Latest news with #PowerQuery
Geeky Gadgets
23-05-2025
- Geeky Gadgets
Say Goodbye to Power Query: Unpivot Excel Data Effortlessly
Have you ever stared at a beautifully organized table in Excel—rows and columns perfectly aligned—only to realize it's a nightmare for analysis? Cross-tab layouts, while visually appealing, can feel like a roadblock when it comes to tasks like creating pivot tables, running trend analyses, or modeling data. The common solution? Power Query. But what if you don't have access to it, or simply prefer sticking to formulas? Here's the good news: you don't need Power Query to unpivot your data. With the right Excel formulas, you can transform even the most stubborn datasets into an analysis-ready format—all within the familiar confines of your workbook. It's a fantastic option for anyone looking to simplify their workflow without sacrificing control. Excel Off The Grid reveal two powerful approaches to unpivoting data in Excel: a quick, automated method using pre-built Lambda functions and a hands-on, customizable approach with advanced formulas. Whether you're dealing with merged cells, blank spaces, or complex layouts, these techniques will equip you to tackle it all. You'll learn how to extract key insights from cross-tab data, organize it into a column-based structure, and even automate updates for real-time accuracy. By the end, you won't just know how to unpivot data—you'll understand why formulas can sometimes outshine Power Query in flexibility and accessibility. After all, mastering your tools isn't just about efficiency; it's about unlocking new possibilities for your data. Unpivoting Data in Excel Why Cross-tab Layouts Pose Challenges Cross-tab layouts, where data is organized in a grid format across rows and columns, are visually intuitive but analytically restrictive. For instance, sales data displayed by regions and product categories in a table may look organized but complicates tasks such as aggregations, trend analysis, or pivot table creation. To make such data usable for analysis, it must be converted into a column-based structure, where each row represents a single data point. This process, known as 'unpivoting,' is essential for creating datasets that are compatible with Excel's analytical tools and functions. Efficient Solution: Pre-built Lambda Functions For users seeking a quick and automated way to unpivot data, pre-built Lambda functions offer a streamlined solution. These functions, such as `FX Unpivot` and `FX Fill Blank Cells`, can either be accessed from a function library or created within Excel. Here's how they simplify the unpivoting process: Unpivoting Data: The `FX Unpivot` function automatically converts cross-tab layouts into a column-based structure. It extracts headers and values, organizing them into a clean, tabular format suitable for analysis. The `FX Unpivot` function automatically converts cross-tab layouts into a column-based structure. It extracts headers and values, organizing them into a clean, tabular format suitable for analysis. Handling Merged Cells: Merged cells often create blank spaces in datasets. The `FX Fill Blank Cells` function fills these gaps with the appropriate values, making sure data consistency and completeness. This method is particularly useful for users who prioritize speed and automation over manual control. It eliminates the need for complex formulas, making it an excellent choice for straightforward unpivoting tasks. Unpivot with Formulas in Excel… No Power Query!! Watch this video on YouTube. Dive deeper into Microsoft Excel with other articles and guides we have written below. Manual Approach: Advanced Excel Formulas For those who prefer greater control or need to handle unique data structures, Excel's advanced formulas provide a powerful alternative. Below is a step-by-step guide to manually unpivot data: Create Row and Column Indices: Use the `SEQUENCE` function to generate a list of row and column numbers. These indices serve as a map for rearranging your cross-tab data into a column-based format. Use the `SEQUENCE` function to generate a list of row and column numbers. These indices serve as a map for rearranging your cross-tab data into a column-based format. Extract Data: Apply the `INDEX` function to retrieve specific values from your dataset based on the generated indices. This step allows you to extract key elements such as product names, regions, or numerical values. Apply the `INDEX` function to retrieve specific values from your dataset based on the generated indices. This step allows you to extract key elements such as product names, regions, or numerical values. Organize Data: Combine the `ROUNDUP` and `MOD` functions to group data into logical categories, such as items, colors, or sizes. This ensures your dataset is systematically structured for analysis. Combine the `ROUNDUP` and `MOD` functions to group data into logical categories, such as items, colors, or sizes. This ensures your dataset is systematically structured for analysis. Fill Blank Values: Use the `SCAN` function to propagate values across rows or columns, addressing issues caused by merged cells or incomplete data entries. Although this method requires more effort and familiarity with Excel formulas, it offers unmatched flexibility. You can customize the process to suit specific requirements, making it ideal for complex datasets or unique analytical needs. Comparing Formulas and Power Query While Power Query is a robust tool for data transformation, formula-based unpivoting has distinct advantages. Here's a comparison of the two methods: Workflow Integration: Formulas are embedded directly within your workbook, making them ideal for scenarios where Power Query is unavailable or impractical. Formulas are embedded directly within your workbook, making them ideal for scenarios where Power Query is unavailable or impractical. Automation: Formula-based solutions automatically update when data changes, eliminating the need for manual refreshes and making sure real-time accuracy. Formula-based solutions automatically update when data changes, eliminating the need for manual refreshes and making sure real-time accuracy. Flexibility: Formulas can be tailored to handle unique data structures or specific analytical requirements, offering a level of customization that Power Query may not provide. The choice between formulas and Power Query depends on your workflow, the complexity of your data, and the level of automation you require. Key Considerations for Choosing a Method When deciding how to unpivot your data, several factors should guide your choice: Complexity: For simple datasets, pre-built Lambda functions or manual formulas are often sufficient. For more intricate transformations, Power Query may be better suited. For simple datasets, pre-built Lambda functions or manual formulas are often sufficient. For more intricate transformations, Power Query may be better suited. Automation: If you need your data to update automatically without additional steps, formula-based solutions are ideal for maintaining efficiency. If you need your data to update automatically without additional steps, formula-based solutions are ideal for maintaining efficiency. Compatibility: If you plan to share your workbook with others who may not have access to Power Query, formulas ensure broader accessibility and usability. By carefully evaluating these factors, you can select the most effective method for your specific use case, making sure both efficiency and accuracy in your data preparation process. Mastering Data Transformation in Excel Unpivoting data is a critical step in preparing datasets for analysis and modeling. Whether you choose the simplicity of pre-built Lambda functions or the precision of advanced formulas, Excel provides versatile tools to transform cross-tab layouts into structured, column-based datasets. While Power Query remains a powerful option, formula-based unpivoting offers flexibility, automation, and seamless integration within Excel workflows. By mastering these techniques, you can streamline your data preparation process and unlock deeper insights from your analyses. Media Credit: Excel Off The Grid Filed Under: Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Geeky Gadgets
10-05-2025
- Geeky Gadgets
The Fastest Way to Unstack Data in Power Query : 3 Methods Compared
Have you ever found yourself staring at a long, messy column of data, wondering how to quickly transform it into a clean, structured table? For anyone working with Power Query, this challenge is all too familiar. The process of unstacking data—converting a single column into multiple columns—isn't just a technical task; it's often the key to unlocking insights hidden in your dataset. But here's the catch: not all methods are created equal. Some are intuitive but slow, while others are lightning-fast yet require technical know-how. So, how do you choose the best approach? In this exploration, Excel Off The Grid tackles this question head-on, comparing three distinct methods to unstack data in Power Query. Whether you're a beginner or a seasoned pro, there's something here that could transform how you work with your data. By the end of this guide, you'll uncover the strengths and trade-offs of each method, from the simplicity of Power Query's user interface to the speed and precision of advanced M code techniques. Along the way, you'll gain insights into which approach is best suited for your specific needs—whether you're dealing with incomplete datasets, large volumes of data, or tight deadlines. Curious about which method is the fastest or how to balance speed with accuracy? Let's explore these methods and reveal the one that could save you hours of manual work while making sure your data remains reliable and ready for analysis. Sometimes, the smallest tweaks in your workflow can lead to the biggest breakthroughs. Unstacking Data in Power Query Method 1: Using the User Interface This method uses Power Query's built-in tools, making it an accessible option for users with minimal technical expertise. The process involves the following steps: Add an index column to assign a unique identifier to each row. to assign a unique identifier to each row. Group rows into patterns that correspond to your desired column structure. Use the 'Pivot Column' feature to transform rows into columns. feature to transform rows into columns. Promote the first row to headers for better organization and clarity. The user interface method is intuitive and ideal for beginners or those who prefer a no-code approach. However, it involves multiple manual steps, which can make it slower when working with large datasets. While this method is effective for straightforward tasks, it may not be the best choice if speed or automation is a priority. Method 2: M Code with Table Functions The second method uses M code, specifically table-based functions, to programmatically control the unstacking process. Key functions include: ` : Divides the dataset into smaller tables based on a specified pattern. : Divides the dataset into smaller tables based on a specified pattern. ` : Rearranges rows into columns within each table. : Rearranges rows into columns within each table. ` Merges the transformed tables back into a single dataset. This approach offers a significant improvement in speed compared to the user interface method. Additionally, it handles incomplete data effectively, preserving rows with missing values or errors. However, it requires a basic understanding of M code, which may pose a challenge for users unfamiliar with scripting. For those comfortable with coding, this method strikes a balance between performance and reliability, making it a versatile option. 3 Ways to Unstack Data in Power Query Watch this video on YouTube. Here are more detailed guides and articles that you may find helpful on Power Query. Method 3: M Code with List Functions The third method employs advanced M code techniques, focusing on list-based functions to directly manipulate data at the list level. The process involves: ` : Divides the dataset into smaller chunks based on a defined structure. : Divides the dataset into smaller chunks based on a defined structure. ` : Applies transformations to each chunk of data. : Applies transformations to each chunk of data. ` Converts the processed lists back into a structured table format. This method is the fastest of the three, making it highly efficient for performance-critical tasks. However, it is best suited for clean and complete datasets, as it skips rows with errors or missing values, potentially leading to data loss. While this approach is ideal for advanced users working with well-prepared data, its limitations in handling incomplete datasets should be carefully considered. Performance Comparison Each method offers distinct advantages and trade-offs, making it essential to evaluate them based on your specific requirements: Speed: Method 3 is the fastest, followed by Method 2, with Method 1 being the slowest due to its manual steps. Method 3 is the fastest, followed by Method 2, with Method 1 being the slowest due to its manual steps. Handling Incomplete Data: Methods 1 and 2 effectively manage missing or inconsistent values, while Method 3 does not. Methods 1 and 2 effectively manage missing or inconsistent values, while Method 3 does not. Complexity: Method 1 is the simplest, requiring no coding, whereas Methods 2 and 3 demand varying levels of M code proficiency. Your choice should depend on the nature of your dataset, your familiarity with Power Query, and the importance of speed versus data integrity. Recommendations To determine the most suitable method for your needs, consider the following guidelines: Choose Method 3 if speed is your top priority and your dataset is clean and free of errors or missing values. if speed is your top priority and your dataset is clean and free of errors or missing values. Opt for Method 2 if you require a balance between performance and the ability to handle incomplete data effectively. if you require a balance between performance and the ability to handle incomplete data effectively. Use Method 1 if you are new to Power Query or prefer a straightforward, no-code solution. By understanding the strengths and limitations of each method, you can make an informed decision that aligns with your goals. Power Query's versatility ensures that, regardless of your skill level, you can efficiently transform and unstack your data to meet your analytical needs. Media Credit: Excel Off The Grid Filed Under: Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Yahoo
11-04-2025
- Business
- Yahoo
Data Wrangling Market Size to Surpass USD 12.6 Billion by 2032, Owing to Growing Integration of AI and Machine Learning in Data Processing
The market is undergoing rapid transformation as businesses across various industries recognize the importance of data in driving strategic decisions. Pune, April 11, 2025 (GLOBE NEWSWIRE) -- Data Wrangling Market Size Analysis: 'The Data Wrangling Market size was USD 3.2 billion in 2023 and is expected to reach USD 12.6 billion by 2032, growing at a CAGR of 16.59% over the forecast period of 2024–2032.'Get a Sample Report of Data Wrangling Market@ Major Players Analysis Listed in this Report are: Trifacta – Trifacta Wrangler Talend – Talend Data Preparation IBM – IBM Data Refinery Alteryx – Alteryx Designer Informatica – Informatica Data Preparation DataRobot – DataRobot Paxata TIBCO Software – TIBCO Clarity Microsoft – Power Query (Excel & Power BI) Google – Google Cloud Dataprep Oracle – Oracle Data Integrator AWS – AWS Glue DataBrew SAS Institute – SAS Data Preparation Hitachi Vantara – Pentaho Data Integration Qlik – Qlik Data Integration Datameer – Datameer Spectrum Data Wrangling Market Report Scope: Report Attributes Details Market Size in 2023 USD 3.2 Billion Market Size by 2032 USD 12.6 Billion CAGR CAGR of 16.59% From 2024 to 2032 Base Year 2023 Forecast Period 2024-2032 Historical Data 2020-2022 Key Regional Coverage North America (US, Canada, Mexico), Europe (Eastern Europe [Poland, Romania, Hungary, Turkey, Rest of Eastern Europe] Western Europe [Germany, France, UK, Italy, Spain, Netherlands, Switzerland, Austria, Rest of Western Europe]). Asia Pacific (China, India, Japan, South Korea, Vietnam, Singapore, Australia, Rest of Asia Pacific), Middle East & Africa (Middle East [UAE, Egypt, Saudi Arabia, Qatar, Rest of Middle East], Africa [Nigeria, South Africa, Rest of Africa], Latin America (Brazil, Argentina, Colombia Rest of Latin America) Key Growth Drivers • Rising data generation across industries is fueling demand for efficient data wrangling tools to enhance analytics and decision-making. Do you have any specific queries or need any customization research on Data Wrangling Market, Make an Enquiry Now@ AI-Powered Cloud Solutions Drive Surge in Global Data Wrangling Adoption Across Key Industries The data wrangling market is expanding at a high pace, supported by rapid data generation in industries such as finance, healthcare, and retail. Organizations are turning to cloud-based solutions to handle huge amounts of structured and unstructured data due to their scalability and affordability. The combination of AI and ML is revolutionizing data wrangling as it automates processes like anomaly detection, data classification, and cleansing, lowering human effort and increasing accuracy. The U.S. Data Wrangling Market size was USD 1.05 billion in 2023 and is projected to reach USD 4.09 billion by 2032, growing at a CAGR of 16.3% during the forecast period of 2024–2032. The U.S market is growing substantially, as industries like finance, healthcare, and retail are leveraging data-driven decision-making more and more. Organizations are focusing on optimizing data processing to manage escalating amounts of structured and unstructured data. Market growth is also being driven by the extensive use of cloud technology and advanced analytics tools. Furthermore, incorporating AI and machine learning in data-wrangling solutions allows for greater automation, precision, and efficiency in operations. By Component, Solutions Segment Dominates Data Wrangling Market While Services Segment Poised for Rapid Growth The solutions segment led the market, and it held 74% of the revenue share in 2023. The reason behind the leadership of this segment is the rise of end-to-end platforms that encompass data integration, preparation, and analysis. Large cloud vendors such as AWS, Google Cloud, and Microsoft Azure are actively backing such platforms, hence driving the solutions segment further. The services segment is expected to exhibit the quickest expansion, with organizations investing in consulting, training, and implementation services to turn their data-wrangling operations more streamlined and derive value from datasets. By Deployment, On-Premises Solutions Dominate Data Wrangling Market Amidst Growing Cloud Adoption In 2023, on-premises data-wrangling platforms maintained the largest market share, largely because of the security and compliance requirements of regulated sectors such as healthcare and finance. Cloud solutions are, however, predicted to expand at the highest rate, fueled by their scalability, cost-effectiveness, and ease of integration. The transition to a hybrid and multi-cloud strategy is fueling demand for cloud-native wrangling platforms that can manage intricate, distributed workloads. As companies aim to steer clear of vendor lock-in and enhance agility, cloud adoption within the data wrangling arena is poised to gain momentum, transforming deployment preferences across sectors. By Enterprise Size, Large Enterprises Dominate Data Wrangling Market as SMEs Rapidly Embrace Scalable Solutions Large enterprises led the market for data wrangling in 2023, using complex tools to tackle and analyse their large and sophisticated data landscapes. Such companies need advanced solutions in order to drive accurate data, adhere to compliances, and drive meaningful insights economically. On the other hand, small and medium businesses (SMEs) are increasingly taking on scalable data-wrangling solutions to underpin their increasing data requirements. The presence of affordable, cloud-based technologies facilitates SMEs' ability to enact data-driven solutions without heavy upfront infrastructure investments, thereby improving competitiveness within the shifting market environment. By End-User, BFSI Sector Dominates Data Wrangling Market Amidst Rapid Growth in IT and Telecom The BFSI industry led the way because of its strict compliance requirements and heavy dependence on high-quality, real-time information. Data wrangling solutions guarantee data accuracy, governance, and security cornerstones for the financial industry. The IT and Telecom industry is expected to expand at the most rapid rate because these firms deal with dynamic data streams in CRM systems, billing systems, and customer support operations, which necessitate efficient data wrangling for service quality and performance enhancement. Data Wrangling Market Segmentation: By Component Solution Services By Deployment Cloud On-premises By Enterprise Size SMEs Large Enterprises By End-Use BFSI Government Manufacturing Retails Healthcare IT & Telecom Others (Media & Entertainment, Transportation)Buy an Enterprise-User PDF of Data Wrangling Market Analysis & Outlook 2024-2032@ North America Dominates Data Wrangling Market While Asia Pacific Emerges as Fastest-Growing Region North America held a dominant 43% market share in 2023, and leadership is fueled by the region's mature technology infrastructure and broad embrace of AI and automation. American-based companies are increasingly using intelligent data-wrangling platforms to improve operational agility, especially within the finance, e-commerce, and healthcare industries. The availability of large technology companies and data-driven innovation culture continue to provide North America a strong advantage. The Asia Pacific is evolving as a high-growth region as its digital growth is surging. As its Internet user base expands rapidly, mobile penetration rises, and e-commerce and financial markets boom, the region is facing an explosion in data volume. This is making the requirement for good data-wrangling tools a priority. In addition, governments and organizations in other nations such as China, India, and South Korea are investing heavily in AI and big data infrastructure, which is predicted to further accelerate regional market demand. Recent Developments October 2024: Technavio projected that the global data wrangling market would grow by USD 1.49 billion between 2024 and 2028, citing the expanding role of automation, AI, and big data analytics in enterprise decision-making. The report highlights how the increasing complexity of business data is pushing organizations to adopt powerful wrangling tools to simplify data transformation, comply with regulatory mandates, and improve time-to-insight metrics. Table of Contents – Major Key Points 1. Introduction 2. Executive Summary 3. Research Methodology 4. Market Dynamics Impact Analysis 5. Statistical Insights and Trends Reporting 6. Competitive Landscape 7. Data Wrangling Market Segmentation, by Component 8. Data Wrangling Market Segmentation, by Deployment 9. Data Wrangling Market Segmentation, by Enterprise Size 10. Data Wrangling Market Segmentation, by End User 11. Regional Analysis 12. Company Profiles 13. Use Cases and Best Practices 14. Conclusion Access Complete Report Details of Data Wrangling Market Analysis Report 2024-2032@ [For more information or need any customization research mail us at info@ SNS Insider Offering/ Consulting Services: Go To Market Assessment Service Total Addressable Market (TAM) Assessment Competitive Benchmarking and Market Share Gain About Us: SNS Insider is one of the leading market research and consulting agencies that dominates the market research industry globally. Our company's aim is to give clients the knowledge they require in order to function in changing circumstances. In order to give you current, accurate market data, consumer insights, and opinions so that you can make decisions with confidence, we employ a variety of techniques, including surveys, video talks, and focus groups around the world. CONTACT: Contact Us: Jagney Dave - Vice President of Client Engagement Phone: +1-315 636 4242 (US) | +44- 20 3290 5010 (UK)
Associated Press
28-01-2025
- Business
- Associated Press
Excel Mum Inspires Professionals to Work Smarter, Not Harder in Secrets of Self-Made Success Book
'Excel Mum,' shares her inspiring journey of turning tedious work into opportunities for growth with Excel automation in the bestselling book Secrets of Self-Made Success. Her insights help readers streamline their work and reclaim their time. Excel Mum to her followers, knows all too well the challenges of juggling endless tasks in a demanding career. As a finance professional, she found herself buried under repetitive processes that left her little time for meaningful work or personal life. But everything changed when she discovered the power of Excel automation. Her chapter, How I Transformed My Workflow with Excel Automation, in the book Secrets of Self-Made Success, takes readers on her journey from frustration to empowerment. With relatable anecdotes and practical tips, Excel Mum explains how mastering tools like macros, pivot tables, and Power Query transformed her workflow—and her life. 'There was a time when I felt completely stuck,' Excel Mum recalls. 'I'd spend hours compiling data, only to make a mistake that set me back to square one. I knew there had to be a better way.' That better way came through automation. By exploring Excel's advanced features, Excel Mum turned time-consuming tasks into seamless processes. Reports that once took her hours were completed in minutes, freeing her to focus on strategic projects and high-impact work. But the benefits didn't stop there. Automation also gave her something even more valuable: time with her family. 'I used to miss out on so much because I was always playing catch-up with work,' she says. 'Automation gave me back my evenings—and my peace of mind.' In Secrets of Self-Made Success, she shows readers that automation isn't just for tech experts. Her advice is approachable and actionable, encouraging anyone to take small steps toward big results. 'Start with one thing,' she advises. 'Whether it's a simple macro or a pivot table, each success will build your confidence.' Excel Mum's story resonates deeply with the themes of the book, which brings together insights from diverse professionals who have carved their own paths to success. The book emphasizes the importance of taking control of your career and creating systems that work for you, rather than the other way around. Since its release, Secrets of Self-Made Success has become a must-read for anyone striving for professional growth and personal fulfillment. Readers have praised Excel Mum's chapter for its down-to-earth tone and practical guidance, making it a standout in an already powerful collection. For Excel Mum, the response has been overwhelming. 'It's amazing to hear how my experience is helping others,' she says. 'If my story can inspire even one person to work smarter, not harder, it's all been worth it.' Whether you're a seasoned professional or just starting out, Excel Mum's journey offers a valuable lesson: with the right tools and mindset, you can transform your work—and your life. Secrets of Self-Made Success is available now on Amazon in print and digital formats. To learn more about Excel Mum and her approach to automation, visit Excel Mum Official. 'Excel Mum,' is a finance professional who has mastered Excel automation to streamline workflows and reclaim time for what matters most. She is passionate about helping others discover simple, effective ways to work smarter. Media Contact Phone: +6597719422 Country: Singapore
