Real-Time Financial Data Integration

🔗 SAP Datasphere & Apache Kafka: The Future of ERP Integration SAP ERP is the backbone of enterprises worldwide, but integrating it with other platforms, databases, and APIs is a major challenge. 🚀 This is where SAP Datasphere and Apache Kafka come in—together, they create a scalable, real-time, and open data fabric for seamless ERP connectivity. Key Takeaways: ✅ SAP Datasphere – A next-gen cloud-based data platform for SAP ERP integration ✅ Apache Kafka – A real-time data streaming powerhouse for scalable, event-driven architectures ✅ Hybrid & Multi-Cloud Ready – Connect on-prem SAP ECC & S/4HANA with cloud-native applications ✅ Seamless Data Flow – Synchronize real-time, batch, and request-response interfaces Why Apache Kafka for SAP Integration? • Real-time event streaming for operational & analytical workloads • Decoupling systems for better flexibility and scalability • Transaction support & exactly-once semantics for ERP-critical processes • Built-in integration with SAP Datasphere, Snowflake, Databricks, and other modern platforms Confluent & SAP: A Strategic Partnership Confluent is now available in the SAP Store, offering fully managed Kafka-powered data streaming. Enterprises can now build event-driven architectures for ERP modernization, just-in-time operations, predictive analytics, and more. 📌 How does your organization handle SAP integration today? Are you exploring real-time event-driven architectures? Let’s discuss in the comments! 🔗 Read the full blog post here: https://lnkd.in/eSd-ZKAY #DataStreaming #SAP #Kafka #S4HANA #ERPIntegration #EventDriven #Cloud #RealTimeData #ApacheKafka #Confluent

Google ADK + Zerodha MCP + LLMs: Autonomous portfolio analysis in action. Modern financial analysis is rapidly moving toward automation and agentic workflows. Integrating large language models (LLMs) with real-time financial data unlocks not just powerful insights but also new ways of interacting with portfolio information. This experiment brings together secure browser-based authentication, live data retrieval from Zerodha’s MCP, and LLM-driven risk and performance analytics—all orchestrated autonomously. This is a starter kit to get you going, but it can be extended to support sophisticated, fully automated quantitative models—simply by crafting effective prompts. I've made the experiment available on my GitHub repo. Please feel free to explore or adapt it for your own agentic financial analysis workflows. Code and documentation: https://lnkd.in/gme977GG #agenticai #mcp

Financial platform Outfund launched over 10 new commerce integrations in just a few months — builds that once took its engineering team 960 hours. We asked Outfund's COO, Alan Lin, to find out how the team pulled it off. Here's what we learned 👇 1️⃣ Challenge: To efficiently offer financing, Outfund needs to evaluate applicants' real-time financial and sales data from QuickBooks, Shopify, and other commerce platforms. However, those integrations are notoriously difficult to build and maintain. That's when Outfund found Rutter. 2️⃣ Solution: With Rutter's Commerce API, Outfund eliminated the need for custom integrations with banking and commerce platforms — without engineering effort. 3️⃣ Results: ✅ 10+ integrations shipped ✅ 960 hours of engineering saved ✅ Key integrations include Woo and Amazon To learn more, read the full case study: https://lnkd.in/edqDakxF

Monthly financial reports are officially outdated. In 2025, if you’re not using a real-time dashboard, you’re already behind. Think about it, would you drive a car using only the rearview mirror? That’s exactly what waiting 30 days for financial insights feels like. By the time the report lands on your desk, the problems have already grown. This year, the game is changing. Real-time financial dashboards aren’t just another “tech trend.” They’re becoming the standard for smart, agile businesses. Here’s why: ✅ Instant clarity: See cash flow, expenses, and revenue daily, not weeks later. ✅ Faster action: Spot late payments or overspending before they snowball. ✅ Stronger trust: Clients and teams feel confident when you can share live insights, not old snapshots. At FinAcc Global, we’ve helped companies plug tools like Xero and QuickBooks into live dashboards and the result? Decision-making speed jumped by almost 50%. But here’s the truth: a dashboard is only as good as the data it’s built on. Messy books and wrong KPIs mean garbage in, garbage out. 2025 is the year to stop reacting late and start steering your business in real time. Be honest : are you still waiting for month-end reports, or have you made the switch to real-time? #FinanceTrends #RealTimeAccounting #FinancialDashboards #SmartBusiness #AccountingTech

𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐋𝐢𝐧𝐪-𝐄𝐦𝐛𝐞𝐝-𝐅𝐢𝐧𝐚𝐧𝐜𝐞, 𝐨𝐧𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐭𝐨𝐩-𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥𝐬 𝐢𝐧 𝐟𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐝𝐚𝐭𝐚 𝐫𝐞𝐭𝐫𝐢𝐞𝐯𝐚𝐥 As an engineer in the hedge fund industry, where 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐢𝐬 𝐜𝐨𝐧𝐬𝐭𝐚𝐧𝐭𝐥𝐲 𝐞𝐯𝐨𝐥𝐯𝐢𝐧𝐠 𝐚𝐧𝐝 𝐦𝐮𝐬𝐭 𝐛𝐞 𝐮𝐩𝐝𝐚𝐭𝐞𝐝 𝐢𝐧 𝐫𝐞𝐚𝐥 𝐭𝐢𝐦𝐞, we understand the importance of retrieving relevant insights both quickly and accurately. This is why we've developed 𝐋𝐢𝐧𝐪-𝐄𝐦𝐛𝐞𝐝-𝐅𝐢𝐧𝐚𝐧𝐜𝐞 as the core embedding model powering our Retrieval Augmented Generation (RAG) capabilities. RAG is critical for pulling in real-time data and reflecting the latest financial information, ensuring hedge fund professionals can act on up-to-date and precise insights. 𝐋𝐢𝐧𝐪-𝐄𝐦𝐛𝐞𝐝-𝐅𝐢𝐧𝐚𝐧𝐜𝐞 delivers 𝟐𝟎-𝟑𝟎% 𝐡𝐢𝐠𝐡𝐞𝐫 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 compared to general-purpose models like OpenAI and Cohere, particularly excelling in multi-step queries and large-scale document searches. It’s specifically designed for handling complex financial documents such as earnings reports and public filings, offering unmatched precision. This model will also be central to our upcoming 𝐀𝐈-𝐝𝐫𝐢𝐯𝐞𝐧 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐒𝐲𝐬𝐭𝐞𝐦 (𝐑𝐌𝐒), helping hedge funds efficiently manage and search both internal and external data. If you're working on an LLM-powered RMS or curious about how this technology can transform your research workflows, feel free to reach out. For more details, check out the blog: https://lnkd.in/ghsFd7vq

Top 5 reasons why financial firms are still using batch systems in their Risk Management Systems 😕😕—and how to fix it 👇 Many trading firms and financial institutions still rely on batching systems to calculate risk. But why? It’s not because they don’t see the limitations—it’s due to complex legacy systems and the challenges of upgrading. Here’s why they’re stuck with batching, and what they can do to change it: 1️⃣ Legacy Systems Are Too Deeply Embedded Many firms have legacy systems that are so linked to operations that replacing them seems impossible without disrupting the whole business. 💡 Add Real-Time Layers on Top of Your Existing System Instead of overhauling everything, add modular real-time layers to your current system. This lets you move some functions to real-time processing without breaking the system. Over time, you can transition more areas without major disruptions. 2️⃣ Poor Connectivity Between Internal and External Systems Firms struggle to connect their risk management systems with market data or internal tools like OMS/EMS. This makes real-time updates difficult. 💡 Use APIs for Real-Time Data Integration Replace batch data with API-driven integrations to get real-time data from market feeds and internal systems. This keeps your risk team updated and enables quicker reactions to market changes. 3️⃣ Fear of Disruption and High Costs Switching to a real-time system seems expensive and risky, and firms worry about operational interruptions. 💡 Take a Phased Approach Start by moving critical risk functions like volatility-sensitive calculations or real-time exposure monitoring to real-time. This reduces disruption and shows the value of the shift. Gradually, expand real-time processing to other areas. 4️⃣ Overwhelmed by Too Much Data The sheer volume of data—market data, trades, and external feeds—can overwhelm batch systems designed for simpler data flows. 💡 Use Stream Processing Stream processing allows your system to handle data continuously, keeping your risk models updated in real-time and helping you stay on top of fast-moving markets. 5️⃣ Performance Bottlenecks During High-Volume Trading Batch systems may handle regular trading, but they slow down when trade volumes spike, leading to delays in risk calculations. 💡 Use Adaptive Scaling for Real-Time Risk To avoid slowdowns during market volatility, use adaptive scaling with cloud or distributed systems. This helps your RMS adjust to high volumes, keeping risk calculations fast and accurate even during market stress. If you have other interesting ideas, I’d love to hear them and discuss more. #RiskManagement #risk #RMS #financial #markets

Quantitative Finance: Wolfram Language + Interactive Brokers TWS In our latest video, we showcase how the Wolfram Language is transforming quantitative finance through powerful API integrations and data connectivity. Key highlights: - Seamless connection between the Equities Entity Store and Interactive Brokers' TWS platform - Real-time data retrieval, order execution, and P&L tracking - Unparalleled flexibility for building sophisticated investment models The Wolfram Language offers a unified platform for conceiving, developing, and deploying cutting-edge investment research. Its robust computational abilities and extensive financial knowledge base empower quants to iterate faster and create more advanced models. What sets it apart? - Instant deployment through APIs and cloud integrations - Interoperability with languages like Python and C++ - Handling of diverse data formats and protocols For quantitative analysts seeking to elevate their research capabilities, this integration is nothing short of a game-changer. Stay tuned for our upcoming series, where we'll demonstrate an end-to-end quantitative research workflow powered by the Equities Entity Store and the Wolfram Language. Are you ready to take your quant research to the next level? Watch the full video to learn more! More information on the Equities Entity Store: https://lnkd.in/epg-5wwM #QuantitativeFinance #WolframLanguage #APIIntegration #FinTech https://lnkd.in/dGsxst-M