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Sales Analytics

A complete working example of an Akili data product. Ingests order and customer data from a source database via change data capture, aggregates into daily metrics by region and segment, enforces quality gates, and serves via the analytics engine and a lookup store.

%%{init: {'flowchart': {'curve': 'basis'}}}%%
flowchart LR
    subgraph Source["Source Database"]
        ORDERS[orders]
        CUSTOMERS[customers]
    end

    subgraph Akili["Akili Platform"]
        CDC[Change Data Capture] --> TRANSFORM[SQL Transform]
        TRANSFORM --> QUALITY[Quality Gates]
        QUALITY --> LAKE[Data Lake]
    end

    subgraph Serving["Serving Stores"]
        SR[Analytics Engine]
        PG[Lookup Store]
    end

    ORDERS --> CDC
    CUSTOMERS --> CDC
    LAKE --> SR
    LAKE --> PG

Manifest Files

Section titled “Manifest Files”

Every Akili data product is defined by 6 YAML manifests in a .akili/ directory. Here is the complete set for the sales analytics product.

product.yaml

Section titled “product.yaml”

Product identity — name, schedule, tier, and ownership.

apiVersion: akili/v1
kind: DataProduct
metadata:
  name: daily-order-summary
  description: >
    Daily aggregated order metrics by region and product category.
    Sources from the production orders database via change data capture.
  owner: data-engineering@acme-corp.com
  tags:
    - sales
    - analytics
    - daily
spec:
  schedule: "0 6 * * *"  # Daily at 6 AM UTC
  tier: silver

sources.yaml

Section titled “sources.yaml”

Two source tables from a production relational database. Orders use change data capture; customers use incremental sync.

apiVersion: akili/v1
kind: Sources
sources:
  - name: orders
    connection: production_db
    resource: public.orders
    strategy: cdc
    columns:
      - order_id
      - customer_id
      - product_id
      - quantity
      - unit_price
      - total_amount
      - region
      - order_date
      - created_at


  - name: customers
    connection: production_db
    resource: public.customers
    strategy: incremental
    columns:
      - customer_id
      - name
      - segment
      - country

transforms.yaml

Section titled “transforms.yaml”

References a SQL file that joins orders with customers and aggregates into daily metrics.

apiVersion: akili/v1
kind: Transforms
transforms:
  - name: daily_summary
    type: sql
    sql_file: transforms/daily_summary.sql

The SQL transform uses {{ ref() }} to reference upstream sources:

-- transforms/daily_summary.sql
SELECT
    DATE_TRUNC('day', o.order_date) AS date,
    o.region,
    c.segment AS customer_segment,
    COUNT(DISTINCT o.order_id) AS order_count,
    COUNT(DISTINCT o.customer_id) AS unique_customers,
    SUM(o.quantity) AS total_units,
    SUM(o.total_amount) AS total_revenue,
    AVG(o.total_amount) AS avg_order_value
FROM {{ ref('orders') }} o
LEFT JOIN {{ ref('customers') }} c ON o.customer_id = c.customer_id
GROUP BY 1, 2, 3

quality.yaml

Section titled “quality.yaml”

Quality checks enforced after every pipeline run. Critical checks block data promotion; warnings are logged but don’t block.

apiVersion: akili/v1
kind: Quality
checks:
  - name: no_null_dates
    type: sql
    sql: "SELECT COUNT(*) FROM {{ this }} WHERE date IS NULL"
    severity: critical
    threshold: 0


  - name: positive_revenue
    type: sql
    sql: "SELECT COUNT(*) FROM {{ this }} WHERE total_revenue < 0"
    severity: critical
    threshold: 0


  - name: reasonable_order_count
    type: sql
    sql: "SELECT COUNT(*) FROM {{ this }} WHERE order_count > 1000000"
    severity: warning
    threshold: 0
sla:
  freshness: 24h
  completeness: 0.99

serving.yaml

Section titled “serving.yaml”

Analytical intent routes queries to the analytics engine (StarRocks). Materialization keeps results fresh.

apiVersion: akili/v1
kind: Serving
intent: analytical
materialization:
  enabled: true
  refresh_interval: "1h"

governance.yaml

Section titled “governance.yaml”

Internal classification with owner and reader access grants.

apiVersion: akili/v1
kind: Governance
classification: internal
access:
  - team: data-engineering
    role: owner
  - team: analytics
    role: reader

Deploy This Example

Section titled “Deploy This Example”

  1. Initialize the project

    Terminal window
    mkdir daily-order-summary && cd daily-order-summary
    akili init --name daily-order-summary

    Replace the generated manifest templates with the files above.

  2. Validate manifests

    Terminal window
    akili validate .akili/

  3. Create the product in the platform

    Terminal window
    akili product create \
      --name daily-order-summary \
      --namespace sales

  4. Deploy

    Terminal window
    akili product deploy daily-order-summary

  5. Trigger an execution

    Terminal window
    akili run trigger daily-order-summary

  6. Query results via API

    Terminal window
    curl -H "Authorization: Bearer $TOKEN" \
      "https://api.akili.systems/api/v1/analytics/query" \
      -d '{"sql": "SELECT region, SUM(total_revenue) FROM daily_order_summary GROUP BY region"}'
Section titled “Related”