Real-Time Data Sharing Technologies

Real-time isn’t a nice-to-have anymore. Netflix loses engagement when recommendations lag by minutes. Uber risks millions if pricing algorithms are delayed by milliseconds. By the end of 2025, 𝟯𝟬% of global data will be consumed in real time. 𝟴𝟵% of IT leaders now treat streaming as a top priority. The hard part is choosing the right stack without overbuilding. In this guide, we break down 7 enterprise-grade platforms (Apache Kafka, Spark Streaming, ApachePulsar, AWS Kinesis, Redpanda Data etc.) with: 🔹 Real-world benchmarks (e.g., Kafka’s 420 MB/sec vs. Redpanda’s 38% speed boost) 🔹 Cost traps (e.g., “free” open-source tools that cost 3x more in DevOps overhead) 🔹 Implementation stories (how Uber, Toyota, and Comcast avoided disasters) Read the full comparison (with pricing tables + pitfalls to avoid): https://lnkd.in/ekekj8ez #RealTimeAnalytics

🔍 How Do You Handle Real-Time Data Replication from SQL to NoSQL? 🤔 Imagine this: Your system has massive amounts of data in SQL Server, but you need high-performance reads for your analytics app running on a NoSQL database like MongoDB, Cassandra, or Cosmos DB. The challenge? Near real-time replication from SQL Server to the NoSQL database. 🌐 What approaches come to mind for ensuring real-time data availability while handling large datasets efficiently? 💡 Here are some options that are widely used: 1️⃣ SQL Server Integration Services (SSIS): Did you know that SSIS can use Change Data Capture (CDC) to track incremental changes from SQL Server and push them into NoSQL? It's a classic ETL tool, but can it keep up with real-time needs? 🤔 2️⃣ Azure Data Factory (ADF): What if cloud tools could do the heavy lifting for you? ADF offers CDC support and native integration with NoSQL databases like Cosmos DB and MongoDB. Is ADF the solution for handling real-time ETL pipelines? 3️⃣ Apache NiFi: What about open-source tools? NiFi enables real-time streaming of data from SQL Server to NoSQL using JDBC connectors. How well do you think NiFi fits into a high-throughput system for real-time processing? 4️⃣ Kafka Connect with JDBC Source: For those who lean towards distributed streaming platforms, Kafka Connect offers JDBC connectors to stream SQL changes in real-time and push them to NoSQL databases. Can Kafka scale seamlessly for real-time data flows in high-traffic environments? 5️⃣ Custom ETL Pipelines with Python/Spark: Feeling creative? Building custom pipelines with Python or Apache Spark gives you the flexibility to handle data ingestion and streaming just the way you want it. Could this approach give you more control in balancing real-time and batch processing? 💡 Which Approach Would You Choose? Here’s the challenge: Each of these solutions has its strengths, but which would best meet your performance, scalability, and real-time requirements? The world of real-time data pipelines is rapidly evolving, and the right choice could make all the difference in scalability and high performance for your applications. 🚀 #DataEngineering #RealTimeData #SQLtoNoSQL #Kafka #AzureDataFactory #SSIS #NiFi #DataPipelines

Zalando just dropped a fantastic blog on how they’re using Delta Sharing to power secure, real-time data exchange — and it’s a must-read for anyone still stuck in the swamp of FTP, CSV exports, or vendor lock-in. Why does this matter? Because Delta Sharing is changing the game in Retail: 🔓 Zero data copying – Share live data without duplicating or moving it ☁️ Cross-cloud and cross-platform – AWS, Azure, GCP, Snowflake, Power BI? No problem ⚡ Real-time and secure – Deliver governed access without delays 💸 Major cost savings – No need for expensive, proprietary license models Zalando’s implementation is elegant, open, and future-proof — exactly what modern data collaboration should look like. It's also why we're seeing a surge of interest from Retailers looking to both reduce their costs while drive stronger business results through real-time collaboration. Read the full post: https://lnkd.in/edTJiRgT And if your data team is still “sharing” via email attachments… it might be time to catch up. #DataSharing #OpenStandards #DeltaSharing #Databricks #ModernDataStack #DataEngineering #Zalando

🚗⛓️ Real-time governed data sharing on Databricks is enabling instant visibility across thousands of suppliers, manufacturers, and logistics partners. Instead of relying on outdated batch reports that create blind spots during disruptions, manufacturers can now share live inventory levels, production schedules, and quality metrics securely with each tier of supplier, allowing a Tier 1 supplier to instantly see real-time demand from OEMs while protecting sensitive pricing data. Or when semiconductor shortages hit or weather disrupts logistics, partners can respond within hours rather than days because they're working from the same real-time data streams.