Vishal Panchal’s Post

Q: How to design a data lake? Ans: Designing a data lake requires a strategic approach to manage vast amounts of structured and unstructured data efficiently. A well-planned data lake facilitates advanced analytics and empowers decision-making by centralizing data storage. Here’s a streamlined guide: Define Objectives: Start by understanding your goals, the types of data you will store, and who will use the data. This clarity helps in tailoring the architecture and governance to meet your needs. Select the Right Technology: Choose a technology stack that supports scalability, diverse data types, and is cost-effective. Cloud platforms like AWS, Google Cloud, and Azure are popular for their robust data lake solutions, offering scalability and security. Ensure Scalability: Design your data lake to scale horizontally with data growth. This requires a flexible architecture that can adapt to new data types and sources without major overhauls. Data Governance: Implement strong data governance to maintain data quality and security. This includes setting up policies for data access, cataloging, and lineage to ensure data is manageable and compliant. Security Measures: Security is critical. Implement encryption, access controls, and audit logs to protect data and comply with regulations like GDPR and HIPAA. Data Ingestion: Develop a strategy for ingesting data, both batch and real-time, ensuring the system can handle various data velocities and maintain the integrity of raw data for analysis. Organize Data: Organize your data lake into zones (e.g., raw, curated, and consumption layers) to support different use cases and simplify data management. Enable Analytics: Provide tools and interfaces for data exploration and analysis. Integration with analytics and BI tools is essential for deriving insights from your data. Monitor and Optimize: Continuously monitor data quality and usage. Be prepared to adjust your strategy to maintain efficiency and alignment with business objectives. By following these steps, you can design a data lake that not only consolidates your data but also transforms it into a valuable asset for your organization, fostering innovation and informed decision-making. Good Luck! 👍 Let me know how it goes 👊 https://www.TBiOS.com

With the #sapanalyticscloud 2024 Q2 QRC released to all customers over the weekend, here are some key highlights to look out for: 1. Model Replacement is now supported for models that have the same data source type and same number and type of structure dimensions. Learn more here: https://lnkd.in/gKSPKUNB 2. We've released Presentation Mode which aims to bring the main features that are available with the traditional Digital Boardroom directly into the Optimized Experience. Learn more here: https://lnkd.in/gsUBsBtT 3. As technology evolves and user expectations change, we have decided to deprecate the Classic Experience and other non-strategic features and replace it with the Optimized Experience. Learn more here: https://lnkd.in/g3E76ucZ #sapanalyticscloud #sac #productguidance #keyupdates

HAPI FHIR is an open-source implementation of the HL7 FHIR (Fast Healthcare Interoperability Resources) standard, designed to facilitate healthcare data exchange. The image illustrates the architecture and data flow of a healthcare application using HAPI FHIR, highlighting its key components and interactions. 1. Your Application:   - This represents the client application that interacts with the HAPI FHIR server.   - It converts model objects into HAPI FHIR objects, which are then parsed into XML or JSON format. 2. HAPI FHIR Server:   - Acts as the central hub for processing and storing FHIR resources.   - It receives requests from both internal applications and external FHIR clients.   - The server parses incoming data (XML/JSON) into FHIR resources and stores them in a database. 3. External FHIR Clients:   - These are third-party applications or systems that interact with the HAPI FHIR server.   - They send and receive FHIR resources using standard HTTP protocols. 4. External Server:   - Represents other servers that the HAPI FHIR server communicates with.   - Data is exchanged in XML/JSON format, ensuring interoperability between different systems. 💢 Data Exchange Process 💥 Model to FHIR Conversion:  - Your application converts its internal data models into HAPI FHIR objects.  - These objects are then serialized into XML or JSON format using the HAPI FHIR parser. 💥 HTTP Communication:  - The serialized data is sent to the HAPI FHIR server via HTTP.  - The server processes the incoming data, converting it back into FHIR resources and storing it in the database. 💥 Interoperability:  - The HAPI FHIR server can communicate with external FHIR clients and servers.  - This ensures seamless data exchange and integration across different healthcare systems. 💢 Benefits of HAPI FHIR 💥 Interoperability:  - HAPI FHIR enables different healthcare applications to communicate and exchange data efficiently.  - It supports the FHIR standard, which is widely adopted in the healthcare industry. 💥 Open-Source:  - Being open-source, HAPI FHIR is freely available for use and modification.  - It has a strong community of developers contributing to its continuous improvement. 💥 Flexibility:  - HAPI FHIR can be integrated into various healthcare applications, from electronic health records (EHR) systems to mobile health apps.  - It supports multiple data formats (XML, JSON) and communication protocols (HTTP). 💢 Conclusion HAPI FHIR is a powerful tool for healthcare data interoperability, providing a robust framework for exchanging FHIR resources. Its open-source nature and flexibility make it an ideal choice for developers looking to integrate FHIR standards into their applications. The architecture and data flow illustrated in the image demonstrate how HAPI FHIR facilitates seamless communication between different healthcare systems, ensuring efficient and accurate data exchange.

Enterprise Application Integration (EAI): A Modern Necessity For Today's Enterprises Modern businesses require real-time data to make timely and informed decisions. EAI allows applications to exchange data utilizing MoM, APIs and Database Integration, so that changes in one application are instantly shared across all integrated systems, allowing stakeholders to make decisions timely. Uniting databases and workflows associated with business applications is important to ensure data consistency, accuracy, and alignment with organizational objectives. Enterprise Application Integration is vital for organizations due to the complexity of modern digital architecture. At NeuralKode, our experts use all vital methods to ensure effective Enterprise Application Integrations from Message-oriented Middleware (MoM) to Database Integration. We guarantee seamless data exchange and system interoperability for optimized business operations. Visit NeuralKode.ai to empower your enterprise with our sophisticated EAI solutions. #business #enterprise #EAI #APIs #technology #middleware #data #database #integration #NeuralKode #workflowmanagement #workflowoptimization

Please share few notes for Healthcare HL7, FHIR.

The Delivery of Captured Data functionality is a crucial step in the Change Data Capture (CDC) process, which involves providing the captured data to appropriate destinations for analysis, further processing, or storage. These destinations may include data storage systems, data warehouses, data lakes, real-time analytics systems, business applications, or other target systems. *Get our free plan and isolate resources, have high availability and much more! Check our website for other contidions and plans. #API #JoinAPI #Freeplans #Technology #Solutions #Management #APImanager #APIs #SAAS #EventDriven #StreamDataLake #CDC #Realtime #Delivery

Engineering 270 Requests to Obtain 271 Information in Eligibility Verification Systems: A Technical Exploration Abstract: The complexity of healthcare eligibility verification often revolves around the use of X12 EDI standards, particularly the 270/271 transaction set. The challenge lies in obtaining complete and actionable eligibility information, particularly when the payer’s response mechanisms and data fidelity introduce gaps. This essay delves into advanced engineering methods to design, structure, and implement 270 request systems capable of eliciting nuanced 271 responses that overcome typical implementation limitations. It emphasizes strategies for filling in missing information, optimizing query configurations, and leveraging auxiliary tools to achieve a high degree of fidelity in eligibility responses. 1. Introduction Eligibility verification is the backbone of revenue cycle management in healthcare. The X12 270/271 transaction set, a widely used standard for electronic data interchange (EDI), underpins real-time exchange of eligibility and benefits information between providers and payers. However, simple implementations of 270 requests often fail to elicit complete 271 responses due to limitations in payer configurations, data completeness, and the inherent rigidity of the EDI schema. To address this, advanced techniques are necessary to engineer requests that not only comply with the 270 standard but also maximize the likelihood of receiving robust 271 responses. This essay explores these techniques, focusing on error mitigation, iterative query construction, and response validation. 2. The Anatomy of the 270/271 Transaction The 270/271 transaction pair provides a standardized mechanism to query eligibility: - The 270 Request: Contains details about the patient, provider, and the requested information scope. - The 271 Response: Returns details about the patient’s eligibility, including benefit coverage, copayments, deductibles, and plan-specific restrictions. Despite the formal structure, real-world variability in payer implementations creates challenges: 1. Incomplete Data Mapping: Payers may provide partial or obfuscated responses. 2. Overly Broad or Narrow Queries: Misaligned request scopes can lead to inadequate data retrieval. 3. Transaction Limits: Systems may cap the amount of data returned in a single 271 response. To address these issues, it is necessary to refine the mechanics of 270 requests beyond standard practices. 3. Key Challenges in Extracting 271 Information 3.1 Variability in Payer Implementations Continued (see bio)…

Real-time data requirements call for a rethinking of our #IntegrationArchitecture. With the additional options provided by #EventDrivenArchitecture, we now have the ability to cover more and more ground. While the old integrations still function, they are no longer 100% sufficient to meet today’s #real real-time data needs across all systems. (Act whenever it happens).. In his must-read article, Shawn McAllister offers insights into the dimensions of this total integrated approach. I would confidently say that achieving this is entirely possible with the solutions currently available on the market, so why not apply that for your transformation projects? #EventDrivenArchitecture #IntegrationArchitecture #TechnicalArchitect #Solace #AdvancedEventMesh #SAPBTP #BTP #IntegrationSuite #SAPEventMesh #SAPAdvancedEventMesh https://lnkd.in/e44dwiv2

The Change Data Capture (CDC) Capture functionality is a critical part of the process, allowing for the identification and recording of data changes as they occur in database systems or other data sources. *Get our free plan and isolate resources, have high availability and much more! Check our website for other contidions and plans. #API #JoinAPI #Freeplans #Technology #Soltions #Management #APImanager #APIs #SAAS #EventDriven #StreamDataLake #CDC

It feels like heresy – but sorry, I have to say it! 🚀 #EnterpriseArchitecture (EA) Methods do not work with #PLM! Do You Know why? Here is the answer. 🔍 Have you also been advised by expensive strategy or methodology consultants who throw around grandiose names like Enterprise Architecture, #TOGAF, #ArchiMate? Have you modeled elaborate diagrams with them, such as process maps or capability maps that ended up being useless? 🎨📊 Were they perhaps also methodological dogmatists (these are the worst 😉) and didn't allow any deviations or other methods? 😤 Well, save your money!  💸 💰 For PLM and also for large parts of ERP, the EA methods have huge blind spots! 🚫👀 This doesn't mean that the methodology and the mindset behind it aren't helpful. It's just that if you've painstakingly modeled everything, you still don't understand your PLM case and can't adequately delineate the content of your projects. The same chaos that prevails in the majority of PLM projects is therefore very likely to occur. ⚠️🔥 Thus, I recommend limiting the use of EA to the bare minimum. ✅ Well, why do I see it that way? 🤔 When we talk about PLM, it is essentially about explicating the information network of a company and mapping it cleanly in existing “standard” IT-Systems on the market. It is necessary to explain the business objects (part, document, design, material ...), their time behavior and the semantics of their aggregation in different structure types (#EBOM, #MBOM, TOS, classification trees ...). 🏗️📚 This is the core of the first discussion! Then you can assign use cases, user stories, epics (you name it) and draw capability maps in all their beauty. 🎯📝 Well, most EA methods completely dispense with modeling the semantics of the information! Many of you will probably counter that data modeling and data models are present in EA models. 📊💡 This is true, but the problem is that they are designed to prepare the implementation of a completely new data model and not to penetrate existing information structures from their meaningful perspective. 🧩🔍 Thats my best so far view. What do you think? Now I'm really looking forward to the discussion and your views on this. 💬🤓 And by the way, with our STZ-RIM Reshape Information Management Company Cubing (https://lnkd.in/eNFmFQnR) we have developed a method that avoids blind spots in PLM and ERP implementations. 🌟🔧 If you are interested, get in touch today here👉: https://lnkd.in/eei3UYdm #EnterpriseArchitecture #BusinessStrategy #DigitalTransformation #DataModeling #ITStrategy #ProjectManagement