Tag: Databricks

  • Wipro Seeks Azure Data Engineer with Databricks, SQL, and PySpark Expertise

    Wipro Seeks Azure Data Engineer with Databricks, SQL, and PySpark Expertise

    Wipro, a global leader in IT consulting and business process services, is hiring an Azure Data Engineer skilled in Databricks, SQL, and PySpark. The role is based in major Indian cities including Bengaluru, Hyderabad, Chennai, Mumbai, and Pune. The ideal candidate will design scalable data systems, resolve technical challenges, and enhance operational support within active data engineering teams.

    Key Responsibilities

    • Oversee daily team performance by reviewing transactions and process quality parameters.
    • Provide technical guidance to production specialists and continuously improve process efficiency.
    • Document client queries, resolutions, and troubleshooting steps systematically.
    • Handle technical escalations with effective diagnosis and resolution within defined SLA timelines.
    • Conduct training sessions to close skill gaps and boost technical capabilities.

    Essential Qualifications

    • Bachelor’s degree in Computer Science or a related field.
    • Extensive Azure Data Engineering experience with Databricks and PySpark.
    • Strong SQL proficiency for data querying, transformation, and database management.
    • Proven ability to troubleshoot data pipelines and resolve production issues.
    • Experience in enterprise environments with SLA-based delivery models and client support.

    Preferred Skills

    • At least 2 years of experience with cloud-based data platforms and distributed processing frameworks.
    • Strong analytical and problem-solving abilities for complex data engineering challenges.
    • Mentoring and training skills to instruct team members on technical topics.
    • Familiarity with performance monitoring dashboards and reporting tools.
    • Excellent communication skills for client interaction and cross-team collaboration.

    Location: Bengaluru, Hyderabad, Chennai, Mumbai, Pune
    Experience: 5–8 years
    Job Type: Full-time
    Apply: Submit your application.

  • 6 Best AI Agents for Data Analytics and Business Intelligence in 2026

    6 Best AI Agents for Data Analytics and Business Intelligence in 2026

    Business intelligence (BI) has evolved far beyond static dashboards and manual reports. In 2026, AI agents are transforming how organizations interact with data—automating reporting, generating insights, and enabling faster, smarter decision-making. These tools can monitor key metrics, write SQL queries, explain trends, detect anomalies, and recommend actions, all through natural language interactions.

    Here are six standout AI agents that leading enterprises and growing startups are adopting this year.

    1. Microsoft Power BI Copilot

    Microsoft Power BI Copilot integrates AI directly into the company’s BI platform. Users can ask questions in plain English, generate reports, create DAX formulas, and summarize dashboards without manual effort. Its tight integration with Microsoft Fabric, Excel, and Teams makes it a practical choice for organizations already using the Microsoft ecosystem.

    2. ThoughtSpot Sage

    ThoughtSpot Sage is built around search-driven analytics. Users simply type business questions and receive charts, explanations, and follow-up insights. The platform identifies unusual trends and suggests additional areas to explore, making it popular with sales, finance, and marketing teams that need quick answers without relying on technical experts.

    3. Databricks AI/BI Genie

    Databricks AI/BI Genie is designed for organizations with large data volumes. The application converts human language into SQL code, enabling users to analyze data without writing any code themselves. Built on the Databricks Lakehouse platform, it provides a seamless experience for data-heavy workflows.

    4. Tableau Agent

    Tableau has extended its visual analytics platform with AI-based assistance. Tableau Agent helps users create dashboards faster, summarize reports, and explain trends using natural language. The visualization process becomes quicker without compromising Tableau’s rich analytical capabilities.

    5. Google Looker AI

    Google Looker AI combines business intelligence with Gemini-powered AI. Users can query enterprise data conversationally while working within a governed semantic model, ensuring consistent metrics across teams. It is especially useful for organizations already relying on Google Cloud for data storage and analytics.

    6. Tellius AI

    Tellius AI focuses on finding answers rather than just displaying data. It investigates changes in business performance, identifies likely causes, and highlights the factors driving results. The platform also delivers predictive insights, helping decision-makers respond to issues before they affect business outcomes.

    Final Thoughts

    The biggest shift in business intelligence in 2026 is the move from AI assistants to AI agents. These platforms do more than generate reports—they analyze data, investigate anomalies, and surface insights with minimal user input. Whether it’s Power BI Copilot for enterprise reporting, ThoughtSpot Sage for self-service analytics, or Tellius AI for automated investigations, businesses now have tools that make data analysis faster, more accessible, and more actionable.

  • 10 Essential Data Science Tools for Modern Analytics in 2026

    10 Essential Data Science Tools for Modern Analytics in 2026

    Modern analytics depends on an integrated ecosystem of programming languages, big data engines, machine learning platforms, and business intelligence tools. The top 10 data science tools highlighted here enable efficient data processing, AI model development, visualization, and scalable analytics workflows, helping organizations turn complex data into actionable business insights.

    How the Pieces Fit Together

    A typical analytics setup layers four pieces. Python or R handles exploration and modeling. Apache Spark processes data at scale. A platform such as Databricks or SAS Viya ties data engineering and model building into one pipeline. Tableau or Power BI turns the output into dashboards that non-technical teams can actually read. Few organizations build all four from scratch. Most pick tools that already work well together and layer new ones in as needs grow.

    The Ten Tools, Ranked by Role

    • Python + Jupyter Notebooks – The default workspace for cleaning data, building models, and automating reports. Teams stay with it because they can move from exploration to production code without switching tools.
    • R + RStudio – Still strong in statistics-heavy fields like pharmaceuticals and finance. Its deep library of specialized statistical methods makes it the better pick for formal statistical work.
    • Apache Spark – Handles large-scale and streaming datasets that go beyond a single machine. Mature ecosystem and wide cloud support keep it central to distributed processing.
    • Databricks – Brings data engineering, shared notebooks, and model deployment in one platform. Cuts down manual work of managing pipelines and scales easily.
    • SAS Viya – Fits regulated industries needing audit-ready analytics. Its edge comes from built-in governance, security, and compliance.
    • KNIME Analytics Platform – Visual, node-based interface to build analytics pipelines with minimal coding. Great for teams wanting open-source with reusable workflows.
    • RapidMiner – Offers guided, reusable workflows from data prep to deployment. Works well for organizations valuing commercial support and polished interface.
    • Tableau – Interactive dashboards built for exploration and storytelling. Stands out when visual customization and analytical flexibility matter.
    • Power BI – Ties closely into Microsoft ecosystem. Low cost and familiarity keep it widely used for business reporting.
    • ThoughtSpot + Qlik Sense – Let business users ask questions in plain language and get governed answers. Close the gap between technical teams and decision-makers.

    Choosing the Right Stack

    Start with the team’s skill mix. Code-heavy teams lean on Python, R, and Spark. Mixed teams benefit from more visual tools. Match the stack to data scale: smaller datasets work fine in notebooks with a BI tool, while large or streaming data calls for a Spark-based platform. Watch for tool sprawl—adding overlapping tools without a clear reason slows a team down.

    What This Means Going Forward

    The tools on this list will keep evolving, but the underlying logic will not. Teams that match their stack to actual data volume, skill set, and reporting needs will keep outpacing those chasing every new platform release.

    Final Thoughts

    The best analytics stacks come from intent, not size. Organizations get the most out of their tools when each one has a clear job, fits into the wider workflow without friction, and can grow alongside rising data and AI demands.

  • 5 Top AI Engineer Certifications to Land a Job in 2026

    5 Top AI Engineer Certifications to Land a Job in 2026

    Artificial intelligence is no longer confined to research labs—it now powers products, services, and operations across every industry. As companies invest heavily in generative AI, automation, and machine learning, the demand for skilled AI engineers continues to surge. Recruiters, however, are looking for more than theoretical knowledge; they want candidates who can apply AI to solve real-world problems.

    A recognized certification can strengthen your resume, validate your skills, and help you stand out during the hiring process. While it is not a substitute for practical experience, it complements a strong portfolio and improves your chances of landing interviews. Below are five industry-recognized AI certifications that can help you get hired in 2026.

    1. Google Cloud Professional Machine Learning Engineer

    The Google Professional Machine Learning Engineer certification is highly valued by employers for enterprise-scale AI roles. It covers the development, deployment, and management of machine learning models using Vertex AI and Google Cloud. Topics include model optimization, MLOps, monitoring, and responsible AI. This certification is ideal for professionals with some experience in machine learning and cloud computing.

    2. Microsoft Azure AI Engineer Associate (AI-102)

    This certification is designed for developers and cloud professionals who want to build AI-powered applications on Microsoft’s Azure platform. Learners gain hands-on skills with Azure AI services, Azure OpenAI, computer vision, speech services, and natural language processing. The course also emphasizes building secure and responsible AI applications.

    3. AWS Certified AI Practitioner

    AWS Certified AI Practitioner is an entry-level certification for those building a foundation in artificial intelligence. It introduces AI concepts, generative AI, prompt engineering, responsible AI, and AWS AI services. This is a practical starting point for IT professionals, software developers, and cloud engineers transitioning into AI roles.

    4. Databricks Certified Generative AI Engineer Associate

    As organizations deploy AI assistants and enterprise chatbots, generative AI skills have become highly sought after. This certification focuses on Retrieval-Augmented Generation (RAG), large language models, vector databases, prompt engineering, and AI agent development. It is aimed at engineers working on modern LLM-powered applications.

    5. IBM AI Engineering Professional Certificate

    IBM’s certification emphasizes project-based learning over theory. Participants learn Python, machine learning, deep learning, TensorFlow, and PyTorch through practical projects. This program is an excellent way for beginners to combine technical skills with a portfolio of AI projects.

    Final Takeaway

    Candidates are now evaluated on their ability to create rather than just their credentials. While a certification can make your profile stand out, practical experience is what gets you through the door. Recruiters prefer candidates who have built AI products, contributed to GitHub repositories, deployed models using cloud technology, and understand vector databases, prompt engineering, and MLOps. A relevant certification can give you the edge over other applicants, but remember: earning a certification is only the first step in your AI career journey.

    Frequently Asked Questions

    What is the best AI certification for beginners in 2026?

    AWS Certified AI Practitioner and IBM AI Engineering Professional Certificate are top choices for beginners. They cover AI fundamentals, generative AI, Python, and practical skills without requiring extensive machine learning experience.

    Which AI certification is most valued by employers?

    Google Cloud Professional Machine Learning Engineer and Microsoft Azure AI Engineer Associate are highly regarded, especially for enterprise AI, cloud computing, and production-scale machine learning roles.

    Do AI certifications guarantee a job?

    No. Certifications improve your resume and validate your knowledge, but employers also expect practical experience through AI projects, GitHub portfolios, cloud deployments, and problem-solving skills during technical interviews.

    Which certification is best for generative AI careers?

    The Databricks Certified Generative AI Engineer Associate is a strong choice for those interested in large language models, RAG, prompt engineering, vector databases, and enterprise AI applications.

    How should I prepare for an AI engineering role in 2026?

    Build a strong foundation in Python, machine learning, and cloud platforms. Earn a recognized AI certification, create real-world projects, and maintain an active portfolio to showcase your technical abilities.

  • Databricks Reaches $188 Billion Valuation With New Strategic Funding for Enterprise AI

    Databricks Reaches $188 Billion Valuation With New Strategic Funding for Enterprise AI

    Data and AI company Databricks has announced a new strategic funding round that values the company at $188 billion, signaling strong investor confidence in enterprise artificial intelligence. The financing, led by existing investor Coatue with participation from other new and existing investors, is expected to close later this summer.

    The fresh capital will be used to strengthen Databricks’ AI portfolio, expand research efforts, and support future acquisitions as the company meets rising enterprise demand for AI-powered business solutions.

    Focus on AI Products

    Databricks said the funding will accelerate development across three key offerings: Unity AI Gateway, Genie, and Lakebase.

    Unity AI Gateway enables enterprises to manage AI models from multiple providers while improving governance, security, and cost control. Genie acts as an AI-powered coworker that helps employees turn business data into actionable insights. Lakebase is a serverless PostgreSQL database designed to support AI agents and modern enterprise applications.

    According to the company, these products aim to address what it describes as the ‘context gap’ in enterprise AI, where disconnected data systems, governance challenges, and rising infrastructure costs prevent organizations from deploying AI effectively at scale.

    Enterprise AI Strategy

    Commenting on the funding, Databricks Co-founder and CEO Ali Ghodsi said enterprises are increasingly prioritizing measurable business value over indiscriminate AI spending.

    ‘Enterprises are moving from tokenmaxxing to valuemaxxing. They don’t want to burn expensive tokens on the smartest model for every task, they want the best outcome per dollar. That means having the freedom to choose the right AI for the job,’ Ghodsi said.

    He added that the new investment will help the company strengthen its multi-AI strategy by enhancing Unity AI Gateway, expanding Genie, and advancing Lakebase.

    Growing Enterprise Adoption

    Headquartered in San Francisco, Databricks serves more than 20,000 organizations worldwide, including Adidas, AT&T, Bayer, Block, Mastercard, Rivian, and Unilever. The company says around 70% of Fortune 500 companies use its Data + AI Platform to build AI applications, analytics tools, and autonomous AI agents.

    The latest funding further strengthens Databricks’ position in the rapidly expanding enterprise AI market as businesses continue to invest in scalable, governed, and cost-efficient AI infrastructure.