Documents
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. DataOps For The Uninitiated

What Is DevOps?

The second leg of the DataOps stool is DevOps. Here is another one of those terms that people throw around but don’t really know what it means.

What is DevOps? I’ll tell you what DevOps isn’t. DevOps is not software. Not gonna lie. That was really confusing for a long time. I literally thought DevOps was learning how to use continuous delivery continuous development or CD/CI tools like Jenkins or Travis CI. It’s not that at all. I was super excited to learn DevOps. I was quite unsatisfied at the very touch feely nature of it.

DevOps is purely philosophical. It’s an intellectual outgrowth of Agile. The fundamental goal of DevOps is to get the people that build the software working closer together with the people that run the software. I’m not talking about users. I’m talking about DBAs, site reliability engineers, server admins etc. Frequently it’s the case that people that build the software, don’t release it to the wild nor manage it once it’s out there.

In the olden days of yore, there used to be a real disconnect between these two groups of people. DevOps attempts to bridge that gap. Dev. Ops. Get it?

Don’t get me wrong. DevOps is all about CD/CI. It’s about getting software out there faster on the same pace as Agile demands. It’s iterative. However, while Agile is about rapid product management, DevOps is about rapid product release. We’re talking about the physical processes of moving code from dev to test to prod.

This process of constantly releasing software, requires a butt load of automation. I mean like nine or ten butt loads worth of automation. DevOps is where you hear concepts like infrastructure as code and application management solutions like Docker come into play. This is reason number 842,559 why I’m screaming to learn to code your ETL processes. You can’t do this stuff with no code low code tools. I don’t care what the sales guy says.

But, also, like I said, there is a lot of hippy dippy warm and fuzzy stuff too like establishing DevOps values in the enterprise. Values like, encouraging teamwork, reducing silos, practicing systems thinking, embracing failure, communication, accepting feedback, and automating processes.

Does any of this sound familiar? Is it just me or is this pretty much everything I’m teaching intuitively without being a real expert on the subject matter? There is a reason for that that I’ll tell you about in a second.

PreviousWhat Is Agile Software Development?NextWhat Is Statistical Process Control?

Last updated 3 years ago