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!

What is Industrial Automation?

Automation helps save a lot of time and effort and at the same time, it helps get a job done more accurately. Industrial automation involves the use of control systems, computers and robots to handle and perform certain operations. A survey by Fortune Business Insights reported that the global industrial automation market will reach 296.70 billion dollars by 2026. In this blog, let’s explore the top 10 advantages of industrial automation. In this blog, by Industrial Automation, we are referring more to the advancements of this century, i.e. after the time computers and electronics started to play a key role in the industrial sector.

Industrial Automation in Manufacturing

Automation is greatly transforming the manufacturing industry. This revolution is bringing cyber-physical systems into existence. Many manufacturing units are already employing robots and using digital technology to automate processes. A few examples of automation in manufacturing are:

• Automation of food and beverage packing to reduce chances of human contamination
• Numerical control in the machine tool industry
• Use of thermal sensors to monitor high activity areas on the floor
• Automatic sorting in the production line
• 3D printing

Top 10 Advantages of Industrial Automation

1. Reduces Cost

One of the top advantages of automation is reduction in manufacturing costs. Instead of having a floor full of workers, you can now have a few supervisors and have robots do the job. The initial investment will be a little high, but then the operation costs will reduce, which will be beneficial in the long run. Your expenses will only include maintenance, repairs, and energy. AI and data analytics have also helped reduce production costs by providing insights and information to make the right production decisions. Automation helps improve productivity, quality and system performance, which in turn reduces your operating expenses (OpEx). At the same time, automated preventive maintenance can improve the life and performance of the machines. It enhances the value of your assets and in turn decreases your capital expenses (CapEx).

2. Increases Productivity

Automated productivity lines consist of workstations connected by transfer lines. Each work station takes care of one part of the product process. Robotic process automation can be used to mimic many human actions. The system can be configured to login to applications and take care of the administrative work related to the business process. Robots can also be used on the production floor to handle raw materials, clean equipment, operate high-pressure systems, and do lots more. For example, in an automobile manufacturing unit, auto components are cut and shaped in different press working stations. All the parts are then brought together to one place where a robot puts them together to build the vehicle. Process automation greatly speeds up the production process.

3. Enhances Quality

Industrial automation also helps increase and maintain consistent quality of the output. In manual processes, the error rate can vary considerably. On the other hand, automated machines in the manufacturing industry have an error rate that is as low as 0.00001%. Adaptive control and monitoring help check every level of the manufacturing process to reduce the margin of errors.

4. Industrial Safety

A huge benefit of automation is improved safety at the workplace. Using robots for loading and unloading materials or transferring huge machine parts reduce risks of accidents. Safety curtains keep workers from going too close to the assembly lines or fast moving components, thereby improving safety. Thermal sensors continually check the temperatures in the production area. In case, they identify any spike in temperature, the sensors will send an alert. Immediately, precautions can be taken to ensure the safety of everyone on the production floor.

5. Accurate Results

Data automation is based on accurate data integration and connectivity. When accurate information is used in the production process, you can be assured of precise results. AI and ML solutions help you get detailed data that can be analyzed using data analytics tools to get accurate information.

Deep learning algorithms are used to build self-healing digital grids that use data analytics and intelligent energy forecasts to manage energy generation. Machine Learning apps have been used to build a self-learning quality control system for assembly line. ML and AI solutions are scalable and self-learning. Both these features ensure that the automated systems deliver accurate results every time, without fail.

6. Better Working Conditions and Value-Addition

One of the prime benefits of industrial automation is that it ensures consistent production and results. Computers, robots, and automated machines work at a steady pace. It allows you to have a better grip on the production rate. Automation not only delivers consistent production, but also consistent quality. In a flexible manufacturing system, the tools, processing machines, and material-handling robots are connected and controlled by a central computer system. Once the entire process is computed, the production goes on continuously without any drop in the pace or the results.

Flexible automation process lets you design or reconfigure a machine to suit a different product measurement or new product. In traditional production processes, it may take days or weeks to train employees. Another problem is that it can be difficult for workers to get used to the new process, which could cause production delays or quality issues. On the other hand, reprogramming a machine or a robot is easier and takes up less time. Plus, after a few trials, you will be ready to go into full production.

Automation frees up employees from working on tedious and repetitive tasks. This means that they can focus on other areas where they can do a value add. They can help with research and process development. Also, workers can effectively use robotic tools and machines to deliver faster and quicker results. Employees also experience the feel-good factor of doing positive and progressive work.

Industrial automation also helps improve working conditions. As the automated machines are able to step up the production, workers need not work long shifts or overtimes. Work hours are reduced, leading to an improved quality of life. In the United States, industries that adopted automation solutions were able to set a standard work time of 40 hours per week.

7. Industrial Communication

Without industrial communication, industrial automation can be near impossible. The communication system helps monitor and operate entire production lines, manage power distribution, and control machines. Some of the popular protocols for industrial communication are Foundation Fieldbus, PROFIBUS, EtherCAT, EtherNET/IP, and CANopen. Industrial communication allows for faster data analysis and real-time decision making.

8. Monitoring & Predictive Maintenance

A huge benefit of industrial automation is that it helps in monitoring and predictive maintenance. Production lines and production floor can be continuously monitored using sensors. These sensors track temperature, acoustics, time, frequency, oil pressure and other parameters related to the production process. If the sensors detect any change in these parameters, they will immediately send an alert. When the alert is received, the technicians can immediately identify the cause for the change. If it is noted that the changes in parameters may cause equipment problems or issues in the production process, then immediate service or repairs can be done. Automation can therefore help identify possible issues before they blow up into huge problems that can result in production downtime.

9. Equipment Monitoring

An automated equipment monitoring system helps observe the working condition of all the equipment in the manufacturing unit. Sensors, cameras, and network can be used to observe the equipment from afar. The monitoring system also helps diagnose any issues in the equipment and do the necessary repairs and services. This automated solution can be effectively used in petrochemical plants, manufacturing units, and other industries where large and complex machines are used. The automated system enhances safety, reduces the number of operators on the floor, and improves machine performance and lifespan.

10. Production Traceability

Automating the entire production process can greatly help in production tracing. Traceability is not only important in the food and beverage industry, but also in other industries. Tracing helps increase the quality and value of your product and facilitates mapping. Also, traceability makes root cause analysis more effective and aids continuous development. Automation helps trace the entire lifecycle of a product from the raw material to the location where the final product is shipped. You will also have a clear record of when and who did the product inspections, the assembly status, the condition of the machine when the product was processed.

How can Utthunga help in Industrial Automation?

With more than 13 years of industry experience, Utthunga is a leader in industrial automation. Our team includes experts in the latest digital technologies and industry professionals to provide digital transformation solutions customized to your business process and business goals. A few of our industrial automation solutions include:

• Deploying sensors, PLCs, controllers, etc. for automation
• Using embedded engineering to create smart devices (non-digital to digital)
• Offering data integration solutions that integrate plant floor assets to each other and other systems
• Building applications (desktop, mobile) for commissioning, calibration, local diagnostics and configuration
• Building bespoke application to enable integration of OT data to SCADA, MES, Enterprise systems
• Creating analytics solutions

Contact us to know more about our industrial automation solutions.