Apache NiFi’s Post

The Apache NiFi Team is pleased to announce the release of Apache NiFi 2.0.0 Milestone 4. Apache NiFi 2.0.0-M4 is the fourth milestone release of the next major version of Apache NiFi, with over 150 issues resolved. This release incorporates the rebuilt user interface supporting dark mode! Additional new features and improvements include the following: - Kafka 3 support for both consuming and publishing - New processor for splitting binary packet capture files - New processor for splitting Microsoft Excel XLSX spreadsheets - New interface for Python processors to function as a source for FlowFiles - Dynamic reloading of system certificate and key information for TLS cluster communication https://lnkd.in/g3U8pNkC

The Apache NiFi Team is pleased to announce the release of Apache NiFi 2.0.0 Milestone 1. Apache NiFi 2.0.0-M1 is the initial milestone release of the next major version of Apache NiFi, incorporating over 960 issues resolved, including new features such as: - Initial version of native Python API for Processors - Stateless Execution mode for Process Groups - Flow Analysis Rules API - Kubernetes-based Leader Election and State Management extensions - Python-based Processors for interacting with ChatGPT and Vector Databases - ListenOTLP Processor for collecting OpenTelemetry - ListenSlack and ConsumeSlack Processors for handling Slack messages - EncryptContentAge and DecryptContentAge Processors supporting the age-encryption.org/v1 specification https://lnkd.in/gXdSSETw

Hands-On Intro to Apache Iceberg - 4 - Partitioning and CALL procedures in Apache Spark https://lnkd.in/e9nc2YFw

APACHE ICEBERG QUICK START Use the commands in the picture to quickly start trying out Apache Iceberg in a docker container with Spark 3.4. ----To copy and paste visit the link below---- https://lnkd.in/eE_PV-55 ----Here some other articles to check---- Using Iceberg and Flink https://lnkd.in/eqFdiKkU Using Iceberg with Spark and Dremio https://lnkd.in/eDiC8fc6

I have just finished working on the first pre-release version of the Jena-OWL2 library. This is enhanced Apache Jena Inference RDF Model with OWL2 support. #owl #rdf #jena #owl2 #apachejena #java Next steps: 1) integrate it into ONT-API (owlcs/ont-api), 2) add support for QL, RL, EL OWL-profiles

Delta tables support a number of utility commands! 👏 For many #DeltaLake operations, you enable integration with #ApacheSpark DataSourceV2 and Catalog APIs (since 3.0) by setting configurations when you create a new 𝚂𝚙𝚊𝚛𝚔𝚂𝚎𝚜𝚜𝚒𝚘𝚗. 🔗 View the Delta Lake documentation to learn more: https://lnkd.in/evusP3rM #opensource #oss #linuxfoundation

The #apache #nifi Team is pleased to announce the release of Apache NiFi 2.0.0 Milestone 1. Apache NiFi 2.0.0-M1 is the initial milestone release of the next major version of Apache NiFi, incorporating over 960 issues resolved, including new features such as: - Initial version of native Python API for Processors - Stateless Execution mode for Process Groups - Flow Analysis Rules API - Kubernetes-based Leader Election and State Management extensions - Python-based Processors for interacting with ChatGPT and Vector Databases - ListenOTLP Processor for collecting OpenTelemetry - ListenSlack and ConsumeSlack Processors for handling Slack messages - EncryptContentAge and DecryptContentAge Processors supporting the age-encryption.org/v1 specification https://lnkd.in/gXdSSETw

🚀 Camptocamp is contributing to the #Java #Baremaps project from the The Apache Software Foundation. 🌍 As part of our collaboration with the Overture Maps Foundation, we are working on the new #Geoparquet module, building geospatial support on top of the Apache #Parquet library. 📊 This module enables Baremaps to load Geoparquet #OvertureMaps data inside Baremaps #PostGIS database. So far, we have improved the performance of a multi-threaded parser to load gigabytes of data in a few hours. 🎯 Following https://lnkd.in/e_Vd9ba4, Our next goal is to efficiently handle the life-cycle of the Java objects before storing them in the database. #OpenSource #Geospatial #Innovation #Tech

Dynamic Watermarking With IMGProxy and Apache APISIX https://lnkd.in/duYw4NNS Last week, I described how to add a dynamic watermark to your images on the JVM. I didn't find any library, so I had to develop the feature, or, more precisely, an embryo of a feature, by myself. Depending on your tech stack, you must search for an existing library or roll up your sleeves. For example, Rust offers such an out-of-the-box library. Worse, this approach might be impossible to implement if you don't have access to the source image. Another alternative is to use ready-made components, namely imgproxy and Apache APISIX. I already combined them to resize images on-the-fly.

How to implement the Kalman Filter on a very large set of measurements utilizing multiple sampling techniques, recursion, and Apache Spark.