AnalytixLabs’ Post

Ready to enhance your data lake platform? Join Itamar Syn-Hershko, CTO at BigData Boutique, as he explores the integration of Apache Flink and Apache Iceberg. The talk was originally presented at our recent Apache Flink TLV Meetup event, hosted at Panorays. In this video, you'll learn: ✨ Seamless integration techniques: Discover how to effectively combine the power of #Flink and #Iceberg. ⚡ Efficient data ingestion: Implement continuous ETL pipelines and database mirroring with Flink. 🔍 Advanced querying: Utilize Iceberg's querying capabilities to enhance data management and performance. 📊 Streaming and batch execution insights: Leverage Flink's dual processing capabilities for optimal efficiency. Watch now: https://ow.ly/gE8c50Ssol4

Curious if Flow.BI, the GenAI for Data Vault 2.0, supports real-time data sources such as Kafka? Well, check out our latest blog article: https://lnkd.in/ef8E-SMz

FOSDEM talk recordings and slides are up. Here's my talk on building open source analytics with ClickHouse and Grafana. You can build a basic observability stack with less than 100 lines of code, then scale up to add OTEL, Kafka, and flexible data modeling. Enjoy! https://lnkd.in/ges_CnmS #opensource #observability #clickhouse #grafana #monitoring #fosdem

🚀 Real-Time Data Analytics: Transform Your Decision-Making! 🚀 Why wait on insights? Our latest blog reveals how real-time data analytics helps you stay agile, respond instantly, and gain a competitive edge! ✨ Highlights: Instant data processing with Apache Kafka & Flink Real-time insights for faster, smarter decisions 👉 Read more: https://buff.ly/4fGwkSL

We were excited and intrigued by the TableFlow announcement by Confluent at the recent Kafka Summit London. It's great to see the Streaming Datalake concept getting the traction it deserves! In this latest blog post we highlight some of the considerations that come with the convergence of operational and analytical data systems and how the Streambased and TableFlow approaches differ, including: ⚙ Should you move/transform data for analytical purposes? ⏳ What (if any) latency is acceptable between operational and analytical systems? 📊 How should we surface operational data for analytical purposes? Please enjoy and, as always, join the discussion! https://lnkd.in/eMbgKn-f

Ready to enhance your data lake platform? Join me in exploring the integration of Apache Flink and Apache Iceberg. The talk was originally presented at our recent Apache Flink TLV Meetup event, hosted at Panorays. In this video, you'll learn: ✨ Seamless integration techniques: Discover how to effectively combine the power of #Flink and #Iceberg. ⚡ Efficient data ingestion: Implement continuous ETL pipelines and database mirroring with Flink. 🔍 Advanced querying: Utilize Iceberg's querying capabilities to enhance data management and performance. 📊 Streaming and batch execution insights: Leverage Flink's dual processing capabilities for optimal efficiency. Watch now: https://ow.ly/iarY50SsomF

Try the github events stream (https://lnkd.in/dAZUVhyU) example from pinot if you'd like to see realtime data processing in action. GitHub events API -> Kafka topic -> real-time table #apachepinot

What is Retrieval-Augmented Generation (RAG)? #apachekafka To ensure that the data retrieved is always current, the vector database should be continuously updated with real-time information. This is where Kafka can help a lot. https://lnkd.in/dbHdyUqv

It was an enjoyable journey to learn about the features of Cognite Data Fusion - CDF with the ability to connect to 3rd party services like Grafana and Power PI

Apache Flink is a powerful technology framework for those working in data management and analysis. 🔍 While it might seem complex, you don't need to be a tech expert to wrap your head around what it is and what it can do. To help demystify Apache Flink, we enlisted the help of Maria Berinde-Tâmpǎnariu from Confluent 🤩 Read more 👉https://bit.ly/4cVWO22 #ApacheFlink #Data