Real-Time Data Collection Methods

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.

Polling vs Webhooks As systems grow more complex, choosing the right update strategy becomes crucial. Let me break down the two primary approaches that define real-time data synchronization: Polling: The Traditional Approach • Client periodically requests updates • Predictable but resource-intensive • Full control over request timing • Higher latency, higher costs at scale Webhooks: The Modern Push System • Server notifies client of changes • Event-driven and efficient • Near real-time updates • Better resource utilization Concrete Implementation Examples: Polling Works Best For: 1. Payment status checks 2. Order tracking systems 3. Basic monitoring tools 4. MVP implementations 5. Systems with predictable update patterns Webhooks Excel In: 1. Payment processing (PayPal) 2. Repository events (GitHub) 3. CRM integrations (Salesforce) 4. E-commerce inventory updates 5. Real-time messaging systems Key Decision Factors: - Update frequency requirements - Infrastructure complexity tolerance - Development team expertise - System scalability needs - Budget constraints Currently implementing these in production? Both approaches have their place. The key is matching the solution to your specific requirements rather than following trends.

When I'm building reports on transactional data from database, I always recommend Change Data Capture (CDC)—not just for real-time analytics, but as the best way to replicate data from databases while minimizing impact and ensuring transactional consistency. OLTP systems are built for high-speed, small transactions, heavily relying on buffer cache to maintain efficiency. Running large analytical queries directly on these systems can increase cache pressure, pushing out critical transactional data and slowing down your operational performance. CDC offers an elegant solution. Instead of running heavy queries or full-table scans, CDC works by mining the transaction log, piggy-backing on the database’s existing logging process. This keeps overhead low since the database is already logging those changes. CDC then replicates just the incremental changes, which means your OLTP system stays optimized for its core purpose: handling transactions. Some people might consider "ZeroETL" or federation, but unless there's smart caching, these approaches still put pressure on the source database. Often, CDC is still needed in the background to move the data efficiently. In my experience, CDC is more than just a method for real-time analytics—it’s the best way to replicate transactional data with minimal performance impact while ensuring data consistency across your pipeline.

Setting up trending on a BAS (Building Automation System) network to minimize bandwidth consumption while providing real-time access to data involves a strategic approach to data collection, storage, and retrieval. Here are the steps to achieve this: 1. Adjust Polling Intervals: Set polling intervals based on the criticality and variability of the data. For less critical data, use longer intervals. Event-Driven Polling: Use change-of-value (COV) polling instead of periodic polling for data points that change infrequently. This means data is sent only when a change occurs. 2. Local Aggregation: Aggregate data locally at the field controllers before sending it to the central station. This reduces the amount of data sent over the network. Hierarchical Trending: Use a hierarchical trending approach where data is collected and stored at multiple levels, such as field controllers, supervisory controllers, and the central station. 3. Data Compression: Utilize data compression techniques to reduce the size of the data being transmitted. Niagara Framework supports various data compression methods. Delta Compression: Only send the difference (delta) between the last reported value and the current value. 4. Trend Only Essential Data: Identify and trend only the most critical data points. Avoid trending points that provide little value or insight. Trend Filtering: Apply filters to trend logs to limit data to specific ranges, times, or conditions. 5. Use Historical Databases: Store historical data in an optimized database designed for time-series data. Niagara typically uses the built-in history database, but you can also integrate with external databases. Data Archiving: Implement a data archiving strategy to move older data to long-term storage, reducing the load on the primary database. 6. Data Caching: Cache data locally on the client side to reduce the need for repeated data requests. WebSockets and Push Notifications: Use WebSockets or other push notification mechanisms to provide real-time updates to clients without constant polling. 7. Segment the Network: Use VLANs or other network segmentation techniques to separate BAS traffic from other network traffic, ensuring optimal performance. Quality of Service (QoS): Implement QoS policies to prioritize BAS traffic on the network. 8. Regularly Review and Adjust Trends: Periodically review the trends and adjust configurations as needed based on the usage patterns and network performance. Monitor Network and System Performance: Continuously monitor the network and system performance to identify and address any bottlenecks or issues. By implementing these strategies, you can ensure that the trending on your BAS network is efficient in terms of bandwidth consumption while providing real-time access to critical data for end users.

Launchmetrics implemented customer-facing real-time analytics with Databricks and Estuary in days (link below). Here are some key takeaways for any real-time analytics project. For those who don’t know Launchmetrics, they help over 1,700 Fashion, Lifecycle, and Beauty businesses improve brand performance with analytics built on Databricks and Estuary. 1. Have data warehouses on your short list for real-time analytics Yes. Databricks SQL is a data warehouse on a data lake. And yes, you can implement real-time analytics on a data warehouse. Over the last decade improved query optimizers, indexing, caching, and other tricks have helped get queries down to low seconds at scale. There is still a place for high-performance analytics databases. But you should evaluate data warehouses for customer-facing or operational analytics projects. 2. Define your real-time analytics SLA Everyone’s definition of real-time analytics is different. The best approach I’ve seen is to define it based on an SLA. The most common definition I’ve seen are query performance times of 1 second or less, the "1 second SLA”. Make sure you define latency as well. The data may not need to be up to date. 3. Choose your CDC wisely Launchmetrics was replacing an existing streaming ETL vendor in part because of CDC reliability issues. It’s pretty common. Read up on CDC (links below) and evaluate carefully. For example, CDC is meant to be real-time. If you implement CDC where you extract in batch intervals, which is what most ELT technologies do, you stress out a source database. It does cause failures. SO PLEASE, evaluate CDC carefully. Identify current and future sources and destinations. Test them out as part of the evaluation. And make sure you stress test to try and break CDC. 4. Support real-time and batch You need real-time CDC and many other real-time sources. But there are plenty of batch systems, and batch loading a data warehouse can save money. Launchmetrics didn’t need real-time data yet, though they knew they would. So for now they stream from sources and batch-load Databtricks. Why? It saves them 40% on compute costs. They can go real-time with the flip of a few switches. 5. Measure productivity Yes. Launchmetrics saved money. But productivity and time to production was much more important. Launchmetrics implemented Estuary in days. They now add new features in hours. Pick use cases for your POC that measure both. 6. Evaluate support and flexibility Why do companies choose startups? It’s not just for better tech, productivity, or time to production. Some startups are more flexible, deliver new features faster, or have better support. Every Estuary customer I’ve talked to has listed great support as one of the reasons for choosing Estuary. Many also mentioned poor reliability and support were reasons they replaced their previous ELT/ETL vendor. #realtimeanalytics #dataengineering #streamingETL

When I first worked on data systems, things were simple—but as data sources multiplied, I realised why integration needs different patterns. A single database was usually enough, and integrating data from one or two sources wasn’t challenging. However, as businesses expanded and started collecting information from diverse channels—social media, IoT devices, and customer touchpoints—things became far more complex. I distinctly recall a project where the sheer variety of data sources overwhelmed the traditional methods we relied on. It was clear that a new approach was needed. Data integration has evolved to keep pace with these growing complexities. Today, integration isn’t a one-size-fits-all process. Instead, it requires choosing the correct pattern for the exemplary scenario. Each pattern addresses specific challenges, making data management more effective and scalable. Here are the key data integration patterns that shape modern solutions: ↳ 𝐄𝐓𝐋 (𝐄𝐱𝐭𝐫𝐚𝐜𝐭, 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦, 𝐋𝐨𝐚𝐝): The traditional approach, transforming data before loading it into target systems.   ↳ 𝐄𝐋𝐓 (𝐄𝐱𝐭𝐫𝐚𝐜𝐭, 𝐋𝐨𝐚𝐝, 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦): A modern take, ideal for leveraging the power of data lakes by transforming data after loading.   ↳ 𝐂𝐃𝐂 (𝐂𝐡𝐚𝐧𝐠𝐞 𝐃𝐚𝐭𝐚 𝐂𝐚𝐩𝐭𝐮𝐫𝐞): Captures real-time changes in source systems for immediate updates.   ↳ 𝐃𝐚𝐭𝐚 𝐅𝐞𝐝𝐞𝐫𝐚𝐭𝐢𝐨𝐧: Offers a unified view of data across systems without moving it.   ↳ 𝐃𝐚𝐭𝐚 𝐕𝐢𝐫𝐭𝐮𝐚𝐥𝐢𝐬𝐚𝐭𝐢𝐨𝐧: Allows real-time querying of data from multiple sources without duplication.   ↳ 𝐃𝐚𝐭𝐚 𝐒𝐲𝐧𝐜𝐡𝐫𝐨𝐧𝐢𝐬𝐚𝐭𝐢𝐨𝐧: Keeps systems in sync by regularly updating data across platforms.   ↳ 𝐃𝐚𝐭𝐚 𝐑𝐞𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧: Ensures redundancy and backup by copying data across systems.   ↳ 𝐏𝐮𝐛𝐥𝐢𝐬𝐡/𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞: Efficiently updates interested subscribers when specific data changes.   ↳ 𝐑𝐞𝐪𝐮𝐞𝐬𝐭/𝐑𝐞𝐩𝐥𝐲: Ensures data or services are delivered on-demand. The optimal pattern can simplify processes, reduce inefficiencies, and unlock the full potential of data. Whether you’re dealing with real-time updates, unified views, or system synchronisation, there’s a pattern designed for the task. Which of these patterns resonates most with your experiences? Have you found any of these particularly effective? Cheers! Deepak Bhardwaj