Data cleaning with Power BI: the definitive guide to transforming dirty data into actionable insights
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing, Limited
2024
|
| Ausgabe: | 1st edition |
| Schlagworte: | |
| Online-Zugang: | DE-2070s DE-91 DE-706 Volltext |
| Beschreibung: | 1 Online-Ressource (340 Seiten) |
| ISBN: | 9781805126058 |
Internformat
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Datensatz im Suchindex
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| contents | Cover -- Title Page -- Copyright and Credits -- Dedication -- Contributors -- Table of Contents -- Preface -- Part 1 - Introduction and Fundamentals -- Chapter 1: Introduction to Power BI Data Cleaning -- Technical requirements -- Cleaning your data in Power BI -- Understanding Power Query -- Understanding DAX -- Where do we begin with data? -- Summary -- Questions -- Chapter 2: Understanding Data Quality and Why Data Cleaning is Important -- What is data quality? -- Where do data quality issues come from? -- The role of data cleaning in improving data quality -- Data integrity and accuracy -- Decision-making and business outcomes -- Data ownership and accountability -- A holistic view of the data ecosystem -- Early detection of issues -- Continuous improvement and learning -- Empowerment and collaboration -- Best practices for data quality overall -- Establishing data quality standards -- Summary -- Questions -- Chapter 3: Data Cleaning Fundamentals and Principles -- Defining data cleaning -- Who's responsible for cleaning data? -- Building a process for cleaning data -- Data assessment -- Data profiling -- Data validation -- Data cleaning strategies -- Data transformations -- Data quality assurance -- Documentation -- Understanding quality over quantity in data cleaning -- Summary -- Questions -- Chapter 4: The Most Common Data Cleaning Operations -- Technical requirements -- Removing duplicates -- Removing missing data -- Splitting columns -- Merging columns -- Replacing values -- Creating calculated columns versus measures -- Calculated columns -- Measures -- Calculation group -- Considerations -- Summary -- Questions -- Part 2 - Data Import and Query Editor -- Chapter 5: Importing Data into Power BI -- Technical requirements -- Understanding data completeness -- Understanding data accuracy -- Understanding data consistency Assessing data relevance -- Assessing data formatting -- Assessing data normalization, denormalization, and star schemas -- Dimension modeling and star schema -- Denormalized data in dimension tables -- Summary -- Questions -- Chapter 6: Cleaning Data with Query Editor -- Technical requirements -- Understanding the Query Editor interface -- Data cleaning techniques and functions -- Adding columns -- Data type conversions -- Date/time -- Rounding -- Pivot/unpivot columns -- Merge queries -- Using Query Editor versus DAX for transformation -- Power Query Editor -- Data Analysis Expressions (DAX) -- Workflow -- Summary -- Questions -- Further reading -- Chapter 7: Transforming Data with the M Language -- Technical requirements -- Understanding the M language -- Structure of M -- Common use cases of M -- Filtering and sorting data with M -- Transforming data with M -- Working with data sources in M -- Creating parameters and variables -- Summary -- Questions -- Chapter 8: Using Data Profiling for Exploratory Data Analysis (EDA) -- Understanding EDA -- Exploring data profiling features in Power BI -- Reviewing column quality, distribution, and profile -- Column distribution -- Column quality -- Column profile -- Turning data profiles into high-quality data -- Recommended actions on column distribution -- Value distribution -- Summary -- Questions -- Part 3 - Advanced Data Cleaning and Optimizations -- Chapter 9: Advanced Data Cleaning Techniques -- Technical requirements -- Using Power Query Editor from within Dataflow Gen1 - fuzzy matching and fill down -- Fuzzy matching -- Fill down -- Best practices for using fuzzy matching and fill down -- Using R and Python scripts -- Benefits of using R or Python scripts -- Getting started with using R or Python scripts in Power BI -- Using ML to clean data -- Data cleaning with anomaly detection Data preparation with AutoML -- Data enhancement with AI Insights -- Summary -- Questions -- Chapter 10: Creating Custom Functions in Power Query -- Planning for your custom function -- Defining the problem -- Identifying parameters -- Setting clear objectives -- Using parameters -- Types of parameters -- Defining parameters -- Best practices for using parameters -- Creating custom functions -- Defining the function structure -- Writing M code -- Testing and debugging -- Documentation -- Summary -- Questions -- Chapter 11: M Query Optimization -- Technical requirements -- Creating custom functions -- Filtering and reducing data -- Using native M functions -- Optimizing memory usage -- Parallel query execution -- Using Table.Buffer and Table.Split -- Summary -- Questions -- Further reading -- Chapter 12: Data Modeling and Managing Relationships -- Understanding the basics of data modeling -- Importing versus DirectQuery -- Dimensional modeling -- Snowflake schema -- Intermediate tables -- Calendars and date tables -- Role-playing dimensions -- Aggregating tables -- Incremental refreshes -- Using bidirectional cross-filtering -- What is bidirectional cross-filtering? -- Best practices for bidirectional cross-filtering -- Understanding what's the right cardinality -- Understanding cardinality -- Why cardinality matters -- Choosing the right cardinality -- Handling large and complex datasets -- Understanding big data -- Challenges of working with big data in Power BI -- Best practices for handling big data -- Avoiding circular references -- Understanding circular references -- Best practices for avoiding circular references -- Summary -- Questions -- Further reading -- Part 4 - Paginated Reports, Automations, and OpenAI -- Chapter 13: Preparing Data for Paginated Reporting -- Technical requirements -- Understanding the importance of paginated reports Connecting to data sources within Power BI Report Builder -- Data preparation -- Query -- Fields -- Options -- Filters -- Parameters -- Creating a dataset example -- Using filters and parameters -- Using row groups/column groups -- Organizing and structuring data -- Enhancing readability and presentation -- Summary -- Questions -- Chapter 14: Automating Data Cleaning Tasks with Power Automate -- Technical requirements -- Handling triggers for automation -- Automating notifications -- Automating refreshing of data -- Creating snapshots (temporary tables) of cleaned data -- Best practices with Power Automate -- Summary -- Questions -- Further reading -- Chapter 15: Making Life Easier with OpenAI -- Optimizing efficiency with OpenAI, ChatGPT, and DAX -- Using OpenAI for M queries -- Using Microsoft Copilot -- Tackling challenges with AI -- Summary -- Questions -- Further reading -- Putting it together -- Assessments -- Chapter 1 - Introduction to Power BI Data Cleaning -- Chapter 2 - Understanding Data Quality and Why Data Cleaning is Important -- Chapter 3 - Data Cleaning Fundamentals and Principles -- Chapter 4 - The Most Common Data Cleaning Operations -- Chapter 5 - Importing Data into Power BI -- Chapter 6 - Cleaning Data with Query Editor -- Chapter 7 - Transforming Data with the M Language -- Chapter 8 - Using Data Profiling for Exploratory Data Analysis (EDA) -- Chapter 9 - Advanced Data Cleaning Techniques -- Chapter 10 - Creating Custom Functions in Power Query -- Chapter 11 - M Query Optimization -- Chapter 12 - Data Modeling and Managing Relationships -- Chapter 13 - Preparing Data for Paginated Reporting -- Chapter 14 - Automating Data Cleaning Tasks with Power Automate -- Chapter 15 - Making Life Easier with OpenAI -- Index -- About Packt -- Other Books You May Enjoy |
| ctrlnum | (ZDB-30-PQE)EBC31195583 (ZDB-30-PAD)EBC31195583 (ZDB-89-EBL)EBL31195583 (OCoLC)1424950496 (DE-599)BVBBV049873813 |
| dewey-full | 005.369 |
| dewey-hundreds | 000 - Computer science, information, general works |
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| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| edition | 1st edition |
| format | Electronic eBook |
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| id | DE-604.BV049873813 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-09T13:09:19Z |
| institution | BVB |
| isbn | 9781805126058 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035213271 |
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| owner_facet | DE-2070s DE-706 DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (340 Seiten) |
| psigel | ZDB-30-PQE ZDB-221-PDA ZDB-30-PQE HWR_PDA_PQE ZDB-221-PDA TUM_Paketkauf_2025 |
| publishDate | 2024 |
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| publisher | Packt Publishing, Limited |
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| spelling | Frazer, Gus Verfasser aut Data cleaning with Power BI the definitive guide to transforming dirty data into actionable insights 1st edition Birmingham Packt Publishing, Limited 2024 © 2024 1 Online-Ressource (340 Seiten) txt rdacontent c rdamedia cr rdacarrier Data mining-Computer programs Information visualization-Computer programs Visual analytics Erscheint auch als Druck-Ausgabe Frazer, Gus Data Cleaning with Power BI Birmingham : Packt Publishing, Limited,c2024 https://portal.igpublish.com/iglibrary/search/PACKT0007091.html Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Frazer, Gus Data cleaning with Power BI the definitive guide to transforming dirty data into actionable insights Cover -- Title Page -- Copyright and Credits -- Dedication -- Contributors -- Table of Contents -- Preface -- Part 1 - Introduction and Fundamentals -- Chapter 1: Introduction to Power BI Data Cleaning -- Technical requirements -- Cleaning your data in Power BI -- Understanding Power Query -- Understanding DAX -- Where do we begin with data? -- Summary -- Questions -- Chapter 2: Understanding Data Quality and Why Data Cleaning is Important -- What is data quality? -- Where do data quality issues come from? -- The role of data cleaning in improving data quality -- Data integrity and accuracy -- Decision-making and business outcomes -- Data ownership and accountability -- A holistic view of the data ecosystem -- Early detection of issues -- Continuous improvement and learning -- Empowerment and collaboration -- Best practices for data quality overall -- Establishing data quality standards -- Summary -- Questions -- Chapter 3: Data Cleaning Fundamentals and Principles -- Defining data cleaning -- Who's responsible for cleaning data? -- Building a process for cleaning data -- Data assessment -- Data profiling -- Data validation -- Data cleaning strategies -- Data transformations -- Data quality assurance -- Documentation -- Understanding quality over quantity in data cleaning -- Summary -- Questions -- Chapter 4: The Most Common Data Cleaning Operations -- Technical requirements -- Removing duplicates -- Removing missing data -- Splitting columns -- Merging columns -- Replacing values -- Creating calculated columns versus measures -- Calculated columns -- Measures -- Calculation group -- Considerations -- Summary -- Questions -- Part 2 - Data Import and Query Editor -- Chapter 5: Importing Data into Power BI -- Technical requirements -- Understanding data completeness -- Understanding data accuracy -- Understanding data consistency Assessing data relevance -- Assessing data formatting -- Assessing data normalization, denormalization, and star schemas -- Dimension modeling and star schema -- Denormalized data in dimension tables -- Summary -- Questions -- Chapter 6: Cleaning Data with Query Editor -- Technical requirements -- Understanding the Query Editor interface -- Data cleaning techniques and functions -- Adding columns -- Data type conversions -- Date/time -- Rounding -- Pivot/unpivot columns -- Merge queries -- Using Query Editor versus DAX for transformation -- Power Query Editor -- Data Analysis Expressions (DAX) -- Workflow -- Summary -- Questions -- Further reading -- Chapter 7: Transforming Data with the M Language -- Technical requirements -- Understanding the M language -- Structure of M -- Common use cases of M -- Filtering and sorting data with M -- Transforming data with M -- Working with data sources in M -- Creating parameters and variables -- Summary -- Questions -- Chapter 8: Using Data Profiling for Exploratory Data Analysis (EDA) -- Understanding EDA -- Exploring data profiling features in Power BI -- Reviewing column quality, distribution, and profile -- Column distribution -- Column quality -- Column profile -- Turning data profiles into high-quality data -- Recommended actions on column distribution -- Value distribution -- Summary -- Questions -- Part 3 - Advanced Data Cleaning and Optimizations -- Chapter 9: Advanced Data Cleaning Techniques -- Technical requirements -- Using Power Query Editor from within Dataflow Gen1 - fuzzy matching and fill down -- Fuzzy matching -- Fill down -- Best practices for using fuzzy matching and fill down -- Using R and Python scripts -- Benefits of using R or Python scripts -- Getting started with using R or Python scripts in Power BI -- Using ML to clean data -- Data cleaning with anomaly detection Data preparation with AutoML -- Data enhancement with AI Insights -- Summary -- Questions -- Chapter 10: Creating Custom Functions in Power Query -- Planning for your custom function -- Defining the problem -- Identifying parameters -- Setting clear objectives -- Using parameters -- Types of parameters -- Defining parameters -- Best practices for using parameters -- Creating custom functions -- Defining the function structure -- Writing M code -- Testing and debugging -- Documentation -- Summary -- Questions -- Chapter 11: M Query Optimization -- Technical requirements -- Creating custom functions -- Filtering and reducing data -- Using native M functions -- Optimizing memory usage -- Parallel query execution -- Using Table.Buffer and Table.Split -- Summary -- Questions -- Further reading -- Chapter 12: Data Modeling and Managing Relationships -- Understanding the basics of data modeling -- Importing versus DirectQuery -- Dimensional modeling -- Snowflake schema -- Intermediate tables -- Calendars and date tables -- Role-playing dimensions -- Aggregating tables -- Incremental refreshes -- Using bidirectional cross-filtering -- What is bidirectional cross-filtering? -- Best practices for bidirectional cross-filtering -- Understanding what's the right cardinality -- Understanding cardinality -- Why cardinality matters -- Choosing the right cardinality -- Handling large and complex datasets -- Understanding big data -- Challenges of working with big data in Power BI -- Best practices for handling big data -- Avoiding circular references -- Understanding circular references -- Best practices for avoiding circular references -- Summary -- Questions -- Further reading -- Part 4 - Paginated Reports, Automations, and OpenAI -- Chapter 13: Preparing Data for Paginated Reporting -- Technical requirements -- Understanding the importance of paginated reports Connecting to data sources within Power BI Report Builder -- Data preparation -- Query -- Fields -- Options -- Filters -- Parameters -- Creating a dataset example -- Using filters and parameters -- Using row groups/column groups -- Organizing and structuring data -- Enhancing readability and presentation -- Summary -- Questions -- Chapter 14: Automating Data Cleaning Tasks with Power Automate -- Technical requirements -- Handling triggers for automation -- Automating notifications -- Automating refreshing of data -- Creating snapshots (temporary tables) of cleaned data -- Best practices with Power Automate -- Summary -- Questions -- Further reading -- Chapter 15: Making Life Easier with OpenAI -- Optimizing efficiency with OpenAI, ChatGPT, and DAX -- Using OpenAI for M queries -- Using Microsoft Copilot -- Tackling challenges with AI -- Summary -- Questions -- Further reading -- Putting it together -- Assessments -- Chapter 1 - Introduction to Power BI Data Cleaning -- Chapter 2 - Understanding Data Quality and Why Data Cleaning is Important -- Chapter 3 - Data Cleaning Fundamentals and Principles -- Chapter 4 - The Most Common Data Cleaning Operations -- Chapter 5 - Importing Data into Power BI -- Chapter 6 - Cleaning Data with Query Editor -- Chapter 7 - Transforming Data with the M Language -- Chapter 8 - Using Data Profiling for Exploratory Data Analysis (EDA) -- Chapter 9 - Advanced Data Cleaning Techniques -- Chapter 10 - Creating Custom Functions in Power Query -- Chapter 11 - M Query Optimization -- Chapter 12 - Data Modeling and Managing Relationships -- Chapter 13 - Preparing Data for Paginated Reporting -- Chapter 14 - Automating Data Cleaning Tasks with Power Automate -- Chapter 15 - Making Life Easier with OpenAI -- Index -- About Packt -- Other Books You May Enjoy Data mining-Computer programs Information visualization-Computer programs Visual analytics |
| title | Data cleaning with Power BI the definitive guide to transforming dirty data into actionable insights |
| title_auth | Data cleaning with Power BI the definitive guide to transforming dirty data into actionable insights |
| title_exact_search | Data cleaning with Power BI the definitive guide to transforming dirty data into actionable insights |
| title_full | Data cleaning with Power BI the definitive guide to transforming dirty data into actionable insights |
| title_fullStr | Data cleaning with Power BI the definitive guide to transforming dirty data into actionable insights |
| title_full_unstemmed | Data cleaning with Power BI the definitive guide to transforming dirty data into actionable insights |
| title_short | Data cleaning with Power BI |
| title_sort | data cleaning with power bi the definitive guide to transforming dirty data into actionable insights |
| title_sub | the definitive guide to transforming dirty data into actionable insights |
| topic | Data mining-Computer programs Information visualization-Computer programs Visual analytics |
| topic_facet | Data mining-Computer programs Information visualization-Computer programs Visual analytics |
| url | https://portal.igpublish.com/iglibrary/search/PACKT0007091.html |
| work_keys_str_mv | AT frazergus datacleaningwithpowerbithedefinitiveguidetotransformingdirtydataintoactionableinsights |