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Data Warehouse ETL Framework
Data Warehouse ETL Framework
  • Introduction To The ETL Framework
  • Framework Overview
    • Framework Description
    • Why You Need An ETL Framework
      • What is the ETL Framework?
      • Framework Objectives
      • Can I use my own resources to implement The Framework?
    • Download The Framework
    • What's In The Box
    • v1.0 Release
    • Training Resources For Engineers
    • Getting Help
  • General Principles
    • Never Finished
    • Overall Engineering Philosophy
    • Self Service Analytics As The Goal
    • Strict Adherence To Globally Accepted Practice
    • Data Flow Design
    • Effective DataOps
    • Code First Philosophy
    • Melding Kimball And Inmon
    • Master Data Management
    • Open Architecture
    • Decoupled Systems
    • Independent Identically Executing Processes
    • Robust Processes
    • Self-Diagnostics
    • Data Provenance
    • Adverse Reporting
    • Error Recovery
    • Archrival And Recovery
    • Documentation As A Necessity
  • DataOps For The Uninitiated
    • DataOps Initiation
    • What Is Agile Software Development?
    • What Is DevOps?
    • What Is Statistical Process Control?
    • You Take All That And You Wind Up With...
    • The DataOps Manifesto
    • Want To Learn More About DataOps?
  • ETL Developer's Field Guide
    • The Plain English Explanation Of What You Are About To Build
    • Data Warehouse User Profiles
    • Using Data Source Names
    • Why You Should Not Use SSIS
      • SSIS Is Not Forward Compatible
      • SSIS Is Not Very Performant
      • Python Development Is Faster Than SSIS
      • SSIS Is Difficult To Deploy To Production
      • SSIS Is Difficult To Maintain
    • Python Software Engineering Considerations
    • Enterprise Data Ecosystem
      • EDW's Place In Enterprise Architecture
      • EDW ETL Overview
    • ETL Environment Databases
    • Data Acquisition Paradigms
      • Demand Pull
      • Supply Push
    • The Common Model
    • The Semantic Layer
    • Database Object Naming Conventions
      • Columns
      • Indexes
      • Primary Keys
      • Schemas
      • Stored Procedures
      • Tables
      • User Defined Functions
    • ETL Reference Architecture
    • Table Anatomy
      • Sample Dimension Table
      • Sample Fact Table
      • Sample Indexed View
      • Sample Junk Dimension Table
      • Sample Master Data Management Table
      • Sample Stage Table
    • Master Data Management
      • MDM With Exact Match On Source System Key
      • Implementation Details
    • Slowly Changing Dimension Processing
      • General Process
      • Creating Empty Records
      • SCD Processing Types
      • Implementing SCDs As Temporal Tables
    • Handling Calculated Values
    • Warehouse Load Commandments
    • Code Style Guide
      • SSIS
      • Transact SQL
      • Python
      • C#
    • The Role Of Schemas
      • Standard Schemas And Their Definitions
      • Schemas As A Security Device
      • Schemas As A DB Object Differentiator
    • Feedback And Control Systems
      • Having An Engineering Mindset
      • The Mechanics Of Checking For Unprocessed Records
      • Time Series Analysis 101
      • Passive Monitoring System Design Theory
      • Passive Monitoring System Implementation
    • Source Control
    • Loading Historical Data
    • Security Access Model
    • Wrapping Up
  • ETL Environment Set Up
    • Install Anaconda
    • Create Initial Source Control Folders
    • Create Primary Databases And Schemas
    • Customize And Run Deployment Scripts
    • Install Python Packages
    • Create Data Source Name
    • Create The SSIS Catalogue
    • Create The File I/O Directory Structure
    • Create Global Environment In The Integration Services Catalog
    • Create The SQL Server Aliases
    • Create The BIAnalytics Proxy Account
    • Create The Opt Folder
    • Configure Email Alerts
  • Standard SSIS ETL Development Package
    • Starting A New Process
    • Tracking Package Variables
    • Remove Things You Do Not Need
    • Creating New Configuration Settings
  • Sample Script Guide
    • SQL
      • Finalize And Audit Scripts
        • 18 CREATE PROC usp_RecordRowCounts
        • 19 CREATE PROC usp_MarkRecordsAsProcessed
        • 20 CREATE PROC usp_CheckForUnprocessedRecords
      • Monitoring Scripts
        • 21 CREATE PROC usp_DisplayTablesNotLoading
        • 22 CREATE PROC usp_LoadTableLoadReportingTable
        • 23 CREATE PROC usp_TableLoadMonitoring
      • Table Object Sample Scripts
        • sample type I dimension table
        • sample type II dimension table
        • sample fact table
        • sample indexed view
        • sample junk dimension table
        • sample master data management table
        • sample stage table
      • Data Processing Sample Scripts
        • Data Pull
        • batch processing
        • bulk insert
        • data cleansing
        • fact table load
        • remove dups
        • type I dimension processing
        • type II dimension processing
      • Helper Scripts
        • Create Database
        • add rowhash
        • change collation
        • configuration insert sample script
        • documentation block
        • populate fact table with fake data
        • proc execution scripts
        • show all columns in database
        • troubleshooting
        • util
        • start an agent job from T-SQL
    • Python
      • Building Blocks
        • CombineCSVsIntoOneFile
        • ConvertCommaToPipeDelimitedFile
        • ConvertExcelToCSV
        • LoopingOverFilesInADirectory
        • Process Zip File
        • QueryDatabaseAndWriteSmallFile
        • QueryDatabaseAndWriteLargeFile
        • SendEmail
        • StringMatching
        • YAMLConfigImport
      • Full Solutions
        • DownloadMoveAndStoreDataCSV
        • LoadLargeCSVsIntoDataWarehouseStagingTables
        • MoveAndStoreDataExcel
        • ReloadFromArchive
      • Jupyter Notebooks
        • Passive Monitoring System Design Theory
    • PySpark
      • ConnectToSpark
      • LoadCSV
      • ExampleDataProcessing
    • Windows Batch
  • A Methodology To Rapidly Convert OLTP Databases to OLAP Solutions
    • Step 1: Find The Nouns
    • Step 2: Find The Stuff We Want To Do Math On
    • Step 3: Analyze Relationships
  • Data Model Creation Tool
    • Record Count
    • Sample Data
    • Create Stage Table
    • Char Length Analysis
    • Column Notes
    • Column Cleansing Notes
    • Source To Target Mapping
    • Dimension List
    • Fact Table Creation Helper
    • Foreign Key Creation
    • Process Fact Script Helper
    • View Creation Helper
    • Date Role Play View Helper
    • Data Model View Creation Helper
    • List Population Values
  • Performance Monitoring
    • The Daily Chore
    • Diagnostic Tools
      • Using The Built-In Stored Procs
      • Using The Built-In Views
    • Logging Database Diagram
  • Data Warehouse Troubleshooting Guide
    • Generalized Troubleshooting Steps
  • Business Analytics Capability Maturity Model
    • What Is A Business Analytics Capability Maturity Model?
    • Level 0. Operational Reporting
    • Level 1. Rapid Delivery
    • Level 2. Self Service
    • Level 3. Central Repository
    • Level 4. Open Data 1
    • Level 5. Open Data 2
    • Level 6. Feedback 1
    • Level 7. Data Archaeology
    • Level 8. Crystal Ball
    • Level 9. Feedback 2
    • Level 10. Oil Rig
    • Level 11. The Singularity
  • Appendices
    • Appendix A. What is Medium Data®?
    • Appendix B. The Benefits Of Using Python And T-SQL Over SSIS For ETL
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  1. Data Warehouse Troubleshooting Guide

Generalized Troubleshooting Steps

Any troubleshooting process is part procedural and part detective work. Reading the ETL Developers guide and making sure you have a good understanding of the system will help you fix problems faster than any step by step process. That being said, the following is a good heuristic for finding problems in the warehouse ETL process.

  1. Open the step that blew up and determine what the error was.

  2. If it was related to a proc, open the sub step and pull out the EXEC statements and run them by hand one by one. The trouble proc will blow up. (This is where your .sql file of EXEC statements of every proc comes in handy.)

  3. Determine and fix the error. (This is where you pull out your Sherlock Holmes pipe and go to work. There is no formulaic way to do this.)

  4. Re-run the step that blew up.

The process is designed so that any problems with loading dimensions brings the process to a grinding halt. Loading dimensions is a necessary sufficient condition for loading facts. Facts, on the other hand, can blow up and the rest of the fact tables will be allowed to continue processing.

The processing of dimensions should be really fast so any long run time on dimension processing should be considered a source of suspicion. Below are the top causes for warehouse load problems.

  1. Duplicate records in staging as a result of bad logic in the source pull. The SQL MERGE statement cannot process duplicate records from a single source.

  2. Data truncation. String data type sizes are determined by profiling existing data and then doubling the existing maximum character value. This does not always work over time. You will have to systematically determine which column is causing the problem.

  3. Pulling source data again before processing the prior run. This will result in a duplicate record error.

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Last updated 4 years ago