Initiative Targets Big Data Workloads {Open Hybrid}

Who are tasked with building, deploying and maintaining hybrid that span not just the cloud and the edge, but traditional on-prem data centers too.


Hortonworks, IBM and Red Hat today announced they’re banding together to build a consistent hybrid computing architecture for big data workloads. Dubbed the Open Hybrid Architecture Initiative, the program pledges simplicity of deployment and freedom of movement for data apps.

The rapid ascent of cloud computing platforms like AWS, Azure, and Google Cloud has given enterprises abundant new options for storing data and deploying processing-intensive applications, such as deep learning and real-time stream processing. Throw in the progress being made at the edge, with sensors and speedy ARM chips collecting and processing massive amounts of data, and you have the makings of a computing revolution.

While the computing possibilities in the cloud and on the edge may appear bountiful, the reality is that the underlying architectures for building apps that can span these three modes are just starting to come together. Enterprises today face a dearth of repeatable patterns to guide their developers, administrators, and architects, who are tasked with building, deploying and maintaining hybrid that span not just the cloud and the edge, but traditional on-prem data centers too.


That’s the underlying challenge that’s to be faced by the Open Hybrid Architecture Initiative. Launched today in advanced of the Strata Data Conference in New York City, the group outlined plans to integrate their respective technologies in such a way as to provide customers with greater freedom of movement and run-time options for their big data workloads.

Hybrid computing architecture for big data workloads

which released an “integration cloud” this week that automates key software development bottlenecks

The Mesosphere platform is designed to ease deployment and scaling of applications and data services, the partners said.

SnapLogic, San Mateo, Calif., said its integration with GitHub Cloud and support for the Mesosphere container platform would “provide the glue needed to streamline the software development lifecycle.”

Integration with the sprawling open-source development cloud acquired by Microsoft in June would enable users to host SnapLogic’s tools, pipelines and other tasks created on GitHub while maintaining version control. The feature leaves open the option of working on those assets downstream to deliver updates faster.

The integration with GitHub also attempts to address current DevOps fragmentation in which IT teams often spend as much time pulling together “siloed” tools as they do on actual software development. The result is delays in shipping new application software and services.

The integration platform also embraces agile application containers through support for Mesosphere’s datacenter operating system that supports Kubernetes cluster orchestration as well as linking public and private clouds. The Mesosphere platform is designed to ease deployment and scaling of applications and data services, the partners said.

SnapLogic said Mesosphere support would allow developers to spin up Docker containers in a single click rather than managing them manually. Along with automating container management, the framework would help reduce development errors when moving services to container platforms.

Meanwhile, the company updated its Iris AI platform to help business users manage their data pipelines, freeing DevOps teams to focus on new software releases. The self-service integration tool is designed to recommend components, or “Snaps,” used to create data pipelines. In a getting-from-Point-A-to-Point-B example, a user could select the first and last Snaps of the pipeline. An “integration assistant” would then fill in the pipeline.

SnapLogic has for several years supported early investor

Life science industry with Digital Technology (Rebooting)

Digital technology is connecting genetic information with real world data and companies are already combining drugs, advanced application devices, and apps to be more patient-centric.

Digital technology has been driving change throughout the life science industry for years, however the sector is currently standing on the precipice of revolutionary development – some organizations have already taken the jump towards a more digital future.

Data collection and visualization for decision making to improve the overall performance of themanufacturing supply chain is a huge opportunity for the life science industry, however it’s not about being new – it’s about using proven solutions andapproaches to decision making to improve quality, reliability and reducing waste.

Businesses across the life science industry have been collecting data using large historian systems for years. Many currently have so much data arising from different sources it can be hard to focus on what is important. Right now, almost every device in a GMP manufacturing facility collects data and our clients have been completing projects to physically connect all these devices and systems for many years. The drive to physically connect the systems has come from many strategic objectives, including serialization.

All this excellent work has put the industry in a great position to use the data it is currently collecting in the best possible way.Although the robotics and automotive industries may be in a better position to use Artificial Intelligence (AI) and self-learning systems to improve manufacturing in efforts linked to Industry 4.0 – the life science industry has been using data and evidence to improve its manufacturing for nearly forty years.


New tools and processes are emerging that can enable smart, decentralized production, with intelligent factories, integrated IT systems, the Internet of Things (IoT), and flexible, highly integrated manufacturing systems. In addition, future developments may mean that machine learning algorithms will be able to quickly adjust manufacturing lines and production scheduling. New developments will also pave the way for predictive maintenance and the opportunity to identify and correct issues before they happen.

Integrating with single use systems
The adoption of single-use technologies, such as single-use bioreactors and other unit operations is on the rise. Fueled by the growing pipeline of high potency and biological drugs and coinciding with the growth in personalized medicine and its inherent need for smaller batches, single-use technology will play an increasingly important role in the coming years.

Both upstream and downstream manufacturing processes benefit from single-use systems. During this manufacturing method the biopharma process system is disposed of after use as opposed to being cleaned, enabling quick set up while reducing cleaning and validation need.

Currently,the integration of manufacturing execution system (MES) solutions with start-to-finish technologies and single-use manufacturing platforms is helping the industry to deploy biopharmaceutical manufacturing with increased productivity and efficiency.The upshot is that manufacturers can significantly reduce the time-to-market for new products.

Single-use components are also an enabling technology for smaller scale production of biopharmaceuticals, including antibodies, proteins, vaccines and cell therapies, which would otherwise be much more difficult to produce. Increased productivity and efficiency are also a necessity when it comes to manufacturing smaller batches and a wider range of product. In this environment, single-use technology will naturally flourish as a simple, cost-effective solution.

Digital manufacturing
The first steps towards fully connected, self-optimizing production processes have been taken – the advent of digital manufacturing is on the horizon. Enterprise Manufacturing Intelligence (MI) involves accessing more meaningful data to give a better, more holistic view of operations and allowing for improved analytics and real-time responsive decision-making to drive continuous improvement.

Access to this data, or Big data, also allows for the creation of digital twins. A digital twin can be made up of data captured from the entireend-to-end manufacturing process of a product – this twin can then be used to find invaluable insights. Extension of the traditional ‘golden batch’, where data was very much process control-based, will be supplemented and surrounded with environmental data, raw material data, training data and any other digital data available that goes towards influencing the golden batch.

With this digital information available across multiple
Sites, batches and suppliers, sophisticated analytics can provide a digital twin that best represents the golden batch and alerts controllers to any problems based on these specific data sets.

Patient centricity
Digital technology is connecting genetic information with real world data and companies are already combining drugs, advanced application devices, and apps to be more patient-centric. This push towards customized medicines solutions, driven by technological advances and pressure from patients who want to be more involved in their own care, will take the market in new directions.

The impact on the market, and more notably manufacturers, is that there will be a growing demand for smaller batches which will highlight any inflexibility in a manufacturer’s supply chain.

The propagation of product variants and smaller batch sizes will mean that launch approaches, process technologies and validation concepts will need to be overhauled.


SQL database aimed at real-time processing of Internet of Things

The upgrade also targets SQL developers who previously relied on NoSQL approaches to handle machine data applications.

Startup’s strategy of advancing an open source scale-out SQL database as an alternative to complex NoSQL versions for handling fast-moving machine data appears to be paying dividends with the close of an early funding round and the release of an upgraded version of its platform. this week announced the close of a Series A funding round that garnered $11 million. The round was led by Zetta Venture Partners and Deutsche Invest Equity. Among the other investors is Solomon Hykes, founder of application container pioneer Docker. The funds will be used to accelerate development and adoption of commercial and open source versions of the CrateDB machine data platform, the company said Tuesday (June 19).

The San Francisco-based startup also released the third version of it open source database emphasizing time-series storage and analytics for industrial and other users dealing with large volumes of machine-generated data. The upgrade also targets SQL developers who previously relied on NoSQL approaches to handle machine data applications.

The upgrade also targets users seeking to harness data generated by connected factory equipment along with smart buildings and vehicles. “The capability for real-time processing of machine data [was] a key constraint in many Industry 4.0 endeavors,” noted Torsten Kreindl, managing partner at Deutsche Invest Venture Capital.Industry 4.0 refers to factory automation efforts that incorporate data analytics into manufacturing technologies.

5 Myths About Artificial Intelligence (AI) You Must Stop Believing said it is addressing the requirements with an upgraded platform that includes faster data ingestion and real-time analytics as well as data visualizations. Along with SQL, it loads JSON and other data points in a variety of structures, including nested objects and arrays.

Meanwhile, data platform administration is based on a cloud-native micro-services approach managed around the Kubernetes cluster orchestrator.