{"id":1224,"date":"2024-03-11T11:11:38","date_gmt":"2024-03-11T11:11:38","guid":{"rendered":"https:\/\/dotlabs.ai\/blogs\/?p=1224"},"modified":"2024-04-17T12:47:19","modified_gmt":"2024-04-17T12:47:19","slug":"a-complete-step-by-step-guide-to-building-data-pipeline-for-etl-operation-using-modern-data-engineering-tools","status":"publish","type":"post","link":"https:\/\/dotlabs.ai\/blogs\/2024\/03\/11\/a-complete-step-by-step-guide-to-building-data-pipeline-for-etl-operation-using-modern-data-engineering-tools\/","title":{"rendered":"A Complete Step-by-Step Guide to Building Data Pipeline for ETL Operation Using Modern Data Engineering Tools"},"content":{"rendered":"\n\n\n

Let’s go through a step-by-step\nprocess of ETL Operation using Fivetran connectors to Snowflake, DBT\nTransformations, and Exploration using Looker Studio.

<\/o:p><\/span><\/p>

A little overview of the\nData Pipeline:<\/span>
<\/o:p><\/span><\/h2>

Technology Stack:<\/span>
<\/o:p><\/span><\/h3>

Snowflake:<\/strong><\/span><\/span> Snowflake is\nthe leading data warehouse built for the cloud. Its unique architecture\ndelivers proven breakthroughs in performance, concurrency, and simplicity. For\nthe first time, multiple groups can access petabytes of data at the same time,\nup to 200 times faster and 10 times less expensive than solutions not built for\nthe cloud. Snowflake is a fully managed service with a pay-as-you-go model that\nworks on structured and semi-structured data.

<\/o:p><\/strong><\/span><\/p>

Fivetran<\/span><\/span>:<\/span><\/span><\/strong> Shaped by the\nreal-world needs of data analysts, Fivetran technology is the smartest, fastest\nway to replicate your applications, databases, events, and files into a\nhigh-performance cloud warehouse. Fivetran connectors deploy in minutes,\nrequire zero maintenance, and automatically adjust to source changes; so, your\ndata team can stop worrying about engineering and focus on driving insights.

<\/o:p><\/strong><\/span><\/p>

dbt<\/span><\/span>:<\/span><\/span><\/strong> dbt is a\ndevelopment environment built and maintained by Fishtown Analytics that speaks\nthe preferred language of data analysts everywhere SQL. With dbt, analysts take\nownership of the entire analytics engineering workflow, from writing data\ntransformation code to deployment and documentation.

<\/o:p><\/strong><\/span><\/p>

Looker<\/span><\/span>:<\/span><\/span><\/strong> Looker is a modern platform for data\nthat offers data analytics and business insights to every department at scale\nand easily integrates into applications to deliver data directly into the\ndecision-making process.<\/o:p><\/span><\/p>

 <\/span><\/p>

\n<\/p>

The following figure shows the Fivetran, dbt, and, Snowflake pipelines.<\/o:p><\/span><\/p>\n\n

Lorem Ipsum has been the industry’s standard dummy text ever since the 1500s.<\/p>\n\n\n

Data\nPipeline:<\/span>
<\/o:p><\/span><\/h2>

\n\nIt starts with developing a Python script to establish a Fivetran\nconnector, facilitating the seamless connection of Google Sheets datasets to\nthe Snowflake warehouse. 
<\/span>
Subsequently, the configuration of Snowflake with Data\nBuilt Tools (DBT) is explored for optimal data management. SQL transformations\nare then executed within the DBT environment, leading to the deployment of\nrefined models back into the Snowflake warehouse. The final stage involves\nconnecting the warehouse with Looker Studio, enabling the creation of dynamic\nand interactive visualizations for comprehensive data analysis.

<\/span><\/p>\n\n

Lorem Ipsum has been the industry’s standard dummy text ever since the 1500s.<\/p>\n\n\n\n\n

To do:<\/span><\/o:p><\/span><\/h2>

 In this tutorial, we will be covering\ndifferent phases we completed in building our data pipeline:
<\/span><\/p>

Writing Python script<\/strong>\nto create a Fivetran connector connecting datasets from Google Sheets to data\nfrom our Snowflake warehouse.<\/span><\/o:p><\/span><\/p>

Configuring Snowflake\nwith Data Built tools (DBT<\/strong>).<\/span><\/o:p><\/span><\/p>

Writing SQL\ntransformation<\/strong> in DBT and deploying models back in our warehouse.<\/span><\/o:p><\/span><\/p>

\n<\/p>

Connecting our warehouse\nwith Looker Studio<\/strong> and creating interactive visualizations.

<\/o:p><\/span><\/p>

About the dataset:<\/span><\/o:p><\/span><\/h2>

We download an open-source dataset\nfrom Kaggle of sales data. It contained categories of each product with their\ncost price and price each for which he sold each item, turnover, and quantity\nordered for each product in their category.

Each order is identified with its\nunique order ID and the ordering date. The total dataset contained 185951<\/strong> rows including headers.

<\/o:p><\/span><\/p>

Step 01: Creating Fivetran Connector using Python.<\/span><\/span><\/o:p><\/span><\/h2>

Fivetran provides us REST<\/strong> framework within Python to send\nAPI calls to perform tasks. This can be found in their official API\ndocumentation. In our code, we used POST calls with two predefined endpoints.\nFirst, we used the \u2018connector\u2019<\/strong>\nendpoint with configures defined in our payload variable. Then we used a sync\nconnector with that specified endpoint to sync our data.

<\/o:p><\/span><\/p>

\n<\/p>

It should be noted that the proper\nAPI and other credentials are required which will be present in the Fivetran\nuser profile. Similarly, we configured our warehouse connection while creating\nthe Fivetran user profile to get specific credentials for proper connections\nwhich will be further used in our code.

<\/span><\/o:p><\/span><\/p>

Code:
<\/span>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

## Importing libraries<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

 <\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

import<\/span><\/strong>\n<\/span><\/strong>requests<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

from<\/span><\/strong>\n<\/span><\/strong>requests<\/span><\/strong>.<\/span><\/strong>auth<\/span><\/strong> <\/span><\/strong>import<\/span><\/strong> <\/span><\/strong>HTTPBasicAuth<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

import<\/span><\/strong>\n<\/span><\/strong>json<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

import<\/span><\/strong>\n<\/span><\/strong>colorama<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

from<\/span><\/strong>\n<\/span><\/strong>colorama<\/span><\/strong> <\/span><\/strong>import<\/span><\/strong> <\/span><\/strong>Fore<\/span><\/strong>, <\/span><\/strong>Back<\/span><\/strong>, <\/span><\/strong>Style<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

 <\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

## Account Credentials | This will be obtained from the\nAPI tab within the Fivetran Profile Account<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

 <\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

api_key<\/span><\/strong>\n= <\/span><\/strong>”<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

api_secret<\/span><\/strong>\n= <\/span><\/strong>”<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

a<\/span><\/strong>\n= <\/span><\/strong>HTTPBasicAuth<\/span><\/strong>(<\/span><\/strong>api_key<\/span><\/strong>, <\/span><\/strong>api_secret<\/span><\/strong>)<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

 <\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

## Function for API calls<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

 <\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

def<\/span><\/strong>\n<\/span><\/strong>atlas<\/span><\/strong>(<\/span><\/strong>method<\/span><\/strong>, <\/span><\/strong>endpoint<\/span><\/strong>, <\/span><\/strong>payload<\/span><\/strong>=<\/span><\/strong>None<\/span><\/strong>):<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

 <\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

    <\/span><\/strong>base_url<\/span><\/strong> = <\/span><\/strong>‘https:\/\/api.fivetran.com\/v1’<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

    <\/span><\/strong>h<\/span><\/strong> = {<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

        <\/span><\/strong>‘Authorization’<\/span><\/strong>:\n<\/span><\/strong>f<\/span><\/strong>‘Bearer <\/span><\/strong>{<\/span><\/strong>api_key<\/span><\/strong>}<\/span><\/strong>:<\/span><\/strong>{<\/span><\/strong>api_secret<\/span><\/strong>}<\/span><\/strong>‘<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

    }<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

    <\/span><\/strong>url<\/span><\/strong> = <\/span><\/strong>f<\/span><\/strong>‘<\/span><\/strong>{<\/span><\/strong>base_url<\/span><\/strong>}<\/span><\/strong>\/<\/span><\/strong>{<\/span><\/strong>endpoint<\/span><\/strong>}<\/span><\/strong>‘<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

 <\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

    <\/span><\/strong>try<\/span><\/strong>:<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

        <\/span><\/strong>if<\/span><\/strong>\n<\/span><\/strong>method<\/span><\/strong> == <\/span><\/strong>‘GET’<\/span><\/strong>:<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

            <\/span><\/strong>response<\/span><\/strong>\n= <\/span><\/strong>requests<\/span><\/strong>.<\/span><\/strong>get<\/span><\/strong>(<\/span><\/strong>url<\/span><\/strong>, <\/span><\/strong>headers<\/span><\/strong>=<\/span><\/strong>h<\/span><\/strong>, <\/span><\/strong>auth<\/span><\/strong>=<\/span><\/strong>a<\/span><\/strong>)<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

        <\/span><\/strong>elif<\/span><\/strong>\n<\/span><\/strong>method<\/span><\/strong> == <\/span><\/strong>‘POST’<\/span><\/strong>:<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

            <\/span><\/strong>response<\/span><\/strong>\n= <\/span><\/strong>requests<\/span><\/strong>.<\/span><\/strong>post<\/span><\/strong>(<\/span><\/strong>url<\/span><\/strong>, <\/span><\/strong>headers<\/span><\/strong>=<\/span><\/strong>h<\/span><\/strong>, <\/span><\/strong>json<\/span><\/strong>=<\/span><\/strong>payload<\/span><\/strong>, <\/span><\/strong>auth<\/span><\/strong>=<\/span><\/strong>a<\/span><\/strong>)<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

        <\/span><\/strong>elif<\/span><\/strong>\n<\/span><\/strong>method<\/span><\/strong> == <\/span><\/strong>‘PATCH’<\/span><\/strong>:<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

            <\/span><\/strong>response<\/span><\/strong>\n= <\/span><\/strong>requests<\/span><\/strong>.<\/span><\/strong>patch<\/span><\/strong>(<\/span><\/strong>url<\/span><\/strong>, <\/span><\/strong>headers<\/span><\/strong>=<\/span><\/strong>h<\/span><\/strong>, <\/span><\/strong>json<\/span><\/strong>=<\/span><\/strong>payload<\/span><\/strong>, <\/span><\/strong>auth<\/span><\/strong>=<\/span><\/strong>a<\/span><\/strong>)<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

        <\/span><\/strong>elif<\/span><\/strong>\n<\/span><\/strong>method<\/span><\/strong> == <\/span><\/strong>‘DELETE’<\/span><\/strong>:<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

            <\/span><\/strong>response<\/span><\/strong>\n= <\/span><\/strong>requests<\/span><\/strong>.<\/span><\/strong>delete<\/span><\/strong>(<\/span><\/strong>url<\/span><\/strong>, <\/span><\/strong>headers<\/span><\/strong>=<\/span><\/strong>h<\/span><\/strong>, <\/span><\/strong>auth<\/span><\/strong>=<\/span><\/strong>a<\/span><\/strong>)<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

        <\/span><\/strong>else<\/span><\/strong>:<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

            <\/span><\/strong>raise<\/span><\/strong>\n<\/span><\/strong>ValueError<\/span><\/strong>(<\/span><\/strong>‘Invalid\nrequest method.’<\/span><\/strong>)<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

 <\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

        <\/span><\/strong>response<\/span><\/strong>.<\/span><\/strong>raise_for_status<\/span><\/strong>()\n <\/span><\/strong># Raise exception for 4xx or 5xx\nresponses<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

 <\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

        <\/span><\/strong>return<\/span><\/strong>\n<\/span><\/strong>response<\/span><\/strong>.<\/span><\/strong>json<\/span><\/strong>()<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

    <\/span><\/strong>except<\/span><\/strong> <\/span><\/strong>requests<\/span><\/strong>.<\/span><\/strong>exceptions<\/span><\/strong>.<\/span><\/strong>RequestException<\/span><\/strong>\n<\/span><\/strong>as<\/span><\/strong> <\/span><\/strong>e<\/span><\/strong>:<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

        <\/span><\/strong>print<\/span><\/strong>(<\/span><\/strong>f<\/span><\/strong>‘Request\nfailed: <\/span><\/strong>{<\/span><\/strong>e<\/span><\/strong>}<\/span><\/strong>‘<\/span><\/strong>)<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

        <\/span><\/strong>return<\/span><\/strong>\n<\/span><\/strong>None<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

 <\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

## Creating our connector<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

    <\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

method<\/span><\/strong>\n= <\/span><\/strong>‘POST’<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

endpoint<\/span><\/strong>\n= <\/span><\/strong>‘connectors’<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

 <\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

payload<\/span><\/strong>\n= {<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

    <\/span><\/strong>“service”<\/span><\/strong>:\n<\/span><\/strong>“google_sheets”<\/span><\/strong>,<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

    <\/span><\/strong>“group_id”<\/span><\/strong>:\n<\/span><\/strong>“”<\/span><\/strong>,    <\/span><\/strong>##\nPaste your group_id from the Fivetran Profile Data Tab<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

    <\/span><\/strong>“config”<\/span><\/strong>:\n{<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

        <\/span><\/strong>“schema”<\/span><\/strong>: <\/span><\/strong>“test_sheet_schema”<\/span><\/strong>,<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

        <\/span><\/strong>“table”<\/span><\/strong>: <\/span><\/strong>“test_sheet_table”<\/span><\/strong>,<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

        <\/span><\/strong>“sheet_id”<\/span><\/strong>: <\/span><\/strong>“”<\/span><\/strong>,     <\/span><\/strong>##\nPaste your google sheet link<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

        <\/span><\/strong>“named_range”<\/span><\/strong>: <\/span><\/strong>“test_sheet_range”<\/span><\/strong>,    <\/span><\/strong>##\nReplace you selected named range within the google sheets <\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

        <\/span><\/strong>“auth_type”<\/span><\/strong>: <\/span><\/strong>“ServiceAccount”<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

    }   <\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

}<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

 <\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

response<\/span><\/strong>\n= <\/span><\/strong>atlas<\/span><\/strong>(<\/span><\/strong>method<\/span><\/strong>, <\/span><\/strong>endpoint<\/span><\/strong>, <\/span><\/strong>payload<\/span><\/strong>)<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

 <\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

print<\/span><\/strong>(<\/span><\/strong>response<\/span><\/strong>)<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

 <\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

## Sync data from sheet<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

 <\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

method<\/span><\/strong>\n= <\/span><\/strong>‘POST’<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

connector_id<\/span><\/strong>\n= <\/span><\/strong>”<\/span><\/strong>  <\/span><\/strong>##\nPlace your connector ID here from you Fivetran profile Setup tab<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

endpoint<\/span><\/strong>\n= <\/span><\/strong>f<\/span><\/strong>‘connectors\/<\/span><\/strong>{<\/span><\/strong>connector_id<\/span><\/strong>}<\/span><\/strong>\/sync’<\/span><\/strong>\n  <\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

payload<\/span><\/strong>\n= {<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

    <\/span><\/strong>“force”<\/span><\/strong>:\n<\/span><\/strong>False<\/span><\/strong><\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

}<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

 <\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

response<\/span><\/strong>\n= <\/span><\/strong>atlas<\/span><\/strong>(<\/span><\/strong>method<\/span><\/strong>, <\/span><\/strong>endpoint<\/span><\/strong>, <\/span><\/strong>payload<\/span><\/strong>)<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

print<\/span><\/strong>(<\/span><\/strong>response<\/span><\/strong>)<\/o:p><\/span><\/strong><\/p>\n<\/td>\n<\/tr>\n<\/tbody><\/table><\/o:p><\/span><\/h2>\n\n


Step 02: Configuring Snowflake to DBT:<\/span>
<\/o:p><\/span><\/h2>

After having our dataset in our\nsnowflake warehouse, we configured our connection with DBT where we performed\nour transformation. We followed the setup guide provided by DBT and pasted our\ndesired credentials to configure our DBT environment. DBT environments are\nmanaged by version control git repositories that can either be localized or\npersonal based on the specific requirement.

<\/o:p><\/span><\/p>

In DBT, we linked our required\nschema in the development environment and used the locally managed repository\nby DBT itself to push our changes. We created branches for SQL models and\nperformed transformations which we pushed back into the repository and updated\nthe Snowflake warehouse.

<\/o:p><\/span><\/p>

Step 03: Writing SQL transformation in DBT:<\/span><\/o:p><\/span><\/h2>

\n\n\n\n\n\n\n\n<\/p>

The sales data included Quantity\nOrdered, Price Each (the price at which each item was sold), Cost Price (the\ncost of that item for the shopkeeper), Margin (profit on each item), Products,\nand Categories, along with miscellaneous columns. As per this, we applied the\nfollowing transformations:<\/o:p><\/span><\/p>\n\n\n

Our first transformation aimed to\nobtain the total number of orders, total quantity ordered, and total turnover\nfor each item in their respective categories.<\/span>
<\/p><\/span>


<\/p>\n\n

Lorem Ipsum has been the industry’s standard dummy text ever since the 1500s.<\/p>\n


Our second transformation aimed to obtain the cost price, total turnover, and total profit, along with the profit percentage on each item in their respective categories.
<\/span>
<\/p>\n\n

Lorem Ipsum has been the industry’s standard dummy text ever since the 1500s.<\/p>\n\n


After a successful transformation,\nwe built our models and pushed back changes to our warehouse and the\nDBT-managed repository.

<\/span><\/p>

Step 04: Exploration using Looker Studio:<\/span><\/o:p><\/span><\/h2>

Looker Studio is a BI tool within\nGoogle Cloud that provides us flexibility in creating interactive dashboards\nwith various charts and exporting them in different templates. It not only\nprovides us good data exploration on our data but it also comes with various\nconnectors that enable us to connect to any data source and extract data tables\nfrom pre-defined schemas.

<\/o:p><\/span><\/p>

\n<\/o:p><\/span><\/p>

We used one of these connectors to\nconnect to our snowflake warehouse where our previously transformed data was\npresent. After the correct configuration and successful connection, we fetched\ndata using SQL.

<\/o:p><\/span><\/span><\/p>\n\n

Lorem Ipsum has been the industry’s standard dummy text ever since the 1500s.<\/p>\n\n


In the looker studio, we utilized\nits toolkit to visualize our data in different formats.

<\/o:p><\/span><\/p>

\n<\/p>

First, we used the table kit to\npaste a tabular representation of our overall data (in an interactive way) to\nget a glimpse of the attributes present in our entire dataset. Then we plotted\na bar and pie chart that categorized the product categories concerning Cost\nPrice and Total turnover telling us how much is spent on each category and the\nprice on which it is sold as per the ordered quantity. 

<\/o:p><\/span><\/p>\n\n

Lorem Ipsum has been the industry’s standard dummy text ever since the 1500s.<\/p>\n\n


Then we moved towards the individual product since it linked with each category with one-to-many relation, and created interactive visualization covering the Bar-line chart for each product over cost price, turnover, a pie-chart with several orders on each product, and its heat tree map.

<\/span><\/p>\n\n

Lorem Ipsum has been the industry’s standard dummy text ever since the 1500s.<\/p>\n\n


If\nyou have stayed with us till the end of the article, then feel free to share\nyour comments and read our other technological blogs.

<\/o:p><\/span><\/p>

\nRead more about Data\nPipelines: <\/span>https:\/\/dotlabs.ai\/blogs\/2023\/11\/02\/streamlining-data-pipelines-a-guide-to-etl-and-elt\/<\/span><\/a>

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Embark on a transformative journey in data engineering with our step-by-step guide on building an ETL Data Pipeline. Utilizing cutting-edge tools like Fivetran connectors, Snowflake, DBT transformations, and Looker Studio, streamline your process for seamless data extraction, transformation, and exploration. Learn how to create dynamic visualizations and gain valuable insights.<\/p>\n","protected":false},"author":2,"featured_media":1226,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"pagelayer_contact_templates":[],"_pagelayer_content":"","footnotes":""},"categories":[37],"tags":[],"class_list":["post-1224","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tutorial"],"yoast_head":"\nModern Tools: Building Data Pipeline for ETL Operations | Dot Labs<\/title>\n<meta name=\"description\" content=\"Read through a Complete Step-by-Step Guide to Building Data Pipeline for ETL Operation Using Modern tools of Data Engineering and techniques!\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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