> ## Documentation Index
> Fetch the complete documentation index at: https://docs.dqlabs.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# What Prizm Collects from Snowflake
> Complete field-level breakdown of every metadata object, quality metric, and signal Prizm extracts from Snowflake across all platform jobs.
Prizm runs the following platform jobs against each Snowflake connector. Each job has a defined type, execution flow, and purpose. Select a job below to see the details.
| | |
| :------------ | :---------------------------------------------------------------------- |
| **Type** | CONTEXT |
| **Execution** | Runs immediately after source is configured — first job in the pipeline |
**What this job does:** Extracts accounts, warehouses, and databases from Snowflake. This is the foundation job — all subsequent jobs depend on it completing successfully. Without a successful TECHNICAL run, no other job can start.
| Object | Fields Collected | Snowflake Source |
| :------------ | :------------------------------------------------------------- | :---------------------------- |
| **Account** | Account name, region, cloud provider | `SHOW ACCOUNTS` |
| **Warehouse** | Name, size, type, state, created\_on | `SHOW WAREHOUSES` |
| **Database** | Name, created\_on, owner, comment | `SHOW DATABASES` |
| **Schema** | Name, database, created\_on, owner | `INFORMATION_SCHEMA.SCHEMATA` |
| **Table** | Name, type, last\_altered, row\_count, is\_iceberg, is\_hybrid | `INFORMATION_SCHEMA.TABLES` |
| **View** | Same as Table + view\_definition | `INFORMATION_SCHEMA.VIEWS` |
| **Column** | Name, data\_type, ordinal\_position, is\_nullable | `INFORMATION_SCHEMA.COLUMNS` |
Prizm catalogs `TABLE` and `VIEW` object types. Dynamic Tables, Stored Procedures, External Tables, and Materialized Views are not currently included in the catalog collection.
| | |
| :------------ | :------------------------------- |
| **Type** | CONTEXT / TRUST |
| **Execution** | Starts after TECHNICAL completes |
**What this job does:** Extracts schemas, tables, views, and volume, freshness, and schema information for table assets from Snowflake metadata. Serves a dual purpose — it feeds the Prizm catalog (CONTEXT) and the alerting and anomaly detection pipeline (TRUST). It is the source of all time-series observability signals in Prizm and the upstream dependency for most parallel jobs.
| Signal | Source | Description |
| :---------------- | :------------------------------------------ | :--------------------------------------------------- |
| **Volume** | `INFORMATION_SCHEMA.TABLES` (row\_count) | Row count trend; anomaly alerts on drops/spikes |
| **Freshness** | `INFORMATION_SCHEMA.TABLES` (last\_altered) | Time since last DDL or DML update; SLA breach alerts |
| **Schema Change** | `INFORMATION_SCHEMA.TABLES` (last\_ddl) | Column additions, renames, type changes, removals |
Anomaly detection uses adaptive forecasting models (EWMA, AutoReg, Prophet, SARIMAX) selected automatically based on each metric's historical pattern. Thresholds are recalculated after every run. A new metric spends its first **more than 5 historical collection runs** in **Learning mode** before alerts begin firing — this threshold is counted in collection runs, not elapsed time.
**Pausing and resuming a source.** Freshness is computed at collection time as the interval since the asset was last altered. Pausing collection freezes the displayed freshness value at whatever it was when the pause took effect — Prizm has no way to know the asset went untouched during the pause versus simply not being checked. When collection resumes, the first run measures the entire pause duration as staleness, which typically fires a critical freshness alert on every asset at once and writes that spike into the anomaly-detection baselines, distorting future thresholds.
To avoid both the false alert storm and the baseline contamination when resuming a paused source: let the source's underlying tables receive normal activity first, then let one full collection cycle complete and land *before* re-enabling downstream alerting, rather than re-enabling everything the moment the source itself reconnects.
| | |
| :------------ | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Type** | CONTEXT |
| **Execution** | No scheduled run. Lineage does not appear on the schedule screen and does not fire automatically — it runs only when triggered manually from an individual asset's run menu. |
**What this job does:** Extracts lineage using Snowflake `ACCESS_HISTORY` and `QUERY_HISTORY` to identify object dependencies and data flow.
Lineage has no automatic schedule today. Trigger it manually per asset when you need current lineage data. Snowflake's underlying `OBJECT_DEPENDENCIES` view can lag up to 3 hours behind the actual DDL change, so a manual run immediately after a schema change may not yet reflect it — wait for the latency window to pass before re-running if the lineage graph looks stale.
| Run | Method |
| :---------------------------------------- | :---------------------------------------------------------------------------------------------------------------- |
| **First run — view lineage** | Seeded from `SNOWFLAKE.ACCOUNT_USAGE.OBJECT_DEPENDENCIES` |
| **First run — table lineage** | Extracted per asset using `SNOWFLAKE.CORE.GET_LINEAGE` |
| **Subsequent runs (DDL change detected)** | Re-extracted only for assets with DDL changes; column-level lineage included (`include_attribute_lineage = true`) |
Supported asset types: `TABLE`, `VIEW`, `EXTERNAL_TABLE`, `MATERIALIZED_VIEW`. Default batch size 100, concurrency 8.
| Direction | What Prizm Shows |
| :------------- | :----------------------------------------------------- |
| **Upstream** | Tables and views this asset reads from |
| **Downstream** | Tables, views, and pipelines that depend on this asset |
Lineage — both table-level and column-level — requires Snowflake **Enterprise edition** or above, since it depends on `SNOWFLAKE.CORE.GET_LINEAGE`. Standard edition does not have a query-log-parsing fallback: on a Standard-edition account, the Lineage graph stays empty.
| | |
| :------------ | :------------------------------------------- |
| **Type** | CONTEXT |
| **Execution** | Runs in parallel after OPERATIONAL completes |
**What this job does:** Extracts warehouse usage, credit consumption, and query statistics for the previous 24 hours. Powers the Usage and Performance views in the Prizm asset detail page. Requires `IMPORTED PRIVILEGES` on the `SNOWFLAKE` database.
| Category | Metrics | Source |
| :-------------------- | :------------------------------------------------------------------------------------------------ | :--------------------------------------------- |
| **Query Performance** | Execution time, queue time, compilation time, bytes scanned, bytes written, rows produced, status | `ACCOUNT_USAGE.QUERY_HISTORY` |
| **Warehouse Compute** | Credits used per warehouse per hour | `ACCOUNT_USAGE.WAREHOUSE_METERING_HISTORY` |
| **Storage** | Average bytes stored (tables, fail-safe, stage) | `ACCOUNT_USAGE.DATABASE_STORAGE_USAGE_HISTORY` |
| **Data Transfer** | Bytes transferred to external destinations | `ACCOUNT_USAGE.DATA_TRANSFER_HISTORY` |
| **Access History** | User, query, objects read/written, timestamp | `ACCOUNT_USAGE.ACCESS_HISTORY` |
`ACCOUNT_USAGE` views have a data latency of up to 45 minutes. Prizm retains time-series observability data, quality score history, and query usage history for a default window of **90 days**.
| | |
| :------------ | :------------------------------------------- |
| **Type** | CONTEXT |
| **Execution** | Runs in parallel after OPERATIONAL completes |
**What this job does:** Extracts summarized usage information and generates usage-based criticality inputs using 7 days of activity. Analyses how frequently each asset is queried, by whom, and with what patterns — then feeds those signals into the CRITICALITY RECOMMENDATION job. Assets with high query volume or many unique users are more likely to be recommended as critical.
| What It Produces | Description |
| :--------------------- | :----------------------------------------------------------------- |
| **Usage frequency** | Number of queries per asset over the past 7 days |
| **Unique user count** | Number of distinct users who accessed each asset |
| **Access patterns** | Read vs write activity, peak usage windows |
| **Criticality inputs** | Usage-based signals consumed by the CRITICALITY RECOMMENDATION job |
| | |
| :------------ | :------------------------------------------- |
| **Type** | CONTEXT |
| **Execution** | Runs in parallel after OPERATIONAL completes |
**What this job does:** Extracts governance metadata including tags and business metadata. Keeps Prizm's governance layer in sync with classifications, ownership, and tagging applied directly in Snowflake. Requires `IMPORTED PRIVILEGES` on the `SNOWFLAKE` shared database for tag ingestion.
| Object | Fields Collected | Snowflake Source |
| :----------------------- | :---------------------------------------- | :------------------------------------- |
| **Tags**\*(Enterprise)\* | Tag name, value, associated objects | `ACCOUNT_USAGE.TAGS`, `TAG_REFERENCES` |
| **Business metadata** | Owner, description, classification fields | `INFORMATION_SCHEMA` object properties |
| | |
| :------------ | :------------------------------------------- |
| **Type** | CONTEXT |
| **Execution** | Runs in parallel after OPERATIONAL completes |
**What this job does:** Calculates and recommends criticality scores for assets and attributes. Criticality determines how much observability coverage an asset receives — higher criticality assets get more frequent monitoring, more structural metrics, and tighter alert thresholds. The output is a recommended criticality level (LOW, MEDIUM, HIGH, CRITICAL) per asset, which users can accept or override.
| Input Signal | Source Job |
| :----------------------------- | :---------------------- |
| Usage frequency and user count | USAGE SUMMARY |
| Downstream dependency count | LINEAGE |
| Business metadata completeness | GOVERNANCE |
| Asset type and schema position | TECHNICAL / OPERATIONAL |
| | |
| :------------ | :------------------------------------------- |
| **Type** | TRUST |
| **Execution** | Runs in parallel after OPERATIONAL completes |
**What this job does:** Computes non-metadata metrics such as freshness and volume for non-table assets by running direct queries against Snowflake. The OPERATIONAL job derives these signals from metadata (e.g. `INFORMATION_SCHEMA.TABLES.LAST_ALTERED`) — but for assets like views and external tables, metadata alone is not sufficient. COMPUTE METRIC fills this gap by executing lightweight SQL queries to obtain accurate signals.
| Asset Type | What Is Computed | How |
| :-------------------- | :--------------------------- | :------------------------------------------------ |
| **View** | Row count, freshness | Direct `SELECT COUNT(*)` and query history lookup |
| **External Table** | Row count, last modified | Query against external stage metadata |
| **Materialized View** | Row count, refresh timestamp | Query against materialized view metadata |
| | |
| :------------ | :-------------------------------------------------------------------- |
| **Type** | TRUST |
| **Execution** | Runs in parallel after OPERATIONAL; PROFILE job depends on its output |
**What this job does:** Recommends profiling schedules and configurations based on asset scope definitions. Determines which assets should be profiled, at what frequency, and with what sample settings — taking into account criticality level, asset size, and scope configuration. The PROFILE job will not run until this job has produced its output and created the schedules.
| | |
| :------------ | :----------------------------------------------------------------------- |
| **Type** | TRUST |
| **Execution** | Runs in parallel after OPERATIONAL; STRUCTURAL job depends on its output |
**What this job does:** Recommends structural quality metrics (Essential or Advanced) for each attribute. Analyses the column's data type, profile history, and criticality to determine the appropriate metric set. Essential metrics are always recommended; Advanced metrics are added for columns where deeper analysis is warranted. The STRUCTURAL job executes against this recommendation output.
| | |
| :------------ | :------------------------------------------------------------------------- |
| **Type** | TRUST |
| **Execution** | Triggered after PROFILE RECOMMENDATION completes and schedules are created |
**What this job does:** Executes profiling jobs according to the schedules generated by PROFILE RECOMMENDATION and stores the profiling results. Can also be triggered manually from the asset page at any time.
**Table-level metrics:**
| Metric | Description |
| :---------------------- | :------------------------------------------------ |
| **Row Count** | Total rows at profile time |
| **Duplicate Row Count** | Rows that are exact duplicates of another row |
| **Completeness Score** | % of columns with non-null values across all rows |
**Column-level metrics:**
| Metric | Applicable Types | Description |
| :------------------------ | :---------------------- | :--------------------------------------------- |
| **Null Rate** | All | % of null values |
| **Distinct Count** | All | Number of unique non-null values (cardinality) |
| **Uniqueness Rate** | All | % of values appearing exactly once |
| **Min / Max** | Numeric, Date/Timestamp | Observed range of values |
| **Mean** | Numeric | Arithmetic mean |
| **Standard Deviation** | Numeric | Statistical spread |
| **Median** | Numeric | 50th percentile value |
| **Top N Values** | String, Boolean | Most frequent values and their counts |
| **Pattern Conformance** | String | % matching an expected regex format |
| **Data Type Conformance** | String | % of values parseable as the declared type |
Advanced numeric statistics are computed for types containing `int`, `number`, `decimal`, `float`, or `double`. Snowflake types such as `REAL`, `BIGINT`, and `SMALLINT` still receive min, max, distinct count, and null counts.
**Trigger conditions:**
| Trigger | Conditions |
| :--------------------- | :------------------------------------------------------------------------------------------- |
| **Scheduled run** | Active schedule, `next_run_at` is due, source is valid and active |
| **Manual run** | User triggers Profile from the asset UI (TABLE, VIEW, MATERIALIZED\_VIEW) |
| **Platform mode** | PROFILE job type must be enabled in platform mode |
| **Criticality gating** | Only attributes eligible for the asset's criticality level are profiled |
| **Skip (incremental)** | Skipped if row count is unchanged since last run, or profile is newer than `last_altered_on` |
Supported asset types: TABLE, VIEW, MATERIALIZED\_VIEW (and ATTRIBUTE for column-level context).
| | |
| :------------ | :-------------------------------------------------------------------------- |
| **Type** | TRUST |
| **Execution** | Triggered after STRUCTURAL RECOMMENDATION completes and metrics are created |
**What this job does:** Executes structural quality metrics according to the schedules and configurations generated by STRUCTURAL RECOMMENDATION. Computes distribution, frequency, pattern, and statistics metrics at the attribute level for all in-scope columns.
| Metric Category | What Is Computed |
| :--------------- | :-------------------------------------------------------------------------------------------------------- |
| **Distribution** | Null count, empty, space, distinct, repeating, character composition, leading/trailing/inner space |
| **Frequency** | Enum (value histogram), min/max value, value range, min/max length, length distribution |
| **Pattern** | Short pattern, long pattern, user-defined regular expressions |
| **Statistics** | Mean, median, mode, standard deviation, sum, Q1, Q3, range, skewness, kurtosis, variance, margin of error |
| | |
| :------------ | :-------------------------------------- |
| **Type** | CONTEXT |
| **Execution** | Independent job; no upstream dependency |
**What this job does:** Recommends asset definitions, descriptions, intended audience, semantic context, and execution scope settings (Incremental, Sampling with Filter, or None). Uses AI to analyse the asset's name, column names, sample values, and usage patterns to generate human-readable context that data stewards can review and accept.
| What It Generates | Description |
| :------------------- | :------------------------------------------------------------------------------------------ |
| **Description** | AI-generated plain-language description of what the asset contains and its purpose |
| **Who should use** | Recommended audience — data analysts, engineers, business users, etc. |
| **Semantic context** | Suggested domain, product, application, and tag classifications |
| **Execution scope** | Recommended profiling mode — Incremental (only changed rows), Sampling with Filter, or None |
| | |
| :------------ | :-------------------------------------- |
| **Type** | TRUST |
| **Execution** | Independent job; no upstream dependency |
**What this job does:** Recommends business metrics for assets based on column names, data types, profiling results, and industry patterns. Suggests quality checks that go beyond structural profiling — for example, a not-null check on a primary key column, or a range check on a revenue column. Recommendations appear in the Prizm UI for review before any metric is created.
| | |
| :------------ | :-------------------------------------- |
| **Type** | TRUST |
| **Execution** | Independent job; no upstream dependency |
**What this job does:** Recommends metric descriptions, purposes, and rationale for existing metrics. For each metric already defined on an asset, this job generates a human-readable explanation of what the metric checks, why it matters, and what a failure would indicate. These descriptions appear in the Prizm metric detail panel and help data stewards understand the intent behind auto-generated checks.
| | |
| :------------ | :-------------------------------------- |
| **Type** | CONTEXT |
| **Execution** | Independent job; no upstream dependency |
**What this job does:** Maps assets and attributes to existing glossary terms or generates new business terms where required. Analyses column names, descriptions, and sample values to find matching terms in the Prizm business glossary. Powers the Semantic layer in Prizm — ensuring that columns are aligned to approved business vocabulary.
| Output | Description |
| :------------------- | :--------------------------------------------------------------------------------------- |
| **Term mapping** | Links a column to an existing glossary term (e.g. `customer_id` → "Customer Identifier") |
| **Term proposal** | Suggests a new glossary term where no existing match is found |
| **Confidence score** | Indicates how confident the AI is in the mapping recommendation |
| | |
| :------------ | :-------------------------------------- |
| **Type** | TRUST |
| **Execution** | Independent job; no upstream dependency |
**What this job does:** Clusters and groups related alerts to reduce duplication and improve root-cause analysis. When multiple alerts fire simultaneously on the same asset or related assets — for example, a freshness alert and a volume alert on the same table — this job groups them into a single alert cluster. Reduces alert fatigue by surfacing correlated failures together rather than as separate notifications.
| | |
| :------------ | :-------------------------------------- |
| **Type** | TRUST |
| **Execution** | Independent job; no upstream dependency |
**What this job does:** Generates and refreshes daily asset trend data by aggregating DQ scores, alerts, issues, and asset metadata. Powers the score history charts on the asset detail page — the time-series view of how an asset's quality score has evolved over time. Also feeds domain-level and portfolio-level trend views in the Prizm Scorecard and Analytics pages.
| | |
| :------------ | :-------------------------------------- |
| **Type** | PLATFORM |
| **Execution** | Independent job; no upstream dependency |
**What this job does:** Cleans and maintains the Prizm repository by removing obsolete data. Deletes stale metric history, expired alert records, orphaned job artifacts, and data from deleted or disconnected sources. Runs on a configurable schedule to keep the Prizm database within the configured retention window. Does not affect live assets or active metric data within the retention period.
None of the four recommendation jobs above (Asset Detail, Business Metric, Metric Detail, Term) can be **generated** in bulk — each produces suggestions for one asset at a time. The only bulk control available is **Approve Recommendations** on the asset page, which accepts already-generated suggestions for that asset's pending recommendations; it does not create new ones. There is currently no control that generates recommendations across more than one asset at a time.
## What You See in Prizm
Once Prizm completes its initial catalog and observability runs, every Snowflake table and view gets a unified asset detail page. The tabs below cover what each section shows.
| **Section** | **What Prizm Shows** |
| :------------------- | :----------------------------------------------------------------------------------------------------------------------------------- |
| **Quality Score** | Overall data quality percentage computed across all active metrics |
| **Status** | Review state (e.g., READY FOR REVIEW) and criticality badge |
| **Key Metrics** | VOLUME (row count), SCHEMA (column count), FRESHNESS (time since last update), METRICS (total quality metrics defined) |
| **Description** | AI-generated or manually verified description of the asset |
| **Semantic Context** | Domain, Application, Product, and Tag classifications |
| **Owners** | Business owner, Technical owner, and Steward contacts |
| **Lineage Summary** | One-hop upstream sources and downstream consumers |
| **Context Panel** | AI-generated narrative, context completeness score (0–100%), and freshness timestamps for description, terms, and owner verification |
The Attribute tab lists every column Prizm has cataloged — data type, quality score, alert/issue counts, and the number of quality metrics defined.
Clicking any attribute opens its column profile with four views: Distribution, Pattern, Custom Pattern, and Frequency.
| **Profile Section** | **What Prizm Shows** |
| :------------------ | :-------------------------------------------------------------------- |
| **Completeness** | Count and % of NULL, EMPTY, SPACE, and NON-EMPTY values |
| **Uniqueness** | Count and % of DISTINCT vs REPEATING values |
| **Character** | Distribution across DIGIT, ALPHABET, ALPHA NUMERIC, SPECIAL CHARACTER |
| **Length** | Minimum and maximum character length |
| **Pattern** | Top regex patterns detected across values |
| **Frequency** | Most and least frequent individual values |
For the full breakdown of profiling views, metric types, and how to interpret profile results, see the [Data Profiling](/data-profiling) section.
The Metric tab lists all quality metrics defined on the asset, grouped by context. Each row shows the metric name, column, domain, current score, alert count, issue count, and action controls.
Asset-level health signals. **Monitor: Yes — Score: No**
| Metric | Subcategory | Description |
| :----------- | :---------- | :------------------------------------------------------------ |
| Row | Volume | Total number of rows at the time of last scan |
| Volume | Volume | Combined row count and data size signal |
| Data size | Volume | Physical size of the asset in bytes |
| Freshness | Freshness | Time elapsed since the asset was last updated |
| Last updated | Freshness | Timestamp of the most recent data load or write |
| Column | Schema | Current number of columns |
| Schema | Schema | Full column snapshot — names, types, and order |
| Schema name | Schema | Name of the schema the asset belongs to |
| Duplicates | Duplicate | Count of duplicate rows based on a defined key or all columns |
Auto-generated column-level metrics. **Score: Yes**
**Distribution**
| Metric | Subcategory |
| :---------------- | :----------- |
| Null count | Completeness |
| Empty | Completeness |
| Non-empty | Completeness |
| Space | Completeness |
| Zero value | Numeric |
| Distinct | Uniqueness |
| Repeating | Uniqueness |
| Leading space | Space |
| Trailing space | Space |
| Inner space | Space |
| Outer space | Space |
| Whitespace | Space |
| Character | Character |
| Alpha numeric | Character |
| Digit | Character |
| Special character | Character |
| Alphabet | Character |
| Positive | Numeric |
| Negative | Numeric |
**Frequency**
| Metric | Subcategory |
| :----------- | :---------- |
| Enum | Value |
| Min value | Value |
| Max value | Value |
| Value range | Range |
| Min length | Length |
| Max length | Length |
| Length | Length |
| Length range | Range |
**Pattern**
| Metric | Subcategory |
| :------------------ | :---------- |
| Short pattern | Pattern |
| Long pattern | Pattern |
| Regular expressions | Pattern |
**Statistics**
| Metric | Subcategory |
| :----------------- | :---------- |
| Mean | Descriptive |
| Median | Descriptive |
| Mode | Descriptive |
| Standard deviation | Descriptive |
| Sum | Descriptive |
| Q1 | Quantile |
| Q3 | Quantile |
| Range | Shape |
| Skewness | Shape |
| Kurtosis | Shape |
| Variance | Shape |
| Margin of error | Shape |
User-defined metrics. **Custom, Conditional, Standalone: Score Yes — Behavioral: Score No**
| Metric Type | Description |
| :---------- | :---------------------------------------------------------------------- |
| Custom | User-defined rules, filters, or expressions on a specific asset |
| Conditional | Different logic or thresholds based on a condition |
| Query | SQL query returning a scalar — enables multi-table logic |
| Standalone | Not bound to a specific asset — used for org-wide KPIs |
| Behavioral | Tracks patterns over time — detects drift, not point-in-time violations |
Platform-level signals. **Monitor: Yes — Score: No**
| Metric | Level |
| :-------------------- | :------------------ |
| Total queries per day | Warehouse, Database |
| Query success rate | Warehouse, Database |
| Execution time | Warehouse, Database |
| Job duration | Warehouse, Database |
| Test execution time | Warehouse, Database |
| Avg blocked | Warehouse, Database |
| Avg queue load | Warehouse, Database |
| Avg running | Warehouse, Database |
| Database storage | Database |
| Total credits per day | Warehouse, Database |
| Credits used | Warehouse, Database |
Cross-source comparison metrics.
| Metric Type | Description |
| :---------- | :---------------------------------------------------------------------- |
| Comparison | Compares values, row counts, or aggregates across two registered assets |
| Lookup | Validates column values against a reference table or allowed set |
Domain and grouping level metrics.
| Grouping | Description |
| :---------- | :-------------------------------------------------------------------------- |
| Domain | DQ score rolled up across all assets assigned to a domain |
| Product | Aggregate quality and freshness across a defined data product |
| Application | % of assets in an application meeting quality and freshness SLOs |
| Tag | PII tag coverage across assets with sensitive columns |
| Connection | Aggregate quality score across all assets from a given Snowflake connection |
The Usage tab surfaces per-asset query intelligence derived from the Performance job.
| **Metric** | **Description** |
| :------------------------- | :------------------------------------------------------- |
| **Total Queries** | Total number of queries that touched this asset |
| **Average Execution Time** | Mean query execution time in milliseconds |
| **Query Success Rate** | Ratio of successful queries to total queries |
| **Total Consumed Credit** | Snowflake credits consumed by queries against this asset |
| **Unique Users** | Number of distinct users who queried this asset |
| **Unique Warehouses** | Number of distinct warehouses used to query this asset |
Queries can be filtered by EXPENSIVE, SLOWER, and POPULAR.
The Lineage tab renders an interactive directed graph of all upstream sources and downstream consumers. Click any node to navigate to that asset's detail page.
The Overview tab also shows a one-hop upstream/downstream summary for quick reference. For details on how lineage data is collected from Snowflake, see the **Lineage** tab in the job types section above.
## Next Steps
Review prerequisites and configure the connector.
Common questions about what Prizm collects and why.