompatibilities, no vendor lock-in. Your code, your connectors, and your tooling all work the same way they would against a self-hosted cluster.

Getting started

If you already have a managed database on OVHcloud, the fastest path to a complete pipeline is:

1. Provision Managed Kafka from the OVHcloud console. Choose your plan, pick your region, and your cluster is ready in minutes.
2. Add Managed Kafka Connect and configure a source connector pointing to your database. This is where your change events start flowing.
3. Spin up Managed ClickHouse (for analytics) or Managed OpenSearch (for search and observability), or both, depending on your use case.
4. Configure sink connectors in Kafka Connect to route events from your Kafka topics into your analytics engines.
5. Query your data. ClickHouse speaks SQL, so your existing BI tools and dashboards plug right in. OpenSearch provides its own dashboards for log exploration and search.

The entire setup can be done from the console, with each service connected through the same management interface. No VPN tunnels to configure between providers, no credential juggling across platforms. For teams comfortable with infrastructure as code, Terraform and the OVHcloud API cover the same ground programmatically.

If you want to explore CDC with Debezium, the Kafka Connect foundation is already there. You configure Debezium as a source connector, and it starts capturing row-level changes from your database into Kafka topics. The managed Kafka Connect infrastructure handles running and scaling the connector itself.

Your database is doing its job. Now to complete the picture.

Nine out of ten managed database customers have not connected their data to streaming or analytics. The operational half works. The insight half is waiting.

The tools are managed, the console is unified, and the pipeline pattern is proven. If you are ready to see what your data can tell you, the missing half is a few clicks away.

Get started with Managed Kafka on OVHcloud

[1] Source: McKinsey: https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-debt-reclaiming-tech-equity

At OVHcloud, we are proud to continuously evolve our infrastructure to help customers run their virtualized workloads with greater efficiency, flexibility and performance.

Today, we announce the availability of Premier 2027 hardware generation for Managed VMware vSphere solution, bringing significant improvements in compute power, memory capacity, storage capacity, and configuration flexibility.

The new generation improves upon its predecessors, being designed to better support enterprise virtualization environments, modern cloud workloads, as well as evolving VMware licensing models.

Managed VMware vSphere Welcomes a New Generation of Hardware

The Premier 2027 hardware range is replacing the previous Premier generation introduced in 2020, delivering stronger performance and broader configuration options for a wide variety of use cases.
Powered by the latest Intel Emerald Rapids processors, the new infrastructure enables customers to run demanding workloads while maintaining excellent price-performance efficiency.

Key highlights of the new generation include:

• Up to 40% more CPU cores compared to the previous Premier hardware
• Up to 1.5 TB RAM per host
• High-performance NVMe storage
• Up to 50 Gbps private bandwith included

Together, these improvements provide a more powerful and scalable foundation for virtual infrastructures hosted on OVHcloud, our latest pride and joy.

Premier 2027 Hardware Specifications

Latest Intel Emerald Rapids processors

The new servers use Intel Emerald Rapids CPUs, available in both mono-socket and bi-socket configurations, offering a flexible range from 16 to 72 cores.
This allows organizations to choose the right compute capacity for their workloads while benefiting from:

• up to 40% more cores compared to the previous Premier hardware
• up to 50% more CPU cache compared to the previous Premier hardware
• improved performance for compute-intensive applications
• broader servers’ options with global purpose (GP-x) and storage-optimized (STO-x) configurations.

Expanded memory capacity

Premier 2027 servers support up to 1.5 TB of RAM per host, enabling:

• larger virtual machines
• improved consolidation ratios
• better performance for memory-intensive applications such as databases and analytics platforms.

High-performance NVMe storage

The new hardware generation includes NVMe storage, enabling lower latency and faster data throughput.
Storage-optimized configurations (STO-x) leverage vSAN NVMe drives and can support up to 70 TB of storage per host.
This allows organizations to efficiently run storage-intensive workloads, including large databases (such as CRM), analytics platforms and enterprise applications (such as ERP).

High-speed networking

Premier 2027 servers feature up to 50 Gbps private networking included, providing a fast and reliable connectivity for virtual machines and distributed workloads.
This high bandwidth improves performance for applications requiring high data throughput, such as:

• distributed systems
• backup operations
• large-scale data processing.

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Designed for a Wide Range of Enterprise Use Cases

The Premier 2027 hardware generation is designed to support a wide range of enterprise virtualization scenarios.

Cloud migration

Organizations migrating workloads from on-premises VMware environments to the cloud can benefit from performance consistency and familiar tooling, making migration simpler and reducing operational complexity.

The additional compute and memory capacity also make it easier to consolidate workloads and optimize infrastructure costs.

Disaster recovery and business continuity

For disaster recovery environments, infrastructure performance and scalability are critical.
With high-performance NVMe storage and up to 50 Gbps networking, the new hardware enables faster replication, improved recovery times and reliable failover environments.

Customers also benefit from compatible and resilient backup solutions such as Veeam Managed Backup, a managed backup solution to protect and back up their virtual machines, and/or Zerto, a replication platform installed and updated by OVHcloud, that allows them to resume activity quickly and easily.

Enterprise application hosting

Many enterprise applications require predictable performance and large memory capacity.
The Premier 2027 range allows customers to run demanding applications such as:

• large databases, such as CRM
• ERP systems
• data analytics platforms
• containerized workloads on VMware environments.

Seamless Infrastructure Evolution

Customers already running Managed VMware vSphere can integrate the Premier 2027 servers, either global purpose (GP-x) or storage-optimized (STO-x) configurations, into existing environments.

The new hardware is compatible with the current Premier generation, allowing the creation of heterogeneous clusters and enabling customers to scale their infrastructure without requiring a full migration.

This flexibility allows organizations to expand their infrastructure progressively while maintaining operational continuity.

Future-Ready Infrastructure

The Premier 2027 hardware generation provides a strong foundation for future virtualization needs.

With the latest CPU architecture, NVMe storage and high-speed networking, organizations can confidently support evolving workloads and future platform upgrades while maintaining the performance and reliability expected from enterprise VMware environments.

Conclusion

The introduction of Premier 2027 hardware for Managed VMware vSphere marks an important evolution of the OVHcloud Private Cloud platform.

With more powerful processors, increased memory and storage capacity, and flexible configurations, we are delighted to welcome this new generation of infrastructure enabling organizations to run modern workloads more efficiently, while maintaining full control of their VMware environments.

Discover the new Premier 2027 range: https://www.ovhcloud.com/en-ie/hosted-private-cloud/vmware/prices/

Find out more about VMware on OVHcloud solutions: https://www.ovhcloud.com/en-ie/solutions/vmware/

Migrating to the cloud is exciting, but early decisions define what follows.

Real pressure emerges as usage grows and architectures are tested under sustained load. What worked for initial migration doesn’t always hold up when systems need to scale across regions, teams and workloads.

OVHcloud Professional Services Engineers and Customer Success Managers highlight the often-overlooked decisions that drive successful public cloud migrations and deployments, setting the foundations for scalable, future-ready growth.

What gets overlooked early, but shapes scaling later

The long-term success of a cloud project is defined in the first six to twelve months, where early infrastructure decisions often have the biggest impact and operational readiness is crucial. Networking, regional design and operational foundations may feel secondary during migration, but they quickly become central as environments grow.

Early deployments may run successfully using public interfaces and default configuration, but as services expand, internal traffic increases between APIs, databases and background processing layers. Without a private network model, that communication layer can introduce latency, expose sensitive traffic, and require disruptive redesign later.

“Security must be the priority. A simple ‘lift & shift’, without first designing a scalable and secure landing zone, isn’t enough. Ensure the correct infrastructure is in place at the beginning to account for future needs.” – Olivier Javaux, Professional Services, OVHcloud

Building resilience beyond initial deployment

Regional strategy follows the same pattern. Choices between single- and multi-availability zone (AZ) deployments influence resilience, failover behaviour and operational complexity. Changing these configurations after deployment is challenging, particularly when data dependencies and production traffic are already in place. By factoring in growth, availability requirements and business continuity early on, organisations can embed scalability and sustainability directly into their system design, alleviating the pressures of having to retrofit later.

Landing zones and operational systems are also common afterthoughts despite being central to day-to-day processes, incident response and compliance as systems scale. The key lesson is a necessary shift in focus: cloud teams need to move from simply deploying infrastructure to establishing visibility, governance and operational control from the outset. Managed services and standardised platforms further support this shift by reducing operational burden while improving oversight as environments expand.

The human dimension cannot be overstated. Proper onboarding, training and alignment across operational stakeholders helps reduce confusion and accelerate adoption. When change management is overlooked in the early stages, even well-architected cloud platforms can feel risky, slowing delivery and increasing resistance.

“The correct training must be in place to ensure teams don’t feel like they’re losing grip on the infrastructure.” – Olivier Picquenot, Customer Success Director, OVHcloud

💡 Implementation tip:

Conduct a cloud maturity assessment or small proof-of-concept to validate network design and resiliency. These early choices become the difference that scales later.

The vital work turning high-level goals into architecture decisions

Migration and scaling begin with clear objectives: improve performance, control costs, and support growth. Translating these into infrastructure decisions is where complexity creeps in.

“Scalability must be studied from the start, including load patterns. One of the main advantages of the cloud is the capacity to scale up or down depending on the load.” – Olivier Picquenot

Understanding dependencies, peak load patterns, compliance requirements and team skills gaps early on is essential. Without this visibility, scaling decisions are made reactively using incomplete information.

Teams should clarify assumptions from the start by asking practical questions early:

• How will internal traffic between services behave under load?
• What are the peak times, and how will scaling respond?
• Which compliance or data residency requirements affect where resources are provisioned?
• Does the team have the skills to operate complex platforms long term?

Automation and managed services help bridge the gap. Infrastructure-as-code, managed databases and integrated systems reduce operational overhead and frees teams to focus on building value, rather than maintaining infrastructure.

💡 Implementation tip:

Map business goals to measurable technical outcomes. Automation and managed services turn intent into scalable architecture.

Scaling risks and operational fundamentals

Rapid growth introduces operational risks that can destabilise even well-designed environments. Foundational practices can prevent these issues.

“The most important answer is automation. Using tools like Terraform or OpenTofu, organisations are ready to scale.” – Olivier Javaux

Automating provisioning, scaling and monitoring ensures repeatable deployments and prepares teams to scale efficiently. Meanwhile, defining ownership, standardising environments and reducing tool sprawl simplify troubleshooting and improve compliance as costs lower. Consistency becomes a force multiplier as teams and platforms grow.

Moving from monolithic applications to microservices or Kubernetes is complex, however, a phased migration approach helps to avoid failure. Starting with a low-risk service allows teams to validate the migration process, tune scaling and gradually expand while building confidence.

💡 Implementation tip:

Scale through repeatability. Automation, standardisation and phased delivery create stability as platforms grow.

Avoiding hidden costs

Legacy assumptions and over-provisioning are common traps in cloud migration. Without rethinking design patterns, teams risk creating unnecessary cost and operational complexity.

Cloud environments evolve rapidly. Reviewing architecture regularly ensures teams benefit from improved services and efficient patterns.

“A predictive, transparent pricing model where customers are charged only for the resources they consume, such as storage, compute power or bandwidth, allows for greater confidence in forecasting projects” – Amarjit Toor

💡 Implementation tip:

Avoid lifting legacy patterns unchanged. Continuously assess services and adjust architecture to match real-world demand.

Designing for scale

Scaling cloud projects successfully means designing for growth, security and operational efficiency from day one. Start with the fundamentals – automate relentlessly and leverage expertise when building the foundations for networking, observability, automation and operational readiness.

This is the difference that scales, resulting in a resilient platform and a confident team focused on value, rather than troubleshooting infrastructure.

If you’re navigating growth and want cloud to be one less thing to worry about, explore scalable cloud solutions for growing businesses.

Over the past few years, the speed at which teams can build infrastructure has changed dramatically. Models that once took weeks to train can now be iterated in days. Tooling has improved, workflows have matured, and the overall friction in getting from idea to working output has reduced significantly.

But this acceleration often stops the moment teams try to deploy — and this can make AI deployment frustrating. In this blog, you will learn how Facets can help teams accelerate migration to OVHcloud by making deployment more structured, repeatable and predictable.

What first begins as a fast, iterative process slows down when it enters the infrastructure layer. The work is no longer about models or code. It shifts to provisioning environments, configuring pipelines, managing permissions, setting up networking, and ensuring that everything works reliably outside a controlled development setting. This phase is not inherently complex, but it is fragmented, and that introduces delay.

As a result, there is now a visible gap between how quickly teams can build and how slowly they can move to production. The model may be ready in hours, but the surrounding system required to run it still takes days, weeks, or in some cases even months, to put together.

AI deployment is slow because infrastructure is inconsistent, not because AI is limited

It is tempting to assume that deployment delays are still a technical limitation but, in most cases, that is no longer true. The bottleneck is not the model itself. It is everything around it.

Infrastructure is rarely standardised across teams. Each project tends to define its own setup, its own pipeline, and its own configuration. Even when teams use the same tools, the way those tools are applied differs just enough to create inconsistency.

Over time, these differences accumulate. Environments are recreated, pipelines are reconfigured, dependencies are re-evaluated, and access is managed manually. What should be a repeatable process becomes a fresh effort every time.

This is where deployment slows down. Not because teams cannot build quickly, but because the systems around deployment are not structured for reuse.

Faster deployment starts with reusable infrastructure

Teams that deploy faster do not necessarily rely on fundamentally different tools. They change how infrastructure is organised.

Instead of treating infrastructure as something that needs to be set up for every project, they define it once and reuse it. Environments are created from standard definitions rather than being handcrafted. Deployment workflows follow consistent patterns instead of being recreated each time. Access, policies and guardrails are built into the system rather than handled separately.

This does not eliminate complexity, but it contains it. Systems become easier to understand, easier to operate and easier to extend. Most importantly, they become predictable.

Once infrastructure behaves predictably, deployment stops being a recurring source of delay.

AI improves operations only when infrastructure is already structured as a system

There is growing interest in applying AI to DevOps and infrastructure management, but its role is often misunderstood.

AI does not fix fragmented systems. If the underlying setup is inconsistent, AI will only automate that inconsistency. It may reduce effort in specific tasks, but it does not create structure on its own.

AI becomes valuable when it operates on top of a well-defined system. It can help teams understand system state, identify issues faster, assist with debugging, and reduce manual operational work. But it works best when the infrastructure beneath it is already standardised, reusable and consistent.

Infrastructure needs to shift from project-level setup to a standardised system

As AI accelerates development, the limitations of infrastructure become more pronounced. Teams are able to build faster than they can deploy, and that imbalance introduces friction.

This is not a temporary phase. As development continues to speed up, deployment will remain the limiting factor unless the underlying structure changes.

A different approach is needed. Instead of treating infrastructure as a series of isolated setups, teams need systems that standardise how infrastructure is created, managed and reused.

Facets turns infrastructure into a reusable deployment system

Facets is designed around this model. It brings infrastructure, CI/CD, environments, workflows and guardrails into a single operating system for deployment. It brings infrastructure, CI/CD, environments, workflows and guardrails into a single operating system for deployment. Teams can create environments on demand, follow predefined deployment workflows and retain operational context within the platform instead of relying on scattered documentation or individual knowledge.

This changes how teams interact with infrastructure. The focus shifts from repeatedly setting things up to using a system that is already structured to work.

Choosing the right cloud matters once infrastructure is standardised

Once infrastructure is structured as a system, the choice of where to run it becomes more important.

For AI workloads, teams need reliable compute, predictable cost structures and control over how environments are configured. OVHcloud provides a strong foundation for this, with access to GPU infrastructure, cost predictability, and flexibility in how environments are managed.

However, infrastructure alone does not remove friction. The real advantage comes when teams can move to that infrastructure quickly and operate it consistently without rebuilding their setup.

OVHcloud + Facets: Accelerating Deployment Through Standardisation

OVHcloud provides reliable, on-demand infrastructure for modern workloads. Facets helps teams make that infrastructure deployment-ready.

Together, they address both sides of the problem. OVHcloud provides the cloud foundation, while Facets defines how environments, workflows, policies and guardrails are created and operated on top of it.

Facets.cloud introduces reusable infrastructure blueprints that can be applied across projects. These blueprints define environments, workflows and operational guardrails in advance, allowing teams to deploy standardised systems instead of assembling infrastructure manually each time.

This shifts deployment from a configuration-heavy process to a repeatable, system-driven approach. Teams can reduce the time spent on setup, avoid recreating the same deployment patterns and move workloads onto OVHcloud with greater consistency.

The result is a faster path from migration planning to production-ready environments — helping teams migrate in days, not weeks.

AI speed is ultimately limited by how fast infrastructure can be deployed and operated

AI has already compressed the time it takes to build. The next bottleneck is AI deployment.

And that bottleneck is not solved by adding more tools or more people—it is solved by changing how infrastructure is structured. Because in practice, the speed of AI is defined not by how quickly models are built, but by how quickly they can be deployed and run.

Discover Facets.cloud

For IT leaders, the important question is not whether blockchain is “the next big thing.” 

The question is actually much simpler: where and how does this technology solve real problems better than conventional architectures?

To answer that, let’s begin with the fundamentals.

What blockchain really is, and how it works

A blockchain is a distributed database made up of blocks of data linked together in chronological order. Each new block contains information about transactions or events, along with a cryptographic reference to the previous block. This creates an auditable chain of records that is extremely difficult to alter retrospectively.

Unlike a traditional database, which is generally administered by one organisation, a blockchain is maintained collectively by participants in the network. Copies of the ledger are distributed across multiple systems, helping to ensure synchronisation, resilience and transparency.

When a transaction is submitted to a blockchain network, it is checked according to the rules of that network. Once validated, it is added to a block and propagated across the network so that participants agree on the updated state of the ledger. This agreement process is known as consensus.

The result is a system designed to support tamper-resistant, highly available and traceable record-keeping.

Public, private, permissioned and permissionless blockchains

Not all blockchains work in the same way. For business leaders, one of the most important distinctions is the difference between public and private networks, and between permissioned and permissionless models.

Public blockchains are open networks. In general, anyone can access them, and they are often designed for broad transparency and participation. These networks are especially relevant where openness, interoperability and shared trust are important.

Private blockchains are restricted to a specific organisation or group of participants. They are usually designed for tightly controlled business processes, where governance, confidentiality or performance requirements call for more control.

A second distinction concerns permissions.

In a permissionless blockchain, participants can typically join the network and interact with it without prior approval, provided they follow the network’s technical rules.

In a permissioned blockchain, roles and actions are controlled more tightly. Specific participants may be allowed to read, write, validate or administer the network based on predefined permissions.

These models are not interchangeable. The right choice depends on the use case, the governance model, the sensitivity of the data, and the degree of openness required.

How infrastructure factors in

Blockchain discussions often focus on tokens, protocols or applications. But for IT decision-makers, infrastructure is just as important.

A blockchain environment depends on reliable underlying systems to ensure performance and trust. Three requirements stand out in particular: security, speed and uptime.

1. Security

Security is indispensably foundational. Blockchain networks are designed to preserve data integrity, but they still need to be protected at the infrastructure and operational levels.

This includes safeguarding validator or node environments, protecting sensitive data during processing, enforcing strict identity and access controls, and defending services against network attacks such as DDoS events. In some blockchain contexts, security solutions must also be tailored carefully so that legitimate high-volume traffic is not mistaken for malicious traffic.

2. Speed and low latency

Many blockchain applications are highly sensitive to latency. Validation, data propagation and user-facing responsiveness all depend on fast, stable connectivity.

This is especially relevant for environments such as decentralised finance, exchanges, validator operations, RPC services and analytics pipelines, where milliseconds can affect performance and user experience. The network layer matters a great deal: bandwidth, routing quality, geographic distribution and private interconnection can all influence outcomes.

3. High availability and resilience

Blockchain systems are expected to operate continuously. Downtime can mean missed transactions, degraded service, failed validations or loss of trust.

That makes blockchain’s resiliency critical. Distributed hosting, strong service-level commitments, redundancy, and multi-regional design can all play an important role in keeping blockchain workloads available and synchronised.

For IT leaders evaluating providers or architectures, blockchain should therefore be treated as both an application question and an infrastructure question.

Blockchain goes beyond cryptocurrency

Perhaps the biggest misconception for blockchain is that it begins, and ends, with crypto.

Cryptocurrency is just one application of blockchain, and it is far from the only one. The technology can also support a wide range of business and public-sector use cases where transparency, traceability, automation or shared trust are needed.

Examples include:

• Digital identity and access models, where blockchain can help verify claims or credentials while preserving control and traceability
• Supply chain tracking, where organisations need reliable visibility into provenance, handling and transfers across multiple parties
• Smart contracts, which can automate actions when predefined conditions are met
• Decentralised applications, where services run on distributed infrastructure rather than a single central backend
• Enterprise recordkeeping, where multiple stakeholders need a shared, verifiable source of truth

The key point is that blockchain is valuable beyond the fact that it is decentralised. It is valuable when decentralisation, immutability and shared verification solve a real business or operational challenge together.

A practical view for IT leaders

For IT leaders, blockchain should be approached with the same discipline as any other architecture decision.

And that means asking:

• What trust problem are we solving?
• Do we need a shared ledger across multiple parties?
• What governance model fits the use case?
• What are the performance, security and compliance requirements?
• Which infrastructure will support this reliably at scale?

In some cases, a conventional database will still be the right answer. In others, blockchain offers a stronger foundation for transparency, resilience and distributed trust.

The important thing is to move beyond the hype. Used in the right context, blockchain is a practical architectural model with applications that extend well beyond cryptocurrency.

As adoption grows, IT leaders who understand both the technology and its infrastructure requirements will be those best placed to identify where it can deliver meaningful business value.

This article is based on the guide “The Blockchain Blueprint”, available to download for free.

Blockchain is a decentralized database technology that tries to approach very important questions in a different way to traditional (centralized) databases. Ultimately, these questions boil down to:

• What is the truth?
• Who gets the last word on what the truth is?

In information management circles, we’ve tried to find a way to the ‘single source of truth’ for decades. Information sprawls. Being able to have a definitive answer on any one matter – whether that’s the last version of a memo or the real-time stock in a retail store – is incredibly valuable.

After all, if you’ve worked in an office for any length of time, you’ve probably renamed a file to something like ‘version 2.5 final final THIS ONE’ – and inevitably, it’s not ‘final’. This analogy doesn’t quite work because blockchain isn’t a file storage technology, but we’ll come back to that later.

Blockchain’s answer to defining truth is democratic. As its name suggests, information is stored in blocks, and these blocks are linked together in a chain. The order of this chain is documented, so once a change is made, it becomes an indelible part of ‘history.’ This establishes ‘truth’ by tying historical facts (or transactions) together so that one is linked to the next – and altering one event means altering the entire chain of events, rather like needing to rewrite an entire history book from the change of one small detail.

Let’s look at an example in practice.

When a change occurs in a blockchain network (like buying a cup of coffee with cryptocurrency) the information is sent to the network and preliminary checks are done – for example, checking that you have enough money to buy the coffee.

If there are any blockchain experts reading this, let’s assume it was a very expensive and significant coffee, because smaller transactions are often handled slightly differently in blockchain, in what we call Layer 2 Networks. We’ll come back to these later in the series.

Once the transaction has been verified, it’s then packaged up with other transactions to form a block and linked to the previous block in turn.

Next, the network has to make sure that the block is valid. This is done through a process called consensus, or validation, and is handled slightly differently depending on the network. Bitcoin uses a system called ‘Proof of Work,’ where different users work to verify complex equations that prove that the block’s contents haven’t been altered and that it does really link to the previous block. If anything has changed in the block since it was submitted, the equation doesn’t work and the block is rejected.

This is a computationally intensive process and requires a lot of electricity, particularly at scale. At one point, Bitcoin used as much electricity as all of Norway, so there are many other alternatives. For example, Solana uses ‘Proof of Stake,’ where the parties involved in verifying the block put forward assets (in this case, actual cryptocurrency). Then, the lead validator builds the block and adds a digital signature to it so as to say that they checked it, and that they have a real stake in the matter (i.e. money). This data is then re-checked by other validators, who vote on the validity. If there are enough ‘yes’ votes, then the block is agreed upon.

Of course, this does rely on trust, but there are usually big penalties for validators who ‘cheat.’ For example, they can have their stake removed, and/or they can be restricted from contributing to validation in future.

Solana also uses a system called ‘Proof of History’ to help it process a large number of transactions, which is a bit different to other systems.

But once approved, the block is then officially part of the chain, and the transaction can be completed – and you can get your Americano.

To define blockchain in another way: it creates a system of establishing trust and control without one single authority. Trust is distributed between all the different parties involved in the network. In fact, almost anyone can become a blockchain validator, although each network has its own requirements to do so.

As we’ve seen, blockchain is much bigger than just Bitcoin; the distributed database structure can be applied to all kinds of settings. We’ve seen organizations using blockchain to verify where tuna fish were caught, to ensure that they were ethically fished, and that the handling of said tuna could be traced from supermarket to distributor, and back to the person who caught them in the ocean.

Another major application for blockchain is in identity verification. Blockchain systems can help to check the validity of something without violating its privacy.

For example, imagine that you’ve discovered the famous Narnia wardrobe. You’ve decided to try to prevent people from sneaking in and causing chaos, so you’ve created a password-protected way to get in. You’re trying to qualify for state support to redevelop your house into a tourist attraction, so you need to prove that the wardrobe is the real deal. On the other hand, you don’t want to just give out the password because you’ll have government officials popping in and out all the time, conjuring infinite amounts of Turkish Delight and smuggling talking badgers out under their coats.

Blockchain’s answer to this is simple: you pop inside and take a selfie with Aslan and Mr. Tumnus, showing conclusively that you have access. This is how Self-sovereign identity works, which requires blockchain to link the wardrobe with the selfie. The two need to be linked – after all, you could have just popped into your friend’s magical wardrobe and taken a photo of some other lion and faun!

Many age-verification systems work in similar ways. For example, in many countries it’s illegal to give credit to someone under 18, so having a credit card is proof that you’re older than this, without having to show your passport or driving license. And in this case, it’s vital to show that the credit card is linked to the person using it, or the system doesn’t work.

Finally, it’s worth knowing that there are a number of different ecosystems within blockchain. Although they’re not strictly specialized, we can generalize that Bitcoin is focused on currency, Solana tends to being more of a platform for decentralized apps, and Ethereum is known for facilitating transactions quickly and making use of smart contracts – an ecosystem similar to Avalanche, but Avalanche has a modular approach focused on speed.

This picture is further muddied by the fact that there are public and private blockchains, which are also subdivided, but which essentially dictate who can see the blockchain and who has permission to be on it.

In our next blog post, we’ll go into a bit more detail about the blockchain world, focusing on the truths that IT leaders need to know. In the meantime, if you want a clearer, more practical view of how blockchain fits into real-world infrastructure, you can explore our resources and customer success stories on our website.