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Oracle OpenWorld updates including the first ever autonomous database, Apache Solr 7.0.0 release and more in today’s data science news.

Apache Solr™ 7.0.0 available

Lucene PMC announced the release of Apache Solr 7.0.0 on September 20. Solr 7 has more flexibility with two new replica types, TLOG & PULL, as updates are handled by replicas based on their types. TLOG can use its transaction log to recover and become a leader, while the PULL type replica cannot become a leader as it does not have a transaction log. In earlier releases, any replica could have become a leader when a leader was lost. Autoscaling is another new feature in Solr 7 that helps manage clusters in simpler ways with more automation. Among other features, the new version also provides rich document parsing, enhanced RESTful APIs and parallel SQL.

Oracle OpenWorld in News

Oracle 18c: World’s first self-driving database

What could possibly be the next generation of industry-leading databases, Oracle has launched the first-of-its-kind fully automated database called Oracle 18c. Calling for automation as essential to preventing and handling data theft, Oracle CTO Larry Ellison announced at Oracle OpenWorld conference the new autonomous database that can patch itself in real time without requiring to go offline. Oracle said their aim is to automate both the threat detection and the immediate remediation, without having a delay waiting for “a human to schedule downtime to gracefully implement a patch in a month or two.” Oracle 18c’s data warehouse version will be available in December while the OLTP version will be available in June 2018.

Oracle announces AI platform Cloud Service, chatbots to tap deep learning, machine learning capabilities

At its ongoing OpenWorld event, Oracle has unveiled the AI Platform Cloud Service that may help developers quickly create and deploy enterprise AI services. The company also announced the availability of intelligent, AI-led chatbots in the Oracle Mobile Cloud delivering multi channel platform to companies for integrating machine learning features. “Oracle AI Platform Cloud instances come pre-installed with familiar AI libraries, tools, and deep learning frameworks, including Caffe, Jupyter Notebook, Keras, NymPy, scikit-learn, and TensorFlow, among others,” Oracle said in its release, adding that machine learning practitioners can access Oracle Object Store and easily connect to existing Spark/Hadoop clusters.The AI-powered bots, that will help automate the information processing and customer conversations, will work with Facebook Messenger, Skype, Slack, Kik, Amazon Echo, Amazon Dot, and Google Home.

Oracle Blockchain Cloud Service may enhance security, scalability and supply chains

In a major announcement, Oracle has unveiled it enterprise-grade blockchain cloud service. The advanced cloud platform, fully managed by Oracle, is expected to simplify and secure operations with its continuous backup, in-built monitoring, and point-in-time recovery features. “Enterprises can now streamline operations across their ecosystem and expand their market reach with new revenue streams, sharing data and transacting within and outside the Oracle Cloud,” said Amit Zavery, senior vice president, Oracle Cloud Platform. Oracle recently joined the open source consortium for blockchain project Hyperledger.

In other Data Science News

MathWorks introduces Release 2017b of the MATLAB and Simulink Product Families, adds deep learning capabilities

MathWorks has announced its Release 2017b with several new features in MATLAB and Simulink. The release also includes six new products, and, updates and bug fixes to 86 other products. R2017b boosts deep learning capabilities with several features that simplify the way researchers, engineers, and domain experts design, train, and deploy models. “With R2017b, engineering and system integration teams can extend the use of MATLAB for deep learning to better maintain control of the entire design process and achieve higher-quality designs faster. They can use pretrained networks, collaborate on code and models, and deploy to GPUs and embedded devices. Using MATLAB can improve result quality while reducing model development time by automating ground truth labeling,” said David Rich, MATLAB marketing director, MathWorks.


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