Nekt is a modern data platform that provides comprehensive data engineering solutions for companies. It enables organizations to extract, transform, and load data from various external sources through custom connectors, manage data pipelines, and create powerful analytics workflows.

Configuring Nekt as a Source

In the Sources tab, click on the “Add source” button located on the top right of your screen. Then, select the Nekt option from the list of connectors. Click Next and you’ll be prompted to add your access.

1. Add account access

You’ll need the following credentials from your Nekt account:

API Key: Your personal API key for accessing the Nekt API. You can generate one in your Nekt workspace settings under the “API Keys” section. Make sure the key has the necessary permissions to read the data you want to sync.
Start Date: The earliest record date to sync.

Once you have the required credentials, add the account access and click Next.

2. Select streams

Choose which data streams you want to sync - you can select all streams or pick specific ones that matter most to you.

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

Select the streams and click Next.

3. Configure data streams

Customize how you want your data to appear in your catalog. Select a name for each table (which will contain the fetched data) and the type of sync.

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.

Once you are done configuring, click Next.

4. Configure data source

Describe your data source for easy identification within your organization, not exceeding 140 characters. To define your Trigger, consider how often you want data to be extracted from this source. This decision usually depends on how frequently you need the new table data updated (every day, once a week, or only at specific times). Optionally, you can determine when to execute a full sync. This will complement the incremental data extractions, ensuring that your data is completely synchronized with your source every once in a while. Once you are ready, click Next to finalize the setup.

5. Check your new source

You can view your new source on the Sources page. If needed, manually trigger the source extraction by clicking on the arrow button. Once executed, your data will appear in your Catalog.

For you to be able to see it on your Catalog, you need at least one successful source run.

Streams and Fields

Below you’ll find all available data streams from Nekt and their corresponding fields:

Sources

Runs

Destinations

Transformations

Activities

Source Triggers

Destination Triggers

Data Model

The following diagram illustrates the relationships between the core data streams in Nekt. The arrows indicate the join keys that link the different entities, providing a clear overview of the data platform structure.

Use Cases for Data Analysis

Here are some valuable business intelligence use cases when consolidating Nekt platform data, along with ready-to-use SQL queries that you can run on Explorer.

1. Daily Credits Consumption per Pipeline

Monitor credit consumption for each pipeline in a daily basis. Business Value:

Track pipeline credit consumption
Analyze resource utilization and make future projections
Identify pipelines that can be optmized

SQL code

SELECT
	coalesce(s.slug, t.slug, d.slug) AS identifier,
	DATE(r.ended_at) AS date,
	count(r.id) AS total_runs,
	sum(r.duration_seconds) / 60 AS credits_used
FROM
	"nekt_raw"."nekt_runs" r
	LEFT JOIN "nekt_raw"."nekt_sources" s ON r.source.id = s.id
	LEFT JOIN "nekt_raw"."nekt_destinations" d ON r.destination.id = d.id
	LEFT JOIN "nekt_raw"."nekt_transformations" t ON r.transformation.id = t.id
WHERE
	r.credit_charge_status = 'charged'
GROUP BY
	coalesce(s.slug, t.slug, d.slug),
	DATE(r.ended_at)
ORDER BY
	DATE(r.ended_at) DESC

2. Data Source Performance and Reliability Analysis

Monitor data pipeline performance, execution times, and failure rates across your data sources. Business Value:

Track pipeline execution trends and identify performance bottlenecks
Monitor data pipeline reliability and uptime
Analyze resource utilization and optimize infrastructure costs
Identify sources that need attention

SQL code

WITH
	run_metrics AS (
		SELECT
			r.source.slug AS source_name,
			r.status,
			r.full_sync,
			r.duration_seconds,
			r.started_at,
			r.ended_at,
			r.credit_charge_status,
			DATE_TRUNC ('day', r.started_at) AS run_date,
			CASE
				WHEN r.status = 'success' THEN 1
				ELSE 0
			END AS success_flag,
			CASE
				WHEN r.status = 'failed' THEN 1
				ELSE 0
			END AS failure_flag
		FROM
			nekt_raw.nekt_runs r
		WHERE
			r.started_at >= CURRENT_DATE - INTERVAL '30' DAY
			AND r.started_at IS NOT NULL
	),
	daily_summary AS (
		SELECT
			run_date,
			source_name,
			COUNT(*) AS total_runs,
			SUM(success_flag) AS successful_runs,
			SUM(failure_flag) AS failed_runs,
			AVG(duration_seconds) AS avg_duration_seconds,
			APPROX_PERCENTILE (duration_seconds, 0.5) AS median_duration_seconds,
			MAX(duration_seconds) AS max_duration_seconds,
			COUNT(
				CASE
					WHEN full_sync = TRUE THEN 1
				END
			) AS full_sync_runs
		FROM
			run_metrics
		WHERE
			source_name IS NOT NULL
		GROUP BY
			run_date,
			source_name
	),
	source_reliability AS (
		SELECT
			source_name,
			SUM(total_runs) AS total_runs_30d,
			SUM(successful_runs) AS total_successful_runs,
			SUM(failed_runs) AS total_failed_runs,
			ROUND(
				CAST(SUM(successful_runs) AS DOUBLE) / NULLIF(SUM(total_runs), 0) * 100,
				2
			) AS success_rate_percentage,
			ROUND(AVG(avg_duration_seconds) / 60.0, 2) AS avg_duration_minutes,
			ROUND(AVG(median_duration_seconds) / 60.0, 2) AS median_duration_minutes,
			COUNT(DISTINCT run_date) AS active_days
		FROM
			daily_summary
		GROUP BY
			source_name
	)
SELECT
	source_name,
	total_runs_30d,
	total_successful_runs,
	total_failed_runs,
	success_rate_percentage,
	avg_duration_minutes,
	median_duration_minutes,
	active_days,
	CASE
		WHEN success_rate_percentage >= 95 THEN 'Excellent'
		WHEN success_rate_percentage >= 90 THEN 'Good'
		WHEN success_rate_percentage >= 80 THEN 'Needs Attention'
		ELSE 'Critical'
	END AS reliability_status,
	CASE
		WHEN avg_duration_minutes <= 5 THEN 'Fast'
		WHEN avg_duration_minutes <= 15 THEN 'Normal'
		WHEN avg_duration_minutes <= 60 THEN 'Slow'
		ELSE 'Very Slow'
	END AS performance_status
FROM
	source_reliability
WHERE
	total_runs_30d > 0
ORDER BY
	total_runs_30d DESC,
	success_rate_percentage DESC

3. User Activity and Platform Usage Analysis

Track user engagement, platform adoption, and operational activities across your Nekt workspace. Business Value:

Monitor platform adoption and user engagement
Identify power users and training needs
Track operational patterns and peak usage times
Optimize team collaboration and workflow efficiency

SQL code

WITH
  user_activities AS (
    SELECT
      a.created_by.email AS user_email,
      a.created_by.first_name || ' ' || a.created_by.last_name AS user_name,
      a.created_by.role AS user_role,
      a.created_by.functional_area AS functional_area,
      a.activity_type,
      a.created_at,
      a.created_by_system,
      DATE_TRUNC('day', a.created_at) AS activity_date,
      DATE_TRUNC('hour', a.created_at) AS activity_hour,
      CASE
        WHEN a.source IS NOT NULL THEN 'source'
        WHEN a.destination IS NOT NULL THEN 'destination'
        WHEN a.transformation IS NOT NULL THEN 'transformation'
        WHEN a.visualization IS NOT NULL THEN 'visualization'
        WHEN a.run IS NOT NULL THEN 'run'
        ELSE 'other'
      END AS entity_type
    FROM nekt_raw.nekt_activities a
    WHERE a.created_at >= CURRENT_DATE - INTERVAL '30' DAY
      AND a.created_by_system = false
      AND a.created_by.email IS NOT NULL
  ),
  user_summary AS (
    SELECT
      user_email,
      user_name,
      user_role,
      functional_area,
      COUNT(*) AS total_activities,
      COUNT(DISTINCT activity_date) AS active_days,
      COUNT(DISTINCT activity_type) AS unique_activity_types,
      COUNT(DISTINCT entity_type) AS entity_types_used,
      MIN(created_at) AS first_activity,
      MAX(created_at) AS last_activity,
      ARBITRARY(activity_type) AS most_common_activity,
      ARBITRARY(entity_type) AS most_used_entity_type
    FROM user_activities
    GROUP BY user_email, user_name, user_role, functional_area
  ),
  hourly_patterns AS (
    SELECT
      user_email,
      EXTRACT(hour FROM activity_hour) AS hour_of_day,
      COUNT(*) AS activities_count
    FROM user_activities
    GROUP BY user_email, EXTRACT(hour FROM activity_hour)
  ),
  peak_hours AS (
    SELECT
      user_email,
      hour_of_day,
      activities_count,
      ROW_NUMBER() OVER (PARTITION BY user_email ORDER BY activities_count DESC) AS hour_rank
    FROM hourly_patterns
  ),
  team_collaboration AS (
    SELECT
      functional_area,
      COUNT(DISTINCT user_email) AS team_size,
      SUM(total_activities) AS team_total_activities,
      AVG(total_activities) AS avg_activities_per_user,
      AVG(active_days) AS avg_active_days_per_user,
      SUM(CASE WHEN active_days >= 20 THEN 1 ELSE 0 END) AS highly_active_users
    FROM user_summary
    WHERE functional_area IS NOT NULL
    GROUP BY functional_area
  )
SELECT
  us.user_name,
  us.user_email,
  us.user_role,
  us.functional_area,
  us.total_activities,
  us.active_days,
  ROUND(
    CAST(us.active_days AS DOUBLE) / 30.0 * 100,
    1
  ) AS engagement_percentage,
  us.unique_activity_types,
  us.entity_types_used,
  us.most_common_activity,
  us.most_used_entity_type,
  ph.hour_of_day AS peak_activity_hour,
  us.first_activity,
  us.last_activity,
  tc.team_size,
  ROUND(tc.avg_activities_per_user, 1) AS team_avg_activities,
  CASE
    WHEN us.active_days >= 25 THEN 'Highly Active'
    WHEN us.active_days >= 15 THEN 'Active'
    WHEN us.active_days >= 5 THEN 'Moderate'
    ELSE 'Low Activity'
  END AS activity_level,
  CASE
    WHEN us.entity_types_used >= 4 THEN 'Power User'
    WHEN us.entity_types_used >= 2 THEN 'Regular User'
    ELSE 'Basic User'
  END AS user_type
FROM user_summary us
LEFT JOIN peak_hours ph ON us.user_email = ph.user_email AND ph.hour_rank = 1
LEFT JOIN team_collaboration tc ON us.functional_area = tc.functional_area
ORDER BY 
  us.total_activities DESC,
  us.active_days DESC

Get started

Using Nekt

Understand

Use cases

FAQ

Nekt as a data source

Configuring Nekt as a Source

1. Add account access

2. Select streams

3. Configure data streams

4. Configure data source

5. Check your new source

Streams and Fields

Data Model

Use Cases for Data Analysis

1. Daily Credits Consumption per Pipeline

2. Data Source Performance and Reliability Analysis

3. User Activity and Platform Usage Analysis

Get started

Using Nekt

Understand

Use cases

FAQ

​Configuring Nekt as a Source

​1. Add account access

​2. Select streams

​3. Configure data streams

​4. Configure data source

​5. Check your new source

​Streams and Fields

​Data Model

​Use Cases for Data Analysis

​1. Daily Credits Consumption per Pipeline

​2. Data Source Performance and Reliability Analysis

​3. User Activity and Platform Usage Analysis

Configuring Nekt as a Source

1. Add account access

2. Select streams

3. Configure data streams

4. Configure data source

5. Check your new source

Streams and Fields

Data Model

Use Cases for Data Analysis

1. Daily Credits Consumption per Pipeline

2. Data Source Performance and Reliability Analysis

3. User Activity and Platform Usage Analysis