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Data Export Guide

Comprehensive Methods for Extracting and Organizing Content Audit Data

Why Data Export Matters

Effective data export is the foundation of any successful content audit. It enables you to analyze content at scale, identify patterns, and make data-driven decisions. Whether you're working with a CMS, analytics platform, or custom database, proper export techniques ensure you capture all necessary information for comprehensive analysis.

Key Fact: Organizations that effectively export and analyze their content data are 3x more likely to identify improvement opportunities that drive measurable ROI.

Data Export Framework

1

Define Requirements

Identify needed data points

  • Content metadata
  • Performance metrics
  • User engagement data
  • Technical attributes
2

Choose Export Method

Select appropriate technique

  • Direct database queries
  • API extraction
  • Plugin/extension tools
  • Manual export options
3

Extract Data

Execute export process

  • Run export scripts
  • Validate completeness
  • Handle errors
  • Document process
4

Transform & Clean

Prepare for analysis

  • Standardize formats
  • Remove duplicates
  • Handle missing data
  • Enrich with metadata
5

Structure & Store

Organize for access

  • Create data schema
  • Set up storage system
  • Implement versioning
  • Enable collaboration
6

Analyze & Report

Generate insights

  • Create pivot tables
  • Build visualizations
  • Identify patterns
  • Share findings

CMS Data Export Methods

WordPress Export

  • Native Export: Tools → Export (XML format)
  • WP All Export: Advanced CSV/XML export with custom fields
  • Database Query: Direct MySQL queries for custom extraction
  • REST API: Programmatic access to all content types
Best For: Complete content inventory with metadata

🟦 Drupal Export

  • Views Data Export: Module for CSV/XML/JSON export
  • Migrate API: Structured content extraction
  • Drush Commands: CLI-based bulk export
  • Database Dump: Complete SQL export with filtering
Best For: Complex content relationships and taxonomies

🟪 Contentful/Headless CMS

  • Content Delivery API: RESTful API for published content
  • Content Management API: Full CRUD operations
  • GraphQL API: Flexible query-based extraction
  • Export Tools: CLI tools for bulk export
Best For: Structured content with rich metadata

🟨 SharePoint Export

  • Export to Excel: List and library export functionality
  • PowerShell Scripts: Automated bulk extraction
  • Migration Tools: SharePoint Migration Tool (SPMT)
  • Graph API: Microsoft Graph for programmatic access
Best For: Enterprise document management systems

Analytics Data Export

Google Analytics 4 (GA4)

  • BigQuery Export: Raw event-level data streaming
  • Data API: Programmatic access to aggregated data
  • Report Export: PDF/CSV from interface
  • Google Sheets Add-on: Direct integration
Setup Command: Admin → BigQuery Linking → Configure

Search Console

  • Performance Export: CSV download (1000 row limit)
  • API Access: Full data via Search Console API
  • Bulk Export: Up to 16 months of data
  • BigQuery Integration: Via third-party connectors
API Limit: 50,000 rows per request

Adobe Analytics

  • Data Warehouse: Custom report builder
  • Report Builder: Excel plugin for data extraction
  • Analytics API 2.0: RESTful API access
  • Data Feeds: Raw clickstream data export
Format Options: CSV, TSV, JSON, XML

Export File Formats

CSV (Comma-Separated Values)

  • Universal compatibility
  • Small file size
  • Excel/Sheets compatible
  • No data types
  • Limited to flat structure

JSON (JavaScript Object Notation)

  • Preserves data types
  • Nested structures
  • API-friendly
  • Larger file size
  • Requires parsing

XML (Extensible Markup Language)

  • Self-documenting
  • Complex relationships
  • Schema validation
  • Verbose format
  • Processing overhead

XLSX (Excel Workbook)

  • Multiple sheets
  • Formatting preserved
  • Formulas included
  • Size limitations
  • Proprietary format

Automated Export Tools

Web Scraping Tools

  • Screaming Frog: SEO spider for content crawling
  • Octoparse: Visual web scraping platform
  • ParseHub: Point-and-click data extraction
  • Import.io: Web data integration platform

API Integration Tools

  • Zapier: No-code automation platform
  • Make (Integromat): Visual workflow builder
  • Postman: API testing and automation
  • Airbyte: Open-source data integration

Database Tools

  • phpMyAdmin: MySQL/MariaDB management
  • pgAdmin: PostgreSQL administration
  • DBeaver: Universal database tool
  • TablePlus: Modern database management

ETL Platforms

  • Talend: Enterprise data integration
  • Apache NiFi: Data flow automation
  • Pentaho: Business analytics platform
  • FiveTran: Automated data pipelines

BigQuery Export Setup (GA4)

Setup Process

  1. Create GCP Project: Set up Google Cloud Platform account
  2. Enable BigQuery API: Activate in GCP Console
  3. Link GA4 Property: Admin → BigQuery Linking
  4. Configure Export: Choose streaming or daily export
  5. Set Permissions: Grant necessary IAM roles
  6. Verify Data Flow: Check tables in BigQuery console

BigQuery Schema

  • Events Table: All user interactions and custom events
  • Users Table: User-level aggregated data
  • Items Table: E-commerce product data
  • Pseudo Tables: Intraday streaming data
Cost: ~$5/million events for storage

Sample Queries

-- Page views by title
SELECT
 (SELECT value.string_value FROM UNNEST(event_params) 
 WHERE key = 'page_title') AS page_title,
 COUNT(*) as views
FROM `project.dataset.events_*`
WHERE event_name = 'page_view'
 AND _TABLE_SUFFIX BETWEEN '20240101' AND '20240131'
GROUP BY page_title
ORDER BY views DESC

Data Transformation Best Practices

Data Cleaning

  • Remove HTML tags from text
  • Standardize date formats
  • Normalize URLs (trailing slashes)
  • Handle encoding issues

Data Enrichment

  • Add content categories
  • Calculate word counts
  • Extract meta information
  • Append performance metrics

Data Validation

  • Check for missing values
  • Verify data types
  • Validate against source
  • Test sample records

Data Documentation

  • Create data dictionary
  • Document transformations
  • Note assumptions made
  • Version control changes

Common Export Challenges

Rate Limiting

Problem: API request limits blocking bulk export

Solution: Implement exponential backoff, batch requests, use pagination

Data Size Limits

Problem: Export files too large to handle

Solution: Chunk exports by date range, use streaming, compress files

Format Inconsistencies

Problem: Different systems use different formats

Solution: Create transformation scripts, use ETL tools, standardize schemas

Missing Relationships

Problem: Lost connections between content pieces

Solution: Export relationship tables, maintain foreign keys, document links

Permission Issues

Problem: Insufficient access to export all data

Solution: Request elevated permissions, work with IT, use service accounts

Real-time Sync

Problem: Need up-to-date data continuously

Solution: Set up webhooks, use streaming APIs, implement CDC

Export Automation Scripts

Python Export Example

import pandas as pd
import requests
from datetime import datetime

def export_content_data(api_endpoint, api_key):
 """Export content data from API to CSV"""
 
 headers = {'Authorization': f'Bearer {api_key}'}
 all_data = []
 page = 1
 
 while True:
 response = requests.get(
 f'{api_endpoint}?page={page}',
 headers=headers
 )
 data = response.json()
 
 if not data['results']:
 break
 
 all_data.extend(data['results'])
 page += 1
 
 # Convert to DataFrame
 df = pd.DataFrame(all_data)
 
 # Add export metadata
 df['export_date'] = datetime.now()
 
 # Export to CSV
 filename = f'content_export_{datetime.now():%Y%m%d}.csv'
 df.to_csv(filename, index=False)
 
 return filename

SQL Export Query

-- Export content with metrics
SELECT 
 p.ID,
 p.post_title,
 p.post_date,
 p.post_status,
 p.post_type,
 pm.meta_value as word_count,
 COUNT(c.comment_ID) as comment_count
FROM wp_posts p
LEFT JOIN wp_postmeta pm 
 ON p.ID = pm.post_id 
 AND pm.meta_key = 'word_count'
LEFT JOIN wp_comments c 
 ON p.ID = c.comment_post_ID
WHERE p.post_type IN ('post', 'page')
 AND p.post_status = 'publish'
GROUP BY p.ID
INTO OUTFILE '/tmp/content_export.csv'
FIELDS TERMINATED BY ','
ENCLOSED BY '"'
LINES TERMINATED BY '
';

Data Storage Solutions

Cloud Storage

  • Google Cloud Storage
  • Amazon S3
  • Azure Blob Storage
  • Dropbox Business

Data Warehouses

  • Google BigQuery
  • Amazon Redshift
  • Snowflake
  • Azure Synapse

Databases

  • PostgreSQL
  • MySQL
  • MongoDB
  • Elasticsearch

Collaboration Tools

  • Google Sheets
  • Airtable
  • Notion databases
  • Microsoft 365

Export Checklist

Pre-Export Checklist

  • Define all required data fields
  • Verify access permissions
  • Test export on small sample
  • Estimate data volume and time
  • Prepare storage location
  • Document export parameters

During Export

  • Monitor progress and errors
  • Log all activities
  • Validate data integrity
  • Handle exceptions gracefully
  • Create backup of raw export

Post-Export

  • Verify record counts
  • Check for missing data
  • Validate against source
  • Document any issues
  • Create data dictionary
  • Set up regular updates

Need Help with Data Export?

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