{
  "slug": "How-an-AI-Prompt-Turned-a-Complex-Multi-Database-ETL-Project-Into-Hours-of-Work-bjw7e",
  "title": "How an AI Prompt Turned a Complex Multi-Database ETL Project Into Hours of Work",
  "subtitle": "",
  "excerpt": "In large organizations, data rarely lives in one place. Some information is stored in Oracle databases, while other datasets live in PostgreSQL.",
  "date": "2026-07-25",
  "tags": [
    "AI",
    "ETL",
    "Data Engineering"
  ],
  "readingTime": "5 min",
  "url": "https://www.linkedin.com/pulse/how-ai-prompt-turned-complex-multi-database-etl-hours-mobin-shaterian-bjw7e",
  "hero": "https://media.licdn.com/dms/image/v2/D4E12AQFgL43OD4VavQ/article-cover_image-shrink_720_1280/B4EZ.ZkljYH4AU-/0/1784987938374?e=2147483647&v=beta&t=cONHzViH8-Y-pkwG3xu5YjOQycvPGfygjE3OTRZwSxY",
  "content": [
    {
      "type": "paragraph",
      "html": "In large organizations, data rarely lives in one place.<!---->"
    },
    {
      "type": "paragraph",
      "html": "Some information is stored in Oracle databases, while other datasets live in PostgreSQL. Each system can use different structures, naming conventions, data types, and quality standards. Even when the data represents the same business domain, integrating it into a reliable DataHub may require days—or weeks—of engineering work.<!---->"
    },
    {
      "type": "paragraph",
      "html": "Recently, I used AI-assisted development to build a production-style ETL pipeline for telecom network data. The experience demonstrated something important:<!---->"
    },
    {
      "type": "heading",
      "level": 3,
      "text": "The Business Problem"
    },
    {
      "type": "paragraph",
      "html": "Our organization works with telecom data distributed across several databases. These systems contain information about network sites, cells, capacity, and other technical attributes.<!---->"
    },
    {
      "type": "paragraph",
      "html": "This data is valuable, but only when people can access it consistently.<!---->"
    },
    {
      "type": "paragraph",
      "html": "Without integration, organizations encounter several problems:<!---->"
    },
    {
      "type": "list",
      "ordered": false,
      "items": [
        "Reports use different definitions for the same information.<!---->",
        "Network identifiers are inconsistent.<!---->",
        "Analysts repeatedly clean the same data.<!---->",
        "Business applications place additional load on operational databases.<!---->",
        "Historical and operational information is difficult to compare.<!---->",
        "Decision-makers cannot easily access one trusted version of the network.<!---->"
      ]
    },
    {
      "type": "paragraph",
      "html": "Our goal was to integrate this information into a centralized DataHub with standardized structures, clean business terminology, ETL metadata, and repeatable synchronization processes.<!---->"
    },
    {
      "type": "heading",
      "level": 3,
      "text": "The Challenge of Building a New ETL Pipeline"
    },
    {
      "type": "paragraph",
      "html": "The new pipeline needed to transform raw network data into a business-friendly model.<!---->"
    },
    {
      "type": "paragraph",
      "html": "The source included technical, operational, geographical, environmental, historical, and capacity-related attributes. Building this pipeline manually required several careful steps:<!---->"
    },
    {
      "type": "list",
      "ordered": true,
      "items": [
        "Understand the existing ETL architecture.<!---->",
        "Study previous database migrations.<!---->",
        "Create a transformation layer.<!---->",
        "Normalize inconsistent values.<!---->",
        "Reproduce the source structure in the destination.<!---->",
        "Match database data types correctly.<!---->",
        "Register the pipeline metadata.<!---->",
        "Generate source-to-target mappings.<!---->",
        "Add appropriate indexes.<!---->",
        "Execute a complete synchronization.<!---->",
        "Diagnose failures and tune performance.<!---->",
        "Verify that no information was lost.<!---->"
      ]
    },
    {
      "type": "paragraph",
      "html": "The difficult part was not writing a single SQL query. It was ensuring that every component worked correctly with the existing metadata-driven ETL platform.<!---->"
    },
    {
      "type": "heading",
      "level": 3,
      "text": "The Prompt Became an Engineering Workflow"
    },
    {
      "type": "paragraph",
      "html": "Instead of simply asking AI to “write an ETL,” I created a reusable implementation prompt.<!---->"
    },
    {
      "type": "paragraph",
      "html": "The prompt required the AI to:<!---->"
    },
    {
      "type": "list",
      "ordered": false,
      "items": [
        "Inspect existing repository conventions<!---->",
        "Follow the established migration sequence<!---->",
        "Create the source transformation<!---->",
        "Register a new pipeline job<!---->",
        "Inspect the actual source schema<!---->",
        "Create a compatible destination structure<!---->",
        "Include standard ETL metadata<!---->",
        "Generate and review field mappings<!---->",
        "Add useful indexes<!---->",
        "Create focused tests<!---->",
        "Execute synchronization<!---->",
        "Report assumptions and test results<!---->",
        "Preserve unrelated code and changes<!---->"
      ]
    },
    {
      "type": "paragraph",
      "html": "This distinction is important.<!---->"
    },
    {
      "type": "paragraph",
      "html": "A vague prompt may produce code that looks correct. A structured prompt produces a process that can be inspected, tested, and repeated.<!---->"
    },
    {
      "type": "paragraph",
      "html": "AI became more than a code generator. It acted as an implementation partner that followed the project’s engineering rules.<!---->"
    },
    {
      "type": "heading",
      "level": 3,
      "text": "Transforming Raw Data Into Trusted Information"
    },
    {
      "type": "paragraph",
      "html": "The transformation layer normalized several common data-quality problems.<!---->"
    },
    {
      "type": "paragraph",
      "html": "For example, network identifiers could contain prefixes, suffixes, inconsistent capitalization, or additional descriptions. The transformation standardized these values so different representations of the same network element could be recognized consistently.<!---->"
    },
    {
      "type": "paragraph",
      "html": "Regional data also required normalization. Empty values and placeholders such as “Please Fill,” “N/A,” and “Unknown” were converted into proper null values. Different region formats were transformed into one consistent representation.<!---->"
    },
    {
      "type": "paragraph",
      "html": "Operational statuses were standardized as well. Different spellings and capitalization styles were converted into shared business values.<!---->"
    },
    {
      "type": "paragraph",
      "html": "These transformations may appear small, but they create significant value. Standardized data improves filtering, reporting, aggregation, integration, and decision-making.<!---->"
    },
    {
      "type": "heading",
      "level": 3,
      "text": "What the AI Built"
    },
    {
      "type": "paragraph",
      "html": "Based on the prompt and the repository’s existing patterns, the AI implemented a complete pipeline containing:<!---->"
    },
    {
      "type": "list",
      "ordered": false,
      "items": [
        "A database transformation view<!---->",
        "A registered ETL job<!---->",
        "A destination table with compatible data types<!---->",
        "Lookup indexes for frequently queried fields<!---->",
        "Source-to-target field mappings<!---->",
        "A mapping-generation test<!---->",
        "A full synchronization test<!---->"
      ]
    },
    {
      "type": "paragraph",
      "html": "The source and destination structures were not based on guesses. The applied database view was inspected through the database’s metadata schema, and the actual result became the source of truth for destination types and mappings.<!---->"
    },
    {
      "type": "paragraph",
      "html": "This detail matters. In data engineering, incorrect assumptions about data types can silently truncate, convert, or corrupt information.<!---->"
    },
    {
      "type": "heading",
      "level": 3,
      "text": "AI Was Fast, but Testing Made It Reliable"
    },
    {
      "type": "paragraph",
      "html": "The first complete synchronization identified a real technical limitation.<!---->"
    },
    {
      "type": "paragraph",
      "html": "The configured batch size, combined with the width of the dataset, produced more prepared-statement parameters than PostgreSQL allowed. The synchronization stopped before writing an invalid batch.<!---->"
    },
    {
      "type": "paragraph",
      "html": "The AI analyzed the failure, identified the relationship between the batch size and the number of writable fields, and reduced the batch size to a safe value consistent with existing pipeline conventions.<!---->"
    },
    {
      "type": "paragraph",
      "html": "The synchronization was then executed again successfully.<!---->"
    },
    {
      "type": "paragraph",
      "html": "Final verification confirmed that:<!---->"
    },
    {
      "type": "list",
      "ordered": false,
      "items": [
        "Every source record was represented in the destination.<!---->",
        "Every required field had one active mapping.<!---->",
        "Mapping order values were unique.<!---->",
        "No records failed during synchronization.<!---->",
        "Focused pipeline tests passed.<!---->",
        "Internal application tests passed.<!---->",
        "Formatting and repository checks passed.<!---->"
      ]
    },
    {
      "type": "paragraph",
      "html": "This illustrates an essential lesson: AI provides speed, but testing provides reliability.<!---->"
    },
    {
      "type": "heading",
      "level": 3,
      "text": "The Business Value of a DataHub"
    },
    {
      "type": "paragraph",
      "html": "This work was not only about saving development time. It was also about protecting and increasing the value of business data.<!---->"
    },
    {
      "type": "paragraph",
      "html": "A centralized DataHub provides long-term benefits:<!---->"
    },
    {
      "type": "list",
      "ordered": false,
      "items": [
        "One trusted source for reporting and analytics<!---->",
        "Less manual data preparation<!---->",
        "Consistent terminology across departments<!---->",
        "Faster integration with dashboards and applications<!---->",
        "Reduced load on operational source systems<!---->",
        "Better traceability through hashes, timestamps, and job metadata<!---->",
        "Repeatable synchronization instead of one-time exports<!---->",
        "A foundation for automation and AI-powered analytics<!---->"
      ]
    },
    {
      "type": "paragraph",
      "html": "For a large organization, data is a critical asset. If it remains fragmented, inconsistent, or inaccessible, much of its value is lost.<!---->"
    },
    {
      "type": "paragraph",
      "html": "ETL pipelines protect that value by transforming operational records into reliable organizational knowledge.<!---->"
    },
    {
      "type": "heading",
      "level": 3,
      "text": "What Made the “Impossible” Feel Fast?"
    },
    {
      "type": "paragraph",
      "html": "The speed did not come from one magical prompt. It came from combining four elements:<!---->"
    },
    {
      "type": "list",
      "ordered": true,
      "items": [
        "A clear business objective<!---->",
        "An existing metadata-driven architecture<!---->",
        "A detailed and reusable implementation prompt<!---->",
        "Continuous execution and verification<!---->"
      ]
    },
    {
      "type": "paragraph",
      "html": "The AI inspected established patterns, created migrations, generated mappings, executed tests, diagnosed a database limitation, corrected the configuration, and validated the final result.<!---->"
    },
    {
      "type": "paragraph",
      "html": "Work that previously required significant context switching was completed through one continuous engineering conversation.<!---->"
    },
    {
      "type": "heading",
      "level": 3,
      "text": "Final Thought"
    },
    {
      "type": "paragraph",
      "html": "AI is changing software and data engineering, but its greatest value is not simply producing code faster.<!---->"
    },
    {
      "type": "paragraph",
      "html": "Its real value appears when we provide it with context, standards, tools, constraints, and measurable success criteria.<!---->"
    },
    {
      "type": "paragraph",
      "html": "With the right prompt, AI can transform a complicated integration request into a repeatable engineering workflow. With proper testing, that workflow can safely integrate data from multiple systems and create a trusted DataHub for the business.<!---->"
    },
    {
      "type": "paragraph",
      "html": "The future of data engineering is not AI replacing engineers.<!---->"
    },
    {
      "type": "paragraph",
      "html": "It is engineers using AI to move from an idea to reliable business value faster than ever before.<!---->"
    }
  ]
}