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Autonomous Database

Machine Learning Performance on Autonomous Database

In many organizations, a data science project likely involves the data scientist pulling data to a separate analytics server - analyzing and preparing data and building machine learning models locally. As enterprises grow their data science teams and data volumes expand, common access to data and the ability to analyze that data in place can dramatically reduce time-to-project-deployment and overall complexity. Building models and scoring data at scale is a hallmark for Oracle’s in-database machine learning - Oracle Machine Learning. Combine this with Oracle Autonomous Database - the converged database with auto-scale capabilities - and a team of data scientists can work comfortably in the same environment. In this blog post, we take a look at factors affecting machine learning model building performance as well as performance numbers illustrating the performance and scalability possible with Oracle Machine Learning. In a subsequent post, we'll discuss scoring performance. Top 7 factors affecting performance Many factors affect machine learning performance, including: Data volume – whether building models or scoring data, the most obvious factor is the amount of data involved – typically measured in the number of rows and columns, or simply gigabytes. Data movement and loading – related to data volume is the performance impact of moving data from one environment to another, or from disk into the analytics processing engine. This time needs to be considered when comparing machine learning tools and processes.  Choice of algorithm – different algorithms can have vastly different computational requirements, e.g., Naïve Bayes and Decisions Tree algorithms have low computational demands compared with those of Generalized Linear Models or Support Vector Machine. Data complexity – some patterns in data are easily identified by an algorithm and result in a model converging quickly. Other patterns, e.g., non-linear, can require many more iterations. In other cases, the cardinality of categorical variables or the density/sparsity of data can significantly impact performance. Algorithm implementation –open source and even proprietary algorithms are often implemented in a non-parallel or single-threaded manner, meaning that, even when run on multi-processor hardware, no performance benefit is realized. Such traditional single-threaded algorithms can often be redesigned to take advantage of multi-processor and multi-node hardware, through parallelized and distributed algorithms implementation. Enabling parallelism is often fundamental for improving performance and scalability. Concurrent users – one data scientist working on a single model on a dedicated machine may or may not see adequate performance relative to the factors identified above. However, when multiple users try to work concurrently to build and evaluate models or score data, the impact on the overall performance for these users may significantly degrade or even result in failures due to exhausting memory or other system resources. The ability for an environment to scale up resources to meet demand alleviates such impact.  Load on the system – while the number of concurrent machine learning users impacts performance, the non-ML sources (interactive users and scheduled jobs) can both impact and be impacted by ML sources. Compute environments that can manage and balance such uses can provide better overall performance Enter Oracle Autonomous Database Oracle Autonomous Database includes the in-database algorithms of Oracle Machine Learning (OML), which addresses the factors cited above that impact machine learning performance. By virtue of being in the database, OML algorithms operate on data in the database such that no data movement is required, effectively eliminating latency for loading data into a separate analytical engine either from disk or extracting it from the database. The OML in-database algorithm implementations are also parallelized—can table advantage of multiple CPUs—and distributed—can take advantage of multiple nodes as found with Oracle Exadata and Oracle Autonomous Database. Oracle Autonomous Database further supports performance by enabling different service levels to both manage the load on the system, by controlling the degree of parallelism jobs can use, and auto-scaling, which adds compute resources on demand—up to 3X for CPU and memory to accommodate both ML and non-ML uses. Performance results To illustrate how Oracle Autonomous Database with Oracle Machine Learning performs, we conducted tests on a 16 CPU environment, involving a range of data sizes, algorithms, parallelism, and concurrent users. Oracle Autonomous Database supports three service levels: high, medium, and low. ‘High’ limits the number of concurrent jobs to three, each of which can use up to the number of CPUs allocated to database instance (here, up to 16 CPUs). ‘Medium’ allows more concurrent users but limits the number of CPUs each job can consume (here, 4CPUs). ‘Low’ allows even more concurrent use but only single-threaded execution, i.e., no parallelism. Let's begin by comparing the single user experience with four popular in-database classification algorithms: Decision Tree (DT), Generalized Linear Model (GLM), Naive Bayes (NB), and Support Vector Machine (SVM). We start with the medium service level, which caps the number of CPUs at 4, and for a 16 CPU environment, the number of concurrent jobs at 20 (1.25 * 16). We use the ONTIME data set with 8 columns (equating to 70 coefficients when accounting for categorical variables). Notice the linear scalability across the range of data set sizes, i.e., a doubling in the number of rows is roughly doubling the run time. While there is some variation in the individual job execution times, this plot depicts the average execution time. Next we look at the effect of the high service level, which enables a job to use up to the number of CPUs allocated to the database instance, in this case 16 CPUs. As we noted earlier, different algorithms respond differently to data, but even to the number of available resources. In some cases, increased parallelism can actually adversely impact performance as we see with SVM above due to the overhead of introducing parallelism for "smaller" data sets. However, at higher data volumes, the additional CPU resources clearly improve performance by about 50% for 800M rows. The remaining algorithms saw performance improvements across the range. As with the medium service level, we see effectively linear scalability across data sizes. Let's now turn our attention to concurrent users. We start with the medium service level using the Generalized Linear Model (GLM) algorithm. It is interesting to note that since each run is limited to 4 CPUs, auto-scale had it's most significant impact at 8 concurrent users. At 4 concurrent users and 4 CPUs each, this consumed the 16 CPU, so this should not be surprising. When we turn auto-scale on, there are more CPUs available for more concurrent users. This illustrates how a team of data scientists can work in the same environment with modest impact on one another and that this can be further mitigated with auto-scale.   Here, we look at the Support Vector Machine (SVM) algorithm regression. The performance benefits with auto-scale enabled are particularly beneficial for 4 and 8 concurrent users with a ~30% reduction in execution time for 400M rows. As discussed above, Oracle Autonomous Database with Oracle Machine Learning provides scalability and performance for data science teams, while providing powerful machine learning tools and autonomous data management capabilities. Thanks to Jie Liu and Marcos Arancibia for their contributions to these performance results.

In many organizations, a data science project likely involves the data scientist pulling data to a separate analytics server - analyzing and preparing data and building machine learning...

Database Cloud Services

Sharding Oracle Database Cloud Service

Oracle Sharding is now available in Oracle Cloud with Oracle Database Cloud Service as well as Kubernetes and Docker containers (OKE). Oracle Sharding enables hyperscale, globally distributed, converged databases. It supports applications that require linear scalability, elasticity, fault isolation and geographic distribution of data for data sovereignty. It does so by distributing chunks of a data set across independent Oracle databases (shards). Shards can be deployed in the cloud or on-premises and require no specialized hardware or software. The following figure shows a table horizontally partitioned across three shards. Figure 1-1 Horizontal Partitioning of a Table Across Shards Benefits of Sharding Linear Scalability. Sharding eliminates performance bottlenecks and makes it possible to linearly scale performance and capacity by adding shards. Fault Containment. Sharding is a shared nothing hardware infrastructure that eliminates single points of failure, such as shared disk, SAN, and clusterware, and provides strong fault isolation—the failure or slow-down of one shard does not affect the performance or availability of other shards. Geographical Distribution of Data. Sharding makes it possible to store particular data close to its consumers and satisfy regulatory requirements when data must be located in a particular jurisdiction. Rolling Upgrades. Applying configuration changes on one shard at a time does not affect other shards, and allows administrators to first test the changes on a small subset of data. Unlike NoSQL solutions, Oracle Sharding provides strong data consistency, the full power of SQL, support for structured and unstructured data, and the Oracle Database ecosystem. Additional Information Please review following links for additional information. Sharding Oracle Database Cloud Service : This is the listing for sharding automation available in Oracle Cloud Infrastructure Marketplace. It automates provisioning and management of Oracle Sharded Database. Automation includes the following features Supports system-managed sharding on Oracle Enterprise Extreme performance database edition. Automatic and uniform distribution of Shards, Catalog and Shard Directors across ADs in a region and Fault Domains within each AD. Supports dataguard based data replication for shards and catalog and thus provides high availability within a region. Provides the ability to easily scale the number of Shards and provides horizontal scalability. Provides the ability to easily scale the number of Shard Directors for high availabillity and load balancing of requests. Sharding with Kubernetes and Docker containers in Oracle Cloud (OKE) - This GitHub repository has deployment procedures for automating provisioning of Oracle Sharded Databases on Oracle Kubernetes Engine (OKE) using Oracle Cloud Infrastructure Ansible Modules and Helm/Chart. Reference Architecture Patterns - These are customer inspired Oracle Cloud deployment architecture patterns with best practices for scalability, availability and security Oracle Database Sharding - This is Oracle Sharding product page which has latest information, customer case studies and links to various resources.  

Oracle Sharding is now available in Oracle Cloud with Oracle Database Cloud Service as well as Kubernetes and Docker containers (OKE). Oracle Sharding enables hyperscale, globally...


Standard Edition High Availability Released - See What's New

The "Standard Edition 2 – We Heard You! Announcing: Standard Edition High Availability" blog post published approximately two months ago resulted in a lot of interest in this new feature - Thank you! It is therefore with great pleasure that I can announce the general availability of Standard Edition High Availability (SEHA) on Linux, Microsoft Windows and Solaris with Oracle Database 19c, Release Update (RU) 19.7. Additional operating systems are planned to be supported later this year. What's New As the Oracle Database 19c New Features Guide under the "RAC and Grid section" states, Standard Edition High Availability provides cluster-based failover for single-instance Standard Edition Oracle Databases using Oracle Clusterware. It benefits from the cluster capabilities and storage solutions that are already part of Oracle Grid Infrastructure, such as Oracle Clusterware, Oracle Automatic Storage Management (Oracle ASM) and Oracle ASM Cluster File System (Oracle ACFS). Standard Edition High Availability is fully integrated with Oracle Grid Infrastructure starting with Oracle Grid Infrastructure 19c, Release Update 19.7. The prerequisites for SEHA database systems are therefore largely the same as for all Grid Infrastructure-based database systems, as discussed under Requirements for Installing Standard Edition High Availability. Standard Edition High Availability databases are not Real Application Clusters (RAC)-enabled. Oracle RAC One Node is a RAC-enabled database in that the RAC-option needs to be enabled in the Oracle Database home from which the database runs. This is not the case for Standard Edition High Availability databases. While both solutions provide cluster-based failover for the Oracle Database, RAC One node supports additional high availability features further reducing planned maintenance related downtime that are not part of the Standard Edition High Availability offering. For more information on how Standard Edition High Availability, Oracle Restart, Oracle RAC and Oracle RAC One Node compare, see the High Availability Options Overview for Oracle Databases using Oracle Clusterware. Standard Edition High Availability databases can be licensed using the “10-day failover rule”, which is described in this document. This rule includes the right to run the licensed program(s) [here the Standard Edition High Availability database] on an unlicensed spare computer in a failover environment for up to a total of ten separate days in any given calendar year. This right only applies when a number of machines are arranged in a cluster and share one disk array, which is the case for Standard Edition High Availability databases by default. In addition, SEHA databases are subject to all licensing regulations that generally apply to a Standard Edition 2 (SE2) single-instance Oracle Database. Note that SEHA databases are not subject to a per-cluster socket limitation, but need to adhere to the per-server socket limitation that applies to any Standard Edition 2 Oracle Database.  Standard Edition High Availability databases can either be freshly installed or configured using an existing single-instance Standard Edition Oracle Database. There is no direct upgrade path from either a single-instance or a pre-19c Oracle RAC Standard Edition 2 database. While the database can be upgraded, configuring it to be a Standard Edition High Availability database requires additional manual steps explained in the Managing Standard Edition High Availability with Oracle Databases section of the Oracle Database Administrator’s Guide. Standard Edition 2 Oracle RAC databases need to be converted to single-instance Standard Edition Oracle Databases prior to upgrading to Oracle Database 19c, as described in My Oracle Support note 2504078.1. Concluding, Standard Edition High Availability provides a fully integrated cluster-based failover for Standard Edition Oracle Databases using Oracle Clusterware. Oracle’s Standard Edition 2 (SE2) customers thereby benefit from the high availability capabilities and storage management solutions that are already part of Oracle Grid Infrastructure, such as Oracle Automatic Storage Management (ASM) and the Oracle ASM Cluster File System (ACFS), free of charge.  Start testing here: Deploying Standard Edition High Availability and let me know your feedback, please.  

The "Standard Edition 2 – We Heard You! Announcing: Standard Edition High Availability" blog post published approximately two months ago resulted in a lot of interest in this new feature - Thank you! It...

Autonomous Database

Freedom to Build - Announcing Oracle Cloud Free Tier with New Always Free Services and Always Free Oracle Autonomous Database

On Monday, September 16th, 2019, Oracle Cloud Infrastructure (OCI) received a major update that enables students, professional developers, and anyone looking to jumpstart their next big idea to build, test, and deploy applications on Oracle Cloud and Oracle Autonomous Database free of charge for an unlimited time. This update adds a new set of Always Free services to OCI – including Always Free Oracle Autonomous Database – and converges them with Oracle’s existing $300, 30-day Free Trial program into a new unified Free Tier now available worldwide. How to Sign Up Getting started with Free Tier, which includes Always Free services and the Free Trial, is easy via a single streamlined sign up. Recognized Oracle users who sign up for Free Tier will see an accelerated onboarding flow with fewer verification steps and no requirement to enter a credit card. For example, students and educators in Oracle Academy; attendees at Oracle OpenWorld and Oracle Code One; designated Oracle Groundbreaker Ambassadors; and prospective customers engaged with Oracle Sales all can sign up without a credit card. Other new users must provide a credit card during sign up for identification purposes only. You can sign up and get started with Always Free services in less than 5 minutes. Simply open the Free Tier webpage at oracle.com/cloud/free and click on the prominent button for signing up. Note while you are signing up, the second step asks you to select a “Home Region”. This is important because Always Free services run only in your home region, and you cannot change it after the account is created. Make sure to click the displayed Regions link, review global data centers where your desired Always Free services are available, and select the home region that works best. Current paying customers who use Oracle Universal Credits and run in a supported commercial OCI Data Region will see new Always Free services added to their accounts automatically – no new sign up required. Always Free Services - What’s Included The new Always Free services that are part of Free Tier cover essential IaaS and PaaS technologies, providing generous resources for free as long as they are used. A summary of Always Free service shapes and specifications available at launch time is below. Not only do these Always Free services run on Oracle’s high-performance mission critical cloud infrastructure, they also incorporate Oracle’s premier cloud database: Oracle Autonomous Database. What You Can Do with Always Free Oracle Autonomous Database Always Free Oracle Autonomous Database delivers a comprehensive database experience, plus built-in tools for developers, administrators, and data scientists – now entirely free. It is a multi-model database for virtually any type of data (including relational, JSON, spatial, graph, multimedia, XML, files, and more) and any interface (full SQL, REST data access, and drivers for popular programming languages). The service supports both Autonomous Transaction Processing and Autonomous Data Warehouse workload types. It includes development tools such as SQL Developer Web for SQL and PL/SQL scripting, along with powerful command-line utilities. Developers also get a fully managed instance of Oracle APEX for rapidly building and deploying low-code apps with a modern aesthetic for mobile and desktop browsers. For data scientists, an included SQL notebook assists with creating Machine Learning models. What You Can Do with Always Free Oracle Cloud Infrastructure Services Always Free Oracle Cloud Infrastructure services include general purpose compute VMs; block, object, and archive storage; and a load balancer. You also get an allocation of free outbound data transfer, monitoring, and notifications. Together with Always Free Oracle Autonomous Database, users can leverage this bundle of free services to develop and deploy applications in diverse languages and frameworks, and to test drive enterprise infrastructure capabilities such as load balancing and storage cloning. In Conclusion Adding it all up, Oracle Cloud Free Tier with new Always Free services and Always Free Oracle Autonomous Database is a breakthrough for developers of all kinds. With easy sign up, a range of useful services, and full Oracle Autonomous Database, developers have the resources to build enterprise class solutions in the cloud quickly and at no cost. And best of all, because it is Always Free, they can do it without worry of losing work at a deadline set by their cloud provider. These new Always Free services greatly expand developers’ freedom to build. * * * * * * * * * * * * * * * * * * * * * To learn more, visit oracle.com/cloud/free. Speak with the experts at Oracle OpenWorld 2019. Join our session on Wednesday, September 18th, 2019 at 10:00am PT in Moscone South Room #203 – Oracle Cloud’s New Free Tier and Always Free Oracle Autonomous Database Speaker: Todd Bottger Session ID: PRO6695

On Monday, September 16th, 2019, Oracle Cloud Infrastructure (OCI) received a major update that enables students, professional developers, and anyone looking to jumpstart their next big idea to build,...

Autonomous Database

Enabling the Autonomous Enterprise

This post was contributed by Senior Principal Product Marketing Director, Ron Craig. Background – data overflow The ability of enterprises to generate data is increasingly outpacing their ability to realize real value from that data. As a result, opportunities for innovation, driven by customer, market and competitive intelligence are being left on the table. And given that only a subset of the avalanche of data is being put to good use, it’s entirely possible that the use of inadequate data is leading to bad decisions. A key source of this problem is that the productivity of human beings simply hasn’t kept pace with the technologies we have developed to help improve our business processes. IDC has predicted that by 2025, 6 billion consumers will have one data interaction every 18 seconds. At that point, the volume of global data will be 175ZB (175,000,000,000,000,000,000,000 bytes), and ~30% of that will be real time data – a 6X increase vs. 2018. The exponential increase in effort required to clean, arrange, secure, maintain and process the data from all those customers means less effort can be dedicated to insights. As a consequence, enterprises are not truly seeing the benefits from their own success in becoming data companies. Abstraction as a game-changer So what’s needed in response? Sometimes it’s good to look to other areas for inspiration, and innovation in the semiconductor industry provides some useful insights. That industry, since its early days, has had to deal with the fact that user productivity has struggled to keep pace with advances in technology, and has surmounted those issues with innovations that address those productivity limitations head on. Digital designs - the creations that comprise everything from the silicon chips to operate a timer on a microwave oven all the way up to the ability to forecast the weather with a supercomputer - are at their essence created from logical components, known as gates. These logic gates perform pretty routine Boolean operations, and effectively allow decisions or calculations to be made based on sets of inputs, and propagate those calculations in real time. Chip designers working at the gate level could be expected to produce verified designs (effectively combinations of connected gates) at a rate of ~50 gates per day - a productivity level that’s remained pretty constant over time. The processors in today’s high end cellphones may contain around 100 million gates, so a team of 100 chip designers working at the gate level would take 80 years to put such a chip together. In reality though, today such chips are often developed in two years or less, as a result of innovations introduced in the chip design flow over the last twenty years. For the purposes of this blog, and since it provides a useful analogy, the innovation we’ll focus on is the introduction of the hardware definition language (HDL). An HDL effectively works like software, allowing the chip designer to describe logic in a way that resembles what it does, as opposed to how it’s built, hence freeing the designer from the details of how that logic operation is implemented in hardware. HDL-based design goes hand in hand with automated synthesis algorithms, which translate those higher level descriptions into the equivalent gates that perform the same function, and which ultimately can be realized in silicon. As a result of these innovations, the semiconductor industry has enabled designers to keep up with the capacity of silicon chips by allowing them to be less and less concerned about the lower level implementation details of the chips they are designing, and put their focus on what those chips actually do. Chip designers take care of the ‘what’, where they can bring their creativity and experience to bear, and automation takes care of the ‘how’ in a reliable and repeatable fashion. Oracle Autonomous Database - Automating a path to innovation The semiconductor industry experience provides a useful blueprint for a path that the data industry must also take, demonstrating why automation is the key to unlocking the potential of today’s data, in the same way that innovation in the semiconductor industry has allowed designers to fully exploit the capacity of silicon chips. Across a range of industries, corporations differentiate themselves by what they do with the data they generate and collect, not in the effort they expend to manage and secure that data. To have maximum impact, database experts need to be able to maximize their focus on what their data is telling them (the ‘what’), and rely on automation to keep that data available and secure (the ‘how’). 95% of the respondents to a recent Oracle user survey noted that they are having difficulty keeping up with the growth in their data, and the majority of data managers are performing multiple major database updates per year. In addition to simply keeping the database up and running, the survey noted that significant manual effort continues to be dedicated to general troubleshooting and tuning, backup/recovery tasks, and provisioning to handle usage peaks and troughs. Data security also stands out as an area that can benefit significantly from automation, not only because automation can reduce manual effort, but because it can reduce risk. In an age where managers of on premises database setups much continuously juggle the urgency of patch installation with the cost of the downtime needed to install those patches, it comes as no surprise that a recent Verizon survey noted that 85% of successful data breaches exploited vulnerabilities for which patches were available for up to a year before the attack occurred. It makes perfect sense to instead make use of Oracle Autonomous Database to automatically apply security patches with no downtime. In total, these automated capabilities reduce administrative costs by 80%, meaning that the Autonomous Enterprise taking advantage of these advances can dedicate significantly more effort to innovation. Coming back to our semiconductor analogy, innovations in how design is done didn’t make chip designers less necessary, rather it made them significantly more productive and enabled them to make more innovative use of advances in technology. We expect the Oracle Autonomous Database to have the same impact for DBAs and data managers in the Autonomous Enterprise. Learn more at Oracle Open World 2019 To learn more about how enterprises who have already become autonomous, visit the sessions below at the 2019 Oracle Open World event – Drop Tank: A Cloud Journey Case Study, Tuesday September 17, 11:15AM – 12:00PM Oracle Autonomous Data Warehouse: Customer Panel, Tuesday September 17, 1:45PM – 2:30PM Oracle Autonomous Transaction Processing Dedicated Deployment: The End User’s Experience, Tuesday September 17, 5:15PM – 6:00PM Managing One Of the Largest IoT Systems in the World With Autonomous Technologies, Wednesday September 18, 9:00AM – 9:45AM

This post was contributed by Senior Principal Product Marketing Director, Ron Craig. Background – data overflow The ability of enterprises to generate data is increasingly outpacing their ability to...

Autonomous Database

Making Compromise Obsolete with Oracle Gen 2 Cloud

This post was contributed by Senior Principal Product Marketing Director, Ron Craig. There’s an old saying in the automotive world – “Cheap, fast, reliable – pick any two”. What’s interesting is as self-driving and automation in general progresses, the introduction of enabling technologies means those old trade-offs may no longer apply. Out of all of those advances, one that I find particularly intriguing (for as long as the concept of a multi-speed transmission remains relevant!) is the idea of the predictive transmission. A predictive transmission addresses the problem that a regular automatic transmission will typically only react to a steep hill or sharp bend by downshifting after encountering that change in road topology to match your slower engine speed, meaning there’s a period of time during which the car isn’t performing optimally. With a predictive transmission, now available in many production cars, the transmission is linked to the car’s navigation system, giving it the intelligence to get into the appropriate gear in advance. Having knowledge of what is ahead allows the transmission to shift appropriately before a change in road conditions, avoiding that period of sluggishness we’ve all encountered where most automatic transmissions are still figuring out how to react. The end result is the best of both worlds – better fuel economy and better performance – two things that you typically would have had to choose between in the past. In short, compromise becomes obsolete. In the same way, by being proactive rather than reactive, Oracle Generation 2 Cloud is anticipating and acting on your needs rather than always waiting for your guidance, meaning you don’t necessarily need to choose between cost, speed and reliability. Oracle Generation 2 Cloud goes beyond first generation cloud technology to offer organizations advanced capabilities that allow them to run their existing and future workloads better and faster. The Oracle Generation 2 Cloud also allows you to get the most out of your data, while also ensuring it’s available and secured. On the database front, integrated machine learning (ML) capabilities in Oracle Autonomous Database allow you to extract more value from your data – moving algorithms to the data rather than the other way around. Oracle Autonomous Database provides options to extract value from the database using SQL analytics, or gain insights and make predictions via native ML. ML algorithms are available as native SQL functions – in effect we’ve taught the database how to do higher level math. Models that used to take weeks to build can now be built in minutes, and multiple data scientist user roles are supported (DBAs, app developers, data scientists etc.).   Oracle Machine Learning Notebook Moving the analytics to the database opens up a new world of possibilities. For example, you can now dedicate more time to things like identifying which machines in your factory will soon require maintenance and only shut down what really needs to be shut down, rather than pausing production on all equipment for routine, perhaps unnecessary, maintenance. Oracle Cloud technology is helping innovators disrupt a broad range of industries. We’re seeing financial services use AI for automatic forecasting without human intervention, to smart manufacturing utilizing real-time IoT data for equipment optimizations. In a particularly compelling example, a medical lab testing company is using Autonomous technologies to shorten results from weeks to minutes. This allows for faster time to diagnosis, which means fast time to treatment, which ultimately means the shortest time to recovery. Cyber Security company FireEye expects to not only better protect their customers through the ability to analyze tens of millions of emails daily, but also reduce disaster recovery costs by 40-50% by moving to Oracle Cloud. Oracle Cloud Blockchain Technology is helping logistics and shipping management company CargoSmart simplify the otherwise complex documentation process and deliver a single source of truth for trusted, real-time sharing of information, increasing trust and boosting the efficiency of ocean freight shipping. OUTFRONT Media is using Oracle Analytics Cloud and Oracle Autonomous Data Warehouse to gain the insight that help their clients make the most effective use of their advertising budgets. What do all of these companies have in common? That’s simple - none of them is a database or compute infrastructure management company. They leave that to Oracle and the Oracle Generation 2 Cloud, while they focus their efforts on growing their businesses by providing differentiated value to their customers. Learn more at Oracle Open World 2019 To learn more about Oracle’s Generation 2 Cloud or to hear from our customers themselves, visit the sessions below at the 2019 Oracle OpenWorld event - The Power of Insights: Better Business Decisions with Oracle Autonomous Data Warehouse, Monday September 16, 1:45 PM – 2:30 PM | Moscone West – Room 3007B Oracle Cloud: A Path and Platform, Tuesday September 17, 11:15 AM – 12:00 PM | YBCA Theater Solution Keynote: Oracle Cloud Infrastructure Strategy and Roadmap, Tuesday, September 17, 03:15 PM – 05:00 PM | Moscone South – Room 207/208 Customer Case Study Session: Oracle Cloud Infrastructure Enables Threat Detonation in World-Leading Security Products, Monday, September 16, 01:45 PM – 02:30 PM | Moscone South – Room 152A Oracle Autonomous Data Warehouse: Customer Panel, Tuesday, September 17, 01:45 PM – 02:30 PM | Moscone South – Room 211

This post was contributed by Senior Principal Product Marketing Director, Ron Craig. There’s an old saying in the automotive world – “Cheap, fast, reliable – pick any two”. What’s interesting is as...

Autonomous Database

The Oracle Autonomous Data Warehouse: Architecting Advanced Data Platforms to Support Data Management

Part 2 of 3-Post Series: Architecting Advanced Data Platforms to Support Data Management ‘Smart’ or cognitive solutions are all the rage in today’s data-driven world. Specifically, solutions like AI, machine learning and data analytics that help your business ‘think’ and leverage data more effectively. The future of these cognitive solutions is expected to flourish in the coming years due to billions of investments pouring into the cognitive solutions vertical. But what’s driving this market for cognitive data solutions? First, data is really, really important and valuable. Secondly, there is a general desire to make business processes more intuitively intelligent to drive competitive advantage in the modern business environment. This smartening, if you will, of the business is absolutely necessary to allow leaders to respond to market trends, positively impact customer experience, and to adapt to a quickly evolving digital economy. Without these smarter solutions, businesses are anchored and are left responding reactively to market dynamics.  The bottom line: This increase in demand for data-driven solutions is a direct outcome of the digitization and generation of a massive amount of data across industries. And that data footprint will only continue to grow. In the first post of this three-part article series, I noted that the amount of data generated in the world will grow from 33 Zettabytes (ZB) in 2018 to 175 ZB by 2025. To put that into perspective, 90 percent of the world’s data was generated in the past two years. And when it comes to managing all of this data, the market is responding: Analysts forecast that the global cognitive solution market will grow at a CAGR of almost 50% during the period 2018-2022. Compelling stats but you’re probably asking why should I care? Fair and here’s why you should: It’s these cognitive solutions that draw insights from business process data and make data-based predictions to augment human decision-making capabilities. That alone is a pretty big deal, but imagine what you can do if you had to do nothing – ie, if you could automate and data ingestion and processing? If you ask me, that’s when the exciting part (the piece that gets the executive office engaged) takes place. It’s exciting because the business can leverage smarter solutions to make better decisions with a level of visibility at the forefront of digital transformation, allowing organizations to truly capture the momentum of the market. Imagine being able to tell which products are doing better in specific markets based on patterns that your data warehouse helps you find proactively. Or, being able to dynamically customize retail and shopping experiences based on data and previous client interactions. From a competitive perspective, solutions like sentiment analysis allow you to leverage machine learning to better understand market sentiment around your services, as well as those of competitors. There are security examples when it comes to cognitive solutions as well. AI-driven security features can detect anomalous behavior and take action proactively. Similarly, cognitive systems can also help detect and stop fraud based on data metrics. When it comes to cognitive engines, smart quickly becomes the new normal. Oracle Autonomous Data Warehouse does exactly this. It uses applied machine learning to self-tune and automatically optimize performance while the database is running. You’re literally working with a solution that gives you foresight into the market to help disrupt and transform the kinds of service and products you deliver. Built on next-generation autonomous technology, Oracle Autonomous Data Warehouse uses artificial intelligence to deliver unprecedented reliability, performance, and highly elastic data management to enable data warehouse deployment in seconds. Oracle Autonomous Data Warehouse brings new meaning to the concept of automatic or ‘autonomous’ data management. Here’s what that means: Self-Driving. If Oracle Autonomous Data Warehouse had wheels, it would drive itself. However, the self-driving element here refers to a fully managed data warehouse cloud service that takes care of network configuration, storage, and database patching and upgrades for you. No customer DBA required. Self-Securing. This part is truly revolutionary and a first of its kind in our industry. The self-securing part of the database ensures that the architecture always runs the latest security patches. It will also detect anomalous behavior and conduct updates, all on its own, autonomously while still running! Further, the data at rest is encrypted by default using Transparent Data Encryption (TDE). Finally, database clients use SSL/TLS 1.2 encrypted and mutually authenticated connections. (We’ll get much more into the unique self-securing architecture in the third blog of our series.) Self-Repairing. No one wants to experience an outage. It causes a lot of stress and can impact, even halt, the flow of your business. Oracle Autonomous Data Warehouse has automated protection from downtime, purpose-built into the core of the design. High availability is built into every component, and backups are completely automated. This means you can get your nights and weekends back knowing you’ve got a data platform that’s actively working to keep your stuff operating. Sounds cool, right? Let’s pop the hood of this autonomous data vehicle and see how it really works. Here’s what the Oracle Autonomous Data Warehouse looks like: Oracle Autonomous Data Warehouse is your direct engine and integration point into the entire DevOps process. Most of all, data-driven applications can leverage machine learning to deliver powerful results while utilizing local services alongside third-party solutions. This means that your existing developer tools, data integration services, data visualization, and cloud object storage easily integrate with Oracle Autonomous Data Warehouse while still leveraging the power of cloud. To get the most value out of the data, you can visualize it in any way you require. Leverage the Oracle Data Visualization Desktop or use your own third-party business intelligence or data visualization solution. Oracle Data Visualization Finally, and this is more of the revolutionary part, Oracle machine learning provides a notebook application designed for SQL users and provides interactive data analysis that lets you develop, document, share, and automate reports based on sophisticated analytics and data models. Oracle Machine Learning Notebook Oracle Machine Learning SQL notebooks, which are based on Apache Zeppelin technology, enable teams to collaborate, build, evaluate, and deploy predictive models and analytical methodologies. From there, this SQL notebook acts as an interface for data scientists to perform machine learning in the Oracle Autonomous Data Warehouse (ADW). These notebook technologies support the creation of scripts while supporting the documentation of assumptions, approaches and rationale to increase data science team productivity. With greater levels of built-in automation and intelligence, you can couple the data warehouse with powerful machine learning and cognitive solutions. This allows for fast and easy collaboration between data scientists, developers, and business users as it leverages the scalability and performance of the Oracle platform and its cloud services. Let’s recap. In a nutshell, here are some of the benefits of a data-driven, autonomous solution, specifically, Oracle Autonomous Data Warehouse: Simplified, end-to-end management of data and data warehouse resources Fully tuned and ‘ready to go’ for your data requirements – including high performance, out of the box Fully elastic scaling with intelligence around idle-compute shut off Auto-scaling based on dependencies and real-time workload requirements Ability to support solutions running on premise, hybrid, or multi-cloud Ability to leverage native or third-party data integration tools High-performance queries and concurrent workloads: Optimized query performance with preconfigured resource profiles for different types of users Powerful data migration utilities to move vast amounts of data Deep integration with data storage, repository, and processing engines including Amazon AWS Redshift, SQL Server, and other databases If you’re worried about security (and who isn’t), we’ll cover that critical topic in part three of our series where I’ll dig deeper into how Autonomous Data Warehouse stores all data (in rest and motion) in encrypted format. Final Thoughts: The Future is Data-Driven. Make Sure You and Your Data are Ready It doesn’t matter what vertical you’re in or how big your company is, data impacts your future. Those organizations that find ways to not only leverage data, but also make it easy to do so, will find meaningful, competitive advantages in a digital economy. From my point of view, Oracle’s Autonomous Data Warehouse provides an easy-to-use, fully autonomous database that scales elastically, delivers fast query performance and requires no database administration. This is the kind of technology that removes the complexity around working with data. Most of all, it allows you to truly leverage the power of data to do innovative and bottom-line enabling activities in a data-driven future.

Part 2 of 3-Post Series: Architecting Advanced Data Platforms to Support Data Management ‘Smart’ or cognitive solutions are all the rage in today’s data-driven world. Specifically, solutions like AI,...