#DataAccess

[bitcot]

OneLake’s One Copy feature - Bitcot

OneLake’s One Copy feature empowers you to work with a single copy of your data across various domains and engines. Curious how this game-changing feature works? Get the full breakdown on the Bitcot Blog and discover the future of data management!

Read complete blog - https://bit.ly/4fHz55M

[govindhtech]

Agentic RAG On Dell & NVIDIA Changes AI-Driven Data Access

Agentic RAG Changes AI Data Access with Dell & NVIDIA

The secret to successfully implementing and utilizing AI in today’s corporate environment is comprehending the use cases within the company and determining the most effective and frequently quickest AI-ready strategies that produce outcomes fast. There is also a great need for high-quality data and effective retrieval techniques like RAG retrieval augmented generation. The value of AI for businesses is further accelerated at SC24 by fresh innovation at the Dell AI Factory with NVIDIA, which also gets them ready for the future.

AI Applications Place New Demands

GenAI applications are growing quickly and proliferating throughout the company as businesses gain confidence in the results of applying AI to their departmental use cases. The pressure on the AI infrastructure increases as the use of larger, foundational LLMs increases and as more use cases with multi-modal outcomes are chosen.

RAG’s capacity to facilitate richer decision-making based on an organization’s own data while lowering hallucinations has also led to a notable increase in interest. RAG is particularly helpful for digital assistants and chatbots with contextual data, and it can be easily expanded throughout the company to knowledge workers. However, RAG’s potential might still be limited by inadequate data, a lack of multiple sourcing, and confusing prompts, particularly for large data-driven businesses.

It will be crucial to provide IT managers with a growth strategy, support for new workloads at scale, a consistent approach to AI infrastructure, and innovative methods for turning massive data sets into useful information.

Raising the AI Performance bar

The performance for AI applications is provided by the Dell AI Factory with NVIDIA, giving clients a simplified way to deploy AI using a scalable, consistent, and outcome-focused methodology. Dell is now unveiling new NVIDIA accelerated compute platforms that have been added to Dell AI Factory with NVIDIA. These platforms offer acceleration across a wide range of enterprise applications, further efficiency for inferencing, and performance for developing AI applications.

The NVIDIA HGX H200 and NVIDIA H100 NVL platforms, which are supercharging data centers, offer state-of-the-art technology with enormous processing power and enhanced energy efficiency for genAI and HPC applications. Customers who have already implemented the Dell AI Factory with NVIDIA may quickly grow their footprint with the same excellent foundations, direction, and support to expedite their AI projects with these additions for PowerEdge XE9680 and rack servers. By the end of the year, these combinations with NVIDIA HGX H200 and H100 NVL should be available.

Deliver Informed Decisions, Faster

RAG already provides enterprises with genuine intelligence and increases productivity. Expanding RAG’s reach throughout the company, however, may make deployment more difficult and affect quick response times. In order to provide a variety of outputs, or multi-modal outcomes, large, data-driven companies, such as healthcare and financial institutions, also require access to many data kinds.

Innovative approaches to managing these enormous data collections are provided by agentic RAG. Within the RAG framework, it automates analysis, processing, and reasoning through the use of AI agents. With this method, users may easily combine structured and unstructured data, providing trustworthy, contextually relevant insights in real time.

Organizations in a variety of industries can gain from a substantial advancement in AI-driven information retrieval and processing with Agentic RAG on the Dell AI Factory with NVIDIA. Using the healthcare industry as an example, the agentic RAG design demonstrates how businesses can overcome the difficulties posed by fragmented data (accessing both structured and unstructured data, including imaging files and medical notes, while adhering to HIPAA and other regulations). The complete solution, which is based on the NVIDIA and Dell AI Factory platforms, has the following features:

PowerEdge servers from Dell that use NVIDIA L40S GPUs

Storage from Dell PowerScale

Spectrum-X Ethernet networking from NVIDIA

Platform for NVIDIA AI Enterprise software

Together with the NVIDIA Llama-3.1-8b-instruct LLM NIM microservice, NVIDIA NeMo embeds and reranks NVIDIA NIM microservices.

The recently revealed NVIDIA Enterprise Reference Architecture for NVIDIA L40S GPUs serves as the foundation for the solution, which allows businesses constructing AI factories to power the upcoming generation of generative AI solutions cut down on complexity, time, and expense.

A thorough beginning strategy for enterprises to modify and implement their own Agentic RAG and raise the standard of value delivery is provided by the full integration of these components.

Readying for the Next Era of AI

As employees, developers, and companies start to use AI to generate value, new applications and uses for the technology are released on a daily basis. It can be intimidating to be ready for a large-scale adoption, but any company can change its operations with the correct strategy, partner, and vision.

The Dell AI factory with NVIDIA offers a scalable architecture that can adapt to an organization’s changing needs, from state-of-the-art AI operations to enormous data set ingestion and high-quality results.

The first and only end-to-end enterprise AI solution in the industry, the Dell AI Factory with NVIDIA, aims to accelerate the adoption of AI by providing integrated Dell and NVIDIA capabilities to speed up your AI-powered use cases, integrate your data and workflows, and let you create your own AI journey for scalable, repeatable results.

What is Agentic Rag?

An AI framework called Agentic RAG employs intelligent agents to do tasks beyond creating and retrieving information. It is a development of the classic Retrieval-Augmented Generation (RAG) method, which blends generative and retrieval-based models.

Agentic RAG uses AI agents to:

Data analysis: Based on real-time input, agentic RAG systems are able to evaluate data, improve replies, and make necessary adjustments.

Make choices: Agentic RAG systems are capable of making choices on their own.

Dividing complicated tasks into smaller ones and allocating distinct agents to each component is possible with agentic RAG systems.

Employ external tools: To complete tasks, agentic RAG systems can make use of any tool or API.

Recall what has transpired: Because agentic RAG systems contain memory, like as chat history, they are aware of past events and know what to do next.

For managing intricate questions and adjusting to changing information environments, agentic RAG is helpful. Applications for it are numerous and include:

Management of knowledge

Large businesses can benefit from agentic RAG systems’ ability to generate summaries, optimize searches, and obtain pertinent data.

Research

Researchers can generate analyses, synthesize findings, and access pertinent material with the use of agentic RAG systems.

Read more on govindhtech.com

[aiwikiweb]

Could AI Tools Like Ai2sql Replace the Need for SQL Knowledge?

Imagine a future where AI tools like Ai2sql completely handle the process of writing SQL queries. Could this mean that learning SQL is no longer necessary, or is there still value in understanding SQL syntax and database management?

Scenario: Consider a future where business users, analysts, and even developers rely entirely on AI to generate SQL queries. Instead of learning SQL, users simply describe what they need in plain language, and the AI takes care of the rest. This could democratize data access, making it possible for anyone to interact directly with databases without the technical knowledge of SQL.

Analysis:

Potential Benefits:

Increased Accessibility: AI tools like Ai2sql could make data access available to everyone, removing the need for specialized SQL training and allowing more people to gain insights from data.

Efficiency Gains: Users could get the data they need faster without relying on technical teams, leading to quicker decision-making and improved productivity.

Challenges:

Complex Queries: While AI can handle many common queries, complex SQL commands that involve multiple joins, subqueries, or specific optimizations may still require human expertise. Would relying solely on AI limit the complexity of the queries users can create?

Understanding Data Structure: Writing SQL queries helps users understand the structure of the database and the relationships between tables. Without this understanding, could users miss important context when interpreting the data?

Do you think AI tools like Ai2sql could completely replace the need for SQL knowledge, or is there still value in understanding how databases work? Would you prefer to generate queries using natural language or write SQL manually? Share your thoughts!

Join the conversation on the future of data analysis. Could AI be the answer, or will SQL knowledge always be essential? Share your views and explore more at aiwikiweb.com/product/ai2sql/

[softlist]

Things to Consider About SFTP Port

[jpmellojr]

MicroStrategy Launches AI-Powered Self-Service Analytics Tool

In what could be a peek at the future of AI integration in enterprises, MicroStrategy on Tuesday announced a new addition to its platform that simplifies access to business analytical data within organizations. https://jpmellojr.blogspot.com/2024/03/microstrategy-launches-ai-powered-self.html

[anusha-g]

Key points to learn the Spring Framework

key points to learn the Spring Framework:

Java Basics: Ensure solid understanding of Java fundamentals.

Spring Basics: Read official Spring documentation for an overview.

Core Concepts: Learn Inversion of Control (IoC) and Dependency Injection (DI).

Spring Boot: Explore Spring Boot for quick setup and configuration.

Dependency Injection: Understand IoC containers, XML, and annotation-based configurations.

Spring MVC: Grasp Model-View-Controller architecture for web applications.

Data Access: Explore data access using JDBC, ORM (like Hibernate), and Spring Data.

Security: Learn Spring Security for authentication and authorization.

RESTful Web Services: Build RESTful APIs using Spring MVC or WebFlux.

Aspect-Oriented Programming (AOP): Understand AOP for handling cross-cutting concerns.

Testing: Write unit and integration tests using tools like JUnit.

Real-World Projects: Apply knowledge through practical projects.

Community Engagement: Join Spring community discussions and forums.

Advanced Topics: Explore Spring Cloud, Spring Batch, and other advanced features.

Continuous Learning: Stay updated with the latest releases and features.

[briskwinits]

Database services are essential for any business that wants to store and manage its data effectively. We offer a wide range of database services to meet your specific needs, including database design, development, implementation, and support.

For more, visit: https://briskwinit.com/

[unstructured-data-management]

Unlocking the power of #DataDemocratization transforms organizations by promoting transparency and collaboration. Empowering every team member with data access leads to informed decisions and drives innovation.

[newfangled-vady]

VADY Conversational Analytics Platform Makes Enterprise Data Easily Accessible

VADY’s conversational analytics platform simplifies data analytics for business by allowing users to access insights through natural language queries. Instead of relying on complex dashboards or technical teams, decision-makers can simply ask questions and receive real-time, AI-powered responses. This user-friendly approach democratizes data, enabling employees at all levels to make data-driven decisions without requiring technical expertise. Whether monitoring sales performance, analyzing customer behavior, or tracking financial metrics, VADY ensures that enterprise data is easily accessible, driving efficiency and innovation.

[thevirtualupdate]

Making your structured data accessible is the key to driving better insights and decisions. Discover why accessibility matters and how it can transform your data game. 📊💡

👉 Read now: https://thevirtualupdate.com/make-your-structured-data-accessible/

[govindhtech]

NASA’s Earth Copilot Uses Microsoft AI To Share Tricky Data

From inquiries to revelations: Microsoft AI capabilities are included into NASA’s new Earth Copilot to democratize access to intricate data.

NASA satellites orbit the planet daily, gathering data to help us comprehend it. This large Earth Science data set on climate change and wildfires can benefit science, politics, agriculture, urban planning, and disaster relief.

It can be difficult to navigate the more than 100 petabytes of data gathered, which is why NASA and Microsoft have partnered to investigate the usage of a custom copilot utilizing Azure OpenAI Service to create NASA’s Earth Copilot. This might revolutionize how users engage with Earth’s data.

Because geospatial data is so complicated, navigating it frequently calls for some technical know-how. Because of this, only a few number of scientists and academics often have access to this data. These complications only increase as NASA gathers more data from additional satellites, which could further restrict the pool of possible researchers and developers of apps that could advance civilization.

NASA decided to make its data more usable and accessible after understanding this issue. NASA’s Office of the Chief Science Data Officer intends to democratize data access for scientists, educators, politicians, and the public by reducing technical barriers.

The difficulty: Handling the intricacy of the data

NASA’s Earth Science Data Systems Program is in charge of gathering an astounding array of data from instruments and sensors in orbit. This information covers a wide range of topics, including ocean temperatures, land cover changes, and atmospheric conditions. Nevertheless, the magnitude and intricacy of this data can be debilitating. Very few non-technical users have the specific skills necessary to navigate technological interfaces, comprehend data formats, and grasp the nuances of geospatial analysis, which are necessary for many people to uncover and extract insights. AI might expedite this procedure, cutting the amount of time needed to extract insights from Earth’s data to just a few seconds.

This problem has practical ramifications; it is not merely a convenience issue. Policymakers who wish to investigate deforestation trends in order to enact environmental restrictions, or scientists who must evaluate past hurricane data in order to enhance prediction models, might not have easy access to the information they require. Many industries are impacted by this inaccessibility, including as agriculture, urban planning, and disaster relief, where prompt insights from spaceborne data could have a big impact.

Furthermore, NASA is always confronted with the task of developing new tools to manage and make sense of this expanding library as new satellites with new instrumentation continue to launch and gather more data. The organization looked into cutting-edge technology that could improve accessibility and speed up data discovery, allowing more individuals to interact with the data and gain fresh perspectives.

The answer is Microsoft Azure’s AI-powered data access

In order to tackle these issues, NASA IMPACT collaborated with Microsoft to create Earth Copilot, an AI-powered customer copilot that may make data access easier and inspire more people to engage with its Earth Science data. Together, they created the proof of concept AI model that would revolutionize how people search for, find, and analyze NASA’s geospatial data by utilizing Microsoft’s Azure cloud platform and cutting-edge AI capabilities.

Cloud-based solutions such as Azure OpenAI Service, which give developers access to strong AI models and natural language processing capabilities so they can include intelligent, conversational AI into their apps, are essential to NASA’s Earth Copilot. This strategy enables NASA to incorporate AI into VEDA, its current data analysis platform. When combined, these technologies facilitate users’ ability to find, search for, and evaluate Earth Science data.

Earth Copilot combines these technologies to allow people to utilize plain language queries to connect with NASA’s data repository. Alternatively, they might only pose queries like “How did the COVID-19 pandemic impact air quality in the United States?” or “What was the impact of Hurricane Ian in Sanibel Island?” After that, AI will obtain pertinent datasets, resulting in a smooth and user-friendly process.

Open research through democratizing data

A wider spectrum of users may now interact with NASA’s science data with the solution developed due to the partnership between Microsoft and NASA IMPACT. The scientific community will profit greatly from this since researchers may now focus more on analysis and discoveries and less on retrieving data. For instance, agricultural professionals can learn more about soil moisture levels to enhance crop management, and climate scientists can rapidly access historical data to examine trends.

Involving pupils in Earth Science in real-world circumstances can spark their curiosity and create future scientists and engineers. Policymakers can make informed decisions on disaster preparedness, urban growth, and climate change with the latest data.

This AI prototype supports NASA’s Open Science program, which promotes scientific research transparency, diversity, and cooperation. NASA and Microsoft are laying the groundwork for a new era of discovery by removing obstacles to data discovery. This era will allow anybody who is interested in the world to explore and gain new insights.

Looking Ahead: Connecting the dots between ideas and data

Currently, NASA scientists and researchers can use the NASA Earth Copilot to investigate and evaluate its capabilities. Strict evaluations are necessary for every ethical AI technology implementation to guarantee that the data and results cannot be abused. Following a phase of internal testing and assessments, the NASA IMPACT team will investigate how to incorporate this feature into the VEDA platform.

This partnership exemplifies how technology can empower individuals, spur creativity, and bring about constructive change. Such solutions will be crucial to guaranteeing that the advantages of data are widely disseminated, allowing more people to interact with, evaluate, and act upon the information that influences its world.

Read more on govindhtech.com

[aiwikiweb]

How Ai2sql Empowers Business Analysts to Access Data Without Coding

Business analysts often need to access data for reporting and decision-making, but not all analysts have the technical skills required to write SQL queries. Ai2sql provides a solution by enabling analysts to generate SQL queries using natural language, making data access faster and more intuitive.

Problem Statement: Accessing data from databases often requires knowledge of SQL, which can be a barrier for business analysts who need quick insights. Relying on developers to write queries can lead to delays in data retrieval and decision-making.

Application: Ai2sql allows business analysts to simply describe their data requirements in natural language, such as "show total sales by product for the last quarter." The tool then converts this request into an SQL query that can be run against the database. This makes it easy for analysts to access the data they need without relying on technical assistance.

Outcome: By using Ai2sql, business analysts can retrieve data independently, leading to faster insights and better decision-making. The ability to generate SQL queries without coding also helps bridge the gap between business and technical teams.

Industry Examples:

Retail Analytics: Retail analysts use Ai2sql to generate reports on sales trends, inventory levels, and customer behavior, enabling data-driven decision-making.

Financial Services: Financial analysts use the tool to access transaction data and generate custom reports for financial analysis and forecasting.

Healthcare: Healthcare analysts use Ai2sql to query patient data, helping healthcare providers make informed decisions based on data insights.

Additional Scenarios: Ai2sql can also be used by small business owners to create sales and inventory reports, by educators to analyze student performance data, and by marketers for campaign performance analysis

Discover how Ai2sql can help you access the data you need without coding. Get started today at aiwikiweb.com/product/ai2sql/

[rwnnews]

The Rise of Bluesky: Scientists' New Favorite Platform.

Scientists are increasingly joining the social media platform Bluesky, which was created as an alternative to X (formerly known as Twitter)

The Rise of Bluesky: Scientists’ New Favorite Platform. SAN FRANCISCO, CALIFORNIA – In recent months, scientists have been flocking to the social media platform Bluesky, drawn by its promise of a more controlled and collaborative online environment. This surge in interest highlights the growing dissatisfaction with traditional platforms like X (formerly known as Twitter) and underscores the need…

[excelworld]

[notehowto]

How to Clear iCloud Storage: A Comprehensive Guide to Clearing Space