Welcome to the new world of semantic technology or alternatively described as an open standards-based graph technology! The announcement of Amazon Neptune is just the latest acknowledgment of the mainstream adoption of semantic technology and the value it provides over other types of graph technologies. Semantic graph technology is becoming an important tool in Digital Transformation initiatives across many industries. Global brands are realizing that to create a true “Enterprise Information Fabric” they need to invest in open and standards based semantic platforms that can leverage their existing IT & Data investments.
It is not just companies adopting semantic technology, but also regulators and industry groups that require common frameworks and terminologies (business ontologies) to communicate across boundaries. At Cambridge Semantics, we’ve witnessed that the speed of adoption of graph technology and specifically semantic technology has rapidly accelerated in the last few years. The US Federal Drug Administration (FDA) is mandating that all clinical trial submissions follow the open semantic standards developed by CDISC. Membership in CDISC has soared. In the financial services industry, the Enterprise Data Management (EDM) Council has rolled out semantic models for the financial industry. They are also seeing a surge in use of the Financial Industry Business Ontology (FIBO) and in the membership in their Council. In healthcare, HL7 has created the Fast Healthcare Interoperability Resources (FHIR) standards based on open semantic standards.
The transformative value of semantic graph technology was confirmed in a recent client visit at a Fortune 100 pharmaceutical company. The senior executive mentioned that, with data doubling every two years and the number of data sources exploding, too much time and effort was being spent on discovering, understanding, preparing and analyzing the data. Getting a drug to market costs around $600M to $2B and takes 8 to 14 years, according to this executive. How can pharma companies reduce the cost and time it takes to develop and commercialize a drug by 30-50%? Their approach is to use semantic graph technology to provide an overlay knowledge graph approach to streamline data management, improve the data discovery and analytics, and make the right data available to the right people when they need it.
Amazon Neptune Announcement
“Amazon Neptune is a fast, reliable, fully-managed graph database service that makes it easy to build and run applications that work with highly connected datasets. The core of Amazon Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency.”
“Amazon Neptune is highly available, with read replicas, point-in-time recovery, continuous backup to Amazon S3, and replication across Availability Zones. Neptune is secure, with support for encryption at rest and in transit. Neptune is fully-managed, …” – all according to Amazon’s recent announcement.
What Does Amazon Neptune Provide and How Do Products Like Cambridge Semantics Anzo Complement Amazon Neptune?
Amazon Neptune provides a highly available semantic graph Online Transaction Processing (OLTP) system in the cloud that has high performance and scalability, is highly secure, fully managed and provides for open graph APIs. All this is provided as a service.
Cambridge Semantics’ Anzo Smart Data Lake® (ASDL) is a complementary offering to Amazon Neptune. ASDL is an enterprise scale data lake management and analytics platform that provides an open standards-based, semantic layer infrastructure for big data management and connected data analytics. ASDL provides the data management and data analytics layer to securely move, manage, govern and analyze data across Amazon Neptune and other systems. Business users can use a simple and intuitive interface to leverage the power of the knowledge graph and perform rapid self-discovery and exploratory analytics without any coding. Anzo also exposes a powerful and extensible set of APIs as well as the open standards-based query language SPARQL to create custom applications and interfaces. ASDL is powered by the Anzo Graph Query Engine (AGQE), an in-memory MPP Graph Online Analytical Processing (GOLAP) engine, that is massively scalable (can handle trillions of facts or triples), and has been purpose built for high speed graph analytics.
ASDL can serve as an analytics overlay for Neptune much like it does for Hadoop and many other data repositories. While Neptune provides core OLTP features like graph data persistence and high availability services, ASDL provides a complementary data management and analytics layer where Amazon customers can:
- Build a true end-to-end enterprise scale knowledge graph based on open standards for both OLTP and OLAP applications
- Rapidly move and manage data on-demand without writing ETL code
- Extend the value of their Neptune investment with a large scale data analytics on-demand and in-memory graph OLAP capability
- Link and push data to third party systems or such as BI tools, R & advanced analytics tools as well as Machine Learning and Deep Learning tools
- Allow IT to develop secure hybrid semantic graph-based solutions that span both their on-premise data sources and Amazon cloud services
- Provide an open standards-based knowledge graph information fabric for embedding value-add natural language processing, Machine Learning and Deep Learning technologies within the semantic layer
Take-Away for Businesses
As Mike Atkins at the EDM Council recently mentioned in the December 2017 EDM Council meeting, 2018 is not about managing data as an asset but it is about putting data to work.
Graph technology with open standards-based semantic understanding makes it easy for business users to discover, find and ask questions of their data without going back to IT for help to prepare, move or manage the data. Using semantic graph technology, businesses and IT departments are able to far more easily automate data movement and management with governance, security and provenance.
Data from diverse sources, whether data from databases or text from unstructured sources, can be linked and analyzed resulting in easier understanding of relationships, improved scientific discovery, quicker root cause analysis, deeper fraud analysis, improved customer 360 understanding and more effective customer targeting, among other use cases. And with Amazon Neptune, it is not just available in select regional markets but available in the cloud for broader and quicker customer adoption.
To learn more about how ASDL’s Anzo Graph Query Engine was able to establish a key milestone for loading and querying big data in 1.98 hours versus the previous record of 220 hours, download the Benchmark Report.