Amazon Enhances Redshift with New AI Features to Improve Efficiency and Productivity

Amazon Redshift is leveraging artificial intelligence (AI) to enhance operational efficiency and boost productivity through two newly launched capabilities now available in preview.

Enhanced Amazon Redshift Serverless

Firstly, Amazon Redshift Serverless has been upgraded to become more intelligent. It automatically and proactively adjusts capacity based on factors such as query complexity, frequency, dataset size, and more, providing tailored performance improvements. This means you can devote less time to tuning your data warehouse and focus more on deriving value from your data.

Introduction of Amazon Q Generative SQL

Secondly, the introduction of Amazon Q generative SQL in the Amazon Redshift Query Editor allows for SQL recommendations to be generated from natural language prompts. This significantly enhances productivity when extracting insights from your data.

Advancements in Amazon Redshift Serverless

Let’s delve into the advancements in Amazon Redshift Serverless. By opting into the preview of AI-driven scaling and optimizations, users can benefit from a system that learns from their usage patterns, including the number of concurrent queries, their complexity, and execution time. This system then automatically fine-tunes serverless endpoints to achieve optimal price-performance targets. According to internal AWS tests, this capability can offer up to ten times better price performance for variable workloads, all without the need for manual intervention.

The AI-driven scaling and optimizations reduce the time and effort necessary to manually resize your workgroup and plan optimizations based on workload demands. The system consistently performs automatic optimizations at the most beneficial times, averting performance dips and time-outs.

This new feature surpasses the existing self-tuning functionalities of Amazon Redshift Serverless, which already included machine learning (ML) techniques for adjusting compute resources, modifying database schemas, and managing materialized views. The latest enhancements incorporate a broader range of dimensions for decision-making regarding compute adjustments and necessary background optimizations. We also coordinate ML-based optimizations for materialized views and workload management as queries demand it.

During the preview phase, users must opt in to activate these AI-driven features. They can configure the system to prioritize either price or performance through a single slider in the console.

As always, you can monitor resource usage and associated modifications via the console, Amazon CloudWatch metrics, and the system table SYS_SERVERLESS_USAGE.

Exploring Amazon Q Generative SQL

Now, let’s explore the Amazon Q generative SQL feature in the Amazon Redshift Query Editor. Imagine being able to utilize generative AI to assist analysts in crafting SQL queries faster. This is the enhanced experience now available in the Amazon Redshift Query Editor, our web-based SQL editor.

You can describe the data you wish to analyze using natural language, and the system will generate SQL query recommendations. Under the hood, Amazon Q generative SQL employs a large language model (LLM) and Amazon Bedrock to create SQL queries. It utilizes techniques such as prompt engineering and Retrieval Augmented Generation (RAG) to contextualize your request based on the connected database, schema, query history, and optionally, the query history of other users on the same endpoint. The system even retains past inquiries, allowing you to refine previously generated queries.

The SQL generation model leverages metadata specific to your schema, including table and column names and the relationships among tables. Furthermore, your database administrator can permit the model to utilize the query history of all users within your AWS account to enhance the relevance of generated SQL statements. Importantly, we do not share your query history with other AWS accounts, nor do we train our generation models using data from your AWS account, ensuring the privacy and security you expect from us.

Utilizing generated SQL queries aids in discovering new schemas, handling the complexities of identifying column names and table relationships. Senior analysts benefit from the capability to articulate their requirements in natural language while the SQL statements are automatically generated, allowing for direct review and execution from their notebooks.

Practical Example

Consider a scenario where I am a data analyst at a concert ticket sales company. My manager requests an analysis of ticket sales data to send thank-you notes with discount coupons to the highest-spending customers in Seattle.

I connect to the Amazon Redshift Query Editor and set up a new notebook. Instead of crafting a SQL statement, I use the chat panel to type, “Find the top five users from Seattle who bought the most number of tickets in 2022.” After verifying the generated SQL statement for accuracy, I execute it. The query returns the list of the top five buyers in Seattle.

Generative SQL is not confined to single interactions; you can engage in a dialogue to refine your queries dynamically. For instance, I might ask, “Which state has the most venues?” The generative SQL suggests an appropriate query, and if you’re curious, the answer is New York, with 49 venues.

Should I decide to know the top three cities with the most venues, I can simply rephrase my request: “What about the top three venues?” I add the revised query to my notebook and run it, receiving the anticipated results.

Best Practices for Effective Prompting

Here are a few tips to maximize the effectiveness of your prompts:

Be Specific: Provide detailed requests to help the system understand your needs. Instead of saying “find the top venues that sold the most tickets,” specify “find the names of the top three venues that sold the most tickets in 2022.” Consistent entity names like venue, ticket, and location will also help avoid confusion.
Iterate: Simplify complex requests into multiple straightforward statements. Follow up with additional questions for deeper analysis. Start with “Which state has the most venues?” and then ask a follow-up like “Which is the most popular venue from this state?”
Verify: Always review the generated SQL for accuracy before execution. If you notice errors, provide corrective instructions rather than rephrasing your entire request, such as indicating, “provide venues from year 2022.”

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