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Amazon DSP To Snowflake -Made Easy

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Replicate Amazon DSP to Snowflake in minute

Amazon DSP (Demand-Side Platform), displays your product ads on Amazon products like Fire T.V, Kindle, and Amazon Prime (OTT Platform). The platform provides maximum exposure to your product. Amazon DSP generates a wide range of data that includes: Advertising Campaigns, costs, creative placements, and advertising inventory. This platform help sellers analyze and focus on the best marketing strategy. Amazon DSP helps to discover from which creative ad placement, you are getting maximum customer traffic on your product page. Also, how many old customers returned to buy after watching Amazon DSP ads? Data helps to find and analyze such information. However, first, you must consolidate data into a scalable data warehouse like Snowflake. Such a data warehouse offers high-speed SQL queries using Google’s infrastructure’s processing power. Replicating your Amazon DSP data to Snowflake  can help you summarize data to improve analysis and, in turn, increase productivity and performance.

This article will take you through the overview of Amazon DSP and Snowflake. It will explain the reasons you should consider while integrating Amazon DSP and Snowflake. Finally, there are two approaches to replicate your data so you can decide which suits your business the best.

 

Why integrate Amazon DSP to Snowflake?

Let’s take a simple case to understand why data integration to Snowflake is essential. First, as you know that you can calculate your business profits by this formula:

Profits/Losses = Sales – Expenses.

For the profit/loss calculation, you can retrieve the sales data from the seller-central reports. And retrieve the advertising expense data from the Amazon DSP’s dashboard. In this article, we will focus on the advertisement expense data. Even though there are other expense data like shipping, packaging, warehousing, and not to mention commissions. Hence, the final equation would look like this:

Profits/Losses = (sales data from seller central – advertising expenses incurred in Amazon DSP Ads).

The sales and advertising expenditure data have to be put on the excel sheet or any equivalent software. This will help to perform the calculation for further analysis. And there are high chances of getting errors in final profit/loss results if your managers manually perform the task. Secondly, there will be no real-time results as the data retrieval, processing, and calculation needs time. It will be a cumbersome task to daily calculate the profit/loss figures. Especially, if you are advertising your product on other e-commerce sites or social media platforms. Hence, Daton, an ETL tool, will help pull all your data to a data warehouse like Snowflake. This data warehouse will consolidate your sales, advertising, customer behavior, and business operation data for deeper analysis. And, also help to optimize your business performance.

 

Amazon DSP Overview

Amazon has created its DSP (Demand Side Platform). This platform helps those online sellers who are always searching for new ways to enhance their conversion rates. Amazon DSP lets you endorse your brand products all over the web with high-quality audio and video product display ads. Also, this platform further helps sellers to increase customer acquisition and retention rates.

 

Snowflake Overview

Snowflake is a flexible, fast and robust data warehouse that helps to analyze big data. It offers high-speed SQL queries against petabytes of data using the processing power of Google’s infrastructure. It lets you upload a large proportion of datasets into Snowflake machine learning so you can understand your data better. Snowflake is a highly trusted source to process your data. This data warehouse enables you to securely and inexpensively process all the relevant data. And, also transform it into actionable insights for your enterprise.

 

How to replicate Amazon DSP to Snowflake?

Here are two approaches you can use to replicate Amazon DSP data to Snowflake. These approaches will not only allow you to evaluate the pros and cons of both but also, help choose the one that best suits your requirement.

Build your own data pipeline

This process needs a lot of experience and consumes time and manpower. Hence, the chances of getting errors are more due to multiple integrated steps to be executed one after the other. Therefore, you need to extract data using Amazon DSP APIs & then connect it properly with the Snowflake data warehouse. In conclusion, this whole process to build a custom data pipeline requires a regular intervention which makes it cumbersome.

Use Daton to integrate Amazon DSP to Snowflake

Integrating Amazon DSP to Snowflake with Daton is the fastest & easiest way to save your time and effort. Leveraging a cloud data pipeline like Daton significantly simplifies and accelerates the time it takes to build automated reporting.

Configuring data replication on Daton only takes a few minutes and a few clicks. Your analysts do not have to write any code or manage any infrastructure. And yet you can get access to Amazon DSP data in a few hours.

Daton’s simple and easy-to-use interface allows analysts and developers to use UI elements. to configure data replication from Amazon DSP to Snowflake.

Daton takes care of:

  • Authentication
  • Rate limits
  • Sampling
  • Historical data load
  • Incremental data load
  • Table creation, deletion, and reloads
  • Refreshing access tokens
  • Notifications
    and many more important functions for data analysts to focus on analysis rather than worrying about data replication.

 

Steps to integrate Amazon DSP with Daton

Daton - The Data Replication Superhero

 

  • Sign in to Daton.
  • Select Amazon DSP from the integrations page.
  • Provide Integration Name, Replication Frequency, and History. Integration name would be used in creating tables for the integration and cannot be changed later.
  • You will be redirected to Amazon DSP log in for authorizing Daton to extract data periodically.
  • Post successful authentication, you will be prompted to choose from the list of available Amazon DSP accounts.
  • Select required tables from the available list of tables.
  • Then select all required fields for each table.
  • Submit the integration.

For more information on Amazon DSP Data Connector, you can visit the linked article.

Sign up for a trial of Daton Today!

 

Here are more reasons to explore Daton for Amazon DSP and Snowflake Integration 

  • Low Effort & Zero Maintenance – Daton automatically takes care of all the data replication processes and infrastructure once you sign up for a Daton account and configure the data sources. No need to manage infrastructure or write manual code.
  • Faster integration – Amazon DSP to Snowflake is one of the integrations Daton can handle very conveniently and seamlessly. By following a few steps you can easily connect Amazon DSP to Snowflake.
  • Enterprise-grade data pipeline at an unbeatable price to help every business become data-driven. Get started with a single integration today for just $10 and scale up as your data needs grow.
  • Data consistency guarantee and an incredibly friendly customer support team ensure you can leave the data engineering to Daton and focus on analysis and insights!
  • Support for all major cloud data warehouses including Snowflake, Snowflake, Amazon Redshift, Oracle Autonomous Data Warehouse, PostgreSQL, and more.
  • Robust Scheduling Options: This allows you to schedule jobs based on their requirements using a simple configuration step.
  • Flexible loading options allow you to optimize data loading behavior to maximize storage utilization and ease of querying.
  • Enterprise-grade encryption gives your peace of mind.
  • Support for 100+ data sources – In addition to Amazon DSP, Daton can extract data from a varied range of sources such as Sales and Marketing applications, Databases, Analytics platforms, Payment platforms, and much more.
  • Daton, a fully managed modern ETL data pipeline gives you a fuss-free means to quickly and easily replicate your data from Amazon DSP to Snowflake.
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