On March 27th, Oracle announced availability of the Oracle Autonomous Data Warehouse Cloud, a service that will spin up a data warehouse and provide automated security, high availability, performance tuning, scaling, patching, and administration at a cost guaranteeed to be half of equivalent Amazon Web Services resources through May 2019. Built on Oracle Database 18c, this new service is both a godsend and a warning call for IT.
As Amalgam said last December, Oracle’s push towards what they are calling the “Autonomous Database” and “Autonomous Cloud” is an important step forward in envisioning an new generation of IT where the operational tasks of rules-based administration, monitoring, and iterative performance tuning are handled without direct human intervention. This will allow IT departments to drive more infrastructure into the cloud and reduce the overall Total Cost of Ownership. This is a fundamental change and differs radically from cloud providers such as Amazon and Microsoft that are providing granular services, but are not replacing the management of those services.
Here’s what you should expect
From Oracle, expect this to be just one of many ways that data, analytics, platform, and application management are increasingly automated. This step is the beginning of the end of IT administration as a standalone job. Although the role probably has another generation left in it, the writing is on the wall. And Oracle will know how to sell this vision: I have often said that Oracle runs a disciplined business and no tech executive understands how to position enterprise data better than Larry Ellison. Oracle has been the most imposing enterprise vendor of our generation based on its database and platform innovations and intellectual property. This announcement continues that legacy ande I predict the Autonomous Cloud service launches occurring will eventually known as the start of the era of Autonomous IT that will eventually replace the current emergent era of IT-as-a-Service.
CIOs should be excited. From Amalgam’s perspective, this is the most fundamental change to Oracle Database’s capabilities since Oracle 12c was first launched in July 2013 and provided a multitenant database option. Quite frankly, this announcement can and should drive database upgrades and cloud migrations, especially for CIOs that are looking for greater performance and flexibility in their data environments. My advice to CIOs: crunch the numbers on Total Cost of Ownership and figure out how to reallocate your potential database administration savings towards digital transformation, machine learning, and end user productivity (mobile and SaaS) efforts.
DBAs, on the other hand, should rightfully look at this service and wonder “what is left for me to do?” To you, the message is simple: it’s time to go back to class, Lynda, or wherever you upgrade your skills. There are still plenty of interesting data and analytic challenges. However, anyone staking their job on database provisioning, tuning, monitoring, patching, and continuity.
As a data enthusiast and one-time DBA, I would suggest some of the following areas:
Graph data and analytics: This emerging space allows data-savvy employees to take advantage of OLAP logic in developing enterprise data networks. With the emergence of graph databases like Neo4j and Amazon Neptune and hypergraph databases such as PatternSpace and HypergraphDB, the structural foundation to support graph analytics is now in place. This skill will provide value across social analytics, blockchain analysis, and multi-relational business model inquiries.
Fast data: From a practical perspective, fast data from my perspective starts with message queuing (MQTT) and the tech ecosystem related to this messaging protocol. To get past traditional structured data and transactions to the new world of IoT, geolocation, and mobile alerts, start learning more about MQTT brokers and servers. The business value of the DBA is in managing enterprise data and to do that, you first need to understand the nature of data.
Predictive analytics and data science: The misnomer of “data science,” (Hint: not a science at all) belies the importance of creating analytic and algorithmic models for the business. This practice is really about defining the data and analytic logic that drives key business decisions. To get into this field, continue learning Python and go back to class for predictive modeling and statistics classes. The advantage you have over current “data scientists” is your deep knowledge of existing data and business practices. Build on your core.
Be prepared for the future. Oracle is working on the future of IT, which will render low-value IT jobs based on manual monitoring obsolete. Those who prepare now for the full ramifications of this ongoing movement will provide their organizations with a strategic advantage. Those who do not will wonder why their IT departments and tactical data roles are being diminished over time.
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