DBAs Unleashed: How Autonomous Databases Slay GenAI Challenges

AI models are like demanding toddlers — they need constant supervision and a steady diet of pristine data to deliver valuable insights. GenAI might offer some wiggle room, but garbage in, garbage out still applies. The problem? Data is often scattered, siloed, and riddled with inconsistencies. It's the worst nightmare for a database adminstrator (DBA).

And now, GenAI is adding fuel to the fire. Complex queries, fueled by power-hungry GPUs, are limiting resource utilization. DBAs are drowning in mundane tasks, while sky-high GPU costs and water consumption are raising eyebrows in the C-suite.

Oracle thinks it has the antidote: Autonomous Database (ADB). Announced in 2017 and released as Autonomous Data Warehouse Cloud Service in 2018, this self-driving database is getting a fresh look in the age of GenAI and LLMs.

Mythbusting ADBs

First things first, ADB isn't some newfangled gizmo. It's built on Oracle's bedrock technology — Oracle Database Enterprise Edition, running on Exadata — and has quietly revolutionized database management for years. The latest iteration, powered by Oracle Database 23ai, is supercharged by AI for the AI era.

Essentially, Oracle wants you to forget the days of painstaking tuning of SQL queries and fretting over infrastructure. ADB is like self-driving cars in the data world. They handle the mundane tasks of patching, upgrading, and optimizing, freeing DBAs to focus on more strategic initiatives. Security is baked in (and with Database 23ai, security against SQL injection is part of the product), while it helps to develop and deploy AI applications faster.

“We've heard from many customers for many years that this is the biggest issue they have with the Oracle database,” says Çetin Özbütün, executive vice president for data warehouse and autonomous database technologies at Oracle. “In the age of the cloud, we said, okay, let Oracle be responsible for the entire management.”

So, why are ADBs a big deal for DBAs besides having the same alphabet letters? With their mundane workloads reduced, they can start doing the “fun” part, like data modeling. They can also work closely with developers, data scientists, and data architects.

AI inside and out

ADB isn't just managed by machine learning; it's infused with it. ML algorithms hum beneath the surface, constantly profiling data and optimizing queries for lightning-fast performance. This means your AI applications can access and process the information they need with minimal latency.

“As soon as [DBAs] load data into it, there's a lot of AI algorithms built into the database to profile their data, to make it ready, very easily query-able, with high performance,” Özbütün explains.

However, building AI applications directly within the autonomous database is the true advantage. Imagine querying your data using English instead of complex SQL. With ADB's Select AI feature, this is a reality.

“You can use any one of the 35 or so different LLMs that we support from seven different vendors to query the database in your natural language,” says Özbütün. “Autonomous database helps take that native language query and turn it into SQL and then execute it.”

This democratization of data access adds a new dimension to working with databases. Now, your non-technical colleagues can glean data insights easily without adding to the DBA’s pipeline of tasks.

No more hardware headaches

Remember those eye-watering quotes for GPUs? The new ADB can help there, too. With its pay-per-use model for GPU-powered VMs, you can run your AI models without breaking the bank.

“GPUs are very hard to get,” Özbütün acknowledges. “But as part of the autonomous database, you can just say, ‘I want to run this model on a GPU,’ and it will rent you one on an hourly basis.”

This flexibility is crucial in the rapidly evolving world of AI, where new models and hardware emerge constantly.

But ADB isn't just about the basics. It also embraces cutting-edge AI techniques like Retrieval Augmented Generation (RAG) and vector databases by running Oracle Database 23ai. “You get all of the capabilities like vector searching and vector indexing,” Özbütün confirms.

These features allow you to build more sophisticated AI applications to understand and analyze complex relationships within your data. Remember that with RAG, your data size increases with embeddings, etc. ADBs keep the workload more manageable and allow DBAs and data engineers to look at chunking strategies together to make RAG scalable.

And what about data sovereignty, a growing concern in today's globalized world? ADB has that covered, too, with support for multiple cloud regions and on-premises deployments.

“We're offering lots of public cloud regions,” says Özbütün. “And with the whole multi-cloud thing, we're offering it in Azure, Google, and AWS data centers.”

The future of databases is autonomous

As AI becomes increasingly intertwined with our lives, the database will be mission-critical. ADB offers a lifeline for DBAs, allowing them to escape the daily grind and become true AI innovators.

It's not about replacing DBAs; it's about empowering them to shape the future of data management. With ADB, they can finally break free from the mundane and embrace a world of possibilities.

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