The Oracle Autonomous Data Warehouse: a three-part series

Part 1: Injecting intelligence and autonomy into data management

Today’s connected society is driven, if not given life, by data. Don’t believe me? Consider that by 2025, each connected person will have at least one data interaction every 18 seconds. That’s 4,800 times per day! Furthermore, by that same year, more than a quarter of data created will be real time in nature, with real-time IoT data making up more than 95% of this.

All these devices and interaction points with new, connected systems are slated to grow the global data footprint 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. The figures become even more staggering when you imagine the exponential amount of data we’ll create over the next five years.

If you look at the data-driven market in general, solutions around data management are growing and show no signs of slowing. To better manage the vast growth around data creation, IDC pointed out that big data and business analytics (BDA) related software are quickly becoming the two largest software categories.

“Digital transformation is a key driver of big data and analytics spending with executive-level initiatives resulting in deep assessments of current business practices and demands for better, faster, and more comprehensive access to data and related analytics and insights,” said Dan Vesset, group vice president, Analytics and Information Management at IDC. “Enterprises are rearchitecting to meet these demands and investing in modern technology that will enable them to innovate and remain competitive.”

The net? As sure as the sun will rise tomorrow, so will the volume of data continue to grow and compound. It’s critical for businesses to learn the value of data and how to best leverage it. This also means learning new ways to ingest different data types, how to intelligently process it, and certainly how to secure and protect it, along with how data can enable better market decisions. Because without data-driven solutions, businesses will stall, overcome with the inability to gain real value from their own hard-earned natural resources – data.

So, what’s the reality if businesses don’t actually invest in data-driven solutions? It’s actually pretty grim. Reminder: whether you like it or not, your business will generate data. And, you’ll likely generate a lot of it over the course of a year.

That data contains information about your products, services, solutions, customers, and so much more. Without good data engines that help correlate and understand the information, your data sits idle and will never reach the working potential that you need.

Without a data-driven solution, you’ll quickly see both your competitors and the market pass you by. You won’t be able to react to industry trends as quickly, nor will you have a good vision into customer requirements, and most of all, you’ll lose serious dollars because you won’t have reliable insights to make better decisions.

So how do you know if you’re actually using data properly — or using proper data? This is where a good partner can help. But, from an even more straightforward perspective, you can ask yourself some pretty basic questions:

1. Do I have the ability to aggregate various data points to make better business decisions?

2. Is my data sitting idle in silos doing nothing?

3. Do I have a means to leverage data analytics engines to see relationships between the market, my customers, and my data?

4. Am I able to impact business direction with clear data points?

5. Do I have good visibility into my data streams or is it sitting unguarded and unused?

The first step to gaining control of your data platform is to understand that you might have a data management challenge. But with so much data being created every single day, designing an intelligent data processing solution isn’t always easy.

In this three-part series, we’ll examine the world of data warehousing, how autonomous data warehouses are built, along with details about how to create an effective security strategy with data and autonomous data warehouses.

The Difference Between Simple Data Storage and Intelligent Data Warehousing

Our reliance on data has also created new ways to process, store, and utilize information. In fact, there are now ways to store data where it sits idle (simple storage), as well intelligent data warehousing – where intelligence is directly integrated into the storage and data ingestion process.

  • Traditional Simple Storage: Think of this as a storage array that sits in your data center. Similarly, it can be a storage repository that’s in a colocation or the cloud. These ‘simple’ storage mechanisms literally store only data. They’re highly resilient, capable of archival storage or even all-flash performance. They’ll even have some stats and analytics around storage utilization, file types, and ways to optimize performance and experience. However, the data-driven analytics portion of these solutions will usually be minimal at best. That means that although you can leverage some of the best systems out there to store your data, you’ll still need a data engine that can help you find ways to apply and use the data.
  • Intelligence-Driven Data Storage and Warehousing: Unlike traditional storage and file repositories, think of these systems as powerful data ingestion engines. They can store your data. But they also connect to various data streams like ERP systems, cloud applications, backend databases, and more to ingest, quantify, correlate, and process vast amounts of data. This data can come from all sources and can be different in structure. Unlike the many simple storage solutions out there, intelligence-driven data warehousing also lets you work with data that’s structured, unstructured, semi-structured, and so on. From there, you can leverage powerful data-driven tools to make even better business decisions. These tools are applications and services that are designed to find patterns and trends impossible to spot using traditional storage methods.

The New Wave of Database Automation Is Self-Driving

Here’s a specific example: Coupled with solutions around data analytics and big data processing, data warehousing allows you to take valuable information to a new level. Powerful data warehouse solutions help you create data visualization to make better decisions around your business and the market. A data warehouse helps with data ingestion and is a decision support system which stores historical data from across the organization, processes it, and makes it possible to use the data for critical business analysis, reports, and dashboards.

Now, let’s take this a step further. Let’s say you’re a large organization dealing with massive streams of data. Or, maybe you’re a company that needs to respond to certain trends and data analysis points. How do you take your data warehouse solution and create levels of autonomous behavior? Most of all, how do you work with cognitive solutions like AI, machine learning, and even advanced data analytics to get even more value out of your data?

Recently, IDC estimates that the amount of the global datasphere subject to data analysis will grow by a factor of 50 to 5.2ZB in 2025; the amount of analyzed data that is “touched” by cognitive systems will grow by a factor of 100 to 1.4ZB in 2025!

From that perspective, to leverage data, you need to not only support new data center initiatives, but also enable the business to become a part of a data-driven world.

This is where data warehouses that are autonomous and capable of cognitive data processing can revolutionize the way you leverage data, work with intelligent systems, visualize information, and deliver powerful new competitive advantages to your business.

A dashboard offering a personalized data view from a range of data sources, aggregated within the Oracle Autonomous Data Warehouse.

As pictured in the image above, you can see how new solutions – in this case, specifically, Oracle Autonomous Data Warehouse – are changing the way organizations can leverage data to create competitive advantages.

Basically, the autonomous solutions leverage cognitive systems to allow the data to be processed far more effectively to help your business react to major market trends and customer demands. Beyond that, you can actually leverage powerful dashboards which make data analytics far easier and proactive to the market. And here’s the cool part, so much of this is automated for you when you leverage an autonomous data warehouse! But, before we go too much further, it’s important to note what we actually mean by a data warehouse that is autonomous:

The Oracle Autonomous Data Warehouse provides an easy-to-use, fully autonomous data warehouse that scales elastically, delivers fast query performance and requires no database administration. It is designed to support all standard SQL and business intelligence (BI) tools, and provides all the performance of the market-leading Oracle Database in an environment that is tuned and optimized for data warehouse workloads.

As a service, the Oracle Autonomous Data Warehouse does not require database administration. With Oracle Autonomous Data Warehouses, you do not need to configure or manage any hardware or install any software. The Autonomous Data Warehouse creates the data warehouse, backs up the database, patches and upgrades the database, and grows or shrinks the database as needed, automatically.

Oracle Autonomous Data Warehouse

“Oracle has thought a great deal about how to give businesses the ability to take advantage of that potential, without adding a tremendous amount of strain on their IT resources which is why we developed Oracle Autonomous Database,” Oracle Product Marketing Manager, Christopher McCarthy says.

Remember, the entire idea here is to simplify the way you work with and manage vast amounts of different types of data. This is a big reason why Oracle’s Autonomous Data Warehouse makes working with data way easier because it does not require any tuning. By design, Autonomous Data Warehouse is a ”load-and-go” service. You start the service, define tables, load data, run queries, and then get value out of the data. That’s it. Seriously.

A good example is car rental company, Hertz who are utilizing Autonomous Data Warehouse to reduce IT administration, increase data security, and analyze their data in Oracle Autonomous Analytics Cloud. Before Autonomous Data Warehouse, it would take two weeks, end-to-end, for Hertz to get the approvals necessary to provision a new database, provision it, tune it and be ready to load data. With Oracle Autonomous Data Warehouse, they were able to provision and load their data in just 8 minutes!

Again, this is specifically designed for ease of use and getting results from data, fast. When you use Autonomous Data Warehouse, no tuning is necessary. You do not need to consider any details about parallelism, partitioning, indexing, or compression. The service automatically configures the database for high performance queries.

Furthermore, Autonomous Data Warehouse includes a cloud-based service console for managing the service (for tasks like creating or scaling the service), and monitoring the service (for tasks such as viewing the recent levels of activity on the data warehouse).

Autonomous Data Warehouse also includes a cloud-based notebook application that provides simple querying, data-visualization, and collaboration capabilities. The notebook is designed for use alongside other business intelligence applications. These Oracle Machine Learning notebooks, based on Apache Zeppelin technology, enable teams to collaborate to build, evaluate, and deploy predictive models and analytical methodologies in the Oracle Autonomous Data Warehouse.

Multi-user collaboration lets multiple users have the same notebook open at the same time. Changes made by one user are immediately updated for other team members. Plus, these data-driven solutions are also really smart when it comes security. Supporting enterprise requirements for security, authentication, and auditing, Oracle Machine Learning notebooks adhere to all Oracle standards and supports privilege-based access to data, models, and notebooks.

Putting It All Together

There’s absolutely no question that we’re creating more data. And, we know that this data is very valuable to every business that’s trying to compete. The Oracle Autonomous Data Warehouse isn’t just another data processing engine. It’s designed to ‘think’ while making data ingestion and processing much easier. You’re working with a technology that can understand various types of data inputs and actually adjust, automatically, based on the data set and the type of queries you’re creating. What does this mean? You can focus more on the results of the data and less on trying to set it all up.

To that extent, it’s really important to grasp the architecture behind the Oracle Autonomous Data Warehouse. That’s why I lifted up the hood on the product and took a look inside.

Conclusion

In part two of our three-part blog series, we’ll review the Oracle Autonomous Data Warehouse architecture, where there are key benefits, and even how data visualization is impacted by new and advanced autonomous solutions.

In the meantime, register for this webcast to learn more about Oracle Autonomous Data Warehouse, its’ key benefits and advantages (including how it’s 8-14x faster than AWS) and how easy it is to get started with the world’s first self-driving database.

For more information on how Oracle Cloud outperforms the competition, follow #LetsProveIt on Twitter and LinkedIn. And if you haven’t yet tried Oracle Cloud, sign up for a free trial.

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