Adapting legacy systems such as SCADA, DCS to the service-oriented digital streams is a challenging task for companies, especially those in the manufacturing sector. One of the major concerns while adapting the new-age automation systems is to re-engineer the existing systems and upgrade them to a newer version. This upgrade is referred to as ‘Migration’.

When the migration strategies of the legacy applications make use of cloud computing technology, the whole process is referred to as cloud migration. Simply put, cloud migration is the process that essentially involves cloud integration of legacy applications by formulating process objectives that aim to deliver desired functionalities and improves efficiency in processing the data.

The cloud application development of legacy applications is generally carried out on a large scale, as it includes both infrastructure and applications. The outdated legacy systems lack robust security. The cloud integrations make these applications secure against data breaches and failure.

Not only this, migrating legacy systems to the cloud makes them more agile and scalable to meet the real-time needs of the consumer. However, a single mistake during the cloud integration could interrupt the industrial processes and cost heavily to the organization.

To avoid such a situation, you need to understand what could go wrong while migrating legacy applications to the cloud. In this blog, we will discuss the factors that must be considered and also the crucial mistakes you need to be careful of during the cloud migration process.

What are factors to be considered while migrating applications to the cloud?

Most of the legacy applications are mission-critical industrial systems but lack basic data security, and futuristic vision. These often cannot be replaced by new technology as it would not only increase the operational costs but also hamper the industrial processes.

Therefore, data migration services help these organizations in shifting the legacy system safely to the cloud, to make it more scalable, agile to the emerging needs and cloud technologies.

To attain the goal of cloud migration, product engineering companies are leveraging DevOps services so that they:

• Reduce time to market of the product
• Meet customer demands
• Lower the costs related to:
  • Product Development
  • Product Testing
  • Deployment
  • Operations

With the cloud migration services, businesses can take the best foot forward in modernizing their legacy systems. Some of the major factors that these services consider while shifting legacy applications to the cloud are:

• Understand existing systems using the documents available, plant design, and system interfaces.
• Analyze business needs and prioritize the legacy applications accordingly. The main motive of moving applications to the cloud is to increase productivity. So, the ones that matter the most in this context, must be given the highest priority.
• Analyze risks associated with migrating legacy applications to the cloud. It helps in monitoring the possible sources of cloud integration failure and take proactive actions accordingly.
• Performance monitoring is a must. Your cloud integration strategy must include a blueprint of how you are going to track the performance of the legacy systems during and after their transition.
• Proper closure helps in evaluating the cloud migration project in terms of performance, KPI, and other relevant metrics. These give you an insight into how cloud migration services have helped your business grow and at what scale.

Cloud and data integrations with your legacy application decide how well your manufacturing plant is ready for the fourth industrial revolution. However, there are few common pitfalls you must look out for while carrying out the same.

5 mistakes to avoid while migrating a legacy application to Cloud

Here’s what you need to be careful of while modernizing your legacy applications.

1. Limited understanding of technical and business requirements
2. Overlooking the importance of risk assessment
3. Lack of visibility
4. Overlooking the security aspects
5. Lack of technical support

Training the staff to use the new technologies will help bridge the intellectual gap and truly make the migration process a success for your company.

Conclusion

The cloud computing effect is now omnipresent in almost every industrial realm. As technology advances at lightning speed, the fourth industrial revolution will soon be replaced by its fifth version. To make your plants ready for the present and future, you need to look out for legacy applications that are essential yet disrupting for your business to adapt to the much-required changes.

Utthunga’s digital transformation consulting includes cloud migration services and data migration services. Leverage our years of technical expertise in helping companies attain their digital transformation goal in the range of domains Cloud, Mobility, IIoT, Analytics, and much more.

Get in touch with us to know more.

Not all databases are built the same, and not all are a good fit for industrial applications. Some are great at handling huge volumes of real-time sensor data. Others are better suited for transactional integrity or geographic redundancy. The important thing is this: whatever system you choose has to scale with your data, integrate well with your existing tools, and perform reliably in complex industrial environments.

Let’s break down how database technology has evolved to meet the demands of modern industry and which systems are actually built to keep up.

A decade ago, most industrial systems were siloed. You had PLCs feeding into SCADA systems, with databases mostly handling archival logs or simple reporting. That’s changed.

Today, industrial applications demand flexible data handling—whether it’s real-time sensor readings, complex spatial data, or transactional logs. That evolution reflects the shift toward automated factories, connected devices, and smart supply chains.

Let’s break down how database technology has evolved to meet the demands of modern industry and which systems are actually built to keep up.

Industrial automation runs on data—from IoT sensors, edge controllers, and cloud platforms. That constant stream needs structure, and fast. A solid database infrastructure doesn’t just collect machine data; it makes sure everyone from engineers to plant managers can access it when they need it.

In industrial applications, missing or delayed data doesn’t just weaken insights—it can lead to downtime.

A well-designed database lets teams share information across departments securely and efficiently. It reduces duplication, improves response times, and gives decision-makers real visibility into what’s happening on the ground.

It’s easy to think of product tracking as something limited to retail or logistics. But in an industrial setting, it’s just as critical.

Databases help manage complex product information across every stage of production—raw materials, sub-assemblies, final goods, and post-sale servicing. When a plant manager can query current inventory levels or trace a batch number in seconds, that’s database power in action.

For brand managers, engineers, or logistics teams, database-driven systems make it easier to update product data, remove obsolete items, and monitor performance in the field. In industrial applications, that can mean tracking thousands of components across global facilities.

Data inconsistencies cost time, money, and trust, especially in industries where compliance isn’t optional.

If your maintenance team is working off outdated readings or your audit logs are incomplete, you’re exposed to both safety risks and regulatory penalties. A proper database management system ensures that everyone across the organization is working from the same set of facts.

That consistency matters for industrial audits, ISO certifications, FDA compliance, and cybersecurity standards. With a well-maintained database, compliance is not just a fire drill, but a part of how the system works.

When industrial teams have access to clean, current data, they move faster. Maintenance crews identify failure patterns before breakdowns happen. Engineers tweak processes in real time. Procurement adjusts orders based on actual usage instead of guesswork.

Good database design reduces the friction between data and action. The interface doesn’t need to be fancy. It just needs to work consistently, quickly, and without making users jump through hoops.

That’s what productivity looks like in the world of industrial applications: fewer delays, smarter decisions, and better coordination between systems and people.

Let’s look at the systems that are widely used in industrial applications today. Each one brings specific strengths to the table, depending on your needs.

• Strong single-server consistency
• Supports a wide variety of data types: SQL, JSON, XML, spatial, RDF
• Includes Blockchain Tables for verifiable records
• Handles both transactional (OLTP) and analytical (OLAP) workloads

Used in large-scale industrial ERP systems where reliability and structure are critical.

• Full ACID compliance for transaction safety
• Integrates with T-SQL, .NET, R, Python, Java
• Works with spatial data, JSON, and structured formats
• Supports hybrid deployments (on-premise + cloud)

A good fit for industrial MES systems and financial reporting platforms.

• Horizontal scalability with built-in replication
• Distributed multi-document ACID transactions
• Flexible query options including full-text, graph, and geo-search
• Atlas Search and Atlas Data Lake for advanced analytics

Often used in IoT-driven environments where unstructured and semi-structured data come in fast.

• High-speed search across massive datasets
• Schema-less, REST-based interface
• Supports real-time logging, monitoring, and observability
• Built-in cross-cluster replication

Ideal for tracking machine logs, production metrics, and diagnostics in real time.

• Built for massive scale and fault tolerance
• Excels at time-series data and distributed workloads
• No single point of failure
• CQL (Cassandra Query Language) for simplified querying

Perfect for sensor-heavy environments, predictive maintenance, and long-term data archiving.

The success of any industrial system comes down to how well it can collect, store, and use data. Whether you’re running a production line, managing distributed assets, or building connected equipment, your choice of database matters.

Don’t just look at performance specs. Consider how each system fits your actual workflows and goals. Industrial applications aren’t generic—they have real constraints, real risks, and real opportunities. And your data infrastructure should reflect that.

If you’re not sure which database fits your setup, that’s a conversation worth having.

The substantial rise in edge analytics applications is due to the widespread use of the internet of things (IoT), widely acknowledged by industries as the most important tech trend due to the broad range of IoT services and domain capabilities.

How does edge analytics work?

When used as a data-driven approach to extracting tangible and measurable metrics from the IoT, edge analytics enables businesses to obtain data faster by deploying advanced analytics and machine learning at the point of data collection via connected devices and real-time intelligence.

In terms of the industrial IoT industry, it is expected that industries using edge computing will grow further owing to the edge analytics advantages of dispersed aggregation and offering shop-floor data gathering across all industrial sectors.

The idea of edge analytics allows for developing an optimum model for managing data transmission from the edge and ‘smart’ data storage in data centers. Advanced analytics techniques, independent of their application area, operate behind the scenes to anticipate system component failure, keep devices operational, and ensure a continuous workflow.

Overall, edge analytics skills are actively used to develop smart machines that perform self-responsive activities and anticipate consequences.

Edge AI video-based analytics have found widespread use in a variety of areas, most notably education. Indeed, it can revolutionize the teaching-learning process by providing innovative methods for delivering instructional films in novel ways. Sony, for example, demonstrated its AI edge analytics-based solution for producing video content: its primary unit, in conjunction with different licensed appliances, demonstrates how video streaming may be elevated to a new level.

Why do edge analytics matter in 2021?

Industry 4.0 is now facing significant obstacles that are impeding its development. Computing at the edge is the answer to many of these problems. Here are some of the most significant benefits that the edge offers to Industry 4.0.

Increased interoperability

IoT systems may be a hurdle to smart manufacturing’s growth. However, an IoT network is just as good as its interoperability.Concerns about interoperability are among the most significant obstacles to its adoption since there is no standard protocol. By relocating computer operations to the edge, part of the requirement for a universal standard is eliminated. When devices can transform signals on their own, they will operate with a wider range of systems.

The edge also acts as a link between information and operational technologies.

Reduced latency

When sending data to a distant cloud data center for analysis, the latency is lengthy and unpredictable. For time-sensitive use-cases, the chance to act on the data may be lost.

Edge computing provides predictable and ultra-low latency, making it suitable for time-critical scenarios such as any use-case where the process is mission-critical or in motion. Delays in moving cars, equipment, components, people, or fluids may result in lost opportunities, excessive expenses, or security concerns.

Analytics-enabled technologies

Companies decide to include edge computing technology in their network architecture due to the number of benefits it offers. There is something for everyone, from real-time AI data analysis and improved application performance to significantly reduced operational expenses and planned downtime.

Technologies enabling Edge Analytics:

Edge Computing with Multiple Access

Multi-access Edge Computing or MEC is an architecture that allows for computing and storage resources inside a radio access network (RAN). The MEC contributes to increased network efficiency and content delivery to end-users. This device can adjust the load on the radio connection to enhance network efficiency and reduce the need for long-distance backhauling.

Internet of Things

The idea of the Cloud of Things (CoT) is still in its early stages, but it has many potentials. All computing power in a Cloud of Things is obtained from the extreme edge at the end-user.

We all have powerful gadgets in our hands, such as industrial tablets, smartphones and Internet of Things (IoT) devices in our plants. These gadgets are often underused. Although IoT devices currently have limited computing capacity, many modern devices are very powerful.

Using mobile or IoT enabled devices, cloud services may be provided directly at the edge. These devices may be coordinated to provide edge cloud services. For example, a vehicle traveling through traffic may transmit traffic warning alerts to others and re-arrange alternative routes without user involvement.

The idea of the Cloud of Things is akin to that of fog computing. All IoT devices in a CoT form a virtualized cloud infrastructure. All computing in the CoT is done by the IoT devices themselves, which are shared resources.

Fog Computing

Fog computing, fog networking, or simply “fogging” refers to dispersed computer architecture. It brings cloud computing (data centers) to the network’s edge while also locating data, storage, and applications in a logical and efficient location. This location occurs between the cloud and the data source and is often referred to as being “out in the fog.”

Future of edge analytics

Edge computing has gained traction because it provides an efficient answer to growing network issues connected to transferring massive amounts of data that today’s businesses generate and consume. It’s not simply a matter of quantity. It’s also an issue of time; applications rely on processing and responses that are becoming more time-critical.

According to projections, there will be 21.5 billion linked IoT devices globally by 2025. Consider if half of them could do computational work for other devices and services. This massive, linked computer network would be especially useful for smart manufacturing.

Conclusion

Edge analytics is a burgeoning area, with companies in the Industrial Internet of Things (IIOT) industry increasing their investments every year. Leading OEM and suppliers are actively spending in this rapidly growing industry. Edge analytics offers real business benefits by decreasing decision latency, scaling out analytics resources, addressing bandwidth problems, and cutting expenditures in industries such as retail, manufacturing, energy, and logistics.

Utthunga’s expertise in edge analytics and edge computing along with IIoT technologies enables organizations to scale their processing and analytics capabilities exponentially. Contact us for your edge analytics requirements to leverage the real-time business insights from the edge.

Industrial safety protocols are communication standards used to keep machines, systems, and people safe in manufacturing plants, processing facilities, and other industrial environments. They make sure critical data—like shutdown signals, sensor alerts, or control commands—gets where it needs to go without delay, distortion, or loss.

These protocols catch issues like message corruption, missing data, or duplicated commands. They play a central role in how automated systems stay in sync, especially when multiple devices are working together under strict safety conditions.

Most automation systems involve a mix of control, motion, synchronization, and safety tasks. Industrial safety protocols manage all of that. One example is the Common Industrial Protocol (CIP), which uses an object-based structure to model devices and define how they exchange information. From basic sensors to complex motion systems, protocols like CIP give these components a safe, reliable way to communicate.

Here’s a breakdown of the protocols most commonly used in industrial safety systems:

A baseline network technology. Fast, scalable, and widely supported.

Known for high-speed data exchange. Common in Asian manufacturing setups.

Combines analog and digital signals. Still widely used in process industries.

Real-time communication between sensors and actuators.

Traditional serial communication methods. Still used in legacy systems.

Built on the Common Industrial Protocol. Handles safety-specific messaging.

Integrates with PROFINET and PROFIBUS. Frequently used in European automation systems.

• Ethernet-based, open standard. Platform-independent and flexible.

EtherCAT’s safety extension. Fast and reliable for high-performance systems.

Each one is used depending on system architecture, safety requirements, and regional preferences.

Mobile apps are now a core part of safety infrastructure. They give workers and supervisors real-time access to safety data pulled directly from systems running on these protocols. Before mobile tools, much of this was tracked manually, which meant delays, gaps, and avoidable risks.

Now, industrial mobile apps tie directly into protocols through IoT sensors and cloud-based systems. They allow you to:

• Detect early signs of equipment failure
• Monitor safety alerts and react faster
• Track inspections and compliance in real time
• Store incident data in the cloud for later analysis

These tools help site managers, technicians, and contractors respond faster, keep better records, and reduce the chance of injury or downtime. They’re not just for convenience. They’re now part of how modern safety gets done.

Industrial safety protocols aren’t optional. They’re built into the core of any serious industrial operation. They make sure systems talk to each other clearly, act when needed, and alert humans before something goes wrong.

Add mobile technology into the mix, and you get faster response, better record-keeping, and fewer gaps in communication. If your safety systems aren’t connected and accessible, they’re incomplete.

Need help choosing a protocol or building mobile tools around one? Let’s talk!