Automated Data Integrity Checks After Migration
Data migration is always stressful. You've moved thousands of records, but are you sure nothing was lost? Relations intact? URLs not broken? Without automated checks, you're guessing. Manual spot-checking is a lottery: you verify 5% of records, but errors hide in the remaining 95%. Especially with nested comments, meta fields, or SEO tags — a single miss can cost traffic or functionality.
Recently we worked with a client: 10,000 posts, 50,000 comments migrating from WordPress to Laravel. Manual spot-checking (3 days, two engineers) found 30 broken links. Automated checks — in 1 day — uncovered 200 lost comments, 5% duplicate URLs, and 150 pages missing SEO titles. The difference is clear: automation is 3× faster and 20× more accurate.
We are a team of engineers with 5+ years of experience in complex migrations. We've developed a script set that gives a full integrity picture in 1–2 days. The scripts adapt to your CMS and database structure — whether it's PostgreSQL, MySQL, or MongoDB.
Why Automated Integrity Checks Are Essential
Manual spot-checking is inefficient. You risk losing 2–5% of records (especially nested comments or meta fields), breaking parent-child relations, creating duplicate URLs, and leaving SEO tags missing — all of which hurt ranking. Automation eliminates these risks. Compare the two approaches:
| Characteristic | Manual Check | Automated (Our Approach) |
|---|---|---|
| Coverage | Random sample | 100% of records |
| Time | 3–5 days | 1–2 days |
| Error miss rate | ~30% | <1% |
| Documentation | None | Detailed HTML/JSON report |
| Repeatability | One-off | Multiple runs (CI/CD possible) |
Typical consequences of missed errors:
| Error Type | Consequence |
|---|---|
| Lost records | Content loss, functionality degradation |
| Broken relations | 404 errors, non-working comments |
| Duplicate URLs | Search engine penalties, traffic loss |
| Missing SEO tags | Ranking drops |
What We Check: Detailed Checklist
Our engineers have implemented scripts for five key checks. Each comes with concrete metrics.
How Checksum Verification Works
We compare aggregated MD5 over critical fields. It detects even minor changes in data. Source: Wikipedia, MD5.
def checksum_check(source_db, target_db): """Compare checksums over critical fields""" # PostgreSQL source_hash = source_db.query_one(""" SELECT md5(string_agg( md5(id::text || coalesce(email,'') || coalesce(slug,'')), ',' ORDER BY id )) as hash FROM articles WHERE status = 'published' """) target_hash = target_db.query_one(""" SELECT md5(string_agg( md5(legacy_id || coalesce(email,'') || coalesce(slug,'')), ',' ORDER BY CAST(legacy_id AS INTEGER) )) as hash FROM articles WHERE status = 'published' """) return source_hash == target_hash Record Count Verification
We compare record counts per content type, considering status filters (published/draft). Example code:
class MigrationValidator: def __init__(self, source_db, target_db): self.source = source_db self.target = target_db self.results = [] def check_counts(self): tables = [ ('posts', 'articles', "status='publish'", "status='published'"), ('users', 'users', None, None), ('comments', 'comments', "approved=1", "status='approved'"), ('categories', 'categories', None, None), ] for src_table, tgt_table, src_where, tgt_where in tables: src_count = self.source.count(src_table, src_where) tgt_count = self.target.count(tgt_table, tgt_where) status = 'OK' if src_count == tgt_count else 'MISMATCH' self.results.append({ 'check': f'count_{src_table}', 'status': status, 'source': src_count, 'target': tgt_count, 'diff': tgt_count - src_count }) Referential Integrity Check
We look for orphan records: articles without an author, comments without a parent article or parent comment.
def check_referential_integrity(target_db): issues = [] # Articles without author orphaned_posts = target_db.query(""" SELECT a.id, a.title FROM articles a LEFT JOIN users u ON a.author_id = u.id WHERE a.author_id IS NOT NULL AND u.id IS NULL """) if orphaned_posts: issues.append(f"Articles without valid author: {len(orphaned_posts)}") # Comments pointing to non-existent posts orphaned_comments = target_db.query(""" SELECT c.id FROM comments c LEFT JOIN articles a ON c.post_id = a.id WHERE a.id IS NULL """) if orphaned_comments: issues.append(f"Orphaned comments: {len(orphaned_comments)}") # Child comments without parent broken_threads = target_db.query(""" SELECT c.id FROM comments c LEFT JOIN comments p ON c.parent_id = p.id WHERE c.parent_id IS NOT NULL AND p.id IS NULL """) if broken_threads: issues.append(f"Comments with missing parent: {len(broken_threads)}") return issues URL Availability Check
We asynchronously check every published URL for HTTP 200, 404, 500, and redirect chains.
import asyncio import aiohttp async def check_urls(urls, base_url, concurrency=20): errors = {'404': [], '500': [], 'redirect_chain': []} semaphore = asyncio.Semaphore(concurrency) async def check_one(session, path): async with semaphore: url = f"{base_url}{path}" try: async with session.get(url, allow_redirects=True) as resp: if resp.status == 404: errors['404'].append(path) elif resp.status >= 500: errors['500'].append(path) elif len(resp.history) > 2: errors['redirect_chain'].append(f"{path} ({len(resp.history)} redirects)") except Exception as e: errors['500'].append(f"{path} (error: {e})") async with aiohttp.ClientSession() as session: tasks = [check_one(session, url) for url in urls] await asyncio.gather(*tasks) return errors # Run urls_to_check = get_all_published_urls(target_db) results = asyncio.run(check_urls(urls_to_check, 'https://new-site.com')) SEO Metadata Check
We look for pages missing title/description, duplicate titles, and missing canonical tags.
def check_seo_completeness(target_db): issues = [] # Pages without title no_title = target_db.query(""" SELECT slug FROM articles WHERE (seo_title IS NULL OR seo_title = '') AND status = 'published' """) if no_title: issues.append(f"Pages without SEO title: {len(no_title)}") # Pages without meta description no_desc = target_db.query(""" SELECT slug FROM articles WHERE (seo_description IS NULL OR seo_description = '') AND status = 'published' """) if no_desc: issues.append(f"Pages without meta description: {len(no_desc)}") # Duplicate titles dup_titles = target_db.query(""" SELECT seo_title, COUNT(*) as count FROM articles WHERE status = 'published' GROUP BY seo_title HAVING COUNT(*) > 1 """) if dup_titles: issues.append(f"Duplicate SEO titles: {len(dup_titles)} groups") return issues Example CI/CD Integration
We package the checks into a Docker image. Your pipeline runs the container, passing environment variables for database connections. Results are published as artifacts in JUnit XML format.
Our Process
- Schema Analysis — Study source and target database structures, identify critical tables and fields.
- Script Adaptation — Tailor checks to your specific stack (PostgreSQL/MySQL, CMS).
- Run & Collect Results — Execute scripts, generate report in HTML/JSON/JUnit.
- Analysis & Recommendations — Break down each failed test, propose remediation plan.
- CI/CD Deployment (optional) — Package checks into Docker container, connect to pipeline.
What's Included
- Check scripts (Python, configurable for your project).
- Detailed report with results table and list of problematic records.
- Remediation recommendations for each error.
- Documentation on running and interpreting results.
- Training for one client engineer.
- Guarantee: If after fixes a discrepancy is found, we refine the script at no extra cost.
Estimated Timeline
Script development and final report: 1–2 working days. Complexity depends on number of tables and data volume. Cost is calculated individually — contact us for an accurate estimate.
Why Choose Us
- 5+ years of migrations: from WordPress to custom Laravel solutions.
- 200+ data transfer projects.
- Certified engineers in PostgreSQL and Docker.
- Result guarantee: all checks run on a test environment before main execution.
Request a consultation on post-migration data integrity — we'll assess the scope and recommend the optimal set of checks. Contact us to get a detailed work plan and cost estimate.