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Snowflake Destination

This page will guide you through setting up an ELT pipeline using Snowflake to fetch data from a KYVE data pool and import it into a Snowflake data warehouse.

About Snowflake​

The KYVE Data Pipeline enables easy import of KYVE data into any data warehouse or destination supported by Airbyte. With the ELT format, data analysts and engineers can now confidently source KYVE data without worrying about its validity or reliability.

A data warehouse is a centralized repository of data that is optimized for querying and analysis. It is designed for analytical processing (OLAP). A data warehouse can handle large volumes of data and complex queries, while a relational database is more suitable for handling small transactions in real-time. A data warehouse provides a way for businesses to consolidate data from various sources into a single location, which can then be used to make better decisions.

Snowflake is used by a wide range of businesses and industries, from startups to large enterprises. Some main user groups that utilize Snowflake include business analysts, data scientists, data engineers and executives.

Snowflake Setup​

Prerequisites​

These instructions assume that you already have a Snowflake account. For more information on how to get started with an account, go here.

Once the Snowflake data warehouse is set up, we can configure it as a destination for Airbyte. We are going to be utilizing the UI via the Web browser:

  1. Open a web browser and go to the Snowflake web application.

  2. Log in to your account using your Snowflake username and password.

  3. Once logged in, click on the Worksheet option from the sidebar. Then, click on the + Worksheet button on the top right of the page to open a new worksheet.

In order to create a connection, you will need the following:

  • Host
  • Username
  • Role
  • Password
  • Warehouse
  • Database
  • Default Schema

Below are the instructions to create/get the required parameters.

NOTE: The worksheet you just opened can act as your query editor. All the SQL queries you'll see below need to be run there.

Configuring Snowflake​

Host​

Go to Admin > Accounts and then locate your user. Hover over the clip icon beside the account name to get your host. It should be in the format <your_account.snowflakecomputing.com>.

Username​

To create a user you can either create it on the UI or using SQL:

  • From the UI, under Admin > Users & Roles > + User

  • Using SQL:

    • To run this command, the current user must have a USERADMIN role or one with higher privileges (run this command in the worksheet):

      NOTE: An owner with ACCOUNTADMIN privileges will work as well since it has higher level of access than USERADMIN.

      CREATE USER <user_name_of_choice>;

    This will serve as the username when creating the connection.

Role​

  • To find the user role, go to Admin > Users & Roles.
  • Select your user to expand and get detailed information including the role.

Password​

  • If no password is set up, you can run this SQL command for your username (run this command in the worksheet):

    ALTER USER <your_username> SET PASSWORD = <your_pass_as_string>;

Warehouse​

To create a warehouse you can either create it on the UI or by using SQL:

  • From the UI, under Admin > Warehouses > + Warehouse

    1. Input a Name and choose the Size(here he chose X-Small for the sake of this tutorial).

  • Using SQL

    To run this command the current user must have a SYSADMIN role or higher (run this command in the worksheet):

    CREATE OR REPLACE WAREHOUSE <warehouse_name_of_choice> WITH warehouse_size=<size_of_warehouse_as_string>;

Database​

To create a database you can either create it on the UI or by using SQL:

  • From the UI, under Data > Databases > + Database

    1. Input a Name and click on Create to create the database.

  • Using SQL

    • To run this command the current user must have a SYSADMIN role or higher (run this command in the worksheet):

      CREATE DATABASE <db_name_of_choice>;

Default Schema​

The default schema is the schema you want to work with. Typically, it is public.

Create an ELT pipeline​

Now you are ready to go on the Airbyte app (http://localhost:8000/) and create a new connection.

  1. Set up the source.

    In this step you should specify the KYVE pool from which you want to retrieve data. You can specify a specific Bundle-Start-ID in case you want to narrow the records that will be retrieved from the pool. You can find the bundles of your pool choice in the KYVE app. For example here are the bundles of Pool ID 8, the Evmos chain.

    For this example we choose:


  2. Set up the destination.

    1. Go to Connections -> + New Connection, choose Kyve source (existing source) and click on the Use existing source button:

    2. Select as destination type Snowflake. You will automatically be taken to a page to add the setup details of the Snowflake Warehouse that you set before.

    3. For the authorization method, use Username and Password and enter the parameters you saved before.

  3. Final ELT configuration.

    This is the final step where you can modify the pipeline.

    • In the Transfer field, you can set how often the data should sync to the destination. For this example, you could set it to Manual.

    • In the Streams section, you can modify the Namespace, and you can add a Prefix for the data that will be stored.

    • In the Activate the streams you want to sync section, you can modify the behavior of the stream in each repetition. For this example, you could set it to Incremental|Append, which means that only new records will be stored on each new sync and will be appended in the DB.

View Stored Data​

To view the data you just synced from KYVE, go back to your Snowflake UI, select the worksheet you created earlier and try out the following queries. All variables inside <> need to be changed according to the variables you set.

  • Query the table with the raw data:

    SELECT * FROM <DATABASE_NAME>.PUBLIC._airbyte_raw_pool_8 limit 10;

  • Query the table with the normalized data

    SELECT * FROM <DATABASE_NAME>.PUBLIC.POOL_8_VALUE limit 10;

  • Querying data for a given miner:

    • Finding miners with multiple transactions:

      SELECT miner, COUNT(*) as transaction_count
      FROM <DATABASE_NAME>.PUBLIC.POOL_8_VALUE
      GROUP BY miner
      HAVING COUNT(*) > 1
      ORDER BY transaction_count DESC;

    • Selecting one and displaying results:

      SELECT * FROM <DATABASE_NAME>.PUBLIC.POOL_8_VALUE
      WHERE MINER = '<MINER_STRING>';

    • Showing transactions originating from a given sender:

      -- list senders and receivers
      SELECT "from", "to" FROM <DATABASE_NAME>.PUBLIC.POOL_8_VALUE_TRANSACTIONS limit 10;

      -- query all transactions for a given sender
      SELECT * FROM <DATABASE_NAME>.PUBLIC.POOL_8_VALUE_TRANSACTIONS WHERE "from" = '<RECEIVER_ADDRESS_STRING>';

  • Creating the Transactions View:

    -- to avoid re-writing <DATABASE_NAME>.PUBLIC
    USE DATABASE <DATABASE_NAME>.PUBLIC;

    -- function to convert hex to numeric
      CREATE OR REPLACE FUNCTION hex_to_numeric(str TEXT)
      RETURNS VARCHAR
      LANGUAGE JAVASCRIPT
      AS
      $$
      const n = Math.floor(arguments[0].length / 8);
      let res = BigInt(0);
      str = arguments[0].padStart((n + 1) * 8, '0');
      for (let i = 0; i <= n; i++) {
        res *= BigInt('0x100000000');
        res += BigInt('0x' + str.substr(i * 8, 8));
      }
      return res.toString();
      $$;

    SELECT TO_TIMESTAMP(POOL_8_VALUE.timestamp) AS timestamp,
        POOL_8_VALUE_TRANSACTIONS."from",
        POOL_8_VALUE_TRANSACTIONS."to",
        POOL_8_VALUE_TRANSACTIONS_value.hex,
        hex_to_numeric(SUBSTR(POOL_8_VALUE_TRANSACTIONS_value.hex, 3)) AS int8_value,
        POOL_8_VALUE_TRANSACTIONS.data
    FROM POOL_8_VALUE_TRANSACTIONS
    JOIN POOL_8_VALUE ON POOL_8_VALUE._airbyte_value_hashid = POOL_8_VALUE_TRANSACTIONS._airbyte_value_hashid
    JOIN POOL_8_VALUE_TRANSACTIONS_value ON POOL_8_VALUE_TRANSACTIONS._airbyte_transactions_hashid = POOL_8_VALUE_TRANSACTIONS_value._airbyte_transactions_hashid
    LIMIT 10;

That's it! You've successfully created an end-to-end ELT pipeline fetching data from a KYVE data pool and importing it into a Snowflake Warehouse.