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Labyrinth Global Solutions (USA|UK|UAE|INDIA) #Normalizing #Interpreting and #Exchanging Beyond data collection, normalized data is required to integrate automated processes and #computerize human #decisionmaking. Normalized data is expressed in a single language with a consistent meaning regardless of the source of the information. To make the data usable from point to point and throughout the systems, it needs to be understood from #machine to machine, #hardware to #software, and system to #person. The point of normalization is to make variables comparable to each other. #Normalization is the process of reducing measurements to a #neutral or #standard scale. A fully normalized database allows its structure to be extended to #accommodate new types of data without changing the existing structure too much. As a result, #applications interacting with the #database are minimally affected. #Normalized relations, and the relationship between one normalized relation and another, mirror real-world concepts and their #interrelationships. The ODB++Manufacturing (ODB++M) machine-language specification defines how #information can be exchanged between #automated and #manual processes and systems, including #dataformat and #content. This ability to #communicate is essential for a #reliable and robust data-exchange environment. With a #standardized format, whenever a new machine or process is introduced into any part of the #manufacturing operation, it can be immediately integrated without rewriting software interfaces, #algorithms, and data definitions. ODB++M is designed to be part of the plug-and-play #architecture, for all manufacturing events, such as machine events, material logistics, quality issues, and #productflow control. #SiemensPolarion #DataNormalization #DataInterpretation #DataExchange #Integration #SoftwareEngineering #ALM (Application Lifecycle Management) #RequirementsManagement #Collaboration #DataIntegration #DigitalTransformation #ProcessImprovement #AgileDevelopment #DevOps #lgs #siemens #electronics #manufacturers #ContinuousIntegration #ConfigurationManagement #SoftwareQuality #Traceability #DataAnalysis #PolarionInterpreting #SiemensExchange #InterpretingInPolarion #PolarionCollaboration #LanguageExchangePolarion #PolarionAccessibility #InterpretationInSiemens #PolarionInclusion #LanguageEqualitySiemens #PolarionCommunication #Efficiency

ITDigest’s Weekly News Roundup Featuring Cadence Design Systems, Oracle, ,Quantiphi, Ceva, Inc., and more https://lnkd.in/dheD2AJD #artificialintelligence #Businesstechnology #CloudComputing #InformationTechnology #ITDigest #news

🔧 8 Common System Design Problems and Their Solutions Do you face challenges in large-scale production systems? Here's how to tackle some of the most common system design issues: Read-Heavy System 👉 Solution: Implement caching to speed up read operations. High-Write Traffic 👉 Solution: Use asynchronous workers for processing writes and consider databases powered by LSM-Trees. Single Point of Failure 👉 Solution: Ensure redundancy and failover mechanisms for critical components like databases. High Availability 👉 Solution: Utilize load balancing to route requests to healthy server instances and replicate databases for improved durability and availability. High Latency 👉 Solution: Reduce latency with a content delivery network (CDN). Handling Large Files 👉 Solution: Use block storage and object storage to manage large files and complex data. Monitoring and Alerting 👉 Solution: Implement a centralized logging system using tools like the ELK stack. Slower Database Queries 👉 Solution: Optimize queries with proper indexes and scale the database horizontally with sharding. Mastering these solutions will boost the resilience and performance of your systems! 💪 #SystemDesign #Tech #Scalability #DevOps #SoftwareEngineering

 🚀 Importance of System Design for Software Engineers 🚀 Mastering system design is crucial for software engineers in building scalable, efficient, and reliable software solutions. Here are some core concepts to be learned in system design: 1. Scalability: Understand techniques like sharding, replication, and load balancing to ensure systems can handle increased loads and user growth. 2. Performance optimization: Learn about caching mechanisms, database indexing, and efficient algorithm design to improve system performance. 3. Reliability: Explore fault tolerance, redundancy, and disaster recovery strategies to build resilient and reliable systems. 4. Distributed systems: Gain expertise in designing and managing distributed systems, including concepts like consistency, availability, and partition tolerance. 5. Data modeling: Master the art of designing efficient data models to ensure data storage and retrieval meet performance and scalability requirements. 6. API design: Learn how to design robust and efficient APIs for interoperability and flexibility. 7. Security and privacy: Develop an understanding of secure system design principles and data protection mechanisms. By embracing these concepts, software engineers can build systems that meet the demands of modern technology and set the stage for future innovation. Tagging: #SystemDesign #Scalability #PerformanceOptimization #Reliability #DistributedSystems #DataModeling #APIDesign #Security #Privacy #SoftwareEngineering #TechSkills #CareerDevelopment  

In the intricate landscape of modern technology, industries like manufacturing often grapple with complex systems and interconnected software architectures resembling a perplexing puzzle. In this blog, Nicholas Mera, Manufacturing Intelligence Team Leader talks about how the Hallam-ICS team approached a recurring technical challenge for one of our clients and found a tailored solution for their needs. #manufacturingintelligence #innovation #engineering https://hubs.li/Q02kndZn0

Do you like this #knowledgegraph? It's a base RDF/OWL structure of Rockwell Automation devices* - and we know #RDF is not the only technology for knowledge graphs. Are you a software practitioner that wants to see the knowledge technologies applied to structuring knowledge gained in the process of technology intelligence? Then have a look at the unique opportunity at https://lnkd.in/d55fzw49 - you will have a chance to work closely with the team handling a lot of data that need to be structured, analyzed and most importantly - understood and be easily accessible in a scalable way. * The graph in the image is not directly related to this role - but you will have a chance to meet people who expanded their work on this representation, or people that are also exploiting LLMs for querying etc.

🔍 Monitoring vs. Observability: The Difference 🔍 In the world of software engineering, we often hear the terms monitoring and observability used interchangeably. However, they serve distinct roles in maintaining and improving our systems. Let's dive into what sets them apart! Monitoring Monitoring is all about knowing what’s broken. It involves: Tracking Metrics: Keeping an eye on predefined metrics (e.g., CPU usage, memory utilization). Alerting: Setting up alerts for when things go wrong. Dashboards: Visualizing data through dashboards for a quick overview. Observability Observability, on the other hand, is about understanding why it’s broken. It includes: Contextual Insights: Collecting detailed data (logs, traces, metrics) to understand the internal state of a system. Dynamic Analysis: Adapting to new questions without predefined metrics. Root Cause Analysis: Helping engineers dig deeper into the “why” behind issues. Key Differences Proactivity vs. Reactivity: Monitoring: Often reactive. You know something is wrong when an alert is triggered. Observability: Proactive. Enables you to understand and prevent issues before they escalate. Scope: Monitoring: Limited to predefined metrics and thresholds. Observability: Broader and deeper, providing a more comprehensive view. Adaptability: Monitoring: Less flexible; predefined metrics may miss new or unexpected issues. Observability: Highly adaptable, allowing engineers to explore new questions and scenarios dynamically. In short, monitoring tells you what is wrong, while observability helps you understand why it’s wrong. Together, they form a powerful duo for maintaining robust and resilient systems. #SoftwareEngineering #DevOps #systemdesign #SystemReliability

Are you facing challenges with your engineering data management? Does any of this sound familiar? ❌ Disorganized engineering documentation ❌ Teams struggling to find the latest files ❌ Manufacturing unsure which files to use ❌ Security issues with documentation storage ❌ Lack of team collaboration There’s a solution for these problems that can help you: ✅ Streamline workflows ✅ Improve data management ✅ Control design and development ✅ Automate processes Connect!! ARKANCE IN Private Limited (Formerly Capricot) for Streamline Solution. #Vault #AutodeskVault #PDM #Streamline #Engineering #Mfg #Solution

System integration refers to the process of connecting different subsystems or components within an organization's IT infrastructure to work together as a cohesive whole. Here's a breakdown: Definition: System integration involves combining various software, hardware, and network components to ensure they operate as a unified system, sharing data and functionality seamlessly. Types of Integration: Vertical Integration: Connecting systems or components that are hierarchically related, such as integrating front-end customer-facing systems with back-end databases. Horizontal Integration: Integrating systems or components that serve similar functions across different departments or business units, enabling collaboration and data sharing. Enterprise Application Integration (EAI): Integrating disparate applications and systems across an entire organization to streamline business processes and improve efficiency. Data Integration: Combining data from different sources or formats into a unified format for analysis, reporting, or decision-making purposes. Cloud Integration: Connecting on-premises systems with cloud-based applications and services to leverage the benefits of cloud computing while maintaining integration with existing infrastructure. Challenges: System integration can be complex and challenging due to various factors, including: Technical Heterogeneity: Differences in technology platforms, data formats, and communication protocols among systems. Organizational Silos: Resistance to change and lack of collaboration between departments or teams responsible for different systems. Data Consistency: Ensuring data consistency and accuracy across integrated systems to avoid discrepancies and errors. Security and Compliance: Addressing security risks and compliance requirements when sharing sensitive data across integrated systems. Benefits: Improved Efficiency: Integrated systems streamline business processes, reduce manual tasks, and improve workflow efficiency. Enhanced Data Visibility: Integration provides real-time access to accurate and up-to-date data, enabling better decision-making. Cost Savings: By eliminating duplicate data entry and improving resource utilization, integration can lead to cost savings in the long run. Competitive Advantage: Organizations with well-integrated systems can respond more quickly to market changes and customer demands, gaining a competitive edge. Overall, system integration is essential for organizations looking to optimize their IT infrastructure, improve business processes, and stay competitive in today's digital landscape. #informationtechnology #bhfyp #business #education #cloud #technology #tech #engineering #networking #innovation #network #computer #internet #programming #coding #python #security #developer #java