Description

A focused course, tailored for you

The Data Engineer's Course on Building Reliable Data Lake Pipelines When Legacy SQL Servers Hold Back

Turn fragmented SSMS scripts and manual lake loads into an automated, auditable workflow that scales with your business.

Stop rebuilding the same SSMS load script every month while audit deadlines keep slipping.

$199 one-time

Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

Your team spends countless evenings stitching ad-hoc T-SQL scripts to move data from on-prem SQL Server into a cloud data lake. The process is undocumented, each engineer has a personal copy of the load logic, and any change breaks downstream reports. When the quarterly data quality audit arrives, you scramble to produce logs, and senior leadership questions the reliability of the entire pipeline.

The tooling friction is real: SSMS is used for everything, but there is no version control, no monitoring, and no reusable templates. Your data lake ingestion jobs are built in notebooks that disappear after a sprint, leaving the operations team to manually re-run queries when a table schema shifts. The stakes are high, missed SLAs trigger penalties, and the next promotion hinges on showing a clean, repeatable data flow.

If this continues, you will face another audit with missing evidence, a costly re-engineering effort, and a career setback as the organization looks for a more disciplined data platform owner.

What you walk away with

Create a repeatable ETL framework that moves SQL Server data into the lake with zero manual steps.
Generate audit-ready evidence packs for each load without extra effort.
Implement automated monitoring and alerts for schema changes and load failures.
Standardize version-controlled scripts and notebooks across the team.
Reduce data onboarding time by at least 50 percent.

The 12 modules

Module 1. Mapping Legacy SQL Objects to Lake Targets

Identify and document source tables, views, and stored procedures for migration.

Module 2. Designing a Parameterized Extraction Blueprint

Build reusable extraction scripts that accept dynamic date ranges and filters.

Module 3. Version-Control Integration for T-SQL

Set up source control workflows for all SSMS scripts.

Module 4. Automating Load Jobs with Scheduler

Configure a job scheduler to run extracts and lake writes on a fixed cadence.

Module 5. Schema Change Detection and Mitigation

Implement automated checks that flag source schema drift before loads.

Module 6. Building a Data Quality Dashboard

Create a visual scorecard that tracks row counts, null ratios, and load latency.

Module 7. Generating Audit Evidence Packs

Produce packaged logs and lineage reports for each successful load.

Module 8. Error Handling and Alerting

Define retry logic and notification rules for failed jobs.

Module 9. Secure Credential Management

Store connection strings and keys in a vault and reference them in scripts.

Module 10. Performance Tuning for Bulk Loads

Apply indexing and batch strategies to accelerate data movement.

Module 11. Collaboration and Documentation Practices

Establish a living documentation hub linking scripts to business owners.

Module 12. Continuous Improvement Loop

Set up a feedback cycle to refine pipelines after each audit.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

Module 1 covers Mapping Legacy SQL Objects to Lake Targets , exactly the inventory you need when your team cannot agree on which tables feed the lake.

Module 5 covers Schema Change Detection and Mitigation , that is precisely the alert you miss when a source column is renamed and downstream jobs fail.

Module 7 covers Generating Audit Evidence Packs , exactly the packaged proof you need when the audit committee asks for load logs on short notice.

What you get with this course

A parameterized extraction script library.
A version-controlled repository scaffold.
A pre-populated job scheduler configuration file.
A schema change detection checklist.
A data quality dashboard template.
An audit evidence pack walkthrough guide.
An error handling and alerting runbook.
A secure credential vault integration guide.
A performance tuning cheat sheet.
A documentation hub starter kit.
A continuous improvement feedback form.
A final implementation playbook tailored to your environment.

What you will have in hand by Day 1, Week 1, Month 1

Day 1: tailored playbook in hand, extraction script library pre-populated for your environment, job scheduler template ready.

Week 1: first version of the data quality dashboard live and shared with the analytics lead.

Month 1: recurring automated load cycle operating with zero manual steps, audit evidence pack ready for the next review.

Before and after

Before

You currently maintain separate SSMS scripts scattered across personal drives, with no single source of truth for load logic. Evidence lives in email threads, and each audit request forces you to rebuild logs from scratch. When a schema change occurs, the team loses hours debugging, and leadership receives vague status updates.

After

After the course, you have a centralized, version-controlled repository of extraction scripts, an automated scheduler that runs daily, and a ready-to-share audit evidence pack. A live data quality dashboard provides real-time metrics, and you can confidently present a clean, repeatable pipeline to senior leadership each quarter.

What happens if you do not address this

If you ignore this, the next quarterly audit will arrive with incomplete evidence, forcing you to spend days recreating logs. The data-ops team will miss SLA commitments, and leadership will question your ability to modernize the data platform. Your promotion prospects may stall as the organization looks for a more disciplined owner.

Who it is for

A data engineer who spends most of the day writing T-SQL in SSMS, orchestrating nightly extracts, and building quick-turn notebooks for lake loads. They operate under tight release cycles, need to prove data quality to auditors, and are frustrated by the lack of reusable, documented processes.

Who this is NOT for. This is not for someone who needs a basic introduction to SQL Server fundamentals.

How it arrives

Within 24 hours of purchase your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it. The playbook is hand-built around your specific situation, not LLM-generated boilerplate.

Time investment. 6 hours of focused work spread over a week, saving an estimated 40-60 hours of internal scaffolding effort.

Why $199 is the right number

A half-day consultant would charge $2K-$5K to map your legacy loads, a generic compliance course costs $800-$2K, and building the same framework yourself takes 60+ hours. At $199 you get a ready-to-run system and a custom playbook, delivering immediate ROI.

FAQ

Do I need prior experience with data lake platforms?

The course assumes basic familiarity with cloud storage but teaches the integration steps from scratch.

Will the templates work with my on-prem SQL Server version?

All scripts are written for SQL Server 2016 and newer, which covers the majority of enterprise deployments.

Can I apply the material to existing pipelines without rewriting everything?

Yes, the modules focus on layering automation and version control onto your current processes.

What support is available after I finish the course?

You get access to a community forum and a 30-day Q&A window for implementation questions.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.