Outbrain is the top native advertising platform in the world, assisting customers worldwide in their online discoveries. With its more than 275 billion recommendations, Outbrain naturally improves and personalizes the reading experience.
Snowflake’s Data Cloud is built on a cutting-edge data platform that is delivered as Software-as-a-Service (SaaS). Snowflake provides Data Storage, Processing, and Analytic Solutions that are faster, easier to use, and more flexible than traditional options.
This article discusses the different methods to Connect Outbrain to Snowflake extensively. In addition to that, it also describes Outbrain and Snowflake briefly.
What is Outbrain?
Outbrain is the top discovery platform in the world that focuses solely on anticipating events and creating data-driven relationships between interests and behaviors. It is an unaffiliated third-party platform that provides native advertising.
It functions by allowing both publishers and advertisers access to the platform. By allowing advertisements to run on their website in specific locations, such as the sidebar, below the content, inside the content, or as a banner, publishers can monetize their content. The publisher is paid when specific actions are performed (which may include a predetermined number of impressions, clicks, or video views).
In contrast, the advertiser will pay to have these placements produce results. They will have the option to search for placements that might be useful to them and pay for a certain number of impressions or results. This can be used to boost sales, brand awareness, lead generation, and lift. You Pay per Click for advertisements on Outbrain Cost per Click (CPC).
Key Features of Outbrain
- Businesses can use Outbrain’s API to enhance video views and display video recommendations, as well as partner with the finest content creators.
- Outbrain offers useful features such as advanced conversion and testing tools for those who wish to achieve specific goals.
- Outbrain VR combines editorial judgment and decision-making with data gathered through algorithm recommendations and analytics to simplify Content Programming and enhance Audience Engagement.
- Outbrain’s Amplify service helps businesses reach an engaged audience using a flexible pay-per-click approach that directs traffic to blogs, articles, and video content.
- Outbrain’s Discovery Modules allow users to see the most recent trends in intriguing information. It may be customized and tailored to meet specific business objectives.
What is Snowflake?
Snowflake is a fully managed SaaS (Software as a Service) that combines Data Warehousing, Data Lakes, Data Engineering, Data Science, Data Application Development, and Secure Sharing and Consumption of Real-time / Shared Data into a single platform. To meet the demanding needs of growing businesses, Snowflake includes out-of-the-box features such as Storage and Compute Separation, On-the-fly Scalable Compute, Data Sharing, Data Cloning, and third-party Tool Support.
Snowflake isn’t based on any existing database technology or “Big Data” software platforms like Hadoop. However, it combines a brand-new SQL query engine with cutting-edge Cloud Architecture.
Snowflake is a hybrid of shared-disk and shared-nothing Database architectures, which allows it to deliver results quickly. It uses a central repository for persisted data, similar to a shared-disk database, which is accessible from all compute nodes.
Key Features of Snowflake
Here are some of the features of Snowflake as a Software as a Service (SaaS) solution:
- Snowflake enables you to enhance your Analytics Pipeline by transitioning from nightly Batch Loads to Real-time Data Streams, allowing you to improve the quality and speed of your analytics. By enabling Secure, Concurrent, and Monitoring Access to your Data Warehouse across your organization, you can improve the quality of analytics at your company.
- Snowflake uses the Caching Paradigm to swiftly deliver the results from the cache. To avoid re-generation of the report when nothing has changed, Snowflake employs Persistent (within the session) Query results.
- Snowflake allows you to get rid of silos and ensure access to meaningful insights across the enterprise, resulting in better Data-driven Decision-Making.
- Snowflake allows you to better analyze Customer Behaviour and Product Usage. You can also use the whole scope of data to ensure Customer Satisfaction, drastically improve product offers, and foster Data Science innovation.
- Snowflake allows you to create your own Data Exchange, which allows you to communicate live, controlled data securely. It also encourages you to improve data relationships throughout your business units, as well as with your partners and customers.
Why Integrate Outbrain to Snowflake?
The biggest challenge for marketers using platforms like Outbrain for online advertising is the amount of money lost on pointless ads. Less money may have been spent on a less well-liked product ad, resulting in lower ROIs; failing to take customer feedback into account when developing an advertising strategy may result in incorrect audience targeting.
You need to create advertisements that are more specifically targeted, like showing them to people who have completed an activity. Events can include looking up your product or reading reviews of it, looking up rival products, clicking on the various call-to-action buttons, or adding a specific product to a user’s shopping cart or wish list on a website or mobile device.
Your ability to gather and use data from various sources in Outbrain will determine how well optimized your ad delivery is. All of these data can’t be sent directly to Outbrain. Before using the relevant data to run ad campaigns on Outbrain, gather the relevant data and perform accurate analysis in a data warehouse like Snowflake. The time and effort required to manually complete this process results in a loss of potential revenue.
Outbrain solely focuses on creating data-driven links between user interests and behavior to assist you in identifying that precise behavior and promoting your post on other pieces of content to draw readers from other websites.
Snowflake provides data warehouse-as-a-service, a cloud-based data storage, and analytics service. Employing hardware and software-based in the cloud, businesses can use it to store and analyze data.
When integrated, moving data from Outbrain to Snowflake could solve some of the biggest data problems for businesses. This integration saves money and time and also helps better audience targeting.
In this article, we have described two methods to achieve this:
Method 1: Using Hevo to Set Up Outbrain to Snowflake
Hevo, an Automated Data Pipeline, provides you a seamless solution to connect Outbrain to Snowflake within minutes with an easy-to-use no-code interface. Hevo is fully managed and completely automates the process of not only loading data from Outbrain but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code.
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Method 2: Using Custom Code to Move Data from Outbrain to Snowflake
This method of moving data from Outbrain to Snowflake. would be time-consuming and tedious to implement. Users will have to write custom codes to enable Outbrain to Snowflake migration. This method is suitable for users with a technical background.
Both the methods are explained below.
Methods to Connect Outbrain to Snowflake
Method 1: Using Hevo to Set Up Outbrain to Snowflake
Hevo provides an Automated No-code Data Pipeline that helps you move your Outbrain data to Snowflake. Hevo is fully-managed and completely automates the process of not only loading data from your 150+ Sources(including 40+ free sources) but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
Using Hevo Outbrain to Snowflake Migration can be done in the following 2 steps:
- Step 1: Configure Outbrain as the Source in your Pipeline by following the steps below:
- Step 1.1: In the Asset Palette, select PIPELINES.
- Step 1.2: In the Pipelines List View, click + CREATE.
- Step 1.3: Select Outbrain on the Select Source Type page.
- Step 1.4: Enter the following information on the Configure your Outbrain Source page:
- Pipeline Name: A name for the Pipeline that is unique and does not exceed 255 characters.
- Username: The username in the access credentials obtained from Outbrain.
- Password: The password in the access credentials obtained from Outbrain
- Historical Sync Duration: The amount of time that the historical data must be ingested.
- Step 1.8: TEST & CONTINUE is the button to click.
- Step 1.9: Set up the Destination and configure the data ingestion.
- Step 2: To set up Snowflake as a destination in Hevo, follow these steps:
- Step 2.1: In the Asset Palette, select DESTINATIONS.
- Step 2.2: In the Destinations List View, click + CREATE.
- Step 2.3: Select Snowflake from the Add Destination page.
- Step 2.4: Set the following parameters on the Configure your Snowflake Destination page:
- Destination Name: A unique name for your Destination.
- Snowflake Account URL: This is the account URL that you retrieved.
- Database User: The Hevo user that you created in the database. In the Snowflake database, this user has a non-administrative role.
- Database Password: The password of the user.
- Database Name: The name of the Destination database where data will be loaded.
- Database Schema: The name of the Destination database schema. Default value: public.
- Warehouse: SQL queries and DML operations are performed in the Snowflake warehouse associated with your database.
- Step 2.5: Click Test Connection to test connectivity with the Snowflake warehouse.
- Step 2.6: Once the test is successful, click SAVE DESTINATION.
Here are more reasons to try Hevo:
- Smooth Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to your schema in the desired Data Warehouse.
- Exceptional Data Transformations: Best-in-class & Native Support for Complex Data Transformation at fingertips. Code & No-code Flexibility is designed for everyone.
- Quick Setup: Hevo with its automated features, can be set up in minimal time. Moreover, with its simple and interactive UI, it is extremely easy for new customers to work on and perform operations.
- Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
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Method 2: Using Custom Code to Move Data from Outbrain to Snowflake
Outbrain to Redshift
The first step in Outbrain to Redshift Integration is to get the data. The RESTful Amplify API from Outbrain allows you to extract data about marketers, campaigns, performance, and more. A JSON response from an API query is used for performance data. To prepare the data you’ll need to construct a schema for your data tables if you don’t already have a data structure in which to store the data you obtain for Outbrain to Redshift Integration.
To Load Data into Redshift, you can use the CREATE TABLE statement in the Redshift data warehouse to set up a table to receive all of the data after you know all of the columns you want to put. You can use INSERT and COPY commands to load data to Redshift. To update the Outbrain data select important fields that your script may use to save its progress through the data and return to them as it searches for updated data.
To learn more about connecting Outbrain to Redshift click here.
Redshift to Snowflake
Database Objects Migration
The first step is, to begin with, database objects, which primarily include Schema, Table Structures, Views, etc. Instead of changing the object’s structure during migration, we should prefer to leave it alone because doing so would harm the entire migration process. Later, DB objects in Snowflake must be created with the same structure as those in Redshift.
- Any project involving migration must include this activity. The first step is to determine the historical data sets for each table and how they can be migrated because the data volume will be quite high and we need to take this into account before beginning data migration activity. It is strongly advised to create separate batches for each table (based on the filter options like transaction date or any other audit columns) and migrate data in these batches rather than in one batch.
- After all historical data from all tables has been transferred to Snowflake, it will be relatively easy to move incremental data.
- Using Redshift’s “Unload Command” to unload data into S3 and Snowflake’s “Copy Command” to load the data from S3 into Snowflake tables could be one solution. This may also display some errors caused by compatibility problems, which you have already experienced on several occasions.
- Another strategy involves using any data replication tool that supports Snowflake as a target, which can then be used to load raw data from the source system into Snowflake. ETL/ELT pipelines can fill facts, dimensions, and metrics tables on the Snowflake platform on top of this raw data.
Migrating Code to Snowflake
- Compared to the previous two steps, this one has fewer difficulties and restrictions. In theory, Redshift and Snowflake both support ANSI-SQL, but they use different formats for various items, such as the lack of DISTKEY, SORTKEY, and ENCODE concepts in Snowflake.
- There are many other examples of using functions, but one of the key distinctions between date functions is that Redshift’s GETDATE() and Snowflake’s CURRENT TIMESTAMP() are both date functions. While JSON, AVRO, and PARQUET are semi-structured data types that can be supported by Snowflake’s “VARIANT” datatype, they cannot be directly stored in Redshift.
- To match the JSON field names in Redshift, the target table must be created by examining the JSON source data. Without a set structure, we cannot import it directly. Only the first-level elements can be parsed into the target table by the COPY functions.
- As a result, the multi-level elements are loaded into a single column and treated as strings. Redshift provides JSON SQL functions that must be used to further parse the intricate, multi-level data structures or arrays of JSON files. When migrating code, one must be extremely cautious and convert the code to supported SQL syntax.
Data Comparison between Redshift & Snowflake
- The final step in any migration project is to compare the data sets from the legacy and newly migrated platforms to make sure that everything was successfully migrated and that the output was accurate for the business.
- Given that you are moving from Redshift to Snowflake in this scenario, you must contrast the outcomes from the two systems. Data comparison between Redshift and Snowflake manually is a time-consuming and difficult task.
- Therefore, you created a custom Python script solution to connect to each DB, run some crucial checks against both DBs (Redshift as the source and Snowflake as the target), and compare the results.
- Some crucial checks include Record Counts, Data Type Comparisons, Metrics Comparisons in Fact Tables, DB Object Counts, Duplicate Checks, etc. A daily CRON job can be scheduled or automated to execute this solution.
This article explains how to connect Outbrain to Snowflake using various methods. It also gives an overview of Snowflake and Outbrain.
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Hevo offers a No-code Data Pipeline that can automate your data transfer process, hence allowing you to focus on other aspects of your business like Analytics, Marketing, Customer Management, etc.
This platform allows you to transfer data from 150+ sources (including 40+ Free Sources) such as Outbrain and Cloud-based Data Warehouses like Snowflake, Google BigQuery, etc. It will provide you with a hassle-free experience and make your work life much easier.
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