SAM S. G. Fattahpour’s Post

In this blog, I tired to explain the basics of Apache Kafka and how to use Apache Kafka in Node.js. A small tool to send weekly digest based on user activity. As this is a simple explanation, the possibilities are immense, one can use Kafka to handle much more than this. Kafka has become a fundamental component in building modern data-driven applications, powering use cases ranging from real-time analytics, log aggregation, and event sourcing to Internet of Things (IoT) data processing and beyond. I Hope you may find it helpful! #nodejs #kafka #apacheKafka #dataEngineering #nodemailer #mongodb #cronjobs #emailmarketing #microservices

The latest update for #Honeycombio includes "#OpenTelemetry Best Practices #3: Data Prep and Cleansing" and "Investigating Mysterious Kafka Broker I/O When Using Confluent Tiered Storage". #observability #monitoring https://lnkd.in/dBXCgkP

Data Pipelines: API Integration with Airflow & PostgreSQL https://lnkd.in/gwS9-HAU #AirflowPipelines #APIIntegration #PostgresConnectivity #RealTimeData #ETLwithAirflow #JSONtoCSV #DataIngestion

🚀𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝘄𝗶𝘁𝗵 𝗔𝗽𝗮𝗰𝗵𝗲 𝗙𝗹𝗶𝗻𝗸 🚀 𝘈𝘳𝘦 𝘺𝘰𝘶 𝘳𝘦𝘢𝘥𝘺 𝘵𝘰 𝘶𝘯𝘭𝘰𝘤𝘬 𝘳𝘦𝘢𝘭-𝘵𝘪𝘮𝘦 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘴 𝘸𝘪𝘵𝘩 𝘈𝘱𝘢𝘤𝘩𝘦 𝘍𝘭𝘪𝘯𝘬? My latest article on Medium dives into how to harness 𝗔𝗽𝗮𝗰𝗵𝗲 𝗙𝗹𝗶𝗻𝗸 for high-impact, real-time data analytics. Whether you’re working on social media metrics, IoT data streams, or financial transactions, Flink's streaming-first approach can transform your analytics pipeline. Discover how to:  ✅ Build a scalable, real-time data pipeline ✅ Process millions of events per second ✅ Turn raw data into instant insights Check it out to take your data strategy to the next level! 🔥 #ApacheFlink #DataAnalytics #RealTimeData #BigData #DataEngineering #StreamingAnalytics #LinkedInTech

🚀 Mastering Data in Motion with Apache Kafka 🌐 In today’s digital-first world, real-time data streaming isn’t just an advantage — it’s a necessity. Apache Kafka, the backbone of modern data pipelines, empowers organizations to: 🔄 Streamline Data Integration: Bridge diverse systems with seamless, real-time data flow. ⚙️ Event-Driven Architectures: Enable microservices to respond to events in milliseconds. 📈 Scalable Performance: Process millions of events per second with ease. 🔍 Advanced Analytics: Fuel AI/ML models with real-time data streams for smarter insights. 💡 With features like exactly-once semantics, Kafka Streams API, and distributed fault tolerance, Kafka is revolutionizing how businesses build resilient and scalable systems. From IoT to financial transactions, Apache Kafka is the heartbeat of innovation across industries. Is your business ready to harness its power? Let’s discuss how streaming-first architecture can transform your data strategy. 🚀 #ApacheKafka #DataStreaming #EventDrivenArchitecture #BigData #ScalableSolutions #RealTimeInnovation

Unlocking the Potential of TimescaleDB for Time-Series Data 🕒 Ayush Shrivastava from our team has written an insightful blog exploring the capabilities of TimescaleDB, a powerful PostgreSQL-based solution for handling time-series data efficiently. Highlights from the blog: Hypertables: Simplifies data partitioning, enabling scalability and performance for large datasets. Continuous Aggregation: Automatically precomputes aggregates for real-time and historical analysis. Partitioning: Streamlines query performance and data management through automatic time/space-based partitioning. 🔑 Key Takeaways: 1️⃣ TimescaleDB outperforms other databases like InfluxDB in write speed and query execution for time-series data. 2️⃣ Optimization techniques like parallel processing and compressed storage enhance performance while managing resource usage. 3️⃣ Ideal for applications in IoT, monitoring, financial data, and more. 📊 If you're working with time-series data, this blog is a must-read to harness the full potential of TimescaleDB. Check out the full blog for detailed insights and performance benchmarks! https://lnkd.in/gwcw3C8j #TimeSeries #DataEngineering #TimescaleDB

If you’re managing time-series data, you know the struggle: balancing performance, scalability, and simplicity. For me, the combination of Timescale and PostgreSQL has been a game-changer. With Timescale, you get the best of both worlds: the power of PostgreSQL’s relational database and the efficiency of a purpose-built time-series database. Whether it's IoT, financial data, or monitoring systems, this combo handles it all like a pro. But don't take my word for it—want to know how Timescale compares to other solutions? I found a great resource that lays it all out. From features to performance and pricing, this guide gives you everything you need to make an informed choice. Check out the Timescale alternatives here: https://lnkd.in/dHFrxZvC #PostgreSQL #Timescale #TimeSeriesData

Alternatives to Timescale

The latest update for #InfluxDB includes "Product Update: #HelmCharts for InfluxDB Clustered" and "Data Visualization Tools For InfluxDB: Grafana, Tableau, and Apache Superset". #monitoring #devops #timeseries https://lnkd.in/dchWaMp

#Logs often take up the majority of a company's #data assets. Examples of logs include business logs (such as user activity logs), and Operation & Maintenance logs of servers, databases, and network or IoT devices. Logs are the guardian angel of #business. On the one hand, they provide system risk #alerts and help engineers in #troubleshooting. On the other hand, if you zoom them out by time range, you might identify some helpful trends and patterns, not to mention that business logs are the cornerstone of user insights. However, logs can be a handful, because: - They flow in like crazy.  Every system event or click from user generates a log. A company often produces tens of billions of new logs per day. - They are #bulky.  Logs are supposed to stay. They might not be useful until they are. So a company can accumulate up to PBs of log data, many of which are seldom visited but take up huge storage space. - They must be #quick to load and find.  Locating the target log for troubleshooting is literally like looking for a needle in a haystack. People long for real-time #log writing and real-time responses to log queries. An ideal log processing system should support: - #High-#throughput real-time data #ingestion:  It should be able to write blogs in bulk, and make them visible immediately. - #Low-cost #storage:  It should be able to store substantial amounts of logs without costing too many resources. - #Reatime text #search:  It should be capable of quick text search. Meet #DORIS (https://doris.apache.org/), a modern data warehouse for real-time analytics. It delivers lightning-fast analytics on real-time data at scale