What is the following for Google Cloud Data Data Data? Amnesty International within SQL and more

For decades, the SQL query language was the cornerstone of database technology.

But what happens when SQL combines modern artificial intelligence? This is the question that Google cloud He answers that it is part of a series of database updates that are presented at the next conference of the company’s Google Cloud today.

During the past year, everything Google Cloud databases They added some forms of support for vectors that allow cases of artificial intelligence. In Google Cloud Next, multiple databases including Firestore are updated, which gets the Mongodb consensus. Google Bigtable gets support for concrete distances and support the Oracle database in Google Cloud.

However, the largest and most transformative news, at least from the AI ​​database perspective is at the Alloydb database. Google first Launch alloydb In 2022 as an improved version of the open source posgresql database. At the next Google event in the summer of 2024, The hijackers fell at Alloydb In addition to Duet AI support to enable the database deportation.

Alloydb is expanded today with integration with Google agents, which will also appear for the first time at the next Google Cloud event. Perhaps it is more interesting is the new artificial intelligence query engine that provides natural language expressions directly in SQL Information for the first time.

The API’s API’s inquiry engine brings natural language directly to SQL

The new Google AI’s inquiry engine for ALLOYDB allows developers to use natural language expressions in standard SQL queries – not only SQL replacement, but to enhance it with artificial intelligence capabilities.

“We are preparing an Amnesty International inquiry engine to Alloydb,” I told Andi Gutmans, GM and VP Engineering, Google Cloud Venturebeat in an exclusive interview. “In the SQL query, we will have a operator who can both use natural, basic language models and traditional SQL operators and you can combine them.”

This innovation represents a great development in the database facades. SQL, an acronym that symbolizes the language of organized inquiries, was presented for the first time in 1973. Over the past fifty years, it was the actual criterion for the organized database inquiries. SQL’s original promise was to facilitate the implementation of database with a language that uses English words in a natural way. Common SQL Information and Actions include terms such as “Insert” and “joining”, but it is not completely normal.

“We are a 50 -year -old promise, as SQL must simulate English now,” said Goetmans.

The engine of the query enables the developers to combine the construction of the exact SQL phrase with flexible natural language expressions.

Unlike other methods that simply translate the natural language into SQL, the Google application merges the natural language directly into the language of the query itself. Google operates the two -way models that work as well as the traditional relationship operators in the database engine.

“When SQL came out for the first time in 1973, it was all, hey, we want a natural language for inquiries, and so SQL was a kind of that natural language,” said Goetman. “But in fact, the way you should think about now, this is more than the SQL promise, because you can now use a more natural language as part of your SQL query, but it’s still a good organizer.”

Agentspace Integration Dechatisting Access Database

The Google Cloud also connects Alloydb to its agents platform, which creates a natural language interface that extends the database to beyond technical specialists to almost any employee in the institution.

While developers and databases benefit from the AI’s AI’s inquiry engine, ordinary business users will use Agentspace.

“It is for the ordinary employee of the organization, in an attempt to complete their work,” said Guttanz. “One of the ways to accomplish their job is in fact that you have a natural language interface, and the ability to ask questions about all the institution’s data they can access.”

What makes this integration particularly strong is how to maintain safety while expanding access. Unlike other natural natural language database facades, the Google app takes advantage of the powerful Ogentspace platform, which knows how to think, not only about one data source, but multiple data sources. It can be a search for web, alloydb or other organized data.

Improving the vector provides measuable results

Google has also improved the possibilities of searching in the ALLOYDB significantly, leading to improving performance and ease of use. The SCANN (SCANN) index (SCANN) now provides up to 10 times the search in the lighthouse luxury vectors compared to the HNSW minor world indicators in the record postgresql.

“We have seen the adoption of the search of vectors from Alloydb, more than seven times since the launch of the nearest developed neighbors (SCANN) of the ALLOYDB index in 2024.”

This rapid adoption reflects a real effect of business, as shown in the GIANT TARGET experience. Gutmans noted that Target has used alloydb to improve the online search experience.

He said: “They use research in vectors, and they use these capabilities to really improve accuracy.” “If you are considering 20 % improvement in accuracy that translate into revenue … it targets 20 % better more transfers, and more revenues.”

The actual time processing capabilities are advanced with the custom Bigtable views

One of the most importantly important ads is the new BigTable feature for continuous opinions, designed for applications in highly productive time.

“This is a really great capacity for BigTable,” Gutmans explained. “BigTable is used a lot in Clickstream counters, like the actual time of real time applications, and there is a very low transition time, and standards.”

Unlike the traditional broken display that requires periodic updates, the BigTable implementation is automatically.

This ability eliminates the need for complex data flow pipelines to calculate groups, and simplify the structure of actual time analyzes.

What does this mean to rely on AI

For institutions that develop artificial intelligence applications, Google database improvements offer many immediate advantages. The artificial intelligence inquiry engine allows access to more intuitive data while maintaining the SQL and safety structure. Optimal research provides measurable performance improvements for semantic search applications. Finally, the Agentspace integration extends to the database throughout the institutions without the need for SQL experience.

For institutions looking to drive in adopting artificial intelligence, these innovations mean that the infrastructure of databases can now participate actively in the workflow of artificial intelligence instead of storing data. The convergence of the SQL structure with the elasticity of natural language creates opportunities for the most intelligent applications that benefit from human and automatic intelligence without asking to redesign a complete system.

Perhaps most importantly, Google’s approach shows that institutions do not need to abandon the current database investments to embrace the capabilities of artificial intelligence. Gutmans also said briefly when asked if SQL became outdated: “SQL is dead. Live live sql”.