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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: 28 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

• Layer: choose between the existing layers on your catalog. This is where you will find your new extracted tables as the extraction runs successfully.
• Folder: a folder can be created inside the selected layer to group all tables being created from this new data source.
• Table name: we suggest a name, but feel free to customize it. You have the option to add a prefix to all tables at once 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

• Configure Delta Log Retention and determine for how long we should store old states of this table as it gets updated. Read more about this resource here.
• Determine when to execute an Additional Full Sync. This will complement the incremental data extractions, ensuring that your data is completely synchronized with your source every once in a while.

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5. Check your new source

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Streams and Fields

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API Limitations

• Rate Limits: The API has standard rate limits. The connector handles pagination automatically and respects these limits.
• Nested Data Size: When fetching comments with nested replies, Facebook may return a “too much data” error if the result set is too large. The Post Comments stream uses a reduced page size (20 records per request) to mitigate this issue.
• Unsupported Metrics: Facebook may return errors if certain insight metrics are invalid, deprecated, or not supported for a specific page. The connector automatically probes and drops these unsupported metrics to ensure the extraction succeeds for the remaining valid data.
• Permissions: The user authorizing the connection must have admin or editor access to the Page to successfully retrieve all data streams.

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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.
• The Page Media Views Breakdown and Post Media Views Breakdown streams are Meta’s modern replacements for older unique reach and unique video view metrics. You can extract and parse the JSON string field breakdown to segment views by ads (paid) vs followers (organic).
• The Reviews stream does not have incremental sync capability, so it performs a full table sync on each extraction.
• Unsupported metrics: If specific insights metrics are unsupported or deprecated for your page, they will be automatically dropped from the extraction. The corresponding fields in your table will appear as null.

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API Limits & Performance

• Facebook’s Graph API has rate limits. The connector handles pagination automatically and respects these limits.
• The Post Comments stream uses a reduced page size (20 records per request) to avoid Facebook’s “too much data” error when fetching nested comment structures.
• 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.

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Skills for agents