Last year’s OpenWorld delivered a peek into the future of IT with some of Oracle’s most important announcements in the company’s history. Today, the experience lives on through events taking place across the globe, the most recent being OpenWorld London. At OpenWorld London, Oracle announced several new features that extend the converged architecture capabilities of the Oracle Database to meet the diverse needs that span the entire business, along with new services for its Cloud Infrastructure Data Science Platform.

Oracle enables businesses to easily run different kinds of workloads against the same data with a single database engine that handles everything. And to extend these converged capabilities, Oracle has release the following new features:

  • Oracle Machine Learning for Python (OML4Py): Oracle Machine Learning (OML) inside Oracle Database accelerates predictive insights by embedding advanced ML algorithms which can be applied directly to the data. Because the ML algorithms are already collocated with the data, there is no need to move the data out of the database. Data scientists can also use Python to extend the in-database ML algorithms.
  • OML4Py AutoML: With OML4Py AutoML, even non-experts can take advantage of machine learning. AutoML will recommend best-fit algorithms, automate feature selection, and tune hyperparameters to significantly improve model accuracy.
  • Native Persistent Memory Store: Database data and redo can now be stored in local Persistent Memory (PMEM). SQL can run directly on data stored in the mapped PMEM file system, eliminating IO code path, and reducing the need for large buffer caches. This allows enterprises to accelerate data access across workloads that demand lower latency, including high frequency trading and mobile communication.
  • Automatic In-Memory Management: Oracle Database In-Memory optimizes both analytics and mixed workload online transaction processing, delivering optimized performance for transactions while simultaneously supporting real-time analytics, and reporting. Automatic In-Memory Management greatly simplifies the use of In-Memory by automatically evaluating data usage patterns, and determining, without any human intervention, which tables would most benefit from being placed in the In-Memory Column Store.
  • Native Blockchain Tables: Oracle makes it easy to use Blockchain technology to help identify and prevent fraud. Oracle native blockchain tables look like standard tables. They allow SQL inserts, and inserted rows are cryptographically chained. Optionally, row data can be signed to ensure identity fraud protection. Oracle blockchain tables are simple to integrate into apps. They are able to participate in transactions and queries with other tables. Additionally, they support very high insert rates compared to a decentralized blockchain because commits do not require consensus.
  • JSON Binary Data Type: JSON documents stored in binary format in the Oracle Database enables 4X faster updates, and scanning up to 10X faster.

Beyond its new database features, Oracle also announced the availability of the Oracle Cloud Data Science Platform. The new service makes it quick and easy for data science teams to collaboratively build and deploy powerful machine learning models. Organizations often fail to utilize the full potential of their data because they lack the right data and tools. Models take too long to develop and don’t always meet enterprise requirements for accuracy and robustness, preventing them to make it into production.

Unlike other data science products, Oracle Cloud Infrastructure Data Science focuses on improving the effectiveness of data science teams instead of individual data scientists. Capabilities like shared projects, model catalogs, team security policies, reproducibility and auditability empowers teams by improving overall productivity. It also automatically selects the most optimal training datasets through AutoML algorithm selection and tuning, model evaluation and model explanation. By automating the entire workflow of data science projects, the platform delivers an end-to-end experience designed to accelerate and improve data science results. New data and machine learning services include:

  • Oracle Cloud Infrastructure Data Science: Enables users to build, train and manage new machine learning models on Oracle Cloud using Python and other open-source tools and libraries including TensorFlow, Keras and Jupyter.
  • Powerful New Machine Learning Capabilities in Oracle Autonomous Database: Machine learning algorithms are tightly integrated in Oracle Autonomous Database with new support for Python and automated machine learning. Upcoming integration with Oracle Cloud Infrastructure Data Science will enable data scientists to develop models using both open source and scalable in-database algorithms. Uniquely, bringing algorithms to the data in Oracle Database speeds time to results by reducing data preparation and movement.
  • Oracle Cloud Infrastructure Data Catalog: Allows users to discover, find, organize, enrich and trace data assets on Oracle Cloud. Oracle Cloud Infrastructure Data Catalog has a built-in business glossary making it easy to curate and discover the right, trusted data.
  • Oracle Big Data Service: Offers a full Cloudera Hadoop implementation, with dramatically simpler management than other Hadoop offerings, including just one click to make a cluster highly available and to implement security. Oracle Big Data Service also includes machine learning for Spark allowing organizations to run Spark machine learning in memory with one product and with minimal data movement.
  • Oracle Cloud SQL: Enables SQL queries on data in HDFS, Hive, Kafka, NoSQL and Object Storage. Only CloudSQL enables any user, application or analytics tool that can talk to Oracle databases to transparently work with data in other data stores, with the benefit of push-down, scale-out processing to minimize data movement.
  • Oracle Cloud Infrastructure Data Flow: A fully-managed Big Data service that allows users to run Apache Spark applications with no infrastructure to deploy or manage. It enables enterprises to deliver Big Data and AI applications faster. Unlike competing Hadoop and Spark services, Oracle Cloud Infrastructure Data Flow includes a single window to track all Spark jobs making it simple to identify expensive tasks or troubleshoot problems.
  • Oracle Cloud Infrastructure Virtual Machines for Data Science: Preconfigured GPU-based environments with common IDEs, notebooks and frameworks that can be up and running in under 15 minutes, for $30 a day.

While the future of IT is centered on data, Oracle believes that the driver of that future is our customers. Oracle OpenWorld London was an opportunity not only to showcase the capabilities of our products, but to inspire the possibilities of what companies can achieve with them.

To learn more or see highlights from OpenWorld London, click here.