Datorios

Introducing, real-time?alerts for #ApacheFlink ??? Late data? Discarded records? Bottlenecks? By the time data teams notice, the damage is often already done. Datorios' Real-Time Alerts now let you catch and fix Flink issues the moment they happen. In other words, know what’s wrong before anyone else. Why it matters: ?? Prevent Data Loss – Stop discarded events before they vanish. ?? Ensure Accuracy – Catch late records before they distort aggregations. ?? Optimize Performance – Identify slow operators before they crash your job. How it works: ?? Slack Alerts: Get notified in real-time when lateness, discards, or stalls occur. ?? One-Click Investigation: Jump directly into State Analysis to pinpoint the root cause. ?? Fix Fast: Adjust watermarks, scaling, or lateness policies before issues cascade. Ready to set up real-time alerts for your Flink pipelines? Let’s talk https://lnkd.in/dKgjrnjA Product release notes here - https://lnkd.in/dkDrQZ3c

With real-time data, misunderstanding observability (like in the image below) doesn’t just lead to confusion; it leads to blind spots, operational disruptions, and costly failures. Ronen Korman, CEO of Datorios, just wrote a piece that cuts through the noise: If you’re working with real-time data, you should give this a look.?It might change how you think about observability. #dataobservability #realtimedata

How companies like Royal Caribbean Group, United Airlines, and Booking.com, use #ApacheFlink to power user experience at scale? Batch processing for real-time applications might have worked in the past, but today’s travelers expect frictionless experiences; instant updates, personalized recommendations, and seamless journey. After 2+ years talking to industry leaders and digging into real-world implementations, we’ve put together this case study to highlight how modern online travel agencies (OTAs) and hospitality brands rely on Apache Flink to stay ahead. Use cases covered: ?? Real-time personalization to recommend the right trips, hotels, and activities. ?? Fraud detection to prevent unauthorized bookings and payment fraud. ?? AI-driven customer support that responds instantly to traveler requests. ?? IoT-powered hotel operations to improve efficiency and security. See how real-time stream processing is shaking up the OTA industry. Read it here - https://lnkd.in/db4RKAvj #apacheflink #dataobservability #ota

?? Apache Flink Ecosystem: 2025 ?? A year ago, we shared a map of the #ApacheFlink ecosystem, and the response was overwhelming. People debated runtimes, questioned observability gaps, and pointed out missing integrations. And that motivated us to keep making these. Flink isn’t just growing, it’s evolving. More sources. More sinks. More ways to run and monitor Flink at scale. But more importantly, deeper integrations and interoperability. If you’re working with Flink today, chances are you’re streaming through Kafka, Delta Lake, or Iceberg, pushing insights to Elasticsearch or Redis, and running workloads on Kubernetes or a managed cloud service. Why is this happening? Because real-time data isn’t a luxury anymore, it’s a necessity. And as businesses demand faster insights, better data quality, and resilient pipelines, Flink has solidified its role as a backbone of real-time decision-making. ?? Highlights ? Sources & Sinks are expanding beyond the usual players, with deeper integrations into data lakes and event-driven architectures. ? Runtimes are maturing, whether you’re self-hosted, managed, or fully SaaS. ? Observability & Monitoring are now central, not optional. Faced with regulatory pressures, massive scale, and rising costs, companies are prioritizing real-time data visibility, reliability, and explainability. ?? Sources: As we move into the Flink 2.0 era, everything will be built on the Source API, creating a more unified ecosystem. Streaming and batch sources alike: #Kafka, RabbitMQ, Pulsar, #FileSystem, #DeltaLake, Apache Iceberg, #AmazonKinesis, #GoogleCloud PubSub, #HBase, #MongoDB, #Hive, and others. ?? Sinks: - Popular sinks: #Kafka, #Elasticsearch, MongoDB, RabbitMQ, #DeltaLake, #JDBC - Cloud-integrated solutions: Amazon DynamoDB, #GoogleCloud PubSub, Rockset, Datagen, and more ?? Runtimes: - Self-hosted: #Kubernetes, #HadoopYARN - Managed: #AmazonEMR, #GoogleDataflow, Alibaba Cloud, Databricks, Redpanda Data, StreamNative, Confluent, Decodable, Aiven, Ververica | Original creators of Apache Flink? ?? Observability & Monitoring: - Observability: Datorios - Monitoring tools: Datadog, Prometheus Group, OpenTelemetry, and others. This map is our way of capturing the fast-moving world of Apache Flink. Did we miss anything? Let us know in the comments! #datorios #apacheflink

The worst data issues? The ones you don’t even know exist. Silent data loss is an invisible threat to businesses. Ignored or late records in streaming systems mean missing or delayed events that go unnoticed, until it’s too late. Watch as Colten Pilgreen explains how to ensure operational quality by detecting and preventing silent failures before they impact your business. Full video in the comments. #dataquality #streamingdata #apacheflink #observability #datorios

?? Apache Flink Ecosystem: 2025 ?? #ApacheFlink isn’t just growing - it’s evolving. More sources. More sinks. More ways to run and monitor Flink at scale.? But more importantly, deeper integrations and interoperability. If you’re working with Flink today, chances are you’re streaming through Kafka, Delta Lake, or Iceberg, pushing insights to Elasticsearch or Redis, and running workloads on Kubernetes or a managed cloud service. Why is this happening? Because real-time data isn’t a luxury anymore, it’s a necessity. And, as businesses demand faster insights, better data quality, and resilient pipelines, Flink has solidified its role as a backbone of real-time decision-making. ?? Highlights ? Sources & Sinks are expanding beyond the usual players, with deeper integrations into data lakes and event-driven architectures. ? Runtimes are maturing, whether you’re self-hosted, managed, or fully SaaS. ? Observability & Monitoring are now central, not optional. Faced with regulatory pressures, massive scale, and rising costs, companies are prioritizing real-time data visibility, for reliability, and explainability. The Ecosystem Keeps Growing - this map is our way of capturing the fast-moving world of Apache Flink. Did we miss anything??Let us know in the comments!

We are often asked about security, access control, and deployment options. Here’s a great short explanation by Colten Pilgreen on how to provides full visibility into real-time data, without compromising security. - Control who can access what - Deploy as dedicated SaaS, or BYOC - Mask sensitive data Check out our Youtube channel to learn more: https://lnkd.in/d9RRG7-7

Our new search bar for #ApacheFlink makes navigating and pinpointing data across multiple screens effortless. Now available across all investigation tools: tracing, state insights, job performance, logs, and lineage. Try it out now and experience faster, more efficient data investigations: https://lnkd.in/dKqvzc-m #realtime #dataobservability #apacheflink

#Monitoring shows you the tip of the iceberg - reacting to visible problems. #Observability dives deeper, helping you anticipate and prevent issues before they emerge. At Datorios, we're proud to lead the way in real-time data observability, empowering enterprises to observe, predict, and resolve even the most complex real-time data challenges with confidence. #realtimedata #observability #monitoring

From a research project to real-time greatness! here’s the story of #ApacheFlink by our own Dennis-Mircea Ciupitu ??