AI Capabilities For Streaming Data Solutions

This concept is the reason you can track your Uber ride in real time, detect credit card fraud within milliseconds, and get instant stock price updates.  At the heart of these modern distributed systems is stream processing—a framework built to handle continuous flows of data and process it as it arrives.     Stream processing is a method for analyzing and acting on real-time data streams. Instead of waiting for data to be stored in batches, it processes data as soon as it’s generated making distributed systems faster, more adaptive, and responsive.  Think of it as running analytics on data in motion rather than data at rest.  ► How Does It Work?  Imagine you’re building a system to detect unusual traffic spikes for a ride-sharing app:  1. Ingest Data: Events like user logins, driver locations, and ride requests continuously flow in.   2. Process Events: Real-time rules (e.g., surge pricing triggers) analyze incoming data.   3. React: Notifications or updates are sent instantly—before the data ever lands in storage.  Example Tools:   - Kafka Streams for distributed data pipelines.   - Apache Flink for stateful computations like aggregations or pattern detection.   - Google Cloud Dataflow for real-time streaming analytics on the cloud.  ► Key Applications of Stream Processing  - Fraud Detection: Credit card transactions flagged in milliseconds based on suspicious patterns.   - IoT Monitoring: Sensor data processed continuously for alerts on machinery failures.   - Real-Time Recommendations: E-commerce suggestions based on live customer actions.   - Financial Analytics: Algorithmic trading decisions based on real-time market conditions.   - Log Monitoring: IT systems detecting anomalies and failures as logs stream in.  ► Stream vs. Batch Processing: Why Choose Stream?   - Batch Processing: Processes data in chunks—useful for reporting and historical analysis.   - Stream Processing: Processes data continuously—critical for real-time actions and time-sensitive decisions.  Example:   - Batch: Generating monthly sales reports.   - Stream: Detecting fraud within seconds during an online payment.  ► The Tradeoffs of Real-Time Processing   - Consistency vs. Availability: Real-time systems often prioritize availability and low latency over strict consistency (CAP theorem).  - State Management Challenges: Systems like Flink offer tools for stateful processing, ensuring accurate results despite failures or delays.  - Scaling Complexity: Distributed systems must handle varying loads without sacrificing speed, requiring robust partitioning strategies.  As systems become more interconnected and data-driven, you can no longer afford to wait for insights. Stream processing powers everything from self-driving cars to predictive maintenance turning raw data into action in milliseconds.  It’s all about making smarter decisions in real-time.

SAP BTP Integration Suite with AI: The Next Evolution of SAP CPI SAP has enhanced its Cloud Platform Integration (CPI) capabilities under the SAP Business Technology Platform (BTP) Integration Suite, now infused with AI and automation for smarter, self-healing integrations. Key AI-Powered Features in SAP BTP Integration Suite 1. AI-Assisted Integration Flows (SAP AI Core & Joule) Smart Mapping: AI suggests field mappings between systems (e.g., SAP S/4HANA ↔ Salesforce) by learning from past integrations. Anomaly Detection: AI monitors message processing and flags unusual patterns (e.g., sudden API failures or data mismatches). Self-Healing: Automatically retries failed calls or suggests fixes (e.g., OAuth token renewal). Example: An EDI 850 (Purchase Order) from a retailer has inconsistent product codes. AI recommends corrections based on historical data before forwarding to SAP S/4HANA. 2. Generative AI for Accelerated Development (Joule + OpenAI Integration) Natural Language to Integration Flow: Describe an integration in plain text (e.g., "Sync customer data from Salesforce to SAP every hour"), and Joule generates a draft CPI flow. Auto-Generated Documentation: AI creates integration specs and test cases. Example: A developer types: "Create a real-time API that checks credit risk before approving orders." Joule proposes: A webhook trigger from SAP Commerce Cloud. A call to a credit-scoring API. A conditional router in CPI to approve/reject orders. 3. Event-Driven AI Integrations (SAP Event Mesh + AI) Smart Event Filtering: AI processes high-volume event streams (e.g., IoT sensor data) and forwards only relevant events to SAP systems. Predictive Triggers: AI predicts when to initiate integrations (e.g., auto-replenish inventory before stockouts). Example: A logistics company uses SAP Event Mesh to track shipment delays. AI analyzes weather + traffic data to reroute shipments proactively. 4. SAP Graph + AI for Context-Aware Integrations Unified Data Access: SAP Graph provides a single API endpoint for cross-SAP data (S/4HANA, SuccessFactors, Ariba). AI Adds Context: Example: When fetching a customer record, AI automatically enriches it with related sales orders and support tickets. Real-World Use Case: AI-Powered Invoice Processing Scenario: Automatically validate supplier invoices against POs and contracts. AI Extraction: Invoice arrives via SAP Document Information Extraction (DocAI). AI parses unstructured PDFs into structured data. Smart Matching: CPI calls SAP AI Core to compare invoice line items with SAP Ariba POs. AI flags discrepancies (e.g., price changes, missing items). Self-Healing Workflow: If discrepancies are minor, AI auto-approves. If major, CPI routes to a SAP Build Workflow for human review. Result: 70% faster invoice processing with fewer errors.

🚀 Big AI updates from Current Bengaluru today! Apache Flink is getting some major upgrades in Confluent Cloud that make real-time AI way easier: 🔹 Run AI models directly in Flink –Bring your model and start making predictions in real time. No need to host externally. 🔹 Search across vector databases – Easily pull in data from places like Pinecone, Weaviate, and Elasticsearch as well as your real-time streams. 🔹 Built-in AI functions – Flink now has built-in tools for forecasting and anomaly detection, so you can spot trends and outliers as the data flows in. Additionally, Tableflow for Iceberg is now GA, and Delta Lake is in early access, making it easier to connect real-time data streams to your AI workflows without managing ETL pipelines. 💡 Why this matters – AI needs fresh, fast data. These updates make it way easier to run models, retrieve data, and build real-time AI apps without stitching together a dozen different tools. Exciting times for AI + streaming! #Current2025 #Confluent #ApacheFlink #AI #RealTimeData #StreamingAI

Amidst the excitement about GenAI chatbots, let's not forget the need for traditional elements like streaming data pipelines to provide real-time facts. My latest blog, published on BARC partner Eckerson Group's website, defines streaming data, explain why companies need it, and explores how streaming data pipelines feed multi-faceted GenAI applications. Thank you to our sponsor Striim. This excerpt describes a fictional case study for a container shipping company, WeMoveIt. Feedback welcome! Streaming data pipelines offer a lightweight method of manipulating and delivering myriad events to the data stores that underlie GenAI language models. Unlike legacy pipelines that process batch loads, streaming pipelines can mix and match different sequences of events before arriving at the target. This results in granular, sophisticated and real-time views of fast-changing business conditions. And those views are critical to the success of retrieval-augmented generation (RAG) workflows that retrieve relevant information and use it to augment user prompts so the GenAI language model can respond accurately. Case study As climate change leads to more disruptive storms, WeMoveIt’s customers have started demanding real-time shipment tracking and arrival estimates to help them adjust supply chains in response. WeMoveIt’s data team implements a new chatbot-enabled routing application, assisted by GenAI, RAG and machine learning. The workflow begins with event sources. These include an SAP database that stores cargo records, a proprietary SaaS application that handles customer orders, an Elasticsearch log store that tracks RFID tag scanners and a third-party service that emails hourly weather updates for shipment routes. WeMoveIt’s data team configures a streaming pipeline to capture real-time events from these diverse sources, then reformat, filter and deliver them to Microsoft Azure Synapse. This streaming data pipeline complements the batch pipeline that transforms static documents into embeddings within a vector database. Together, these pipelines support RAG and GenAI. The consolidated tables and files on Synapse become the foundation for RAG. When a customer enters her natural-language request for a shipment update, or a fleet manager requests a re-routing, the application retrieves the appropriate records and injects them into the user prompt. Armed with this latest information, the GenAI language model within the application can have a responsible and reliable conversation with the user. The retrieval workflow also supports a predictive ML model that analyzes weather indicators to anticipate delays, notify customers and suggest alternative routes. Enriched by streaming data, WeMoveIt’s AI initiative improves customer satisfaction and efficiency. #data #ai #genai #streamingdata #realtimedata Dianna Spring Allen Skees Stephanie McCarthy (Allen) Sam Wortman John Kutay Bradley Flemming Victoria Davis Elisabeth Dornbrach