{"id":33091,"date":"2022-08-02T06:21:42","date_gmt":"2022-08-02T06:21:42","guid":{"rendered":"https:\/\/sarasanalytics.com\/?p=33091"},"modified":"2023-04-19T11:10:58","modified_gmt":"2023-04-19T11:10:58","slug":"google-analytics-to-snowflake-made-easy","status":"publish","type":"post","link":"https:\/\/sarasanalytics.com\/blog\/google-analytics-to-snowflake-made-easy\/","title":{"rendered":"Google Analytics to Snowflake \u2013 Made Easy"},"content":{"rendered":"
If you are reading this, you are probably looking for a way to transfer data from Google Analytics<\/a> to Snowflake quickly & efficiently. In this article, we will talk about why using Google Analytics is essential and how you can get data from all your apps and tools together in one place without having to write any code.<\/p>\n The choice for eCommerce businesses when it comes to marketing and selling their merchandise is growing every day. eCommerce vendors have to decide on what channels they want to sell on, which channels they want to spend their advertising dollars on, whether the channels include:<\/p>\n Complexity increases with the addition of every sales channel. For instance, if we consider marketing channels available to support online business, you will find a choice of:<\/p>\n In the competitive digital landscape that we live in, it has become imperative that eCommerce businesses<\/a> of all sizes that aspire to grow and stay profitable have to look into their data deeply and leverage this for growth.<\/p>\n With the increase in competition, eCommerce Companies should strive to be more data-driven for various reasons. Some of these reasons include:<\/p>\n Businesses these days need to be efficient in terms of their data analysis. They are struggling to make sense of the data generated from various applications and tools used to manage different processes efficiently.<\/p>\n Due to the reasons highlighted above, any eCommerce business typically operates at least 10-15 different software\/platforms to deliver on their customer expectations. As a result, data silos are created, which makes it more difficult to consolidate data and use the data for reporting, operations, analysis, and taking informed forward-looking decisions.<\/p>\n Analytics platforms like\u00a0Google Analytics\u00a0generate a substantial amount of data like Traffic, Audience Demography, User Behaviour, Clicks,\u00a0Bounce Rates, Time on Site, Traffic Source, Browsing Device and much more. Additionally, eCommerce companies that sell globally often end up having separate views or dashboards for each country-specific website. It is quite normal to have marketers, product managers, and eCommerce managers needing to review data from multiple GA assets. Imagine a brand selling in three countries; they would have different marketing channels with varying demographics of the audience for each country \u2013 driving traffic to a separate website.<\/p>\n For example:<\/p>\n https:\/\/myshop.us for USA<\/p>\n https:\/\/myshop.de for Germany<\/p>\n https:\/\/myshop.fr for France<\/p>\n It would be tough to get the complete picture of the business in one place if you do not consolidate data generated in tools used by multiple departments. However, it takes a considerable amount of time, skills, and resources to extract all data manually. At times, it is almost impossible, leaving analysts with little time or scope to focus on analysis.<\/p>\n These separate silos make a comprehensive analysis of the business data, challenging. Data Savvy eCommerce businesses try to reduce the effort of reporting and analysis by integrating data from all these channels into a cloud data warehouse like Snowflake. By taking this step, the process of reporting and analysis becomes easy, inexpensive, and consequently done more frequently.<\/p>\n In this post, we will be looking at methods to replicate data from\u00a0Google Analytics to Snowflake.<\/p>\n Before we start exploring the process involved in data transfer, let us spend some time looking at these individual platforms.<\/p>\n <\/p>\n Google Analytics\u00a0is the most popular Business Analytics tool used by Brands. It is free to use and provides detailed reporting and analysis resources for all sizes of websites. Google Analytics helps users to track the traffic of their websites, identify the user experience with their pages and create custom reports based on their company\u2019s needs. Google Analytics gathers, administers, and centralizes website data. This data can be used to understand how visitors interact with the website, blogs, impact of advertisements, videos, and social media on revenue, popular products, and much more. Users can leverage the Google Analytics data for analysis and in the generation of transformational insights. Companies can enhance their marketing strategies and experiment with their web content, website, products, pricing and much more by using the insights. People who use Google Analytics like using it because :<\/p>\n Track many websites in a single dashboard.<\/p>\n <\/p>\n Snowflake\u00a0is a cloud-based data warehouse created by three data warehousing experts at Oracle Corporation in 2012. Snowflake Computing, the vendor behind the Snowflake Cloud Data Warehouse product, raised over $400 million over the past eight years and acquired thousands of customers. One might wonder if another data warehouse vendor is needed in an already crowded field of traditional data warehousing technologies like Oracle, Teradata, SQL Server, and cloud data warehouses like Amazon Redshift and Google BigQuery. Well, the answer is the disruption caused by cloud technologies and cloud opportunities for new technology companies. Public clouds enabled startups to shed past baggage, learn from the past, challenge the status quo, and take a fresh look at cloud opportunities to create a new data warehouse product. You can read\u00a0this article\u00a0to understand the core technology components that make up this modern, cloud-built data warehouse for consumers of cloud technologies.<\/p>\n You can\u00a0register for a $400 free trial of Snowflake\u00a0within minutes. This credit is sufficient to store a terabyte of data and run a small data warehouse environment for a few days.<\/p>\n <\/p>\n <\/p>\n Let\u2019s take a simple example to illustrate why data consolidation to Snowflake<\/a> can be helpful for an eCommerce business.<\/p>\n A global company sells its products in different countries through various channels like online marketplaces like Alibaba, Amazon or their website using platforms like Shopify, Unicommerce runs different marketing campaigns in different channels in each country.<\/p>\n Now let us say that the company wants to calculate its overall business profits. We all know that :<\/p>\n Profits\/Losses = Sales \u2013 Expenses<\/p>\n The sales data will come from Shopify as well as from Amazon and Alibaba. Thus, it becomes a nearly impossible task to pull all of these data from multiple platforms for each country separately, and then analyze all of this data together with the expense data and calculate profits. It involves a lot of working hours which costs money, and there is usually a time lag involved, which reduces the accuracy of the analysis and its effectiveness as the data is not analyzed in real-time. Consolidate all of the data in a data warehouse like Snowflake to simplify the process.<\/p>\n If this company then wants to optimize its profits, they need to increase sales and decrease expenses. Hence they need to associate the traffic flowing from their marketing campaigns to the purchases taking place to understand which marketing activity is generating better ROIs and which needs improvement, this is where Google Analytics comes into play. It captures the flow of traffic from different channels into a website. But Google Analytics fails to capture the sales data accurately and the data from marketing tools like target audience, Ad impressions. To understand the sales funnel clearly, and give accurate attributions to the marketing activities, it becomes vital to check the data from the various data sources in use manually and then tally that data to the data coming from Google Analytics to gain meaningful insights. Hence, this becomes a difficult task when done manually on a scale.<\/p>\n Using Only Google analytics, it is not possible to :<\/p>\n For these reasons, top companies consolidate all of their data from\u00a0Google Analytics and other apps and tools into a data warehouse like Snowflake\u00a0to analyze the data and generate reports at a rapid pace.<\/p>\n <\/p>\n There are two board ways to pull data from any source to any destination. The decision is always a build vs buy decision. Let us look at both these options to see which option provides the business with a scalable, reliable, and cost-effective solution for reporting and analysis of Google Analytics data. The following data is available from\u00a0Google Analytics for replication to a data warehouse.\u00a0You can also retrieve the data from Snowflake any time you want.<\/p>\n <\/p>\n To build support for extracting data using Google Analytics APIs, the developer or analyst will have to follow the steps.<\/p>\n In addition to the steps listed above, developers also have to figure out how to handled sampling-related issues that are common with Google Analytics. You can read more about the infamous Google Analytics sampling\u00a0here.<\/p>\n Once you have automated the extract of data from Google Analytics and you manage to save the data as a CSV or a JSON file, you can use the file to load Google Analytics data into Snowflake. You can leverage Snowflake loading routines to accomplish the task of loading data.<\/p>\n <\/p>\n Building support for APIs is not only tedious, but it is also extremely time-consuming, difficult, and expensive. Engaging analysts or developers in writing support for these APIs takes away their time from more revenue-generating endeavours. Leveraging an\u00a0eCommerce data pipeline like Daton<\/a>, significantly simplifies and accelerates the time it takes to build automated reporting. Daton supports automated extraction and loading of Google Analytics data into cloud data warehouses like Google BigQuery, Snowflake, Amazon Redshift, and Oracle Autonomous DB.<\/p>\n Configuring data replication on Daton on only takes a minute and a few clicks. Analysts do not have to write any code or manage any infrastructure but can still get access to their Google Analytics data in a few hours. Any new data is generated automatically replicated to the data warehouse without any manual intervention.<\/p>\n Daton\u00a0supports replication from Google Analytics to a cloud data warehouse<\/a> of your choice, including Snowflake. Daton\u2019s simple and easy to use interface allows analysts and developers to use UI elements to configure data replication from Google Analytics data into Snowflake. Daton takes care of<\/p>\n And many more important functions that are required to enable analysts to focus on analysis rather than worry about the data that is delivered for analysis.<\/p>\n <\/p>\n Daton is a fully-managed, cloud data pipeline that seamlessly extracts relevant data from many data sources for consolidation into a data warehouse of your choice for more effective analysis. The best part analysts and developers can put Daton into action without the need to write any code.<\/p>\n <\/p>\n We Saras Analytics<\/a>, can help with our eCommerce-focused Data pipeline (Daton) and custom ML and AI solutions to ensure you always have the correct data at the right time. Request a demo and envision how reporting is supercharged with a 360\u00b0 view.<\/p>\n For all sources, check our\u00a0data connectors<\/a>\u00a0page.<\/p>\n Other Articles by\u00a0Saras Analytics<\/a>,<\/p>\n If you are reading this, you are probably looking for a way to transfer data from Google Analytics to Snowflake quickly & efficiently. In this article, we will talk about why using Google Analytics is essential and how you can get data from all your apps and tools together in one place without having to […]<\/p>\n","protected":false},"author":1,"featured_media":35482,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[395,399],"tags":[],"_links":{"self":[{"href":"https:\/\/sarasanalytics.com\/wp-json\/wp\/v2\/posts\/33091"}],"collection":[{"href":"https:\/\/sarasanalytics.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sarasanalytics.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sarasanalytics.com\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/sarasanalytics.com\/wp-json\/wp\/v2\/comments?post=33091"}],"version-history":[{"count":8,"href":"https:\/\/sarasanalytics.com\/wp-json\/wp\/v2\/posts\/33091\/revisions"}],"predecessor-version":[{"id":54320,"href":"https:\/\/sarasanalytics.com\/wp-json\/wp\/v2\/posts\/33091\/revisions\/54320"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sarasanalytics.com\/wp-json\/wp\/v2\/media\/35482"}],"wp:attachment":[{"href":"https:\/\/sarasanalytics.com\/wp-json\/wp\/v2\/media?parent=33091"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sarasanalytics.com\/wp-json\/wp\/v2\/categories?post=33091"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sarasanalytics.com\/wp-json\/wp\/v2\/tags?post=33091"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
Branded Websites<\/h4>\n
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Marketplaces<\/h4>\n
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Retail Stores<\/h4>\n
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Google Analytics Overview<\/h2>\n
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Snowflake Overview<\/h2>\n
Why Do Businesses Need to Replicate Google Analytics to Snowflake<\/h2>\n
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Replicate Google Analytics Data to Snowflake<\/h2>\n
Build your Data Pipeline<\/h2>\n
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Use a Cloud Data Pipeline<\/h2>\n
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Daton \u2013 The Data Replication Superhero<\/h2>\n
Here are more reasons to explore Daton:<\/h3>\n
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