The following section contains a list of the posts grouped by topic.
Just click on the link to jump to the post.General
Azure Synapse & Microsoft Fabrics
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Business Intelligence |
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- Part I: Monitoring the Warehouse
- SQL Databases
- Part I: Creating a View
- Part II: Under the Hood
- Part III: Backups
- Part IV: Automatic Tuning Options
- Part V: Manual Index Maintenance
- Part VI: Index Usage Analysis
- Part VII: Things That Don't Work
- Part VIII: Permissions
- Part IX: From OLTP to OLAP Data Models
- Part X: Templates for Database Objects
- Part XI: Database and Server Properties
- Part XII: Databases
Notes
- Azure
- Azure Data Factory (ADF)
- Azure Data Lake Storage Gen2 (ADLS Gen2)
- Azure service principals (SPN)
- Azure storage account
- general
- continuous integration & continuous deployment [CI/CD]
- data governance
- medallion architecture
- data pipeline
- Hadoop
- Microsoft Fabric
- Data Factory
- data sharing
- data store
- dataflows (Gen1)
- dataflows (Gen2)
- delta lake
- delta tables
- Eventhouses
- Activator
- Eventstreams
- Kusto Query Language (KQL)
- Real-Time Dashboards (RTD)
- Real-Time Hub (RTH)
- Real-Time Intelligence (RTI)
- folders
- governance
- Data Loss Prevention (DLP)
- Purview
- lakehouse
- partitions in lakehouse
- metrics layer
- mirroring
- notebooks
- OneLake
- OneLake Role-Based Access Control (RBAC)
- parquet format
- Python
- query acceleration
- security
- sharding
- shortcuts
- SQL Server
- warehouses
- workspaces
- Part I: General Issues
- Part II: The Complexity Myth
- Part III: Self Service BI
- Part IV: How Big is Your Report?
- Part V: Data Soup - From BI to Analytics
- Part VI: Data Soup - Reports vs Data Visualization
- Part VII: Insights - Aha Moments!
- Part VIII: Insights - The Complexity Perspective
- Part IX: Dashboards are Dead & Other Crap
- Part X: The Top 5 Pains of a BI/Analytics Manager
- Part XI: Ways of Thinking about Data
- Part XII: From Data to Data Models
- Part XIII-XV: From Data to Storytelling (Part I, II, III)
- Part XVI: On the Cusps of Complexity
- Part XVII: Creating Value for Organizations
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Part XVIII: There’s More to Noise
- Part XIX: Data Visualization between Art, Pragmatism and Kitsch
- Part XX: From BI to AI
- Part XXI: Data Visualization Revised
- Part XXII: Queries' Complexity
- Part XXIII: In between the Many Destinations
- Part XXIV: Building Castles in the Air
- Part XXV: Grounding the Roots
- Part XXVI: Monitoring - A Cockpit View
- Part XXVII: A Tale of Two Cities II
- Part XXVIII: Cutting through Complexity
- Part XXIX: Navigating into the Unknown
- Part XXX: The Artificial Intelligence Connection
- Part XXXI: More on Data Visualization
- Learning curve
- Metrics Layer
- Notes
- Metrics Layer [private preview]
- Visual Calculations
- Preparatory Steps for Creating a Power BI Report
- Working with Visual Calculations (new feature)
- A One Man Show
- Part I: Some Personal Background and a Big Thanks!
- Part II: In the Cusps of Complexity
- Part III: The Microsoft Fabric
- Part IV: Data Roles between Past and Future
- Part V: Focus on the Foundation
- Part VI: The Lakehouse Perspective
- A Software Engineer's Perspective
- Part I: Houston, we have a Problem!
- Part II: Major Knowledge Gaps
- Part III: More of a One-Man Show
- Part IV: The Loom of Interactions
- Part V: From Process Management to Mental Models in Knowledge Gaps
- Part VI: The Data Citizen
- Part VII: Think for Yourself!
- Mea Culpa
- Part II: The Beginnings
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Part V: All-Knowing Developers are Back in Demand?
Part VI: A Look Back - Part VII: A Look Forward
- Part VIII: A Look Beyond
- Part IX: A Look Inward
- Part X: A Look Beyond AI
- Approaching a Query
- Learning SQL
- A Reporting Guy’s Issues
- Addressable Questions in Reports’ Creation I
- Best Practices
- Choosing Report’s Attributes
- Filtering Internal Kitchen
- How Big Is Your Report?
- How Many Reporting Systems You Need?
- Levels of Accuracy in Reporting
- Levels of Detail in Reporting
- Reports Types
- Reports’ Lifecycle - Definition
- Drawing Concentric Circles with matplotlib.pyplot
- Installing PySpark and GraphFrames on a Windows 10 Machine
- Plotting Data with the Radar Chart
- Test Drive PySpark and GraphFrames
- Data Summaries without Using a DataFrame
- Data Transformations
- Part I: Temperatures' comparison between F° and C°
- Drawing Function Plots
- Hello World in R & Working with Data Frames
- Using the lessR Package in Microsoft Fabric's Notebooks (Test Drive)
- Visualizing the Iris Dataset
- Covid-19 Data Project
- Matrix Report Display - Fifth Magic Class
- Parameter Dependencies in Dropdowns- Fourth Magic Class
- Poor Design of Parameters
- Ranking Rows in Reports
- Report Formatting - Third Magic Class
- Report Parameters - Second Magic Class
- SQL Server CPU Utilization
- The Good, the Bad and the Ugly
- Wizarding a Report
- Graphical Representations II - Sixth Magic Class
Data Management
- Business Rules
- Part I: An Introduction
- Data Culture
- Part I: No Silver Bullet
- Part II: Leadership is Necessary but not Sufficient
- Part III: A Tale of Two Cities
- Part IV: Quo vadis? [Where are you going?]
- Part V: Quid nunc? [What now?]
- Data Driven
- Data Governance
- Data Integration
- Data Literacy
- Data Operations
- Data Profiling
- Data Quality
- An Introduction
- Data Cleansing
- Data Migration
- Dimensions
- Information Systems' Perspective
- Data Security
- Data Soup
- Master Data Management (MDM)
- Notes
Data Migrations
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Data Warehousing
- Data Vault 2.0
- Data Warehousing
- Data Warehousing and Microsoft Dynamics 365
- A Few Issues to Consider (Part I)
- Building a Data Lakehouse for Dynamics 365 Environments with Serverless SQL Pool
- Data Warehousing and Microsoft Dynamics 365
- Dynamics 365, the Data Lakehouse and the Medallion Architecture
- ETL
- An Introduction
- Push vs. Pull
- SSIS packages vs. SQL code
- Synchronization
- The Extract Subprocess
- The Load Subprocess
- The Transform Subprocess
- Microsoft Fabric
- Data(base) Mirroring in Microsoft Fabric (New feature)
- SSIS
- Aggregate Data Flow Transformation
- Percentage Sampling Data Flow Transformation
- Second Magic Class - SQL Server to Oracle Data Export
- The Conditional Split Data Flow Transformation
- The Good, the Bad and the Ugly
- The Union All Data Flow Transformation
- Third Magic Class - Data Flow Task
- Using Oracle as Data Source
- Visual Studio 2010 CR doesn’t support SSIS Projects
- Wizarding an SSIS Package - First Magic Class
- Concepts
- Definitions
- Part I: The Stored Procedure Case
- Part II: What's in a Name
- Challenges
- Cheat sheets
- Database objects
- Dependencies I - Introduction
- Views I
- Views II
- Table Value Constructors at Work
- Temporary Tables vs. Table Variables and TempDB
- Databases
- An Introduction
- Event Streaming Databases - More of a Kafka’s Story
- Running a Statement for Each Database - CLR Version
- Searching a Value within a Database
- SQL Reloaded
- A Look into Reverse
- Advices on SQL logic split in Web Applications
- Aggregates
- Number of records
- COUNT
- ROWCOUNT in action
- The CLR Version
- The DMV Approach
- via sys.partitions DMV
- via cursor
- via sp_MSForEachTable undocumented stored procedure
- Before and After
- Best practices
- Concatenation
- Common table expressions
- CRUD
- Incremental Update Technique
- Saving Data With Stored Procedures
- Self-Join in Update Query I
- Self-Join in Update Query II
- Stored Procedures from Metadata
- Updating Data With Values From The Same Table
- Cursor and Linked Server for Data Import
- Cursors and Lists
- Data Processing
- Preparing test data
- Processing JSON Files with Complex Structure in SQL Server 2016+
- Processing JSON Files with Flat Matrix Structure in SQL Server 2016+
- Randomized Data Selection
- SQL Server and Excel Data
- Trivial Equalities in Queries
- Useful Functions in Data Conversion
- Ways of Looking at Data
- When Queries Look Like Ducks
- XML on SQL Server 2005
- Dates
- GetDate SQL Server 2000/2005
- More Date-related Functionality I
- More Date-related Functionality II
- Some Notes on Dates
- Dynamic Queries
- Evaluating Textual Expressions with CLR UDFs
- Handling duplicates
- Joins
- Self-Join in Update Query I
- Self-Join in Update Query II
- Self-Joins and Denormalized Data Loading in Normalized Models
- The Power of Joins
- Just in CASE
- Part I: Introduction
- Part II: Clauses and Joins
- Part III: Quest for Performance
- Part IV: Other Scenarios
- Part V: Dynamic Queries
- Functions Useful in Data Migrations
- Lists
- List Based Aggregations - On Hand example
- Lists as Parameters in Stored Procedures
- Lists, Sets and a Little Math
- Table-valued Functions and List of Values
- Math
- Prime Numbers
- To be or not to be a Prime
- 6 out of 49
- Misusing Views and Pseudo-Constants
- Null-ifying the World
- Out of Space
- PIVOT Operator Example
- Partition-based functions
- Successive Price Increases/Discounts via Windowing Functions and CTEs
- STRING_AGG and STRING_SPLIT at Work, and a Bit of Pivoting
- String_Split Function
- Tricks with Strings via STRING_SPLIT, PATINDEX and TRANSLATE
- window functions
- Simple Aggregations (SQL Server 2022)
- Running Totals (SQL Server 2022)
- Ranking (SQL Server 2022)
- Window Functions
- Pulling the Strings of SQL Server
- Part I - An Introduction
- Part II - Creation and Selection
- Part IV - Spaces, Trimming, Length and Comparisons
- Part V - Character Indexes
- Part VI - Subparts of a String
- Part VII - List of Values
- Part VIII - Insertions, Deletions and Replacements
- Part IX - Special Characters
- Part X - Dynamic Queries
- The STUFF function
- Oracle
- Oracle vs SQL Server
- Query Patterns in SQL Server
- SQL Server
- Administration
- Database Recovery on SQL Server 2017
- End of Life for 2008 and 2008 R2 Versions
- Feature Bloat
- Killing Sessions - Killing ‘em Softly and other Snake Stories
- Monitoring the Database Logs
- System.OutOfMemoryException in SQL Server Management Studio and other 32-bit Drawbacks
- Metadata
- Part I: Indexes Overview
- Features
- New
- Undocumented
- Part I: Reading the SQL Server Log
- Part II: Execute Command for Each Table
- Part III: CPU Utilization via the Ring Buffer
- Part IV: DBCC SQLPERF
- Wish List
Notes
- buffer pool
- checkpoints
- compression
- databases
- tempdb
- resource
- garbage collection
- language
- live query statistics
- logging
- minimally logged
- locking
- memory
- memory manager
- NUMA
- objects
- cursor [defs]
- databases [defs]
- model database [defs]
- tempdb database [defs]
- functions
- aggregate functions [defs]
- analytics functions
- system functions
- table-valued functions
- user-defined functions
- indexes [defs]
- clustered index [defs]
- clustered vs non-clustered indexes
- columnstore indexes
- composite indexes [defs]
- covering indexes [defs]
- index selectivity [defs]
- non-clustered indexes [defs]
- keys
- schemas [defs]
- statistics [defs]
- issues
- stored procedures [defs]
- system stored procedures [defs]
- user-defined stored procedures
- tables [defs]
- contained database
- resource database
- scalable shared database
- stretched databases
- table variables
- tempdb
- temporal databases
- temporary tables
- triggers [defs]
- views [defs]
- user-defined views
- indexed views
- system views
- parameter sniffing
- partitions
- plan cache
- queries
- correlated subquery [defs]
- query optimization
- query plan
- query processing
- subqueries
- real-time operational analytics
- resource governor
- resource monitor
- service broker
- sessions
- transaction log [defs]
- transactions [defs]
- variables
- Troubleshooting
- Adventure Works - one year later
- Adventure Works installation error on Vista
- AdventureWorks requires FILESTREAM enabled
- Could not load file or assembly Microsoft.MSXML…
- Errors
- Information Schema Views
- Logical errors
- Login Failed for User
- Microsoft SQL Server 2008 installation error
- Porting 32 bit CLR UDFs on 64 bit Platforms
- Problems in SQL Server 2000 DTS Packages
- Root Blocking Sessions
- Searching the Error Log
- Who is Active
ERP Systems
- Data Migration
- Data Warehousing
- Implementations
- Part I: The Right ERP software
- Part II: General Points of Failure
- Part III: It’s all about Scope I - Functional Requirements
- Part IV: It’s all about Scope II - Nonfunctional Requirements & MVP
- Part V: It’s all about Partnership I - An Introduction
- Part VI: It’s all about Partnership II -
- Part VII: The Process Seems to Be Broken
- Part VIII: It’s a Matter of Complexity
- Part IX: Simplifying the Implementation Project
- Part X: Introducing an Upfront Proof-of-Concept Setup
- Part XI: Tales from the Crypt
- Part XII: The Process Perspective
- Part XIII: On Project Management
- Part XIV: A Never-ending Story
- Part XV: An Ecosystemic View
- Part XVI: It’s All About Politics
- Part XVII: Taming the Monsters
- Part XVIII: The Price of Partnership
- Dynamics 365
- general
- Finance
- Invoice Capture
- Notes
- Business Performance Analytics
- Business Performance Planning
- Business Process Catalog (BPC)
- Fixed Assets
- Financial tags
- Invoice capture
- Vendor Invoices Processing
- Operations
- SQL queries
- Oracle e-Business Suite
Metablogging
MS Office
- MS Excel
- Excel for SQL Developers
- MS Access
Process Management
- Dynamics 365 for Finance & Operations
- ITIL
Project Management
- Methodologies:
- Agility under Eyeglasses
- The Good, the Bad and the Ugly
- Project Planning
- Planning Correctly Misundersood
- Some Thoughts on Planning
- The Butterflies of Project Management
- Project Tools
- Project Management
- Projects' Dynamics
- Redefining Projects' Success I
- The Agile Manifesto Reloaded
Software Engineering
- Application Design
- Business Intelligence
- Copilot Unabridged (Answers to Copilot Prompts, so feel free to experiment with them as well!) 🤖〽️
- Part 1: The Importance of AI in Society: A Transformational Force I
- Part 2: The Importance of AI in Society: A Transformational Force II
- Part 3: Why AI in Society Doesn’t Matter (Or Does It?)
- Part 4: Why Society Doesn't Matter for AI
- Part 5: Why There Are Limits to AI Growth
- Part 6: Why There Are No Limits to AI Growth
- Part 7: The Risks Associated with AI - Challenges in a Rapidly Advancing World
- Part 8: Critical Points in the Development of AI - Milestones and Challenges
- Part 9: The Perils of AI - Risks and Challenges in a Rapidly Evolving Technology
- Part 10: When Will AI Become a Danger for Society?
- Part 11: How the Danger of AI on Society Can Be Alleviated
- Part 12: How Humanity Can Respond When AI Becomes a Danger to Society
- Part 13: What Humanity Can't Do When AI Becomes a Danger to Society
- Part 14: How Fast Humanity Could Face Extinction Due to AI Intervention
- Part 15: What AI Can Use Its Intelligence to Damage Society
- Part 16: How AI Can Use Its Intelligence to Help Society
- Part 17: Can AI Become Self-Conscious? Exploring the Possibilities
- Part 18: How Fast Can AI Surpass Human Intelligence?
- Part 19: How Much AI Influences the Recruitment Process
- Part 20: The Negative Influence of AI on the Recruitment Process
- Part 21: Can AI Be Fooled? Understanding Its Vulnerabilities
- Part 22: How AI Can Fight Against Other AI - The Battle of Algorithms
- Part 23: How AI Can Be Tamed - Ensuring Responsible Development and Use
- Part 24: The Next Steps in the Development of AI
- Part 25: How AI Can Outsmart Us - The Rise of Machine Intelligence
- Part 26: How Humans Can Outsmart AI - The Power of Human Ingenuity
- Part 27: The Negative Impact of AI on the Economy
- Part 28: The Gray Areas of AI: Navigating Ethical and Practical Uncertainty
- Part 29: The Duality of AI - Innovation and Ethical Challenges
- Part 30: The Multiplicity of AI - Expanding Perspectives in Artificial Intelligence
- Part 31: The Potential of AI to Reach Singularity
- Part 32: Can AI Be Stopped from Reaching Singularity?
- Part 33: The Conflict of Interests Among Superintelligent AIs
- Part 34: How the Average Citizen Can Use AI in Everyday Life
- Part 35: How AI Impacts the Average Citizen
- Part 36: How AI Can Reduce Unemployment
- Part 37: How AI Can Increase Unemployment
- Part 38: The Growing Backlog of AI Policies - How Much Needs to Be Done?
- Part 39: How Fast Does Humanity Move in Enforcing Policies to Cope with AI’s Rapid Growth?
- Part 40: How Disruptive Is AI as a Technology?
- Part 41: How AI Can Play Devil’s Advocate - Challenging Assumptions and Expanding
- Part 42: How AI Can Help in Understanding Complex Systems
- Part 43: How Can I Use AI for Blogging?
- Part 44: How Can I Misuse AI for Blogging?
- Part 45: How Fast a Conflict with AI can escaladate
- Part 46: Understanding AI Governance - Balancing Innovation and Responsibility
- Part 47: The Future of AI - How Artificial Intelligence Could Evolve in the Next Decade
- Part 48: AI - Society’s Illusion of Progress
- Part 49: The End of AI - Will We Ever Stop Using Artificial Intelligence?
- Part 50: The Obsolescence Effect - How AI May Render Technologies and Jobs Redundant
- Part 51: Will AI Make Programmers Obsolete?
- Part 52: Will AI Make Project Managers Obsolete?
- Part 53: Will AI Make Software Engineering Obsolete?
- Part 54: The Future of Business Intelligence - Will AI Make It Obsolete?
- Part 55: Will AI Make Data Analysis Obsolete?
- Part 56: AI and the Search for Immortality - A Digital Quest for Eternal Life
- Part 57: AI and the Search for Spirituality - A New Frontier
- Part 58: AI and the Search for Consciousness - Can Machines Truly Think?
- Part 59: The Exploitable Weaknesses of AI - Risks and Challenges
- Part 60: The Foolishness of AI: Weaknesses That Can Be Exploited
- Part 61: The Competitive Gap: AI-Adopting vs. AI-Resistant Organizations
- Part 62: When AI Goes Terribly Wrong: Risks and Consequences
- Part 63: Is AI Making Humanity Dumber? The Risks of Over-Reliance
- Part 64: How AI Can Make Humanity Smarter
- Part 65: AI: A Reflection of Humanity
- Part 66: The Rise of AI: A New Era of Power Transition
- Part 67: The Reality of AI: A World Beyond Human Perception
- Part 68: AI: A Reflection of Intelligence, Not a Replica
- Part 69: AI and the Illusion of Knowledge: Learning Without Understanding
- Part 70: AI and the Illusion of Consciousness: Can Machines Truly Think?
- Documentation
- Knowledge Management
- Mea Culpa
- Programming
- Part I: Learning SQL
- Part II: To get or not Certified?!
- Part III: Football and Software Development
- Part IV: Believe and Not Doubt?!
- Part V: Is MS Access or Excel the Answer to your Problems?
- Part VI: What is Programming About?
- Part VII: Documentation - Lessons Learned
- Part VIII: Pair Programming
- Part IX: Programmer, Coder or Developer?
- Part X: Programming as Art
- Part XI: The Dark Side of Programming
- Part XII: Misconceptions about Programming I
- Part XIII: Misconceptions about Programming II
- Part XIV: Good Programmer, Bad Programmer
- Part XV: Rapid Prototyping - Introduction
- Part XVI: The Software Quality Perspective and AI
- Part XVII: More on AI
Strategic Management
- Lean Management
- Between Value and Waste
- Part I: Introduction
- Notes
- Performance Management
- First Time Right (The Aim toward Operational Excellence)
- Over-Educated, Yet Under-Qualified?
- The Need for Perfection vs. Excellence
- Simplicity
- Part I: Simple, but not that Simple
- Part II: A System's View
- Part III:
- Part IV: Designing for Simplicity
- Part V: ERP Implementations' Story I
- Part VI: ERP Implementations' Story II
- Strategy
- Strategic Perspectives
- Defining the Strategy
- Quality Acceptance Criteria for Strategies and Concepts
- Strategic Planning
- The Reason behind a Strategy
- The Impact of New Technologies
Systems Engineering
- Complex Systems
- Never-Ending Stories in Praxis (Quote of the Day)
- A Play of Problems (Much Ado about Nothing)
- Simplicity
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