Category Archives: Database

Oracle renames Database 23c to 23ai, makes it generally available

Posted by on 2 May, 2024

This post was originally published on this site

Oracle is making the latest long-term support release version of its database offering — Database 23c — generally available for enterprises under the name Oracle Database 23ai.

The change in nomenclature can be attributed to the addition of new features to the database that are expected to help with AI-based application development among other tasks, the company said.

Database 23c, showcased for the first time at the company’s annual event in 2022, was released to developers in early 2023 before being released to enterprises, marking a shift in the company’s tradition for the first time.

Stiff competition from database rivals forced Oracle to shift its strategy for its databases business in favor of developers, who could offer the company a much-needed impetus for growth.

In September last year, Oracle said it was working on adding vector search capabilities to Database 23c at its annual CloudWorld conference.

These capabilities, dubbed AI Vector Search, included a new vector data type, vector indexes, and vector search SQL operators that enable the Oracle Database to store the semantic content of documents, images, and other unstructured data as vectors, and use these to run fast similarity queries, the company said.

AI Vector Search in Database 23c that has been passed onto 23ai along with other features, according to the company, also supports retrieval-augmented generation (RAG), a generative AI technique, that combines large language models (LLMs) and private business data to deliver responses to natural language questions.

Other notable features of Database 23c that have been passed onto 23ai include JSON Relational Duality, which unifies the relational and document data models, SQL support for Graph queries directly on OLTP data, and stored procedures in JavaScript, allowing developers to build applications in either relational or JSON paradigms.

Database 23ai, according to Oracle, will be available as a cloud service as well as on-premises through a variety of offerings, including Oracle Exadata Database Service, Oracle Exadata Cloud@Customer, and Oracle Base Database Service, as well as on Oracle Database@Azure.

While Oracle did not release Database 23ai’s pricing, the developer version of Database 23c continues to be free since its release.

The reason to offer Database 23c for free can be attributed to the company’s strategy to lower the barriers to the adoption of its database as rival database providers also add newer features, such as vector search, to support AI workloads.

Several database vendors, such as MongoDB, AWS, Google Cloud, Microsoft, Zilliz, DataStax, Pinecone, Couchbase, Snowflake, and SingleStore, have all added capabilities to support AI-based tasks.

Vector databases and vector search are two technologies that developers use to convert unstructured information into vectors, now more commonly called embeddings.

These embeddings, in turn, make storing, searching, and comparing the information easier, faster, and significantly more scalable for large datasets. 

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How TigerGraph CoPilot enables graph-augmented AI

Posted by on 30 April, 2024

This post was originally published on this site

Data has the potential to provide transformative business insights across various industries, yet harnessing that data presents significant challenges. Many businesses struggle with data overload, with vast amounts of data that are siloed and underutilized. How can organizations deal with large and growing volumes of data without sacrificing performance and operational efficiency? Another challenge is extracting insights from complex data. Traditionally, this work has required significant technical expertise, restricting access to specialized data scientists and analysts. 

Recent AI breakthroughs in natural language processing are democratizing data access, enabling a wider range of users to query and interpret complex data sets. This broadened access helps organizations make informed decisions swiftly, capitalizing on the capability of AI copilots to process and analyze large-scale data in real time. AI copilots can also curb the high costs associated with managing large data sets by automating complex data processes and empowering less technical staff to undertake sophisticated data analysis, thus optimizing overall resource allocation.

Generative AI and large language models (LLMs) are not without their shortcomings, however. Most LLMs are built on general purpose, public knowledge. They won’t know the specific and sometimes confidential data of a particular organization. It’s also very challenging to keep LLMs up-to-date with ever-changing information. The most serious problem, however, is hallucinations—when the statistical processes in a generative model generate statements that simply aren’t true.

There’s an urgent need for AI that is more contextually relevant and less error-prone. This is particularly vital in predictive analytics and machine learning, where the quality of data can directly impact business outcomes.

Introducing TigerGraph CoPilot

TigerGraph CoPilot is an AI assistant that combines the powers of graph databases and generative AI to enhance productivity across various business functions, including analytics, development, and administration tasks. TigerGraph CoPilot allows business analysts, data scientists, and developers to use natural language to execute real-time queries against up-to-date data at scale. The insights can then be presented and analyzed through natural language, graph visualizations, and other perspectives. 

TigerGraph CoPilot adds value to generative AI applications by increasing accuracy and reducing hallucinations. With CoPilot, organizations can tap the full potential of their data and drive informed decision-making across a spectrum of domains, including customer service, marketing, sales, data science, devops, and engineering.

TigerGraph CoPilot key features and benefits

  • Graph-augmented natural language inquiry
  • Graph-augmented generative AI
  • Reliable and responsible AI
  • High scalability and performance

Graph-augmented natural language inquiry

TigerGraph CoPilot allows non-technical users to use their everyday speech to query and analyze their data, freeing them to focus on mining insights rather than having to learn a new technology or computer language. For each question, CoPilot employs a novel three-phase interaction with both the TigerGraph database and a LLM of the user’s choice, to obtain accurate and relevant responses.

The first phase aligns the question with the particular data available in the database. TigerGraph CoPilot uses the LLM to compare the question with the graph’s schema and replace entities in the question by graph elements. For example, if there is a vertex type of BareMetalNode and the user asks “How many servers are there?,” then the question will be translated to “How many BareMetalNode vertices are there?”

In the second phase, TigerGraph CoPilot uses the LLM to compare the transformed question with a set of curated database queries and functions in order to select the best match. Using pre-approved queries provides multiple benefits. First and foremost, it reduces the likelihood of hallucinations, because the meaning and behavior of each query has been validated. Second, the system has the potential of predicting the execution resources needed to answer the question.

In the third phase, TigerGraph CoPilot executes the identified query and returns the result in natural language along with the reasoning behind the actions. CoPilot’s graph-augmented natural language inquiry provides strong guardrails, mitigating the risk of model hallucinations, clarifying the meaning of each query, and offering an understanding of the consequences. 

tigergraph copilot 01 IDG

Graph-augmented generative AI

TigerGraph CoPilot also can create chatbots with graph-augmented AI on a user’s own documents. There’s no need to have an existing graph database. In this mode of operation, TigerGraph CoPilot builds a knowledge graph from source material and applies its unique variant of retrieval-augmented generation (RAG) to improve the contextual relevance and accuracy of answers to natural language questions.

First, when loading users’ documents, TigerGraph CoPilot extracts entities and relationships from document chunks and constructs a knowledge graph from the documents. Knowledge graphs organize information in a structured format, connecting data points through relationships. CoPilot will also identify concepts and build an ontology, adding semantics and reasoning to the knowledge graph, or users can provide their own concept ontology. Then, using this comprehensive knowledge graph, CoPilot performs hybrid retrievals, combining traditional vector search and graph traversals, to collect more relevant information and richer context to answer users’ questions.

Organizing the data as a knowledge graph allows a chatbot to access accurate, fact-based information quickly and efficiently, thereby reducing the reliance on generating responses from patterns learned during training, which can sometimes be incorrect or outdated.

tigergraph copilot 02 IDG

Reliable and responsible AI

TigerGraph CoPilot mitigates hallucinations by allowing LLMs to access the graph database via curated queries. It also adheres to the same role-based access control and security measures (already part of the TigerGraph database) to assure responsible AI. TigerGraph CoPilot also supports openness and transparency by open-sourcing its major components and allowing users to choose their LLM service.

High scalability and performance

By leveraging the TigerGraph database, TigerGraph CoPilot brings high performance to graph analytics. As a graph-RAG solution, it supports large-scale knowledge bases for knowledge graph-powered Q&A solutions.

TigerGraph CoPilot key use cases 

  • Natural language to data insights
  • Context-rich Q&A

Natural language to data insights

Whether you are a business analyst, specialist, or investigator, TigerGraph CoPilot enables you to get information and insights quickly from your data. For example, CoPilot can generate reports for fraud investigators by answering questions like “Show me the list of recent fraud cases that were false positives.” CoPilot also facilitates more accurate investigations like “Who had transactions with account 123 in the past month with amounts larger than $1000?”

TigerGraph CoPilot can even answer “What if” questions by traversing your graph along dependencies. For example, you can easily find out “What suppliers can cover the shortage of part 123?” from your supply chain graph, or “What services would be affected by an upgrade to server 321” from your digital infrastructure graph.

Context-rich Q&A

TigerGraph CoPilot provides a complete solution for building Q&A chatbot on your own data and documents. Its knowledge graph-based RAG approach enables contextually accurate information retrieval that facilitates better answers and more informed decisions. CoPilot’s context-rich Q&A directly improves productivity and reduces costs in typical Q&A applications such as call centers, customer services, and knowledge search.

Furthermore, by merging a document knowledge graph and an existing business graph (e.g., product graph) into one intelligence graph, TigerGraph CoPilot can tackle problems that cannot be addressed by other RAG solutions. For example, by combining customers’ purchase history with product graphs, CoPilot can make more accurate personalized recommendations when customers type in their search queries or ask for recommendations. By combining patients’ medical history with healthcare graphs, doctors or health specialists can get more useful information about the patients to provide better diagnoses or treatments.  

Graph meets generative AI

TigerGraph CoPilot addresses both the complex challenges associated with data management and analysis and the serious shortcomings of LLMs for business applications. By leveraging the power of natural language processing and advanced algorithms, organizations can unlock transformative business insights while navigating data overload and accessibility barriers. By tapping graph-based RAG, they can ensure the accuracy and relevance of LLM output.

CoPilot allows a wider range of users to leverage data effectively, driving informed decision-making and optimizing resource allocation across organizations. We believe it is a significant step forward in democratizing data access and empowering organizations to harness the full potential of their data assets.

Hamid Azzawe is CEO of TigerGraph.

Generative AI Insights provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss the challenges and opportunities of generative artificial intelligence. The selection is wide-ranging, from technology deep dives to case studies to expert opinion, but also subjective, based on our judgment of which topics and treatments will best serve InfoWorld’s technically sophisticated audience. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Contact

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DBOS: A better way to build applications?

Posted by on 29 April, 2024

This post was originally published on this site

At the end of March 2024, Mike Stonebraker announced in a blog post the release of DBOS Cloud, “a transactional serverless computing platform, made possible by a revolutionary new operating system, DBOS, that implements OS services on top of a distributed database.” That sounds odd, to put it mildly, but it makes more sense when you read the origin story:

The idea for DBOS (DataBase oriented Operating System) originated 3 years ago with my realization that the state an operating system must maintain (files, processes, threads, messages, etc.) has increased in size by about 6 orders of magnitude since I began using Unix on a PDP-11/40 in 1973. As such, storing OS state is a database problem. Also, Linux is legacy code at the present time and is having difficulty making forward progress. For example there is no multi-node version of Linux, requiring people to run an orchestrator such as Kubernetes. When I heard a talk by Matei Zaharia in which he said Databricks could not use traditional OS scheduling technology at the scale they were running and had turned to a DBMS solution instead, it was clear that it was time to move the DBMS into the kernel and build a new operating system.”

If you don’t know Stonebraker, he’s been a database-focused computer scientist (and professor) since the early 1970s, when he and his UC Berkeley colleagues Eugene Wong and Larry Rowe founded Ingres. Ingres later inspired Sybase, which was eventually the basis for Microsoft SQL Server. After selling Ingres to Computer Associates, Stonebraker and Rowe started researching Postgres, which later became PostgreSQL and also evolved into Illustra, which was purchased by Informix.

I heard Stonebraker talk about Postgres at a DBMS conference in 1980. What I got out of that talk, aside from an image of “jungle drums” calling for SQL, was the idea that you could add support for complex data types to the database by implementing new index types, extending the query language, and adding support for that to the query parser and optimizer. The example he used was geospatial information, and he explained one kind of index structure that would make 2D geometric database queries go very fast. (This facility eventually became PostGIS. The R-tree currently used by default in PostGIS GiST indexes wasn’t invented until 1984, so Mike was probably talking about the older quadtree index.)

Skipping ahead 44 years, it should surprise precisely nobody in the database field that DBOS uses a distributed version of PostgreSQL as its kernel database layer.

dbos 01 IDG

The DBOS system diagram makes it clear that a database is part of the OS kernel. The distributed database relies on a minimal kernel, but sits under the OS services instead of running in the application layer as a normal database would.

DBOS features

DBOS Transact, an open-source TypeScript framework, supports Postgres-compatible transactions, reliable workflow orchestration, HTTP serving using GET and POST, communication with external services and third-party APIs, idempotent requests using UUID keys, authentication and authorization, Kafka integration with exactly-once semantics, unit testing, and self-hosting. DBOS Cloud, a transactional serverless platform for deploying DBOS Transact applications, supports serverless app deployment, time-travel debugging, cloud database management, and observability.

Let’s highlight some major areas of interest.

DBOS Transact

The code shown in the screenshot below demonstrates transactions, as well as HTTP serving using GET. It’s worthwhile to read the code closely. It’s only 18 lines, not counting blank lines.

The first import (line 1) brings in the DBOS SDK classes that we’ll need. The second import (line 2) brings in the Knex.js SQL query builder, which handles sending the parameterized query to the Postgres database and returning the resulting rows. The database table schema is defined in lines 4 through 8; the only columns are a name string and a greet_count integer.

There is only one method in the Hello class, helloTransaction. It is wrapped in @GetApi and @Transaction decorators, which respectively cause the method to be served in response to an HTTP GET request on the path /greeting/ followed by the username parameter you want to pass in and wrap the database call in a transaction, so that two instances can’t update the database simultaneously.

The database query string (line 16) uses PostgreSQL syntax to try to insert a row into the database for the supplied name and an initial count of 1. If the row already exists, then the ON CONFLICT trigger runs an update operation that increments the count in the database.

Line 17 uses Knex.js to send the SQL query to the DBOS system database and retrieves the result. Line 18 pulls the count out of the first row of results and returns the greeting string to the calling program.

The use of SQL and a database for what feels like should be a core in-memory system API, such as a Linux atomic counter or a Windows interlocked variable, seems deeply weird. Nevertheless, it works.

dbos 02 IDG

This TypeScript code for a Hello class is generated when you perform a DBOS create operation. As you can see, it relies on the @GetApi and @Transaction decorators to serve the function from HTTP GET requests and run the function as a database transaction.

DBOS Time Travel Debugger

When you run an application in DBOS Cloud it records every step and change it makes (the workflow) in the database. You can debug that using Visual Studio Code and the DBOS Time Travel Debugger extension. The time-travel debugger allows you to debug your DBOS application against the database as it existed at the time the selected workflow originally executed.

dbos 03IDG

To perform time-travel debugging, you first start with a CodeLens to list saved trace workflows. Once you choose the one you want, you can debug it using Visual Studio Code with a plugin, or from the command line.

dbos 04IDG

Time-travel debugging with a saved workflow looks very much like ordinary debugging in Visual Studio Code. The code being debugged is the same Hello class you saw earlier. 

DBOS Quickstart

The DBOS Quickstart tutorial requires Node.js 20 or later and a PostgreSQL database you can connect to, either locally, in a Docker container, or remotely. I already had Node.js v20.9.0 installed on my M1 MacBook, but I upgraded it to v20.12.1 from the Node.js website.

I didn’t have PostgreSQL installed, so I downloaded and ran the interactive installer for v16.2 from EnterpriseDB. This installer creates a full-blown macOS server and applications. If I had used Homebrew instead, it would have created command-line applications, and if I had used, I would have gotten a menu-bar app.

The Quickstart proper starts by creating a DBOS app directory using Node.js.

martinheller@Martins-M1-MBP ~ % npx -y @dbos-inc/create@latest -n myapp
Merged .gitignore files saved to myapp/.gitignore
added 590 packages, and audited 591 packages in 25s
found 0 vulnerabilities
added 1 package, and audited 592 packages in 1s
found 0 vulnerabilities
added 129 packages, and audited 721 packages in 5s
found 0 vulnerabilities
Application initialized successfully!

Then you configure the app to use your Postgres server and export your Postgres password into an enviroment variable.

martinheller@Martins-M1-MBP ~ % cd myapp
martinheller@Martins-M1-MBP myapp % npx dbos configure
? What is the hostname of your Postgres server? localhost
? What is the port of your Postgres server? 5432
? What is your Postgres username? postgres
martinheller@Martins-M1-MBP myapp % export PGPASSWORD=*********

After that, you create a “Hello” database using Node.js and Knex.js.

martinheller@Martins-M1-MBP myapp % npx dbos migrate
2024-04-09 15:01:42 [info]: Starting migration: creating database hello if it does not exist
2024-04-09 15:01:42 [info]: Database hello does not exist, creating...
2024-04-09 15:01:42 [info]: Executing migration command: npx knex migrate:latest
2024-04-09 15:01:43 [info]: Batch 1 run: 1 migrations
2024-04-09 15:01:43 [info]: Creating DBOS tables and system database.
2024-04-09 15:01:43 [info]: Migration successful!

With that complete, you build and run the DBOS app locally.

martinheller@Martins-M1-MBP myapp % npm run build
npx dbos start

> myapp@0.0.1 build
> tsc

2024-04-09 15:02:30 [info]: Workflow executor initialized
2024-04-09 15:02:30 [info]: HTTP endpoints supported:
2024-04-09 15:02:30 [info]:     GET   :  /greeting/:user
2024-04-09 15:02:30 [info]: DBOS Server is running at http://localhost:3000
2024-04-09 15:02:30 [info]: DBOS Admin Server is running at http://localhost:3001

At this point, you can browse to http://localhost:3000 to test the application. That done, you register for the DBOS Cloud and provision your own database there.

martinheller@Martins-M1-MBP myapp % npx dbos-cloud register -u meheller
2024-04-09 15:11:35 [info]: Welcome to DBOS Cloud!
2024-04-09 15:11:35 [info]: Before creating an account, please tell us a bit about yourself!
Enter First/Given Name: Martin
Enter Last/Family Name: Heller
Enter Company: self
2024-04-09 15:12:06 [info]: Please authenticate with DBOS Cloud!
Login URL:
2024-04-09 15:12:12 [info]: Waiting for login...
2024-04-09 15:12:17 [info]: Waiting for login...
2024-04-09 15:12:22 [info]: Waiting for login...
2024-04-09 15:12:27 [info]: Waiting for login...
2024-04-09 15:12:32 [info]: Waiting for login...
2024-04-09 15:12:38 [info]: Waiting for login...
2024-04-09 15:12:44 [info]: meheller successfully registered!
martinheller@Martins-M1-MBP myapp % npx dbos-cloud db provision iw_db -U meheller
Database Password: ********
2024-04-09 15:19:22 [info]: Successfully started provisioning database: iw_db
2024-04-09 15:19:28 [info]: {"PostgresInstanceName":"iw_db","HostName":"","Status":"available","Port":5432,"DatabaseUsername":"meheller","AdminUsername":"meheller"}
2024-04-09 15:19:28 [info]: Database successfully provisioned!

Finally, you can register and deploy your app in the DBOS Cloud.

martinheller@Martins-M1-MBP myapp % npx dbos-cloud app register -d iw_db
2024-04-09 15:20:09 [info]: Loaded application name from package.json: myapp
2024-04-09 15:20:09 [info]: Registering application: myapp
2024-04-09 15:20:11 [info]: myapp ID: d8806829-c5b8-4df0-8b5a-2d1bf87c3322
2024-04-09 15:20:11 [info]: Successfully registered myapp!
martinheller@Martins-M1-MBP myapp % npx dbos-cloud app deploy
2024-04-09 15:20:35 [info]: Loaded application name from package.json: myapp
2024-04-09 15:20:35 [info]: Submitting deploy request for myapp
2024-04-09 15:21:09 [info]: Submitted deploy request for myapp. Assigned version: 1712676035
2024-04-09 15:21:13 [info]: Waiting for myapp with version 1712676035 to be available
2024-04-09 15:21:21 [info]: Successfully deployed myapp!
2024-04-09 15:21:21 [info]: Access your application at
dbos 05IDG

The “Hello” application running in the DBOS Cloud counts every greeting. It uses the code you saw earlier.

DBOS applications

The “Hello” application does illustrate some of the core features of DBOS Transact and the DBOS Cloud, but it’s so basic that it’s barely a toy. The Programming Quickstart adds a few more details, and it’s worth your time to go through it. You’ll learn how to use communicator functions to access third-party services (email, in this example) as well as how to compose reliable workflows. You’ll literally interrupt the workflow and restart it without re-sending the email: DBOS workflows always run to completion and each of their operations executes once and only once. That’s possible because DBOS persists the output of each step in your database.

Once you’ve understood the programming Quickstart, you’ll be ready to try out the two DBOS demo applications, which do rise to the level of being toys. Both demos use Next.js for their front ends, and both use DBOS workflows, transactions, and communicators.

The first demo, E-Commerce, is a web shopping and payment processing system. It’s worthwhile reading the Under the Covers section of the README in the demo’s repository to understand how it works and how you might want to upgrade it to, for example, use a real-world payment provider.

The second demo, YKY Social, simulates a simple social network, and uses TypeORM rather than Knex.js for its database code. It also uses Amazon S3 for profile photos. If you’re serious about using DBOS yourself, you should work though both demo applications.

A tantalizing glimpse

I have to say that DBOS and DBOS Cloud look very interesting. Reliable execution and time-travel debugging, for example, are quite desirable. On the other hand, I wouldn’t want to build a real application on DBOS or DBOS Cloud at this point. I have lots of questions, starting with “How does it scale in practice?” and probably ending with “How much will it cost at X scale?”

I mentioned earlier that DBOS code looks weird but works. I would imagine that any programming shop considering writing an application on it would be discouraged or even repelled by the “it looks weird” part, as developers tend to be set in their ways until what they are doing no longer works.

I also have to point out that the current implementation of DBOS is very far from the system diagram you saw near the beginning of this review. Where’s the minimal kernel? DBOS currently runs on macOS, Linux, and Windows. None of those are minimal kernels. DBOS Cloud currently runs on AWS. Again, not a minimal kernel.

So, overall, DBOS is a tantalizing glimpse of something that may eventually turn out to be cool. It’s new and shiny, and it comes from smart people, but it will be awhile before it could possibly become a mainstream system.

Cost: Free with usage limits; paid plans require you to contact sales.

Platform: macOS, Linux, Windows, AWS.

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Qdrant offers managed vector database for hybrid clouds

Posted by on 16 April, 2024

This post was originally published on this site

Open-source database provider Qdrant has made available Qdrant Hybrid Cloud, a dedicated vector database to be offered in a managed hybrid cloud model.

Qdrant, the open-source foundation of both Qdrant Cloud and Qdrant Hybrid Cloud, is a vector similarity search engine and vector database written in Rust. Qdrant offers a set of features for performance optimization and can handle billions of vectors with scale and memory safety, the company said. 

Qdrant said it lets businesses deploy vector databases across any cloud provider, on-prem data center, or edge location, thus ensuring performance, security, and cost efficiency for AI-driven applications. Vector databases have emerged as a critical component for building generative AI applications.

Qdrant Hybrid Cloud lets customers deploy a vector database in their chosen environment without sacrificing the benefits of a managed cloud service. In addition to running on Google Cloud Platform and Microsoft Azure, Qdrant Hybrid Cloud can run in Oracle Cloud Infrastructure, Red Hat OpenShift, Vultur, DigitalOcean, OVHcloud, Scaleway, Stackit, Civo, or any other private or public infrastructure with Kubernetes support.

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Google adds Gemini to databases to aid faster code development, migration

Posted by on 9 April, 2024

This post was originally published on this site

Google Cloud is adding capabilities driven by its proprietary large language model, Gemini to its database offerings, which include Bigtable, Spanner, Memorystore for Redis, Firestore, CloudSQL for MySQL, and AlloyDB for PostgreSQL, the company announced at its annual Next conference.

The Gemini-driven capabilities, which are currently in public preview, include SQL generation, and AI assistance in managing and migrating databases.

Last year, the company added Duet AI, now rebranded to Gemini, in Spanner and its Database Migration Service.

The SQL generation capability can be accessed via the company’s SQL editor named Database Studio to be found inside Google’s Cloud Console.

As the name suggests, this capability allows developers to easily generate, summarize, and fix SQL code with intelligent code assistance, code completion, and guidance directly inside Database Studio, which in turn improves productivity, the company said, adding that Database Studio supports both MySQL and PostgreSQL dialects.

In addition, Database Studio comes with a context-aware chat interface that can take input in natural language to help build database applications faster, according to the company.

Google is not the only database provider that has added SQL code generation to its list of capabilities, analysts said.

“SQL code generation with assistance from generative AI has become one of the low-hanging fruits for generative AI over the past year,” said Tony Baer, principal analyst at dbInsight.

“The new breed of generative AI database code assistants should eventually have a key advantage over those assistants that cater to general-purpose languages, which is that they are database-specific and can therefore read the metadata of databases to not just form, but also optimize SQL code,” Baer explained.

Managing and migrating databases with Gemini

In order to help manage databases better, the cloud service provider is adding a new feature called the Database Center, which will allow operators to manage an entire fleet of databases from a single pane.

Database Center also provides intelligent dashboards to proactively assess availability, data protection, security, and compliance posture, the company said.

Further, the company is infusing Gemini into the Database Center via a natural language-based chat window that will allow enterprise teams to interact with the databases and find more insights about them.

The chat window also can be used to generate troubleshooting tips for database-related issues, the company said.

Google’s idea to have a single pane to manage multiple databases takes inspiration from Oracle, according to Baer.

While Oracle provides the capability for multiple instances of the same databases, which is multimodal, Google extends the capability to a heterogenous collection of databases, Baer said.

“Having central control means that enterprises can be consistent with their policies for security, data access, and service level agreements (SLAs). That’s a major step toward the simplification that we expect from the cloud,” the principal analyst explained.

Google has also extended Gemini to its Database Migration Service, which earlier had support for Duet AI.

Gemini’s improved features will make the service better, the company said, adding that Gemini can help convert database-resident code, such as stored procedures, functions to PostgreSQL dialect.

Additionally, Gemini-powered database migration also focuses on explaining the translation of the code with a side-by-side comparison of dialects, along with detailed explanations of the code and recommendations.

The focus on explaining the code has been planned to help upskill and retrain SQL developers, the company said.

AlloyDB AI gets new features

In addition to powering databases with Gemini, Google has added new features to AlloyDB AI.

AlloyDB AI, which was introduced last year as part of its AlloyDB for PostgreSQL database service, is a suite of integrated capabilities targeted at helping developers build generative AI-based applications using real-time data.  

The new features include allowing generative AI-based applications to query data with natural language and a new type of database view.

The enablement of querying data with natural language will allow AI-based applications to respond to more sets of questions from enterprise teams, the company said.

On the other hand, the new type of database view — parameterized secure view — allows enterprise teams to secure data based on the end-users’ context.

AlloyDB AI can be downloaded using AlloyDB Omni, which has been made generally available. AlloyDB Omni is a downloadable version of Google Cloud’s PostgreSQL-compatible database service.

Other updates include the addition of Bigtable Data Boost, similar to Spanner Data Boost released last year, and performance enhancements to Memorystore for Redis.

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Using Neo4J’s graph database for AI in Azure

Posted by on 4 April, 2024

This post was originally published on this site

Once you get past the chatbot hype, it’s clear that generative AI is a useful tool, providing a way of navigating applications and services using natural language. By tying our large language models (LLMs) to specific data sources, we can avoid the risks that come with using nothing but training data.

While it is possible to fine-tune an LLM on specific data, that can be expensive and time-consuming, and it can also lock you into a specific time frame. If you want accurate, timely responses, you need to use retrieval-augmented generation (RAG) to work with your data.

RAG: the heart of Microsoft’s Copilots

The neural networks that power LLMs are, at heart, sophisticated vector search engines that extrapolate the paths of semantic vectors in an n-dimensional space, where the higher the dimensionality, the more complex the model. So, if you’re going to use RAG, you need to have a vector representation of your data that can both build prompts and seed the vectors used to generate output from an LLM. That’s why it’s one of the techniques that powers Microsoft’s various Copilots.

I’ve talked about these approaches before, looking at Azure AI Studio’s Prompt Flow, Microsoft’s intelligent agent framework Semantic Kernel, the Power Platform’s Open AI-powered boost in its re-engineered Q and A Maker Copilot Studio, and more. In all those approaches, there’s one key tool you need to bring to your applications: a vector database. This allows you to use the embedding tools used by an LLM to generate text vectors for your content, speeding up search and providing the necessary seeds to drive a RAG workflow. At the same time, RAG and similar approaches ensure that your enterprise data stays in your servers and isn’t exposed to the wider world beyond queries that are protected using role-based access controls.

While Microsoft has been adding vector search and vector index capabilities to its own databases, as well as supporting third-party vector stores in Azure, one key database technology has been missing from the RAG story. These missing databases are graph databases, a NoSQL approach that provides an easy route to a vector representation of your data with the added bonus of encoding relationships in the vertices that link the graph nodes that store your data.

Adding graphs to Azure AI with Neo4j

Graph databases like this shouldn’t be confused with the Microsoft Graph. It uses a node model for queries, but it doesn’t use it to infer relationships between nodes. Graph databases are a more complex tool, and although they can be queried using GraphQL, they have a much more complex query process, using tools such as the Gremlin query engine.

One of the best-known graph databases is Neo4j, which recently announced support for the enterprise version of its cloud-hosted service, Aura, on Azure. Available in the Azure Marketplace, it’s a SaaS version of the familiar on-premises tool, allowing you to get started with data without having to spend time configuring your install. Two versions are available, with different memory options built on reserved capacity so you don’t need to worry about instances not being available when you need them. It’s not cheap, but it does simplify working with large amounts of data, saving a lot of time when working with large-scale data lakes in Fabric.

Building knowledge graphs from your data

One key feature of Neo4J is the concept of the knowledge graph, linking unstructured information in nodes into a structured graph. This way you can quickly see relationships between, say, a product manual and the whole bill of materials that goes into the product. Instead of pointing out a single part that needs to be replaced for a fix, you have a complete dependency graph that shows what it affects and what’s necessary to make the fix.

A tool like Neo4j that can sit on top of a large-scale data lake like Microsoft’s Fabric gives you another useful way to build out the information sources for a RAG application. Here, you can use the graph visualization tool that comes as part of Neo4j to explore the complexities of your lakehouses, generating the underlying links between your data and giving you a more flexible and understandable view of your data.

One important aspect of a knowledge graph is that you don’t need to use it all. You can use the graph relationships to quickly filter out information you don’t need for your application. This reduces complexity and speeds up searches. By ensuring that the resulting vectors and prompts are confined to a strict set of relationships, it reduces the risks of erroneous outputs from your LLM.

There’s even the prospect of using LLMs to help generate those knowledge graphs. The summarization tools identify specific entities within the graph database and then provide the links needed to define relationships. This approach lets you quickly extend existing data models into graphs, making them more useful as part of an AI-powered application. At the same time, you can use the Azure Open AI APIs to add a set of embeddings to your data in order to use vector search to explore your data as part of an agent-style workflow using LangChain or Semantic Kernel.

Using graphs in AI: GraphRAG

The real benefit of using a graph database with a large language model comes with a variation on the familiar RAG approach, GraphRAG. Developed by Microsoft Research, GraphRAG uses knowledge graphs to improve grounding in private data, going beyond the capabilities of a standard RAG approach to use the knowledge graph to link related pieces of information and generate complex answers.

One point to understand when working with large amounts of private data using an LLM is the size of the context window. In practice, it’s too computationally expensive to use the number of tokens needed to deliver a lot of data as part of a prompt. You need a RAG approach to get around this limitation, and GraphRAG goes further, letting you deliver a lot more context around your query.

The original GraphRAG research uses a database of news stories, which a traditional RAG fails to parse effectively. However, with a knowledge graph, entities and relationships are relatively simple to extract from the sources, allowing the application to select and summarize news stories that contain the search terms, by providing the LLM with much more context. This is because the graph database structure naturally clusters similar semantic entities, while providing deeper context in the relationships encoded in the vertices between those nodes.

Instead of searching for like terms, much like a traditional search engine, GraphRAG allows you to extract information from the entire dataset you’re using, whether transcripts of support calls or all the documents associated with a specific project.

Although the initial research uses automation to build and cluster the knowledge graph, there is the opportunity to use Neo4j to work with massive data lakes in the Microsoft Fabric, providing a way to visualize that data so that data scientists and business analysts can create their own clusters, which can help produce GraphRAG applications that are driven by what matters to your business as much as by the underlying patterns in the data.

Having a graph database like Neo4j in the Azure Marketplace gives you a tool that helps you understand and visualize the relationships in your data in a way that supports both humans and machines. Integrating it with Fabric should help build large-scale, context-aware, LLM-powered applications, letting you get grounded results from your data in a way that standard RAG approaches can miss. It’ll be interesting to see if Microsoft starts implementing GraphRAG in its own Prompt Flow LLM tool.

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Microsoft unveils Copilot for Azure SQL Database

Posted by on 27 March, 2024

This post was originally published on this site

Microsoft has announced a private preview of Copilot in Azure SQL Database, an AI assistant that improves productivity in the Azure portal by offering natural language to SQL conversion, along with self-help for database administration.

Microsoft announced the preview on March 21. To sign up for the preview, users can request access.

Copilot in Azure SQL Database enables the Azure portal query editor to translate natural language queries to SQL, making database interactions more intuitive, Microsoft said. Plus, Azure Copilot integration adds Azure SQL Database skills into Microsoft Copilot for Azure, providing self-guided assistance that enables users to manage databases and solve issues independently.

Copilot in Azure SQL Database integrates data and formulates responses using public documentation, database schema, dynamic management views, catalog views, and Azure supportability diagnostics, Microsoft said.

Within the Azure portal query editor, Copilot for Azure SQL Database uses table and view names, column names, primary key, and foreign key metadata to generate T-SQL code. Users then can review and execute the code suggestion. The Copilot also can answer questions with prompts like “Which real estate agent has listed more than two properties for sale?” or “Show me a pivot summary table that displays the number of properties sold in each year from 2020 to 2023,” Microsoft said.

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Redis moves to source-available licenses

Posted by on 25 March, 2024

This post was originally published on this site

Starting with Redis 7.4, all future versions of Redis software will be dual-licensed under the Redis Source Available License (RSAL 2) and the Server Side Public License (SSLPv1), Redis announced. The popular NoSQL database will no longer be distributed under the three-clause Berkeley Software Distribution (BSD) license.

New source-available licenses will allow Redis the company to provide permissive use of its source code, the company said on March 20. Source code will continue to be freely available to developers, customers, and partners through Redis Community Edition.

Future Redis source-available licenses will unify core Redis with Redis Stack, including search, JSON, vector, probabilistic, and time-series data models in one package as downloadable software. This will allow Redis software to be used across a variety of contexts, including key-value and document store, a query engine, and a low-latency vector database powering generative AI applications, the company said.

Redis has faced challenges, the company said. The majority of commercial sales of Redis software are channeled through the largest cloud service providers, who commoditize Redis’s investments and its open source community. Despite efforts to support a community-led governance model and a desire to maintain the BSD license, delivering multiple software distributions simultaneously is at odds with Redis’s ability to drive the technology successfully, the company said.

Under the new licensing, cloud service providers hosting Redis products will no longer be permitted to use Redis source code free of charge. But in practice, nothing changes for the Redis developer community, who will still have permissive licensing under the dual license, Redis said. All Redis client libraries will remain open-source licensed.

RSALv2 is a permissive non-copyleft license, allowing the right to “use, copy, distribute, make available, and prepare derivative works of the software.” RSALv2 has only two primary limitations, the company said: Under RSALv2, users may not commercialize the software or provide it to others as a managed service; and users may not remove or obscure any licensing, copyright, or other notices.

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Nutanix sues DBaaS startup Tessell for IP theft

Posted by on 22 March, 2024

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Datacenter and hybrid multi-cloud software provider Nutanix has filed a lawsuit against database-as-a-service (DBaaS) providing startup, Tessell, alleging that its products were built using Nutanix’s source code and other resources.

The lawsuit, more significantly, is targeted at the three co-founders of the San Francisco-headquartered Tessell, including Bala Kuchibhotla, Kamaldeep Khanuja, and Bakul Banthia. The lawsuit alleged that the co-founders developed Tessell products while being employed at Nutanix.

While Kuchibhotla served as the general manager and senior VP of the Era business, Khanuja and Banthia were senior engineers.

“Kuchibhotla used Nutanix facilities, equipment, services, and even the Nutanix Era source code when developing the Tessell product. Kuchibhotla planned, developed, obtained initial financing for, and demonstrated prototypes of the competing product—all using Nutanix computers,” the company alleged in the lawsuit filed with the Northern District Court of California.

“One of the Tessell prototypes they demonstrated actually ran on Nutanix servers,” the company added.

The lawsuit further goes on to say that the theft of intellectual property was discovered after the company carried out a thorough forensic investigation of their internal resources, which was triggered based on suspicions arising post the launch of Tessell’s offerings.

“When Tessell launched its product in late 2022, however, the speed with which it came to market with features strikingly similar to Era caused Nutanix to commence a full-fledged forensic investigation,” the company said.

Nutanix describes its Era offering as software that helps enterprises manage multiple databases in servers located in their data centers or hybrid cloud environments.

Essentially, Era simplifies the processes for managing databases, including creating, populating, backing up, duplicating, and administering databases, the company explained, adding that Era helps enterprises cut down on time, money, and talent significantly.

Tessell’s offerings, which include targeted products for PostgreSQL, MySQL, SQL Server, Milvus, Oracle, MongoDB, and SQL Server, compete with Nutanix Era, which also supports databases such as Oracle, Microsoft SQL Server, MongoDB, MySQL, and PostgreSQL.

The company sees the act of the three founders as a case of intellectual property theft as it believes that all three former employees were contractually bound to disclose and assign “the stolen IP” to Nutanix.

An email query sent to Tessell about the lawsuit received no response.

In a separate statement to the press, Nutanix said that the lawsuit filed against Tessell and its founders also alleges that they schemed to remove all indicia of Nutanix branding from their prototype, and then tried to cover their tracks by wiping their Nutanix laptops.

“Nutanix is seeking the return of its stolen intellectual property, an injunction to stop further infringement, restitution for the Nutanix resources taken by the three former employees for the founding of Tessell, and money damages in an amount to be proven,” the company said.

Further, Nutanix added that it was commencing separate arbitration proceedings against the Tessell founders per their Nutanix employee agreements.

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Evaluating databases for sensor data

Posted by on 18 March, 2024

This post was originally published on this site

The world has become “sensor-fied.”

Sensors on everything, including cars, factory machinery, turbine engines, and spacecraft, continuously collect data that developers leverage to optimize efficiency and power AI systems. So, it’s no surprise that time series—the type of data these sensors collect—is one of the fastest-growing categories of databases over the past five-plus years.

However, relational databases remain, by far, the most-used type of databases. Vector databases have also seen a surge in usage thanks to the rise of generative AI and large language models (LLMs). With so many options available to organizations, how do they select the right database to serve their business needs?

Here, we’ll examine what makes databases perform differently, key design factors to look for, and when developers should use specialized databases for their apps.

Understanding trade-offs to maximize database performance

At the outset, it’s important to understand that there is no one-size-fits-all formula that guarantees database superiority. Choosing a database entails carefully balancing trade-offs based on specific requirements and use cases. Understanding their pros and cons is crucial. An excellent starting point for developers is to explore the CAP theorem, which explains the trade-offs between consistency, availability, and partition tolerance.

For example, the emergence of NoSQL databases generated significant buzz around scalability, but that scalability often came at the expense of surrendering guarantees in data consistency offered by traditional relational databases.

Some design considerations that significantly impact database performance include:

  • Storage format: The organization and storage format of data on hard drives heavily influences performance. With a rapidly increasing number of businesses storing vast volumes of data for analytical workloads, the adoption of column-based formats like Apache Parquet is on the rise.
  • Data compression: The choice of compression algorithms directly impacts storage costs and query performance. Some algorithms prioritize minimizing data size, while others prioritize faster decompression, improving query performance.
  • Index data structure: The indexing mechanism used by a database is pivotal for peak performance. While primary indexes aid the storage engine, secondary, user-defined indexes enhance read performance, although these could also introduce additional overhead for writing new data.
  • Hot vs. cold storage: Modern database systems facilitate data movement between faster, more expensive, “hot” storage and slower, cheaper, “cold” storage. This tiered approach optimizes performance for frequently accessed data while economizing storage costs for data used less often.
  • Disaster recovery: The disaster recovery mechanisms present in a database architecture inherently influence performance. While robust disaster recovery features enhance data security, they could also introduce performance overhead. For use cases that are not mission-critical, databases can trade certain safety guarantees for improved performance.

These and other factors collectively shape database performance. Strategically manipulating these variables allows teams to tailor databases to meet the organization’s specific performance requirements. Sacrificing certain features becomes viable for a given scenario, creating finely-tuned performance optimization.

Key specialty database considerations

Selecting the appropriate database for your application involves weighing several critical factors. There are three major considerations that developers should keep in mind when making a decision.

Tendencies in data access

The primary determinant in choosing a database is understanding how an application’s data will be accessed and utilized. A good place to begin is by classifying workloads as online analytical processing (OLAP) or online transaction processing (OLTP). OLTP workloads, traditionally handled by relational databases, involve processing large numbers of transactions by large numbers of concurrent users. OLAP workloads are focused on analytics and have distinct access patterns compared to OLTP workloads. In addition, whereas OLTP databases work with rows, OLAP queries often involve selective column access for calculations. Data warehouses commonly leverage column-oriented databases for their performance advantages.

The next step is considering factors such as query latency requirements and data write frequency. For near-real-time query needs, particularly for tasks like monitoring, organizations might consider time series databases designed for high write throughput and low-latency query capabilities.

Alternatively, for OLTP workloads, the best choice is typically between relational databases and document databases, depending on the requirements of the data model. Teams should evaluate whether they need the schema flexibility of NoSQL document databases or prefer the consistency guarantees of relational databases.

Finally, a crucial consideration is assessing if a workload exhibits consistent or highly active patterns throughout the day. In this scenario, it’s often best to opt for databases that offer scalable hardware solutions to accommodate fluctuating workloads without incurring downtime or unnecessary hardware costs.

Existing tribal knowledge

Another consideration when selecting a database is the internal team’s existing expertise. Evaluate whether the benefits of adopting a specialized database justify investing in educating and training the team and whether potential productivity losses will appear during the learning phase. If performance optimization isn’t critical, using the database your team is most familiar with may suffice. However, for performance-critical applications, embracing a new database may be worthwhile despite initial challenges and hiccups.

Architectural sophistication

Maintaining architectural simplicity in software design is always a goal. The benefits of a specialized database should outweigh the additional complexity introduced by integrating a new database component into the system. Adding a new database for a subset of data should be justified by significant and tangible performance gains, especially if the primary database already meets most other requirements.

By carefully evaluating these factors, developers can make educated and informed decisions when selecting a database that aligns with their application’s requirements, team expertise, and architectural considerations, ultimately optimizing performance and efficiency in their software solutions.

Optimizing for IoT applications

IoT environments have distinct characteristics and demands for deploying databases. Specifically, IoT deployments need to ensure seamless operation at both the edge and in the cloud. Here is an overview of database requirements in these two critical contexts.

Requirements for edge servers

The edge is where data is locally generated and processed before transmission to the cloud. For this, databases must handle data ingestion, processing, and analytics at a highly efficient level, which requires two things:

  • High ingest rate: Edge servers must support rapid write capabilities for the huge data streams produced by IoT sensors without loss, even while experiencing latency. Similarly, databases need to handle data bursts while maintaining real-time ingestion to prevent data loss.
  • Fast reads and analytics: Databases at the edge also require quick read capabilities and analytical tools. Local data processing enables real-time decision-making, which is streamlined by databases with built-in analytics functionalities to transform, classify, and aggregate sensor data.

Requirements for cloud data centers

In cloud data centers, databases play a crucial role in collecting, transforming, and analyzing data aggregated from edge servers. Key requirements include:

  • Analysis commands: Database management systems should incorporate built-in analysis commands to streamline data processing and analysis, minimizing operational complexity and overhead.
  • Downsampling and retention policies: Implementing downsampling techniques and retention policies helps efficiently manage historical data. Downsampling ensures high-precision data is retained for short durations, while less precise data is stored to capture longer-term trends. Automated data retention policies facilitate timely data deletion, optimizing storage utilization.
  • Visualization engine: A robust visualization engine is crucial for monitoring the IoT system’s state. It can provide insights into system performance, helping teams make informed decisions based on real-time data visualization.
  • Publish and subscribe mechanism: An efficient publish and subscribe capability allows for seamless communication and data exchange between edge devices and the cloud, ensuring data integrity and timely updates.

Because the database landscape evolves swiftly, developers must stay informed about the latest trends and technologies. While sticking to familiar databases is reliable, exploring specialized options can offer advantages that include cost savings, enhanced user performance, scalability, and improved developer efficiency.

Ultimately, balancing the organization’s business requirements, storage needs, internal knowledge, and (as always) budget constraints gives teams the best chance for long-term success.

Anais Dotis-Georgiou is lead developer advocate at InfluxData.

New Tech Forum provides a venue for technology leaders—including vendors and other outside contributors—to explore and discuss emerging enterprise technology in unprecedented depth and breadth. The selection is subjective, based on our pick of the technologies we believe to be important and of greatest interest to InfoWorld readers. InfoWorld does not accept marketing collateral for publication and reserves the right to edit all contributed content. Send all inquiries to

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