LinkedIn recently detailed open-sourced Kafka Monitor service that they're using to monitor production Kafka clusters as well as extensive testing automation, leading them to identify bugs in the main Kafka trunk and contribute solutions to the open-source community.
Confluent Platform 3.0 messaging system from Confluent, the company behind Apache Kafka messaging framework, supports Kafka Streams for real-time data processing. The company announced last week the general availability of the latest version of the open source Confluent platform.
Cloudera announced their partnership with MIT & Harvard's Broad Institute and detailed some of their experience with the Genome Analytics Toolkit pipeline.
Two years after the first release of Apache Spark, Databricks announced the technical preview of Apache Spark 2.0 , based on upstream branch 2.0.0-preview. The preview is not ready for production, neither in terms of stability nor API, but is a release intended to gather feedback from the community ahead of the general availability of the release.
Amazon has recently announced an update to their Amazon Kinesis Service. In this update, three new features have been added to Amazon Kinesis Streams and Amazon Kinesis Firehose including support for Elasticsearch Service Integration, Shard-Level Metrics and Time-Based Iterators.
AWS engineers Christopher Crosbie and Ujjwal Ratan detail using Spark on EMR for precision medicine data analysis on the ADAM platform with data from the 1000 genomes project.
Genomic data sequencing and subsequent analysis faces large data volume challenges that several organizations are solving with cloud services. The Broad Institute detailed their experience with petabyte scale sequencing pipelines last month through the Google Research Blog and is detailed here by InfoQ.
After months of awaiting details about the NHS and Google DeepMind partnership InfoQ gains insights into recent claims of widespread patient data access.
Hadoop and other big data technologies revolutionized the way organizations run data analytics but the organizations are still facing challenges with operating costs of using these technologies for on-premise data processing. Ashish Thusoo recently spoke at Enterprise Data World Conference about Hadoop as a service offering that helps organizations bridge the gaps with these capabilities.
AirFlow recently joined the Apache Incubator program. AirFlow is a workflow and scheduling system designed to manage data pipelines. Developed by AirBnb for their internal usage, it was open sourced last September, as previously reported by InfoQ.
Operational Data Stream and Batch Processing at Netflix with Mantis
Today at GraphConnect Europe 2016, Neo Technology announced the release of Neo4j 3.0, which includes a new binary protocol for transmitting data between server and client, and a new set of standardised drivers for interacting with the database, along with stored procedure support and higher performance and capacity. InfoQ spoke to Neo Technology to find out more.
Late last month Google released an alpha version of their TensorFlow (TF) integrated cloud machine learning service as a response to a growing need to make their Tensor Flow library to run at scale on the Google Cloud Platform (GCP). Google describes several new feature sets around making TF usage scale by integrating several pieces of the GCP like Dataproc, a managed Hadoop and Spark service.
InfoQ's Rags Srinivas caught up with Stephan Ewen, a project committer for Apache Flink about the 1.0.0 Release and the roadmap
Since announcements late last year about Google open-sourcing TensorFlow, the company’s open-source library for machine learning, and previous coverage at InfoQ, the data-science community has had an opportunity to try out TensorFlow for their own projects.