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Configuring Facebook Pages as a Source

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1. Add account access

• Page ID: The Facebook Page ID to sync data from. This field is automatically populated with the Page ID you select in the dropdown menu.
• Start Date: The earliest date from which records will be synced.
• Lookback Window: (Default: 30 days) The number of days to look back for incremental sync. Since post engagement metrics can change over time, this ensures recent data is re-fetched to capture updates.

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2. Select streams

Tip: The stream can be found more easily by typing its name.

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3. Configure data streams

• Table name: we suggest a name, but feel free to customize it. You have the option to add a prefix and make this process faster!
• Sync Type: you can choose between INCREMENTAL and FULL_TABLE.
  • Incremental: every time the extraction happens, we’ll get only the new data - which is good if, for example, you want to keep every record ever fetched.
  • Full table: every time the extraction happens, we’ll get the current state of the data - which is good if, for example, you don’t want to have deleted data in your catalog.

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4. Configure data source

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5. Check your new source

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Streams and Fields

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Stream to Endpoint Mapping

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Data Model

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Use Cases for Data Analysis

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Page Performance Analysis

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1. Daily Page Engagement Overview

• Monitor daily engagement trends
• Identify high-performing days for content strategy
• Track the balance between organic and paid reach

SELECT
    DATE(end_time) AS date,
    page_views_total,
    page_post_engagements,
    page_posts_impressions_organic,
    page_posts_impressions_paid,
    page_actions_post_reactions_total,
    page_video_views
FROM
    nekt_raw.facebook_pages_daily_page_insights
WHERE
    end_time >= CURRENT_DATE - INTERVAL '30' DAY
ORDER BY
    date DESC

SELECT
    DATE(end_time) AS date,
    page_views_total,
    page_post_engagements,
    page_posts_impressions_organic,
    page_posts_impressions_paid,
    page_actions_post_reactions_total,
    page_video_views
FROM
    `nekt_raw.facebook_pages_daily_page_insights`
WHERE
    end_time >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
ORDER BY
    date DESC

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2. Post Performance Analysis

• Identify top-performing content
• Understand reaction distribution across posts
• Optimize content strategy based on engagement data

SELECT
    p.id AS post_id,
    SUBSTRING(p.message, 1, 100) AS post_preview,
    p.created_time,
    p.shares.count AS share_count,
    pi.post_clicks,
    pi.post_reactions_like_total,
    pi.post_reactions_love_total,
    pi.post_reactions_wow_total,
    (COALESCE(pi.post_reactions_like_total, 0) + 
     COALESCE(pi.post_reactions_love_total, 0) + 
     COALESCE(pi.post_reactions_wow_total, 0) +
     COALESCE(pi.post_reactions_haha_total, 0) +
     COALESCE(pi.post_reactions_sorry_total, 0) +
     COALESCE(pi.post_reactions_anger_total, 0)) AS total_reactions
FROM
    nekt_raw.facebook_pages_posts p
    LEFT JOIN nekt_raw.facebook_pages_post_insights pi ON p.id = pi.post_id
WHERE
    p.created_time >= CURRENT_DATE - INTERVAL '30' DAY
ORDER BY
    total_reactions DESC
LIMIT 20

SELECT
    p.id AS post_id,
    SUBSTR(p.message, 1, 100) AS post_preview,
    p.created_time,
    p.shares.count AS share_count,
    pi.post_clicks,
    pi.post_reactions_like_total,
    pi.post_reactions_love_total,
    pi.post_reactions_wow_total,
    (COALESCE(pi.post_reactions_like_total, 0) + 
     COALESCE(pi.post_reactions_love_total, 0) + 
     COALESCE(pi.post_reactions_wow_total, 0) +
     COALESCE(pi.post_reactions_haha_total, 0) +
     COALESCE(pi.post_reactions_sorry_total, 0) +
     COALESCE(pi.post_reactions_anger_total, 0)) AS total_reactions
FROM
    `nekt_raw.facebook_pages_posts` p
    LEFT JOIN `nekt_raw.facebook_pages_post_insights` pi ON p.id = pi.post_id
WHERE
    p.created_time >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
ORDER BY
    total_reactions DESC
LIMIT 20

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3. Comment Engagement Analysis

• Monitor community engagement
• Identify posts that spark conversation
• Track response patterns and community health

WITH comment_stats AS (
    SELECT
        post_id,
        COUNT(*) AS total_comments,
        SUM(like_count) AS total_comment_likes,
        SUM(comment_count) AS total_replies,
        COUNT(CASE WHEN is_hidden = false THEN 1 END) AS visible_comments
    FROM
        nekt_raw.facebook_pages_post_comments
    WHERE
        post_created_time >= CURRENT_DATE - INTERVAL '30' DAY
    GROUP BY
        post_id
)
SELECT
    p.id AS post_id,
    SUBSTRING(p.message, 1, 80) AS post_preview,
    p.created_time,
    cs.total_comments,
    cs.total_comment_likes,
    cs.total_replies,
    cs.visible_comments
FROM
    nekt_raw.facebook_pages_posts p
    JOIN comment_stats cs ON p.id = cs.post_id
ORDER BY
    cs.total_comments DESC
LIMIT 15

WITH comment_stats AS (
    SELECT
        post_id,
        COUNT(*) AS total_comments,
        SUM(like_count) AS total_comment_likes,
        SUM(comment_count) AS total_replies,
        COUNTIF(is_hidden = false) AS visible_comments
    FROM
        `nekt_raw.facebook_pages_post_comments`
    WHERE
        post_created_time >= DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY)
    GROUP BY
        post_id
)
SELECT
    p.id AS post_id,
    SUBSTR(p.message, 1, 80) AS post_preview,
    p.created_time,
    cs.total_comments,
    cs.total_comment_likes,
    cs.total_replies,
    cs.visible_comments
FROM
    `nekt_raw.facebook_pages_posts` p
    JOIN comment_stats cs ON p.id = cs.post_id
ORDER BY
    cs.total_comments DESC
LIMIT 15

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4. Review Sentiment Analysis

• Track overall customer satisfaction
• Monitor review volume and sentiment over time
• Identify areas for improvement

SELECT
    DATE_TRUNC('week', created_time) AS week,
    COUNT(*) AS total_reviews,
    COUNT(CASE WHEN recommendation_type = 'positive' THEN 1 END) AS positive_reviews,
    COUNT(CASE WHEN recommendation_type = 'negative' THEN 1 END) AS negative_reviews,
    ROUND(
        COUNT(CASE WHEN recommendation_type = 'positive' THEN 1 END) * 100.0 / COUNT(*),
        2
    ) AS positive_percentage
FROM
    nekt_raw.facebook_pages_reviews
WHERE
    created_time >= CURRENT_DATE - INTERVAL '90' DAY
GROUP BY
    DATE_TRUNC('week', created_time)
ORDER BY
    week DESC

SELECT
    DATE_TRUNC(created_time, WEEK) AS week,
    COUNT(*) AS total_reviews,
    COUNTIF(recommendation_type = 'positive') AS positive_reviews,
    COUNTIF(recommendation_type = 'negative') AS negative_reviews,
    ROUND(
        COUNTIF(recommendation_type = 'positive') * 100.0 / COUNT(*),
        2
    ) AS positive_percentage
FROM
    `nekt_raw.facebook_pages_reviews`
WHERE
    created_time >= DATE_SUB(CURRENT_DATE(), INTERVAL 90 DAY)
GROUP BY
    DATE_TRUNC(created_time, WEEK)
ORDER BY
    week DESC

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Implementation Notes

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Data Quality Considerations

• The lookback window is important for post engagement metrics, as reactions and comments can be added to posts days or weeks after publication.
• Daily Page Insights data is fetched in 3-month batches to optimize API usage and avoid rate limits.
• Post Insights metrics provide lifetime values that accumulate over time, while Daily Page Insights provide daily snapshots.

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API Limits & Performance

• Facebook’s Graph API has rate limits. The connector handles pagination automatically and respects these limits.
• For pages with high post volumes, consider selecting only the streams you need to optimize extraction times.
• The page_id field is included in all streams to enable easy joins and filtering when you have multiple pages.