#Datatransformation

[excelworld]

🧩 Power Query Online Tip: Diagram View

Q: What does the Diagram View in Power Query Online allow you to do?

✅ A: It gives you a visual representation of how your data sources are connected and what transformations have been applied.

🔍 Perfect for understanding query logic, debugging complex flows, and documenting your data prep process—especially in Dataflows Gen2 within Microsoft Fabric.

👀 If you're more of a visual thinker, this view is a game-changer!

💬 Have you tried Diagram View yet? What’s your experience with it?

[data-analytics-masters]

🎯 Data Transformation Methods help prepare raw data for analysis.

🔹 Normalization

🔹 Standardization

🔹 One-Hot Encoding

🔹 Log Transformation

✅ Why Choose Us?

✔️ 100% practical training

✔️ Real-time projects & case studies

✔️ Expert mentors with industry experience

✔️ Certification & job assistance

✔️ Easy-to-understand Telugu + English mix classes

📍 Institute Address:

3rd Floor, Dr. Atmaram Estates, Metro Pillar No. A690,

Beside Siri Pearls & Jewellery, near JNTU Metro Station,

Hyder Nagar, Vasantha Nagar, Hyderabad, Telangana – 500072

📞 Contact: +91 9948801222    

📧 Email: dataanalyticsmasters.in@gmail.com

🌐 Website: https://dataanalyticsmasters.in

[spaculus]

Looking to make better business decisions? Hire AI engineers to unlock the full potential of your historical data. With the right AI tools, even years of untapped information can be turned into a smart assistant that guides your decisions every day. Learn how AI can revolutionize your approach to data and drive growth.

[datameticasols]

With the growing demand for cloud-native solutions, Teradata to BigQuery migration is becoming a popular choice for organizations seeking scalable and cost-efficient data platforms. BigQuery’s serverless architecture and real-time analytics capabilities make it an ideal solution for modern data analytics needs.

By migrating from traditional on-premises systems like Teradata or Netezza, businesses can reduce infrastructure costs, scale automatically with data growth, and leverage BigQuery's advanced querying features for faster insights. Unlike legacy systems that require significant investments in physical hardware, BigQuery operates on a flexible pay-per-use pricing model, offering significant cost savings and operational efficiency.

The migration process from Teradata to BigQuery involves careful planning, data transformation, and ensuring compatibility with BigQuery’s cloud architecture. For businesses transitioning from Netezza to BigQuery migration, similar steps apply, ensuring a smooth transition to a more agile, cloud-based solution.

Overall, BigQuery’s integration with Google Cloud services, its scalability, and cost-effectiveness make it a powerful tool for businesses looking to modernize their data infrastructure. Moving to BigQuery enables real-time analytics and enhances decision-making, helping companies stay competitive in a data-driven world.

[newfangled-vady]

VADY AI turns raw data into actionable insights that fuel strategic business decisions. With AI-powered business intelligence, companies can identify hidden opportunities, optimize processes, and predict trends with precision.

Through AI-powered data visualization and automated data insights software, VADY ensures that every data point contributes to business success. From context-aware AI analytics to enterprise-level data automation, VADY helps businesses convert data into profitability.

🚀 Transform your data into a competitive advantage today!

[kanerikablog]

Master Power Query & Transform Data Like a Pro!

Struggling with messy data? Power Query in Power BI simplifies, cleans, and reshapes data for faster insights and smarter decisions.

Learn how to automate transformations, eliminate manual work, and unlock the full potential of your data in our latest guide.

📖 Read more

[expressanalytics]

Data Transformation in Machine Learning: Unlocking the Power of AI

Data transformation is a crucial step in machine learning, involving the conversion and restructuring of data from one format to another. This process enhances data quality, making it more suitable for analysis and modeling. Effective data transformation can significantly improve the performance of machine learning models by ensuring that the data is clean, consistent, and relevant.

Key Aspects of Data Transformation in Machine Learning:

Data Cleaning: Removing duplicates, handling missing values, and correcting errors to ensure data accuracy.

Data Integration: Combining data from different sources to provide a unified view.

Data Reduction: Simplifying data without losing essential information, often through dimensionality reduction techniques.

Data Discretization: Converting continuous data into discrete buckets or intervals.

Data Normalization: Scaling data to fit within a specific range, improving the performance of certain algorithms.

Implementing these transformation techniques helps in unlocking the full potential of AI by providing high-quality data for training machine learning models. For a more info click here: Data Transformation

[bccunited]

BCC-United doesn’t just manage data—we elevate it. Structured, optimized, and insight-driven, we turn data noise into strategic power, fueling smarter operations, sharper insights, and stronger business outcomes. 📊 📈 🗂️

[innovaticsblog]

Optimize your data strategy by designing a data lake framework in AWS. Our guide provides expert advice on creating a scalable, efficient solution

[colisty]

Modern Data Science with SAS Viya & Python for Churn Models | CoListy

Learn data science with SAS Viya & Python to predict churn, manage data, deploy models, & use GitHub for collaboration.

[excelworld]

🔄 Mastering Dataflows (Gen2): Transform Like a Pro

Q: What are some common data transformations in Dataflows Gen2?

✅ A: Here are some of the most used transformations:

🔹 Filter and Sort rows

🔹 Pivot and Unpivot

🔹 Merge and Append queries

🔹 Split and Conditional Split

🔹 Replace values and Remove duplicates

🔹 Add, Rename, Reorder, or Delete columns

🔹 Rank and Percentage calculators

🔹 Top N and Bottom N selections

🧠 These transformations help clean, shape, and enrich your data—making your downstream reporting more effective and insightful.

💬 Which transformation do you use the most in your projects?

Drop your favorite (or most underrated) one in the comments!

#DataPlatform #LowCodeTools

[stigasoft]

Businesses are undergoing a major shift in data management. Traditional Business Intelligence (BI) tools, while effective for analyzing historical data, struggle with modern demands like handling big data, real-time insights, and faster decision-making. AI and Machine Learning (ML) are revolutionizing data analysis by providing predictive, real-time insights, automating manual tasks, and effortlessly managing complex datasets. AI’s adaptability, speed, and continuous learning offer significant benefits, including accurate decision-making, cost savings, and enhanced customer experiences. From retail and healthcare to finance and manufacturing, AI is transforming industries. Embracing AI-driven insights is crucial for businesses to thrive in today’s competitive landscape.

[sudarshannarwade]

SAP PO for Beginners Part – 16 – Brief Overview on XSLT Transformations in SAP PO with Examples

The transformation of XSL code is managed by the XSLT processor. The XSLT processor takes one or more XML source documents along with the XSL file and processes them to produce a result document. For a comprehensive overview on XSLT Transformations in SAP PO with Examples, it’s important to note that the data/source document must be in XML format. read more

[datameticasols]

With the growing demand for cloud-native solutions, Teradata to BigQuery migration is becoming a popular choice for organizations seeking scalable and cost-efficient data platforms. BigQuery’s serverless architecture and real-time analytics capabilities make it an ideal solution for modern data analytics needs.

By migrating from traditional on-premises systems like Teradata or Netezza, businesses can reduce infrastructure costs, scale automatically with data growth, and leverage BigQuery's advanced querying features for faster insights. Unlike legacy systems that require significant investments in physical hardware, BigQuery operates on a flexible pay-per-use pricing model, offering significant cost savings and operational efficiency.

The migration process from Teradata to BigQuery involves careful planning, data transformation, and ensuring compatibility with BigQuery’s cloud architecture. For businesses transitioning from Netezza to BigQuery migration, similar steps apply, ensuring a smooth transition to a more agile, cloud-based solution.

Overall, BigQuery’s integration with Google Cloud services, its scalability, and cost-effectiveness make it a powerful tool for businesses looking to modernize their data infrastructure. Moving to BigQuery enables real-time analytics and enhances decision-making, helping companies stay competitive in a data-driven world.

Learn more about the benefits of migrating from Teradata to BigQuery and how it can transform your data operations.

[newfangled-vady]

VADY is revolutionizing how businesses interact with data by enabling enterprise-wide AI transformation. With automated data insights software, companies can automate workflows, enhance productivity, and achieve operational excellence. Whether you’re a startup or an established enterprise, VADY smart decision-making tools help you stay agile, informed, and competitive. Don’t just analyze data—let VADY power your AI transformation!