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Themis

Connecting to Themis using Depot/Cluster

Prerequisite

Ensure you have an active and running Themis Cluster.

Step 1: Prepare the Lens model folder

Organize the Lens model folder with the following structure to define tables, views, and governance policies:

model
├── sqls
│   └── sample.sql  # SQL script for table dimensions
├── tables
│   └── sample_table.yml  # Logical table definition (joins, dimensions, measures, segments)
├── views
│   └── sample_view.yml  # Logical views referencing tables
└── user_groups.yml  # User group policies for governance

Load data from the data source

In the sqls folder, create .sql files for each logical table, where each file is responsible for loading or selecting the relevant data from the source. Ensure that only the necessary columns are extracted, and the SQL dialect is specific to the data source.

For example, a simple data load from Postgres source might look as follows:

SELECT
    customer_id as p_customer_id,
    cast(purchase_date as timestamp) as purchase_date,
    recency as recency_in_days,
    mntwines,
    mntmeatproducts,
    mntfishproducts,
    mntsweetproducts,
    mntgoldprods,
    mntfruits,
    numdealspurchases,
    numwebpurchases,
    numcatalogpurchases,
    numstorepurchases,
    numwebvisitsmonth,
    numwebpurchases + numcatalogpurchases + numstorepurchases + numstorepurchases as purchases,
    (mntwines+mntmeatproducts+mntfishproducts+mntsweetproducts+mntgoldprods+mntfruits) as spend
FROM
    postgres.public.purchase_data

Similarly, to load the data from another source lakehouse the sql looks as follows:

SELECT 
    customer_id, 
    birth_year, 
    education, 
    marital_status, 
    income, 
    country 
FROM 
    lakehouse.customer_relationship_management.customer_data

Define the table in the model

Create a tables folder to store logical table definitions, with each table defined in a separate YAML file outlining its dimensions, measures, and segments. For example, to define a table for cutomerdata:

tables:
  - name: customer
    sql: {{ load_sql('customer') }}
    description: "This table stores key details about the customers, including their personal information, income, and classification into different risk segments. It serves as the central reference point for customer-related insights."
    public: true

Add dimensions and measures

After defining the base table, add the necessary dimensions and measures. For example, to create a table for sales data with measures and dimensions, the YAML definition could look as follows:

tables:
  - name: customer
    sql: {{ load_sql('customer') }}
    description: "This table stores key details about the customers, including their personal information, income, and classification into different risk segments. It serves as the central reference point for customer-related insights."
    public: true

    joins:
      - name: purchase
        relationship: one_to_many
        sql: "{TABLE.customer_id}= {purchase.p_customer_id}"

    dimensions:   
      - name: customer_id
        type: number
        column: customer_id
        description: "The unique identifier for each customer."
        primary_key: true
        public: true

      #...

    measures:
      - name: total_customers
        sql: "COUNT( Distinct {customer_id})"
        type: number
        description: "The total number of customers in the dataset, used as a basic measure of customer volume."

Add segments to filter

Segments are filters that allow for the application of specific conditions to refine the data analysis. By defining segments, you can focus on particular subsets of data, ensuring that only the relevant records are included in your analysis. For example, to filter for records where the state is either Illinois or Ohio, you can define a segment as follows:

#..
segments:
  - name: country_india
    sql: country = 'India'
To know more about segments click here.

Create the views

Create a views folder to store all logical views, with each view defined in a separate YAML file (e.g., sample_view.yml). Each view references dimensions, measures, and segments from multiple logical tables. For instance the followingtotal_spend view is created.

views:
  - name: total_spending
    description: This metric measures how many marketing campaigns a customer has engaged with. 
    public: true
    meta:
      title: Customer Spending by Product Category
      tags:   
        - DPDomain.Marketing
        - DPUsecase.Customer Segmentation
        - DPUsecase.Product Recommendation
        - DPTier.Consumer Aligned
      metric:
        expression: "*/45  * * * *"
        timezone: "UTC"
        window: "day"
        excludes: 
         - spend
    tables:
      - join_path: purchase
        prefix: true
        includes:
          - purchase_date
          - customer_id
          - total_spend
          - spend

      - join_path: product
        prefix: true
        includes:
          - product_category
          - product_name

To know more about the views click here.

Create user groups

This YAML manifest file is used to manage access levels for the semantic model. It defines user groups that organize users based on their access privileges. In this file, you can create multiple groups and assign different users to each group, allowing you to control access to the model.By default, there is a 'default' user group in the YAML file that includes all users.

user_groups:
  - name: default
    description: this is default user group
    includes: "*"

Multiple user groups can be created in the user_groups.yml . To know more about the user groups click here.

Create Lens manifest file

After preparing the Lens semantic model create a lens_deployemnt.yml parallel to the model folder.

version: v1alpha
name: "themis-lens"
layer: user
type: lens
tags:
  - lens
description: themis lens deployment on lens2
lens:
  compute: runnable-default
  secrets:
    - name: bitbucket-cred
      allKeys: true
  source:
    type: themis #minerva/themis/depot
    name: lenstestingthemis
    catalog: icebase
  repo:
    url: https://bitbucket.org/tmdc/sample
    lensBaseDir: sample/lens/source/themis/model 
    # secretId: lens2_bitbucket_r
    syncFlags:
      - --ref=main #repo-name

The YAML manifest provided is designed for a cluster named themiscluster, created on the Themis source, with a data catalog named icebase. To utilize this manifest, duplicate the file and update the source details as needed.

Each section of the YAML template outlines essential elements of the Lens deployment. Below is a detailed breakdown of its components:

  • Defining the Source:

    • type: The type attribute in the source section must be explicitly set to themis.

    • name: The name attribute in the source section should specify the name of the Themis Cluster. For example, if the name of your Themis Cluster is clthemis the Source name would be clthemis.

    • catalog: The catalog attribute must define the specific catalog name within the Themis Cluster that you intend to use. For instance, if the catalog is named lakehouse_retail, ensure this is accurately reflected in the catalog field.

  • Defining Repository:

    • url The url attribute in the repo section specifies the Git repository where the Lens model files are stored. For instance, if your repo name is lensTutorial then the repo url will be https://bitbucket.org/tmdc/lensTutorial

    • lensBaseDir: The lensBaseDir attribute refers to the directory in the repository containing the Lens model. Example: sample/lens/source/depot/awsredshift/model.

    • secretId: The secretId attribute is used to access private repositories (e.g., Bitbucket, GitHub). It specifies the secret needed to authenticate and access the repository securely.

    • syncFlags: Specifies additional flags to control repository synchronization. Example: --ref=dev specifies that the Lens model resides in the dev branch.

  • Configure API, Worker, and Metric Settings (Optional): Set up replicas, logging levels, and resource allocations for APIs, workers, routers, and other components.

The above manifest is intended for a cluster named lenstestingthemis, created on the themis source, with the Depot or data catalog named icebase. To use this manifest, copy the file and update the source details accordingly.

Step 4: Apply the Lens manifest file

After configuring the deployment file with the necessary settings and specifications, apply the manifest using the following command:

dataos-ctl resource apply -f ${manifest-file-path}

dataos-ctl resource apply -f /lens/lens_deployment.yml -w curriculum
# Expected output
INFO[0000] 🛠 apply...                                   
INFO[0000] 🔧 applying(curriculum) sales360:v1alpha:lens... 
INFO[0001] 🔧 applying(curriculum) sales360:v1alpha:lens...created 
INFO[0001] 🛠 apply...complete
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