SQL Server Docker Basics

Microsoft SQL Server 2022 · Docker Desktop · SQL practice · DataGrip workflow · Data/BI learning path

This repository documents a local Microsoft SQL Server setup for learning relational databases, SQL workflows, data modelling, data quality checks and reporting-oriented queries.

It is part of my broader Data/BI portfolio as an IHK retraining student in Data and Process Analysis. The goal is not to build a production database system, but to create a clean, reproducible foundation for practical SQL and Microsoft-oriented BI learning.

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Why This Project Matters for Data/BI

SQL Server is a central part of many Microsoft-based data environments. For Data/BI work, it is not enough to only write isolated SQL queries. A useful workflow also needs:

• a reproducible local database environment
• clear connection settings and documented tooling
• safe handling of credentials
• structured SQL scripts
• basic validation queries
• reporting-oriented aggregations
• a path from relational data toward Power BI, Fabric and BI-style analysis

This project demonstrates the foundation for that workflow: a local SQL Server instance running in Docker, documented DataGrip access and SQL scripts that can later feed Power BI or Python-based analysis.

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What This Demonstrates

This repository demonstrates:

• running Microsoft SQL Server 2022 locally in Docker
• using .env files for local credentials without committing secrets
• connecting to SQL Server from DataGrip
• verifying a SQL Server instance with basic checks
• organizing SQL scripts in a clear folder structure
• documenting a practical local database workflow
• preparing SQL practice for reporting, data quality and BI-style analysis

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Data/BI-Oriented Stack

Layer	Tool / Concept	Purpose
Database	Microsoft SQL Server 2022	Relational database practice and Microsoft data stack foundation
Runtime	Docker Desktop	Reproducible local SQL Server environment
SQL Client	DataGrip	SQL development, database inspection and query execution
Scripts	SQL files	Repeatable checks, training queries and reporting-oriented examples
Configuration	.env / .env.example	Local credential handling without committing secrets
BI Layer	Power BI	Planned next layer for connecting reports to SQL Server data
Platform Perspective	Microsoft Fabric / Azure	Longer-term Microsoft data platform context

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Current Status

The local Docker-based SQL Server setup is working and documented.

Verified:

• SQL Server container starts successfully
• SQL Server listens on host port 14333
• sqlcmd is available inside the container
• connection test with SELECT @@VERSION works
• DataGrip connection works
• DPA training database scripts are included
• SQL example scripts are organized under sql/examples/

Included:

• docker-compose.yml
• .env.example
• .gitignore
• documented DataGrip workflow
• SQL example scripts
• basic project notes
• folder structure for future notebooks and BI-related extensions

Not included:

• real passwords
• .env files
• database volumes
• private data
• exported local data
• personal or customer data

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Repository Structure

sql-server-docker-basics/
├── README.md
├── LICENSE
├── .env.example
├── .gitignore
├── docker-compose.yml
├── docs/
│   ├── datagrip-workflow.md
│   └── project-notes.md
├── sql/
│   ├── 01_create_database.sql
│   ├── 02_create_schema.sql
│   ├── 03_insert_sample_data.sql
│   ├── 04_analysis_queries.sql
│   └── examples/
│       ├── README.md
│       ├── 01_basic_checks.sql
│       ├── 02_training_queries.sql
│       └── 03_data_quality_checks.sql
└── notebooks/

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Environment Variables

Create a local .env file from .env.example:

cp .env.example .env

Then change the password in .env.

The .env file is intentionally ignored by Git.

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Local Connection

Default local connection settings:

Setting	Value
DBMS	Microsoft SQL Server
Host	127.0.0.1
Port	14333
User	sa
Password	local value from .env

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DataGrip Workflow

The DataGrip workflow is documented in:

docs/datagrip-workflow.md

This includes the local connection setup and the basic workflow for working with the SQL Server container from DataGrip.

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SQL Examples

The repository contains two kinds of SQL scripts:

1. core SQL workflow scripts that build on each other
2. additional standalone example scripts for checks, training queries and data quality practice

Core SQL workflow scripts

Core SQL workflow scripts are available directly under:

sql/

These scripts document a small end-to-end SQL learning workflow:

• 01_create_database.sql creates the local training database in an idempotent way
• 02_create_schema.sql defines a small relational schema with primary keys, foreign keys and uniqueness constraints
• 03_insert_sample_data.sql inserts reproducible synthetic sample data
• 04_analysis_queries.sql contains reporting-oriented SQL queries, including joins, averages, pass-rate calculations and a simple below-target flag

The sample data is synthetic and does not contain real personal or customer data.

Additional standalone example scripts

Additional standalone example scripts are available in:

sql/examples/

Current examples:

• 01_basic_checks.sql
• 02_training_queries.sql
• 03_data_quality_checks.sql

The sql/examples/ scripts are intended as a practical starting point for SQL Server checks, training queries, basic data quality checks and later reporting-oriented SQL examples.

Together, the core workflow scripts and the standalone examples show database setup, relational schema design, reproducible sample data, analysis queries and basic data quality checks.

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Roadmap: From SQL Server to BI Workflow

Planned improvements are intentionally incremental:

1. add a small synthetic training database
2. add data quality checks and validation queries
3. add reporting-oriented aggregation queries
4. add a simple star-schema example for BI learning
5. document a Power BI connection workflow
6. optionally add a Python/pandas notebook for SQL Server data access
7. connect the project conceptually to Microsoft Fabric/Azure as the next platform layer

Power BI and Microsoft Fabric are not treated as finished parts of this repository yet. They are the planned next layers after the SQL Server foundation is clean, documented and reproducible.

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Notes and Limitations

This repository is a local learning setup, not a production database project.

It should not contain:

• real credentials
• private dumps
• personal data
• customer data
• SQL Server database volumes
• generated local exports

The purpose is to document a realistic learning workflow for SQL Server, relational data practice and Data/BI preparation.