#databricks software

[dvt-uk]

Unlocking the Potential of Databricks: Comprehensive Services and Solutions

In the fast-paced world of big data and artificial intelligence, Databricks services have emerged as a crucial component for businesses aiming to harness the full potential of their data. From accelerating data engineering processes to implementing cutting-edge AI models, Databricks offers a unified platform that integrates seamlessly with various business operations. In this article, we explore the breadth of Databricks solutions, the expertise of Databricks developers, and the transformative power of Databricks artificial intelligence capabilities.

Databricks Services: Driving Data-Driven Success

Databricks services encompass a wide range of offerings designed to enhance data management, analytics, and machine learning capabilities. These services are instrumental in helping businesses:

Streamline Data Processing: Databricks provides powerful tools to process large volumes of data quickly and efficiently, reducing the time required to derive actionable insights.

Enable Advanced Analytics: By integrating with popular analytics tools, Databricks allows organizations to perform complex analyses and gain deeper insights into their data.

Support Collaborative Development: Databricks fosters collaboration among data scientists, engineers, and business analysts, facilitating a more cohesive approach to data-driven projects.

Innovative Databricks Solutions for Modern Businesses

Databricks solutions are tailored to address the diverse needs of businesses across various industries. These solutions include:

Unified Data Analytics: Combining data engineering, data science, and machine learning into a single platform, Databricks simplifies the process of building and deploying data-driven applications.

Real-Time Data Processing: With support for streaming data, Databricks enables businesses to process and analyze data in real-time, ensuring timely and accurate decision-making.

Scalable Data Management: Databricks’ cloud-based architecture allows organizations to scale their data processing capabilities as their needs grow, without worrying about infrastructure limitations.

Integrated Machine Learning: Databricks supports the entire machine learning lifecycle, from data preparation to model deployment, making it easier to integrate AI into business processes.

Expertise of Databricks Developers: Building the Future of Data

Databricks developers are highly skilled professionals who specialize in leveraging the Databricks platform to create robust, scalable data solutions. Their roles include:

Data Engineering: Developing and maintaining data pipelines that transform raw data into usable formats for analysis and machine learning.

Machine Learning Engineering: Building and deploying machine learning models that can predict outcomes, automate tasks, and provide valuable business insights.

Analytics and Reporting: Creating interactive dashboards and reports that allow stakeholders to explore data and uncover trends and patterns.

Platform Integration: Ensuring seamless integration of Databricks with existing IT systems and workflows, enhancing overall efficiency and productivity.

Databricks Artificial Intelligence: Transforming Data into Insights

Databricks artificial intelligence capabilities enable businesses to leverage AI technologies to gain competitive advantages. Key aspects of Databricks AI include:

Automated Machine Learning: Databricks simplifies the creation of machine learning models with automated tools that help select the best algorithms and parameters.

Scalable AI Infrastructure: Leveraging cloud resources, Databricks can handle the intensive computational requirements of training and deploying complex AI models.

Collaborative AI Development: Databricks promotes collaboration among data scientists, allowing teams to share code, models, and insights seamlessly.

Real-Time AI Applications: Databricks supports the deployment of AI models that can process and analyze data in real-time, providing immediate insights and responses.

Data Engineering Services: Enhancing Data Value

Data engineering services are a critical component of the Databricks ecosystem, enabling organizations to transform raw data into valuable assets. These services include:

Data Pipeline Development: Building robust pipelines that automate the extraction, transformation, and loading (ETL) of data from various sources into centralized data repositories.

Data Quality Management: Implementing processes and tools to ensure the accuracy, consistency, and reliability of data across the organization.

Data Integration: Combining data from different sources and systems to create a unified view that supports comprehensive analysis and reporting.

Performance Optimization: Enhancing the performance of data systems to handle large-scale data processing tasks efficiently and effectively.

Databricks Software: Empowering Data-Driven Innovation

Databricks software is designed to empower businesses with the tools they need to innovate and excel in a data-driven world. The core features of Databricks software include:

Interactive Workspaces: Providing a collaborative environment where teams can work together on data projects in real-time.

Advanced Security and Compliance: Ensuring that data is protected with robust security measures and compliance with industry standards.

Extensive Integrations: Offering seamless integration with popular tools and platforms, enhancing the flexibility and functionality of data operations.

Scalable Computing Power: Leveraging cloud infrastructure to provide scalable computing resources that can accommodate the demands of large-scale data processing and analysis.

Leveraging Databricks for Competitive Advantage

To fully harness the capabilities of Databricks, businesses should consider the following strategies:

Adopt a Unified Data Strategy: Utilize Databricks to unify data operations across the organization, from data engineering to machine learning.

Invest in Skilled Databricks Developers: Engage professionals who are proficient in Databricks to build and maintain your data infrastructure.

Integrate AI into Business Processes: Use Databricks’ AI capabilities to automate tasks, predict trends, and enhance decision-making processes.

Ensure Data Quality and Security: Implement best practices for data management to maintain high-quality data and ensure compliance with security standards.

Scale Operations with Cloud Resources: Take advantage of Databricks’ cloud-based architecture to scale your data operations as your business grows.

The Future of Databricks Services and Solutions

As the field of data and AI continues to evolve, Databricks services and solutions will play an increasingly vital role in driving business innovation and success. Future trends may include:

Enhanced AI Capabilities: Continued advancements in AI will enable Databricks to offer more powerful and intuitive AI tools that can address complex business challenges.

Greater Integration with Cloud Ecosystems: Databricks will expand its integration capabilities, allowing businesses to seamlessly connect with a broader range of cloud services and platforms.

Increased Focus on Real-Time Analytics: The demand for real-time data processing and analytics will grow, driving the development of more advanced streaming data solutions.

Expanding Global Reach: As more businesses recognize the value of data and AI, Databricks will continue to expand its presence and influence across different markets and industries.

[kittu800]

[basheeransari]

Understanding Data Insights: How Businesses Can Use Data for Growth

In today's digital world, data is everywhere. Every interaction, transaction, and process generates information that can be analyzed to reveal valuable insights. However, the real challenge is using this data effectively to drive informed decision-making, improve efficiency, and predict future trends.

What Are Data Insights?

Data insights refer to the meaningful patterns, trends, and conclusions that businesses derive from analyzing raw data. These insights help organizations understand past performance, optimize current operations, and prepare for future challenges. By leveraging data, companies can make strategic decisions based on facts rather than intuition.

Why Are Data Insights Important?

Data-driven decision-making has become a key factor in business success. Here’s why:

Better Decision-Making – Businesses can use data to evaluate market trends, customer preferences, and operational efficiency.

Enhanced Customer Experience – Understanding customer behavior helps companies tailor products and services to meet specific needs.

Operational Efficiency – Identifying inefficiencies allows organizations to streamline processes and reduce costs.

Risk Management – Analyzing data helps in detecting fraud, assessing financial risks, and improving security.

Competitive Advantage – Companies that leverage data effectively can anticipate market shifts and respond proactively.

Types of Data Analytics

There are several types of analytics, each serving a different purpose:

Descriptive Analytics – Examines historical data to identify trends and patterns. Example: A retail store analyzing sales data to determine seasonal demand.

Diagnostic Analytics – Explains why something happened by finding correlations and causes. Example: A company investigating why customer engagement dropped after a website update.

Predictive Analytics – Uses historical data and statistical models to forecast future outcomes. Example: Predicting customer churn based on past interactions.

Prescriptive Analytics – Recommends the best course of action based on predictive models. Example: An airline optimizing ticket pricing based on demand trends.

Cognitive Analytics – Uses artificial intelligence (AI) and machine learning to interpret complex data and generate human-like insights. Example: A chatbot analyzing user sentiment to improve responses.

How Different Industries Use Data Insights

Data insights are widely used across industries to improve efficiency and drive innovation.

Healthcare : Data insights help predict disease outbreaks and improve patient care by analyzing health patterns and trends. They also play a crucial role in personalized treatment, allowing doctors to tailor medical plans based on a patient's history. Additionally, data-driven approaches accelerate drug development, helping researchers identify effective treatments and potential risks more efficiently.

Retail & E-Commerce : Analyzing customer behavior enables businesses to personalize recommendations, enhancing the shopping experience. Additionally, real-time demand forecasting helps in efficient inventory management, ensuring that products are stocked based on consumer needs.

Finance & Banking : Financial institutions use anomaly detection to identify fraudulent transactions and prevent unauthorized activities. Additionally, analyzing customer spending patterns helps assess credit risk, allowing for better loan and credit approval decisions.

Manufacturing : Predictive maintenance helps prevent equipment failures by analyzing performance data and detecting potential issues early. Additionally, data-driven insights optimize supply chain management and production schedules, ensuring smooth operations and reduced downtime.

Marketing & Advertising : By analyzing consumer data, businesses can create targeted marketing campaigns that resonate with their audience. Additionally, data insights help measure the effectiveness of digital advertising strategies, allowing companies to refine their approach for better engagement and higher returns.

Telecommunications : Predicting potential failures helps improve network reliability by allowing proactive maintenance and reducing downtime. Additionally, analyzing customer feedback enables service providers to enhance quality, address issues efficiently, and improve user satisfaction.

Education : Tracking student performance helps create personalized learning paths, ensuring that each student receives tailored support based on their needs. Additionally, data-driven insights assist in curriculum planning, allowing educators to design more effective teaching strategies and improve overall learning outcomes.

Logistics & Transport : Optimizing delivery routes helps reduce fuel costs by identifying the most efficient paths for transportation. Additionally, predictive analytics enhances fleet management by forecasting vehicle maintenance needs, minimizing downtime, and ensuring smooth operations.

How to Implement Data Insights in a Business

For organizations looking to integrate data analytics, here are key steps to follow:

Define Business Objectives – Identify what you want to achieve with data insights.

Collect Relevant Data – Ensure that you gather high-quality data from various sources.

Choose the Right Tools – Use analytics software and machine learning algorithms to process data efficiently.

Ensure Data Security – Protect sensitive information through encryption and compliance measures.

Interpret Results Accurately – Avoid misinterpreting data by considering multiple perspectives.

Train Employees – Build a data-literate workforce that understands how to use insights effectively.

Continuously Improve – Regularly refine analytics processes to stay updated with new trends.

Data Analytics in Advanced Technologies

Space Technology : AI-driven data analytics enhances satellite imaging, real-time Earth monitoring, and space exploration by processing vast amounts of astronomical data efficiently.

Quantum Computing : Quantum-powered analytics enable faster simulations and predictive modeling, improving data processing for scientific and financial applications.

Large Data Models : AI-driven large data models analyze massive datasets, extracting valuable insights for businesses, healthcare, and research.

Research & Analytics (R&A) Services : AI enhances R&A services by automating data collection, trend analysis, and decision-making for industries like finance and healthcare.

Big Social Media Houses : Social media platforms use AI analytics to track user behavior, detect trends, personalize content, and combat misinformation in real-time.

The Future of Data Analytics

The field of data analytics is evolving rapidly with advancements in artificial intelligence, cloud computing, and big data technologies. Businesses are moving towards automated analytics systems that require minimal human intervention. In the coming years, expect to see:

AI-powered decision-making – Machines making real-time business decisions with minimal human input.

Edge computing – Faster data processing by analyzing information closer to the source.

Ethical data practices – Increased focus on privacy, transparency, and responsible AI usage.

Data insights have transformed how businesses operate, enabling smarter decision-making and improved efficiency. Whether in healthcare, finance, or marketing, data analytics services continue to shape the future of industries. Companies that embrace a data-driven culture will be better positioned to innovate and grow in a highly competitive market.

By understanding and applying data insights, businesses can navigate challenges, seize opportunities, and remain ahead in an increasingly digital world.

FAQs: 

What are data insights?Data insights are patterns and trends derived from analyzing raw data to help businesses make informed decisions.

Why are data insights important?They improve decision-making, enhance customer experience, optimize operations, and provide a competitive advantage.

How do businesses use data insights?Companies use them for customer behavior analysis, fraud detection, predictive maintenance, targeted marketing, and process optimization.

What tools are used for data analytics?Common tools include Python, R, SQL, Tableau, Power BI, and Google Analytics.

What is the future of data analytics?AI-powered automation, edge computing, and ethical data practices will shape the future of analytics.

[azuresolutionsarchitect12]

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Generative AI Solutions | Samprasoft

Harness the power of SampraSoft's specialized Generative AI solutions, including strategic development, custom solution design, and data strategy. Benefit from our expertise to create innovative, customized solutions for your business. Partner with us for advanced Generative AI solutions that drive your success.

[rajaniesh]

Exploring the Latest Features of Apache Spark 3.4 for Databricks Runtime

In the dynamic landscape of big data and analytics, staying at the forefront of technology is essential for organizations aiming to harness the full potential of their data-driven initiatives.

[dostoyevsky-official]

The Young, Inexperienced Engineers Aiding Elon Musk's Government Takeover

WIRED has identified six young men—all apparently between the ages of 19 and 24, according to public databases, their online presences, and other records—who have little to no government experience and are now playing critical roles in Musk’s so-called Department of Government Efficiency (DOGE) project, tasked by executive order with “modernizing Federal technology and software to maximize governmental efficiency and productivity.” The engineers all hold nebulous job titles within DOGE, and at least one appears to be working as a volunteer. The engineers are Akash Bobba, Edward Coristine, Luke Farritor, Gautier Cole Killian, Gavin Kliger, and Ethan Shaotran. None have responded to requests for comment from WIRED. Representatives from OPM, GSA, and DOGE did not respond to requests for comment. [...] Kliger, whose LinkedIn lists him as a special advisor to the director of OPM and who is listed in internal records reviewed by WIRED as a special advisor to the director for information technology, attended UC Berkeley until 2020; most recently, according to his LinkedIn, he worked for the AI company Databricks. His Substack includes a post titled “The Curious Case of Matt Gaetz: How the Deep State Destroys Its Enemies,” as well as another titled “Pete Hegseth as Secretary of Defense: The Warrior Washington Fears.”

these people are nazis orchestrating an illegal, unconstitutional takeover of government agencies and tapping into your personal data. they need to be arrested, charged with crimes, before that doxxed, harassed, etc.

[mariacallous]

Multiple current and former government IT sources tell WIRED that it would be easy to connect the IRS’s Palantir system with the ICE system at DHS, allowing users to query data from both systems simultaneously. A system like the one being created at the IRS with Palantir could enable near-instantaneous access to tax information for use by DHS and immigration enforcement. It could also be leveraged to share and query data from different agencies as well, including immigration data from DHS. Other DHS sub-agencies, like USCIS, use Databricks software to organize and search its data, but these could be connected to outside Foundry instances simply as well, experts say. Last month, Palantir and Databricks struck a deal making the two software platforms more interoperable.

“I think it's hard to overstate what a significant departure this is and the reshaping of longstanding norms and expectations that people have about what the government does with their data,” says Elizabeth Laird, director of equity in civic technology at the Center for Democracy and Technology, who noted that agencies trying to match different datasets can also lead to errors. “You have false positives and you have false negatives. But in this case, you know, a false positive where you're saying someone should be targeted for deportation.”

Mistakes in the context of immigration can have devastating consequences: In March, authorities arrested and deported Kilmar Abrego Garcia, a Salvadoran national, due to, the Trump administration says, “an administrative error.” Still, the administration has refused to bring Abrego Garcia back, defying a Supreme Court ruling.

“The ultimate concern is a panopticon of a single federal database with everything that the government knows about every single person in this country,” Venzke says. “What we are seeing is likely the first step in creating that centralized dossier on everyone in this country.”

DOGE Is Building a Master Database to Surveil and Track Immigrants

[spinnrblog]

In 2013, Databricks was born out of UC Berkeley with one mission: simplify big data and unleash AI through Apache Spark. Founders like Ali Ghodsi believed the future of computing lay in seamless data platforms. With $𝟑𝟑 𝐦𝐢𝐥𝐥𝐢𝐨𝐧 in early backing from Andreessen Horowitz and NEA, Databricks introduced a cloud-based environment where teams could collaborate on data science and machine learning. By 2020, it had over 𝟓,𝟎𝟎𝟎 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬, including Shell and HP. Its 2023 funding round pushed its valuation to $𝟒𝟑 𝐛𝐢𝐥𝐥𝐢𝐨𝐧, cementing it as a leader in the AI infrastructure space. Databricks now powers analytics for over 𝐨𝐯𝐞𝐫 𝟓𝟎% of Fortune 500 companies.

The moral? When you streamline complexity, you don’t just sell software—you unlock transformation.

[vengoai]

In 2013, Databricks was born out of UC Berkeley with one mission: simplify big data and unleash AI through Apache Spark. Founders like Ali Ghodsi believed the future of computing lay in seamless data platforms. With $𝟑𝟑 𝐦𝐢𝐥𝐥𝐢𝐨𝐧 in early backing from Andreessen Horowitz and NEA, Databricks introduced a cloud-based environment where teams could collaborate on data science and machine learning. By 2020, it had over 𝟓,𝟎𝟎𝟎 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬, including Shell and HP. Its 2023 funding round pushed its valuation to $𝟒𝟑 𝐛𝐢𝐥𝐥𝐢𝐨𝐧, cementing it as a leader in the AI infrastructure space. Databricks now powers analytics for over 𝐨𝐯𝐞𝐫 𝟓𝟎% of Fortune 500 companies.

The moral? When you streamline complexity, you don’t just sell software—you unlock transformation.

[intellbiinstitute]

How to Become a Successful Azure Data Engineer in 2025

In today’s data-driven world, businesses rely on cloud platforms to store, manage, and analyze massive amounts of information. One of the most in-demand roles in this space is that of an Azure Data Engineer. If you're someone looking to build a successful career in the cloud and data domain, Azure Data Engineering in PCMC is quickly becoming a preferred choice among aspiring professionals and fresh graduates.

This blog will walk you through everything you need to know to become a successful Azure Data Engineer in 2025—from required skills to tools, certifications, and career prospects.

Why Choose Azure for Data Engineering?

Microsoft Azure is one of the leading cloud platforms adopted by companies worldwide. With powerful services like Azure Data Factory, Azure Databricks, and Azure Synapse Analytics, it allows organizations to build scalable, secure, and automated data solutions. This creates a huge demand for trained Azure Data Engineers who can design, build, and maintain these systems efficiently.

Key Responsibilities of an Azure Data Engineer

As an Azure Data Engineer, your job is more than just writing code. You will be responsible for:

Designing and implementing data pipelines using Azure services.

Integrating various structured and unstructured data sources.

Managing data storage and security.

Enabling real-time and batch data processing.

Collaborating with data analysts, scientists, and other engineering teams.

Essential Skills to Master in 2025

To succeed as an Azure Data Engineer, you must gain expertise in the following:

1. Strong Programming Knowledge

Languages like SQL, Python, and Scala are essential for data transformation, cleaning, and automation tasks.

2. Understanding of Azure Tools

Azure Data Factory – for data orchestration and transformation.

Azure Synapse Analytics – for big data and data warehousing solutions.

Azure Databricks – for large-scale data processing using Apache Spark.

Azure Storage & Data Lake – for scalable and secure data storage.

3. Data Modeling & ETL Design

Knowing how to model databases and build ETL (Extract, Transform, Load) pipelines is fundamental for any data engineer.

4. Security & Compliance

Understanding Role-Based Access Control (RBAC), Data Encryption, and Data Masking is critical to ensure data integrity and privacy.

Career Opportunities and Growth

With increasing cloud adoption, Azure Data Engineers are in high demand across all industries including finance, healthcare, retail, and IT services. Roles include:

Azure Data Engineer

Data Platform Engineer

Cloud Data Specialist

Big Data Engineer

Salaries range widely depending on skills and experience, but in cities like Pune and PCMC (Pimpri-Chinchwad), entry-level engineers can expect ₹5–7 LPA, while experienced professionals often earn ₹12–20 LPA or more.

Learning from the Right Place Matters

To truly thrive in this field, it’s essential to learn from industry experts. If you’re looking for a trusted Software training institute in Pimpri-Chinchwad, IntelliBI Innovations Technologies offers career-focused Azure Data Engineering programs. Their curriculum is tailored to help students not only understand theory but apply it through real-world projects, resume preparation, and mock interviews.

Conclusion

Azure Data Engineering is not just a job—it’s a gateway to an exciting and future-proof career. With the right skills, certifications, and hands-on experience, you can build powerful data solutions that transform businesses. And with growing opportunities in Azure Data Engineering in PCMC, now is the best time to start your journey.

Whether you’re a fresher or an IT professional looking to upskill, invest in yourself and start building a career that matters.

[aitoolswhitehattoolbox]

Software Engineering Associate Advisor - HIH - Evernorth

(JavaScript, Python, R, Ruby, Perl, etc.) Experience being part of Agile teams – Scrum or Kanban. Airflow Databricks / Cloud… Apply Now

[jcmarchi]

Soham Mazumdar, Co-Founder & CEO of WisdomAI – Interview Series

New Post has been published on https://thedigitalinsider.com/soham-mazumdar-co-founder-ceo-of-wisdomai-interview-series/

Soham Mazumdar, Co-Founder & CEO of WisdomAI – Interview Series

Soham Mazumdar is the Co-Founder and CEO of WisdomAI, a company at the forefront of AI-driven solutions. Prior to founding WisdomAI in 2023, he was Co-Founder and Chief Architect at Rubrik, where he played a key role in scaling the company over a 9-year period. Soham previously held engineering leadership roles at Facebook and Google, where he contributed to core search infrastructure and was recognized with the Google Founder’s Award. He also co-founded Tagtile, a mobile loyalty platform acquired by Facebook. With two decades of experience in software architecture and AI innovation, Soham is a seasoned entrepreneur and technologist based in the San Francisco Bay Area.

WisdomAI is an AI-native business intelligence platform that helps enterprises access real-time, accurate insights by integrating structured and unstructured data through its proprietary “Knowledge Fabric.” The platform powers specialized AI agents that curate data context, answer business questions in natural language, and proactively surface trends or risks—without generating hallucinated content. Unlike traditional BI tools, WisdomAI uses generative AI strictly for query generation, ensuring high accuracy and reliability. It integrates with existing data ecosystems and supports enterprise-grade security, with early adoption by major firms like Cisco and ConocoPhillips.

You co-founded Rubrik and helped scale it into a major enterprise success. What inspired you to leave in 2023 and build WisdomAI—and was there a particular moment that clarified this new direction?

The enterprise data inefficiency problem was staring me right in the face. During my time at Rubrik, I witnessed firsthand how Fortune 500 companies were drowning in data but starving for insights. Even with all the infrastructure we built, less than 20% of enterprise users actually had the right access and know-how to use data effectively in their daily work. It was a massive, systemic problem that no one was really solving.

I’m also a builder by nature – you can see it in my path from Google to Tagtile to Rubrik and now WisdomAI. I get energized by taking on fundamental challenges and building solutions from the ground up. After helping scale Rubrik to enterprise success, I felt that entrepreneurial pull again to tackle something equally ambitious.

Last but not least, the AI opportunity was impossible to ignore. By 2023, it became clear that AI could finally bridge that gap between data availability and data usability. The timing felt perfect to build something that could democratize data insights for every enterprise user, not just the technical few.

The moment of clarity came when I realized we could combine everything I’d learned about enterprise data infrastructure at Rubrik with the transformative potential of AI to solve this fundamental inefficiency problem.

WisdomAI introduces a “Knowledge Fabric” and a suite of AI agents. Can you break down how this system works together to move beyond traditional BI dashboards?

We’ve built an agentic data insights platform that works with data where it is – structured, unstructured, and even “dirty” data. Rather than asking analytics teams to run reports, business managers can directly ask questions and drill into details. Our platform can be trained on any data warehousing system by analyzing query logs.

We’re compatible with major cloud data services like Snowflake, Microsoft Fabric, Google’s BigQuery, Amazon’s Redshift, Databricks, and Postgres and also just document formats like excel, PDF, powerpoint etc.

Unlike conventional tools designed primarily for analysts, our conversational interface empowers business users to get answers directly, while our multi-agent architecture enables complex queries across diverse data systems.

You’ve emphasized that WisdomAI avoids hallucinations by separating GenAI from answer generation. Can you explain how your system uses GenAI differently—and why that matters for enterprise trust?

Our AI-Ready Context Model trains on the organization’s data to create a universal context understanding that answers questions with high semantic accuracy while maintaining data privacy and governance. Furthermore, we use generative AI to formulate well-scoped queries that allow us to extract data from the different systems, as opposed to feeding raw data into the LLMs. This is crucial for addressing hallucination and safety concerns with LLMs.

You coined the term “Agentic Data Insights Platform.” How is agentic intelligence different from traditional analytics tools or even standard LLM-based assistants?

Traditional BI stacks slow decision-making because every question has to fight its way through disconnected data silos and a relay team of specialists. When a chief revenue officer needs to know how to close the quarter, the answer typically passes through half a dozen hands—analysts wrangling CRM extracts, data engineers stitching files together, and dashboard builders refreshing reports—turning a simple query into a multi-day project.

Our platform breaks down those silos and puts the full depth of data one keystroke away, so the CRO can drill from headline metrics all the way to row-level detail in seconds.

No waiting in the analyst queue, no predefined dashboards that can’t keep up with new questions—just true self-service insights delivered at the speed the business moves.

How do you ensure WisdomAI adapts to the unique data vocabulary and structure of each enterprise? What role does human input play in refining the Knowledge Fabric?

Working with data where and how it is – that’s essentially the holy grail for enterprise business intelligence. Traditional systems aren’t built to handle unstructured data or “dirty” data with typos and errors. When information exists across varied sources – databases, documents, telemetry data – organizations struggle to integrate this information cohesively.

Without capabilities to handle these diverse data types, valuable context remains isolated in separate systems. Our platform can be trained on any data warehousing system by analyzing query logs, allowing it to adapt to each organization’s unique data vocabulary and structure.

You’ve described WisdomAI’s development process as ‘vibe coding’—building product experiences directly in code first, then iterating through real-world use. What advantages has this approach given you compared to traditional product design?

“Vibe coding” is a significant shift in how software is built where developers leverage the power of AI tools to generate code simply by describing the desired functionality in natural language. It’s like an intelligent assistant that does what you want the software to do, and it writes the code for you. This dramatically reduces the manual effort and time traditionally required for coding.

For years, the creation of digital products has largely followed a familiar script: meticulously plan the product and UX design, then execute the development, and iterate based on feedback. The logic was clear because investing in design upfront minimizes costly rework during the more expensive and time-consuming development phase. But what happens when the cost and time to execute that development drastically shrinks? This capability flips the traditional development sequence on its head. Suddenly, developers can start building functional software based on a high-level understanding of the requirements, even before detailed product and UX designs are finalized.

With the speed of AI code generation, the effort involved in creating exhaustive upfront designs can, in certain contexts, become relatively more time-consuming than getting a basic, functional version of the software up and running. The new paradigm in the world of vibe coding becomes: execute (code with AI), then adapt (design and refine).

This approach allows for incredibly early user validation of the core concepts. Imagine getting feedback on the actual functionality of a feature before investing heavily in detailed visual designs. This can lead to more user-centric designs, as the design process is directly informed by how users interact with a tangible product.

At WisdomAI, we actively embrace AI code generation. We’ve found that by embracing rapid initial development, we can quickly test core functionalities and gather invaluable user feedback early in the process, live on the product. This allows our design team to then focus on refining the user experience and visual design based on real-world usage, leading to more effective and user-loved products, faster.

From sales and marketing to manufacturing and customer success, WisdomAI targets a wide spectrum of business use cases. Which verticals have seen the fastest adoption—and what use cases have surprised you in their impact?

We’ve seen transformative results with multiple customers. For F500 oil and gas company, ConocoPhillips, drilling engineers and operators now use our platform to query complex well data directly in natural language. Before WisdomAI, these engineers needed technical help for even basic operational questions about well status or job performance. Now they can instantly access this information while simultaneously comparing against best practices in their drilling manuals—all through the same conversational interface. They evaluated numerous AI vendors in a six-month process, and our solution delivered a 50% accuracy improvement over the closest competitor.

At a hyper growth Cyber Security company Descope, WisdomAI is used as a virtual data analyst for Sales and Finance. We reduced report creation time from 2-3 days to just 2-3 hours—a 90% decrease. This transformed their weekly sales meetings from data-gathering exercises to strategy sessions focused on actionable insights. As their CRO notes, “Wisdom AI brings data to my fingertips. It really democratizes the data, bringing me the power to go answer questions and move on with my day, rather than define your question, wait for somebody to build that answer, and then get it in 5 days.” This ability to make data-driven decisions with unprecedented speed has been particularly crucial for a fast-growing company in the competitive identity management market.

A practical example: A chief revenue officer asks, “How am I going to close my quarter?” Our platform immediately offers a list of pending deals to focus on, along with information on what’s delaying each one – such as specific questions customers are waiting to have answered. This happens with five keystrokes instead of five specialists and days of delay.

Many companies today are overloaded with dashboards, reports, and siloed tools. What are the most common misconceptions enterprises have about business intelligence today?

Organizations sit on troves of information yet struggle to leverage this data for quick decision-making. The challenge isn’t just about having data, but working with it in its natural state – which often includes “dirty” data not cleaned of typos or errors. Companies invest heavily in infrastructure but face bottlenecks with rigid dashboards, poor data hygiene, and siloed information. Most enterprises need specialized teams to run reports, creating significant delays when business leaders need answers quickly. The interface where people consume data remains outdated despite advancements in cloud data engines and data science.

Do you view WisdomAI as augmenting or eventually replacing existing BI tools like Tableau or Looker? How do you fit into the broader enterprise data stack?

We’re compatible with major cloud data services like Snowflake, Microsoft Fabric, Google’s BigQuery, Amazon’s Redshift, Databricks, and Postgres and also just document formats like excel, PDF, powerpoint etc. Our approach transforms the interface where people consume data, which has remained outdated despite advancements in cloud data engines and data science.

Looking ahead, where do you see WisdomAI in five years—and how do you see the concept of “agentic intelligence” evolving across the enterprise landscape?

The future of analytics is moving from specialist-driven reports to self-service intelligence accessible to everyone. BI tools have been around for 20+ years, but adoption hasn’t even reached 20% of company employees. Meanwhile, in just twelve months, 60% of workplace users adopted ChatGPT, many using it for data analysis. This dramatic difference shows the potential for conversational interfaces to increase adoption.

We’re seeing a fundamental shift where all employees can directly interrogate data without technical skills. The future will combine the computational power of AI with natural human interaction, allowing insights to find users proactively rather than requiring them to hunt through dashboards.

Thank you for the great interview, readers who wish to learn more should visit WisdomAI.

[kamalkafir-blog]

Lead Software Engineer

Job title: Lead Software Engineer Company: Nike Job description: Software Engineer – Platforms with a passion for cloud-native development and platform ownership. You are someone who thrives… of AWS Cloud Services, Kubernetes, DevOps, Databricks, Python and other cloud-native platforms. You should be an excellent… Expected salary: Location: Karnataka Job date: Wed, 21 May 2025 03:13:56 GMT Apply…

[mariacallous]

Elon Musk’s so-called Department of Government Efficiency (DOGE) has plans to stage a “hackathon” next week in Washington, DC. The goal is to create a single “mega API”—a bridge that lets software systems talk to one another—for accessing IRS data, sources tell WIRED. The agency is expected to partner with a third-party vendor to manage certain aspects of the data project. Palantir, a software company cofounded by billionaire and Musk associate Peter Thiel, has been brought up consistently by DOGE representatives as a possible candidate, sources tell WIRED.

Two top DOGE operatives at the IRS, Sam Corcos and Gavin Kliger, are helping to orchestrate the hackathon, sources tell WIRED. Corcos is a health-tech CEO with ties to Musk’s SpaceX. Kliger attended UC Berkeley until 2020 and worked at the AI company Databricks before joining DOGE as a special adviser to the director at the Office of Personnel Management (OPM). Corcos is also a special adviser to Treasury Secretary Scott Bessent.

Since joining Musk’s DOGE, Corcos has told IRS workers that he wants to pause all engineering work and cancel current attempts to modernize the agency’s systems, according to sources with direct knowledge who spoke with WIRED. He has also spoken about some aspects of these cuts publicly: "We've so far stopped work and cut about $1.5 billion from the modernization budget. Mostly projects that were going to continue to put us down the death spiral of complexity in our code base," Corcos told Laura Ingraham on Fox News in March.

Corcos has discussed plans for DOGE to build “one new API to rule them all,” making IRS data more easily accessible for cloud platforms, sources say. APIs, or application programming interfaces, enable different applications to exchange data, and could be used to move IRS data into the cloud. The cloud platform could become the “read center of all IRS systems,” a source with direct knowledge tells WIRED, meaning anyone with access could view and possibly manipulate all IRS data in one place.

Over the last few weeks, DOGE has requested the names of the IRS’s best engineers from agency staffers. Next week, DOGE and IRS leadership are expected to host dozens of engineers in DC so they can begin “ripping up the old systems” and building the API, an IRS engineering source tells WIRED. The goal is to have this task completed within 30 days. Sources say there have been multiple discussions about involving third-party cloud and software providers like Palantir in the implementation.

Corcos and DOGE indicated to IRS employees that they intended to first apply the API to the agency’s mainframes and then move on to every other internal system. Initiating a plan like this would likely touch all data within the IRS, including taxpayer names, addresses, social security numbers, as well as tax return and employment data. Currently, the IRS runs on dozens of disparate systems housed in on-premises data centers and in the cloud that are purposefully compartmentalized. Accessing these systems requires special permissions and workers are typically only granted access on a need-to-know basis.

A “mega API” could potentially allow someone with access to export all IRS data to the systems of their choosing, including private entities. If that person also had access to other interoperable datasets at separate government agencies, they could compare them against IRS data for their own purposes.

“Schematizing this data and understanding it would take years,” an IRS source tells WIRED. “Just even thinking through the data would take a long time, because these people have no experience, not only in government, but in the IRS or with taxes or anything else.” (“There is a lot of stuff that I don't know that I am learning now,” Corcos tells Ingraham in the Fox interview. “I know a lot about software systems, that's why I was brought in.")

These systems have all gone through a tedious approval process to ensure the security of taxpayer data. Whatever may replace them would likely still need to be properly vetted, sources tell WIRED.

"It's basically an open door controlled by Musk for all American's most sensitive information with none of the rules that normally secure that data," an IRS worker alleges to WIRED.

The data consolidation effort aligns with President Donald Trump’s executive order from March 20, which directed agencies to eliminate information silos. While the order was purportedly aimed at fighting fraud and waste, it also could threaten privacy by consolidating personal data housed on different systems into a central repository, WIRED previously reported.

In a statement provided to WIRED on Saturday, a Treasury spokesperson said the department “is pleased to have gathered a team of long-time IRS engineers who have been identified as the most talented technical personnel. Through this coalition, they will streamline IRS systems to create the most efficient service for the American taxpayer. This week the team will be participating in the IRS Roadmapping Kickoff, a seminar of various strategy sessions, as they work diligently to create efficient systems. This new leadership and direction will maximize their capabilities and serve as the tech-enabled force multiplier that the IRS has needed for decades.”

Palantir, Sam Corcos, and Gavin Kliger did not immediately respond to requests for comment.

In February, a memo was drafted to provide Kliger with access to personal taxpayer data at the IRS, The Washington Post reported. Kliger was ultimately provided read-only access to anonymized tax data, similar to what academics use for research. Weeks later, Corcos arrived, demanding detailed taxpayer and vendor information as a means of combating fraud, according to the Post.

“The IRS has some pretty legacy infrastructure. It's actually very similar to what banks have been using. It's old mainframes running COBOL and Assembly and the challenge has been, how do we migrate that to a modern system?” Corcos told Ingraham in the same Fox News interview. Corcos said he plans to continue his work at IRS for a total of six months.

DOGE has already slashed and burned modernization projects at other agencies, replacing them with smaller teams and tighter timelines. At the Social Security Administration, DOGE representatives are planning to move all of the agency’s data off of legacy programming languages like COBOL and into something like Java, WIRED reported last week.

Last Friday, DOGE suddenly placed around 50 IRS technologists on administrative leave. On Thursday, even more technologists were cut, including the director of cybersecurity architecture and implementation, deputy chief information security officer, and acting director of security risk management. IRS’s chief technology officer, Kaschit Pandya, is one of the few technology officials left at the agency, sources say.

DOGE originally expected the API project to take a year, multiple IRS sources say, but that timeline has shortened dramatically down to a few weeks. “That is not only not technically possible, that's also not a reasonable idea, that will cripple the IRS,” an IRS employee source tells WIRED. “It will also potentially endanger filing season next year, because obviously all these other systems they’re pulling people away from are important.”

(Corcos also made it clear to IRS employees that he wanted to kill the agency’s Direct File program, the IRS’s recently released free tax-filing service.)

DOGE’s focus on obtaining and moving sensitive IRS data to a central viewing platform has spooked privacy and civil liberties experts.

“It’s hard to imagine more sensitive data than the financial information the IRS holds,” Evan Greer, director of Fight for the Future, a digital civil rights organization, tells WIRED.

Palantir received the highest FedRAMP approval this past December for its entire product suite, including Palantir Federal Cloud Service (PFCS) which provides a cloud environment for federal agencies to implement the company’s software platforms, like Gotham and Foundry. FedRAMP stands for Federal Risk and Authorization Management Program and assesses cloud products for security risks before governmental use.

“We love disruption and whatever is good for America will be good for Americans and very good for Palantir,” Palantir CEO Alex Karp said in a February earnings call. “Disruption at the end of the day exposes things that aren't working. There will be ups and downs. This is a revolution, some people are going to get their heads cut off.”