Julien Le Dem, the PMC chair of the Apache Arrow project, presented on Data Eng Conf NY on the future of column-oriented data processing. Apache Arrow is an open-source standard for columnar in-memory execution. InfoQ interviewed Le Dem to find out the differences between Arrow and Parquet.
Couchbase 4.6 Developer Preview features full text search improvements, cross data center replication with globally-ordered conflict resolution and connectors for real-time analytics technologies: one for Spark 2.0 and the other for Kafka.
Apache Spark integration with deep learning library TensorFlow, online learning using Structured Streaming and GPU hardware acceleration were the highlights of Spark Summit EU 2016 held last week in Brussels.
Microsoft recently released two new data science tools for interactive data exploration: modeling and reporting. These tools can be reused by data science teams with data specific tasks in their projects. The goal is to ensure consistency and completeness of data science tasks across different projects in the organization.
Javier Lopez and Mihail Vieru spoke at Reactive Summit 2016 Conference about cloud-based data integration and distribution platform used for stream processing in business intelligence use cases. Their solution is based on technologies such as Flink, Kafka and Elasticsearch.
On September 26th, Microsoft announced the Azure DNS service has reached General Availability (GA) in all public Azure regions. Azure DNS allows customers to host their DNS domain in Azure, so they can manage their DNS records using the same credentials, billing and support contract as their other Azure services.
Wolfram, the software company behind computation-centric products like Mathematica and Wolfram|Alpha, shipped a new private cloud appliance targeting companies that want to centralize their computational efforts.
Lambda architecture has been a popular solution that combines batch and stream processing. Kartik Paramasivam at LinkedIn wrote about how his team addressed stream processing and Lambda architecture challenges using Apache Samza for data processing. The challenges described are the late arrival of events and the processing of duplicated messages.
Apache Kafka and Kafka Streams frameworks help with developing stream-centric architectures and distributed stream processing applications. Jay Kreps, CEO of Confluent, gave the keynote presentation on stream processing and microservices at Reactive Summit 2016 Conference last week.
Reactive microservices, data center scale operating system (DCOS), and staging reactive data pipelines were the highlighted topics at Reactive Summit 2016 Conference held this week. InfoQ team attended the conference and this post is a summary of the first day's events at the conference.
Confluent Enterprise latest version supports multi-datacenter replication, automatic data balancing, and cloud migration capability. Confluent, provider of the Apache Kafka based streaming platform, announced last week the new features for Confluent Enterprise, to help build streaming data pipelines and develop stream processing applications.
Last week, Hashicorp released version 0.7 of Consul its open-source distributed service discovery and configuration tool. Tagged a "very large release", it introduces transactions for key/value updates, replication of ACLs across datacenters, improvements to its Raft and Gossip protocol implementations and optimisation of corresponding timings.
Real-time analysis of event streams has a new focus in Big Data platforms, both on-premise and in the cloud. AWS have released Amazon Kinesis Analytics, a rival to Azure StreamAnalytics. Both platforms use a simple SQL language for complex querying, and move Big Data analysis into a SaaS-like space.
A team of scientists at IBM Research in Zurich, have created an artificial version of neurons using phase-change materials to store and process data. These phase change based artificial neurons can be used to detect patterns and discover correlations in Big Data (real-time streams of event based data) and unsupervised machine learning at high speeds using very little energy.
Data science is about the data that you need; deciding which data to collect, create, or keep is fundamental argues Lukas Vermeer, an experienced Data Science professional and Product Owner for Experimentation at Booking.com. True innovation starts with asking big questions, then it becomes apparent which data is needed to find the answers you seek.