#DataEngineer

[kittu800]

[shahida04]

Power BI vs Tableau with Databricks – Which Should You Choose?

If you're a Data Engineer or Analyst working with Databricks, picking the right BI tool can make or break your workflow. This comparison chart breaks down everything you need to know — from connection methods and authentication to real-time analytics and multi-cloud support.

���� Whether you prefer Power BI for real-time queries or Tableau for advanced visuals, this guide will help you integrate smarter and faster.

🚀 Dive deeper in the full blog: 🔗 https://databrickstraining7.blogspot.com/2025/08/databricks-power-bitableau-integration.html

🎓 Ready to master Databricks with real-time projects? 🔗 https://www.accentfuture.com/courses/databricks-training/

[4achieversnoida]

Discover the key differences between a Data Engineer and a Data Scientist, including their roles, skills, tools, and responsibilities in data processing, analysis, and modeling to support data-driven decision-making in organizations.

[pangaeax]

Infographic - Freelance Data Analyst vs. Data Scientist Who Delivers Better ROI?

Thinking about hiring a freelance data expert?

Before you pick between a Data Analyst and a Data Scientist, you should really ask:

Who gives you more ROI for the project you’re working on?

Dashboards vs. predictions. Speed vs. complexity. $5K vs. $50K pipelines. Here’s a visual comparison to help you decide wisely 👇

TL;DR:

Analysts = great for fast, visual insights and tight budgets

Scientists = great for complex ML models and predictive workflows

Right choice = more ROI, fewer delays, and less burn

Best Use Cases:

Hire a Freelance Data Analyst if:

You need dashboards, reports, or cleaned-data insights

Your budget is under $5,000

You want something shipped this week

Hire a Data Scientist if:

You're building ML systems or predictive engines

You’ve already got structured data pipelines

You’re looking to automate, not just analyze

Need Help Finding the Right One?

Check out Pangaea X — the only platform just for Data Analytics & AI freelancers.

[shivam1605]

From Learner to Data Engineer | Vikas Mahindrakar’s Journey with Imarticus Learning

Career transformations don’t happen overnight—but with the right mindset, skills, and support, they do happen. Just ask Vikas Mahindrakar, who is now thriving as an Associate Data Engineer at Atgeir Solutions.

Vikas’s story began with a simple yet powerful desire: to build a meaningful, future-proof career in the world of technology. Like many, he faced the uncertainty of choosing the right path in an industry that’s constantly evolving. But instead of standing still, Vikas decided to take control of his future by enrolling in the Postgraduate Program in Data Science and Analytics at Imarticus Learning.

This decision turned out to be the turning point in his professional journey. Through the program, Vikas not only learned the technical foundations of data science, analytics, and engineering but also gained practical, hands-on experience with real-world projects. From mastering programming languages and statistical techniques to working on data visualization and machine learning models—he built the in-demand skill set that the industry is actively seeking.

But the transformation wasn’t just about technical knowledge. The program also helped Vikas develop critical problem-solving abilities, business acumen, and the confidence to transition into an entirely new field. With the guidance of industry mentors and dedicated career support, he was able to land his role at Atgeir Solutions, where he now works on cutting-edge data projects that shape decision-making and innovation.

Vikas’s journey is proof that with the right education, hard work, and resilience, you can redefine your career and open doors to exciting new opportunities.

[jnitupdates]

✍️ Registration for free : http://bit.ly/4lHpQGr Attend Free demo on Azure Data Engineer with hands-on training on Data Factory, Azure SQL, Python, Data Lake, Databricks & more!

📅 Date: 10th July 2025 🕡 Time: 6:30 PM (IST)

[pythonjobsupport]

Data Engineering Interview Questions in 1 minute #dataengineer #datascience #bigdata

source

[honestheartprison]

डेटा विज्ञान में पायथन की भूमिका क्यों महत्वपूर्ण है?

🔍 डेटा साइंस में पायथन की भूमिका क्यों है इतनी ज़रूरी?

📊 आज के समय में Data Science बिना Python के अधूरी लगती है। इसका सिंपल सिंटैक्स, पावरफुल लाइब्रेरीज़ (जैसे NumPy, Pandas, Scikit-learn, TensorFlow) और Visualization टूल्स (जैसे Matplotlib, Seaborn) इसे Data Analysis, Machine Learning और AI के लिए सबसे पॉपुलर भाषा बनाते हैं।

💡 अगर आप Data Science की दुनिया में कदम रखना चाहते हैं, तो Python सीखना सबसे पहला और जरूरी स्टेप है।

🔗 पूरी जानकारी के लिए पढ़ें ये ब्लॉग: 👉 letsdiskuss.com/data-science-mein-python-ki-bhoomika-kyon-mahatvapurn-hai

#Python #DataScience #MachineLearning #AI #BigData #TechLearning #letsdiskuss #Backlink #CodeWithPurpose

[praveennareshit]

📢 FREE MASTERCLASS

🔷 Azure Data Engineering with Data Factory 🗓️ 19th June | 🕢 7:30 AM IST 👨‍🏫 Trainer: Mr. Venkat Reddy 🔗 https://tr.ee/vepeQC

📌 Learn Data Pipelines, Azure Integration & Real-Time Projects

[giridhar78]

Why Azure Data Engineering Is a High-Demand Career Path

Introduction: Data Is Fueling the Future

We live in a world powered by data. Every click, swipe, and transaction generates information that companies can analyze to make smarter decisions. But raw data isn’t useful on its own—it needs to be cleaned, transformed, and moved to the right systems. That’s where Azure Data Engineers come in.

Azure, Microsoft’s cloud platform, has become a top choice for businesses that want reliable, scalable, and secure data solutions. And with this rise, Azure Data Engineers have become one of the most in-demand roles in the tech industry.

The Boom in Data and the Need for Data Engineers

Businesses are swimming in data, but they need skilled professionals to turn it into actionable insights. Data Engineers act like data plumbers—building the infrastructure that moves and prepares data for analysis.

Why is this role exploding in popularity?

Every company is becoming data-driven.

Cloud adoption is accelerating.

Real-time insights are now critical.

Traditional IT roles can't handle modern big data needs.

This makes Data Engineering, especially on Azure, a key pillar in digital transformation.

Why Azure?

So, why are companies choosing Azure over other cloud platforms? Three main reasons:

Enterprise Trust: Over 95% of Fortune 500 companies use Microsoft tools. For them, Azure is a natural fit.

Integrated Ecosystem: Azure offers a full suite—Data Factory, Synapse Analytics, Data Lake, Databricks, and Power BI—all connected.

Security and Compliance: Azure leads in cloud security, making it ideal for regulated industries like finance and healthcare.

When companies invest in Azure, they need engineers who know how to use its tools—and that’s where Azure Data Engineers come in.

Core Responsibilities of an Azure Data Engineer

An Azure Data Engineer builds and manages data systems that move information from source to destination. Key tasks include:

Creating ETL/ELT pipelines using Azure Data Factory.

Managing big data with Azure Synapse Analytics.

Handling real-time data streams using Event Hubs and Stream Analytics.

Securing data with Azure Key Vault, RBAC, and encryption.

Optimizing performance and cost by monitoring workloads and using best practices.

In short, they make data accessible, reliable, and useful.

Skills That Make You Job-Ready

If you’re looking to become an Azure Data Engineer, here’s what you need to focus on:

Technical Skills:

Strong knowledge of SQL for querying and managing data.

Comfort with scripting languages like Python.

Hands-on experience with Azure tools like ADF, Synapse, Data Lake, and Databricks.

Understanding of data modeling, cloud storage, and API integration.

Soft Skills:

Communication is crucial—you'll work with data scientists, analysts, and managers.

Problem-solving helps in debugging and optimizing data flows.

Flexibility to adapt as technology evolves quickly.

Career Opportunities and Salary Expectations

Azure Data Engineering offers one of the most rewarding tech careers, both in job satisfaction and salary.

Entry-level roles start around $80,000–$100,000 per year.

Experienced professionals can earn $130,000–$180,000+.

Freelancers and consultants with Azure expertise are also in high demand.

What’s more, remote opportunities are abundant. With just a laptop and a strong internet connection, you can work for global firms from anywhere.

Getting Started: Certifications and Projects

To break into this field, the smartest first step is certification.

Start with:

AZ-900: Azure Fundamentals – for beginners.

DP-203: Azure Data Engineer Associate – the main certification for data engineers.

But don’t stop there. Work on hands-on projects using free Azure credits:

Build an ETL pipeline using ADF.

Store data in Azure Data Lake.

Create reports with Power BI.

Document everything on GitHub. A strong portfolio can get you noticed just as much as a resume.

Future Outlook: A Career Built to Last

Azure is not slowing down. With Microsoft heavily investing in AI, cloud innovation, and enterprise tools, demand for Azure professionals will only grow. And as companies rely more on real-time data, predictive analytics, and automation, Azure Data Engineers will become even more critical.

In the future, expect:

Closer integration with AI (Azure OpenAI, Copilot).

New tools and services around data governance and automation.

More hybrid cloud and multi-cloud setups.

This is a field that evolves fast—but it’s also one where you can future-proof your career.

How Global Teq Can Help You Prepare

When it comes to Azure Data Engineering training, Global Teq stands out. We offer expert-led courses, hands-on projects, and certification preparation that align perfectly with industry needs. Our career support team guides you from learning to landing your dream job.

Ready to ace your Azure Data Engineering interview? Visit Global Teq today and start your journey toward a successful, high-paying career!

Conclusion

The tech world is shifting toward data-driven decisions, real-time analytics, and scalable cloud solutions—and Azure sits at the center of it all. As an Azure Data Engineer, you’re not just part of that transformation—you’re leading it.

With the right skills, certifications, and mindset, you can unlock a high-paying, remote-friendly, and deeply impactful career. Whether you're just starting out or looking to switch lanes in tech, now is the perfect time to step into Azure Data Engineering.

FAQs

1. Do I need coding skills to be an Azure Data Engineer? Yes, especially SQL and some Python. Azure tools offer low-code features, but real-world use cases often need scripting.

2. Is Azure better than AWS for data engineering? Both are great, but Azure’s seamless integration with Microsoft tools gives it an edge in many enterprises.

3. What’s the best first step? Start with the AZ-900 certification and build a few hands-on projects using Azure’s free tier.

4. Can I work remotely as an Azure Data Engineer? Absolutely. Many companies offer fully remote or hybrid roles for Azure professionals.

5. How long does it take to become job-ready? With focused learning, 4–6 months is realistic for gaining skills, certification, and project experience.

[nschool]

The Role of Data Science in Healthcare and Diagnosis

Data science is changing many areas, and healthcare is one of the most important ones. Today, healthcare uses data science to help doctors find diseases early, make better decisions, and create treatments that fit each patient. Hospitals, clinics, and researchers have a lot of health data, like patient records, test results, and information from devices like fitness trackers. Data science helps to understand all this data and use it to improve health and save lives.

Why Healthcare Needs Data Science

Healthcare creates huge amounts of data every day. Each patient has a medical history, lab tests, prescriptions, and other information. But this data is often spread out and not easy to use. Data science helps by analyzing this data and finding useful patterns.

Using tools like machine learning and statistics, data scientists find important information that can help doctors and nurses make faster and better decisions. This means patients get the right care at the right time.

How Data Science Helps Healthcare

1. Finding Diseases Early

One of the biggest ways data science helps is by spotting diseases early. Doctors use data science models trained on thousands of medical images and patient data to find signs of diseases like cancer or heart problems before they become serious.

For example, some AI tools can look at breast cancer scans and find tiny changes that a doctor might miss. This helps catch cancer early when treatment is easier and more effective.

2. Predicting Health Problems

Data science can also predict which patients might get sick or need extra care. Hospitals use this to plan treatment and avoid emergencies.

For example, data models can predict if a patient might develop a serious infection like sepsis. If the model alerts the doctors early, they can start treatment sooner and save the patient’s life.

3. Making Treatment Personal

Every person is different, so one treatment might not work for everyone. Data science helps by studying a patient’s genes, lifestyle, and past treatments to suggest the best medicine or therapy for them.

In cancer treatment, for example, doctors use genetic data to choose the drugs that will work best for a patient’s specific type of cancer. This approach is called “precision medicine.”

4. Helping Doctors Read Medical Images

Reading X-rays, MRIs, or CT scans takes time and skill. Data science uses AI to help doctors by quickly analyzing these images and pointing out problems.

For example, AI can find small lung nodules on a chest X-ray, which could be early signs of lung cancer. This helps doctors make faster and more accurate diagnoses.

5. Finding New Medicines

Creating new drugs takes a long time and costs a lot of money. Data science can speed up this process by predicting which chemicals might work as medicines.

During the COVID-19 pandemic, data science helped researchers understand the virus and find possible treatments faster than ever before.

Tools Used in Healthcare Data Science

Healthcare data science uses many computer tools to do its work:

Python and R: These programming languages help analyze data and build models.

TensorFlow and PyTorch: These tools help create AI programs that learn from data.

Tableau and Power BI: These help make charts and graphs to show data clearly.

Cloud platforms like AWS and Azure: These provide places to store and process big amounts of data quickly.

Together, these tools help doctors and data scientists work as a team to improve health care.

Challenges of Using Data Science in Healthcare

Even though data science is very helpful, there are some challenges:

Privacy: Patient data is very private. It must be kept safe and only used in the right ways.

Data Quality: Sometimes data is incomplete or wrong, which can lead to mistakes.

Understanding AI: Doctors need to know how AI makes decisions to trust it, but sometimes AI is hard to understand.

Fairness: If data is biased, AI might make unfair decisions that hurt some patients.

Healthcare providers, data scientists, and regulators must work together to solve these problems carefully.

What the Future Looks Like

The future of healthcare will rely even more on data science. Some examples include:

AI assistants helping with mental health support.

Wearable devices that monitor health and alert doctors in emergencies.

Hospitals using data to manage patient care and resources better.

Digital models of patients that test treatments before trying them in real life.

As technology improves and more data becomes available, healthcare will become faster, safer, and more personal.

Conclusion

Data science is changing healthcare in many good ways. It helps find diseases early, predicts health risks, personalizes treatments, helps doctors read medical images, and speeds up drug discovery. These improvements come from using data and technology together.

[nit2023]

Django Online Training - NareshIT

Django Online Training - NareshIT

Are you looking to build powerful web applications using Python? Then it’s time to explore Django, one of the most popular and robust web frameworks available today. Whether you're a beginner in web development or looking to upgrade your skills, a structured Django course can help you unlock new career opportunities in the field offull-stack development.

What is Django? Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design. Built by experienced developers, it handles much of the hassle of web development, so you can focus on writing your app without needing to reinvent the wheel.

Why Learn Django? Here are some compelling reasons to learn Django:

Fast Development: Django’s built-in features, such as the admin interface, authentication system, and ORM, help you build and scale web applications quickly.

Secure by Default: Django includes built-in protection against many security threats like SQL injection, cross-site scripting (XSS), and cross-site request forgery (CSRF).

Versatile: Django is suitable for building everything from simple websites to large-scale enterprise applications.

Community Support: Being open-source, Django has a strong community that continually contributes to its growth and updates.

What Will You Learn in a Django Course?

A well-structured Django course will cover:

Basics of Python for web development

Django architecture and installation

URL routing and view handling

Working with templates and static files

Forms and validations

Connecting to databases and using Django ORM

Building REST APIs with Django Rest Framework (DRF)

Deployment strategies for Django applications

Best Django Online Training:

If you are looking for Django Online Training, NareshIT offers a comprehensive and practical course that helps you master Django from scratch. The course is designed by industry experts and includes hands-on projects to solidify your learning.

👉 For More Information: Django Online Training at NareshIT

Final Thoughts: Learning Django opens up a wide range of opportunities in web development, backend programming, and full-stack roles. With the right training and guidance, you can become a job-ready Django developer in just a few weeks. Start your journey today with a reliable and industry-approved Django Online Training course.

[icedq-toranainc]

iceDQ Training: Learn ETL Testing and Data Governance

Manual testing won’t cut it in today’s high-volume data environments. The Introduction to iceDQ v1.0 course introduces you to a smarter way to manage quality across your entire data stack.

What’s inside the course:

🧠 Deep dive into the logic of rule creation

🔄 Understand execution and test cycles

📊 Learn to audit BI dashboards and warehouse data

🛠️ Discover scalable data QA strategies

Ideal for:

QA professionals tired of writing SQL scripts for validation

Analysts needing reliable reporting

Developers building ETL pipelines

Businesses requiring continuous data monitoring

Top skills you’ll gain:

Automated data validation

Data observability implementation

Data governance fundamentals

📚 Let automation handle your data quality. Start the Introduction to iceDQ v1.0 Course today and streamline your testing.

[jnitupdates]

✍️ Registration for free : http://bit.ly/4lHpQGr Attend Free demo on Azure Data Engineer with hands-on training on Data Factory, Azure SQL, Python, Data Lake, Databricks & more!

📅 Date: 10th July 2025 🕡 Time: 6:30 PM (IST)

[pythonjobsupport]

Top 5 Companies Hiring for Data Engineer | Highest CTC #faang #mnc #dataengineer #youtube #shorts

Top 5 Companies Hiring for Data Engineer | Highest CTC #faang #mnc #dataengineer #youtube #shorts#faang #mnc … source