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Incept LineageIQ™
Data Architecture & Integration
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Incept LineageIQ™

Function: Lineage Reporter

Answers the two questions that stall every change: where did this come from, and what breaks if I touch it. It walks relationships in both directions and resolves the true root source behind a derived asset, rather than stopping at the nearest warehouse.

3 tools in production, tracing into Snowflake, Databricks and Microsoft Fabric

  • Databricks
  • Snowflake
  • Microsoft Fabric
  • Informatica
  • DataHub
  • Microsoft Purview
3
tools in production
2
directions traced
[Interactive demo]

Walk the pipeline yourself.

Sample data, in your browser. A faithful simulation of the real flow. Approve or reject at step four and watch the score change.

Incept DataIQ™

analytics.sales.customer_master

Sample data · not a client system

Profiling analytics.sales.customer_master

Eight dimensions across every column, five of them shown here. Nothing is written; this is a read.

Column
Type
Null %
Distinct
Detected pattern
Signal
customer_id
string
0.0%
4,812
^C[0-9]{6}$
clean
customer_name
string
0.2%
4,780
free text
clean
tax_id
string
12.2%
4,201
^[0-9]{2}-[0-9]{7}$
issue
country_code
string
0.0%
47
ISO 3166-1 alpha-2
review
email
string
8.1%
3,944
RFC 5322
issue
created_date
date
0.0%
1,204
ISO 8601
clean
status
string
0.0%
4
enum, 4 values
clean
credit_limit
decimal
31.7%
892
numeric
review

4,812 rows · 8 columns · 2 columns flagged · 2 for review

Business metadata, drafted

Table and column descriptions written from the profile and the column context. Every one is a draft until a steward accepts it.

Object
Drafted description
customer_master
Master record for every business customer the organization sells to. One row per customer, keyed on customer_id. Sourced from the order system and enriched with credit attributes.
customer_id
System-generated unique identifier for a customer. Format C followed by six digits. Primary key.
tax_id
Government-issued tax identification number, formatted NN-NNNNNNN. Required for any customer invoiced in the current fiscal year.
country_code
ISO 3166-1 alpha-2 country code for the customer’s registered address.
credit_limit
Approved credit ceiling in reporting currency. Null where no credit assessment has been completed.

1 table + 4 columns described · awaiting steward

Rules recommended, with the evidence

Seven rules proposed from the profile. Each carries the proof that produced it, so approval is a judgment call rather than a leap of faith.

customer_id, Must not be null

Completeness

SELECT COUNT(*) AS failed_records
FROM analytics.sales.customer_master
WHERE customer_id IS NULL;
Evidence

0.0% null across 4,812 rows. Already clean, worth enforcing as a hard constraint before it drifts.

customer_id, Must be unique

Uniqueness

SELECT customer_id, COUNT(*) AS occurrences
FROM analytics.sales.customer_master
GROUP BY customer_id
HAVING COUNT(*) > 1;
Evidence

4,812 distinct values across 4,812 rows. Uniqueness holds today; the rule locks it.

tax_id, Must match the tax ID format

Validity

SELECT COUNT(*) AS failed_records
FROM analytics.sales.customer_master
WHERE tax_id IS NOT NULL
AND tax_id NOT RLIKE '^[0-9]{2}-[0-9]{7}$';
Evidence

97.2% of non-null values match the pattern. 118 do not, mostly nine-digit strings missing the hyphen.

tax_id, Null rate must stay under 5%

Completeness

SELECT COUNT(*) AS failed_records
FROM analytics.sales.customer_master
WHERE tax_id IS NULL;
Evidence

12.2% null against a 5% domain threshold. 587 rows. This is the largest single rule failure in the table.

email, Must be a parseable email address

Validity

SELECT COUNT(*) AS failed_records
FROM analytics.sales.customer_master
WHERE email IS NOT NULL
AND email NOT RLIKE '^[^@\\s]+@[^@\\s]+\\.[^@\\s]+$';
Evidence

4,359 of 4,422 non-null values parse. 63 do not: trailing semicolons and two addresses in one field.

status, Must be one of the accepted values

Validity

SELECT COUNT(*) AS failed_records
FROM analytics.sales.customer_master
WHERE status NOT IN ('ACTIVE','INACTIVE','PENDING','BLOCKED');
Evidence

Exactly 4 distinct values observed, all within the expected set. Cardinality of 4 on 4,812 rows reads as a controlled vocabulary.

country_code, Must exist in the country reference set

Consistency

SELECT COUNT(*) AS failed_records
FROM analytics.sales.customer_master cm
LEFT JOIN ref.country c
ON cm.country_code = c.alpha_2
WHERE c.alpha_2 IS NULL;
Evidence

47 distinct codes. Two (XK, AN) are absent from ref.country, affecting 12 rows. Neither is a current ISO 3166-1 code.

7 rules · 4 quality dimensions

Steward approval

Nothing reaches production without this step. Uncheck anything you would not stand behind. In the real product, the SQL is editable too.

customer_id, Must not be null

Completeness

Evidence

0.0% null across 4,812 rows. Already clean, worth enforcing as a hard constraint before it drifts.

customer_id, Must be unique

Uniqueness

Evidence

4,812 distinct values across 4,812 rows. Uniqueness holds today; the rule locks it.

tax_id, Must match the tax ID format

Validity

Evidence

97.2% of non-null values match the pattern. 118 do not, mostly nine-digit strings missing the hyphen.

tax_id, Null rate must stay under 5%

Completeness

Evidence

12.2% null against a 5% domain threshold. 587 rows. This is the largest single rule failure in the table.

email, Must be a parseable email address

Validity

Evidence

4,359 of 4,422 non-null values parse. 63 do not: trailing semicolons and two addresses in one field.

status, Must be one of the accepted values

Validity

Evidence

Exactly 4 distinct values observed, all within the expected set. Cardinality of 4 on 4,812 rows reads as a controlled vocabulary.

country_code, Must exist in the country reference set

Consistency

Evidence

47 distinct codes. Two (XK, AN) are absent from ref.country, affecting 12 rows. Neither is a current ISO 3166-1 code.

7 of 7 approved, nothing will run

Execute approved rules

Approve at least one rule

Executed on schedule

Approved rules run against the table. Failures produce downloadable rejected records; scores register to the catalog.

97.7%

Overall score

93.9%

Completeness

100.0%

Uniqueness

98.7%

Validity

99.8%

Consistency

Rule
Dimension
Result
Failed rows
customer_id, Must not be null
Completeness
PASS
0
customer_id, Must be unique
Uniqueness
PASS
0
tax_id, Must match the tax ID format
Validity
FAIL
118
tax_id, Null rate must stay under 5%
Completeness
FAIL
587
email, Must be a parseable email address
Validity
FAIL
63
status, Must be one of the accepted values
Validity
PASS
0
country_code, Must exist in the country reference set
Consistency
FAIL
12

No rules approved, so nothing ran.

Back to approval

7 rules executed · 780 rejected rows · scores registered to the catalog

No rules executed · nothing registered to the catalog

[HOW IT WORKS]

The pipeline, step by step.

  1. Trace upstream Walk back through every transformation to the systems of record.
  2. Trace downstream Find every consumer, report and copy that depends on the asset.
  3. Rank by severity Impact reports ordered by consequence, not alphabetically.
  4. Resolve the root source Identify the true origin behind a derived asset.

Lineage that stops at the lake is the most common gap we find. This is the agent that closes it.

[CAPABILITIES]

What it does.

  • Both directions. Upstream to origin and downstream to consumption, from any named asset.
  • Severity-ranked impact. Change review starts where it matters.
  • Root source discovery. Cuts through chains of copies to the actual system of record.
  • Stitched across platforms. Extended into Snowflake, Databricks and Microsoft Fabric, with managed links added where scanning tools cannot see.
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See it on your data.

Discovery is read-only, takes five days, and costs nothing. We assess your environment and tell you honestly whether this agent is worth your time.

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