At their annual re:Invent conference in Las Vegas, AWS unleashed a flurry of announcements about upcoming cloud services. Amazon outlined over two dozen new capabilities coming to the public cloud, including directly querying data in S3 object storage, building code as part of deployment pipelines, provisioning cheap virtual private servers, and moving data in bulk, ETL-style.
The Cloud, infrastructure as code, federated architectures with APIs, and anti-fragile systems: these are technologies for developing software systems that are rapidly coming into focus, claimed Mary Poppendieck. Systems are moving towards the cloud, and APIs are replacing central shared databases and enable the internet of things. We need to develop anti-fragile systems which embrace failure.
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.
Realm has launched an open source object database for Node.js, allowing mobile developers to create and send pre-populated Realms to clients.
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.
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.
Scalability should be considered when developing a Minimum Viable Product (MVP). An MVP needs to be technically scalable and you need to have a plan on how to scale quickly when your MVP attracts many users and becomes successful. Knowing your possible performance bottlenecks and using common sense while developing your MVP will get you very far, says Erik Duindam, CTO at Unboxd.
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.