Snapchat ads are unobtrusive, full-screen advertisements that users see between original content. Images or videos can be used as ads on Snapchat. They must have a minimum resolution of 1080 pixels by 1920 pixels, range in length from 3 seconds to 3 minutes, and be in a 9:16 aspect ratio.
Google BigQuery is a cloud-based enterprise Data Warehouse that allows users to run SQL queries quickly and analyze large datasets interactively. BigQuery is a read-only data processing engine based on Google’s Dremel Technology.
This article talks about how to Connect Snapchat to BigQuery in a few simple steps. In addition to that, it also describes Snapchat and Bigquery briefly.
Table of Contents
- What are Snapchat Ads?
- What is Google BigQuery?
- Why Connect Snapchat to BigQuery?
- Connecting Snapchat to BigQuery
What are Snapchat Ads?
Snapchat is a multi-media instant messaging service and app created in the United States by Snap Inc., formerly known as Snapchat Inc. One of the main characteristics of Snapchat is that images and messages are frequently only accessible for a brief period before they are no longer viewable by their recipients.
The Snapchat advertising platform, Snapchat Ads, gives you access to information about the advertisements, ad squads, and campaigns that are currently active on Snapchat.
Ads Manager, Snapchat’s self-serve advertising platform, lets you create ads, launch campaigns, track performance, and optimize for your objectives all in one location.
All advertisers, from big-name companies to small and medium-sized enterprises, can succeed with Snapchat ads.
- Starting at just $5 per day, Snapchat ads: Own the budget you set and make changes as you go.
- Obtain a Highly Interested Audience: enlist 332 million daily active users as your audience.
- Simple Campaign Optimization: You can real-time optimize your advertising campaigns by using the Snap Pixel and goal-based bidding.
Key Benefits of Using Snapchat Ads
- Drives Traffic: Snaps are made with a brief lifespan in mind. An image or video is sent to you; you click on it, and then, poof, it vanishes. Despite appearing somewhat ineffective, it can increase traffic. Making photos that are intended to vanish gives them an urgency that your audience won’t want to miss.
- Boost engagement: This is ideal for marketers who are organizing events, want to highlight the culture of their business, or want to use them in an integrated marketing effort. With Snapchat’s on-demand geofilters, you can make unique filters that other users can use.
- Build Brand Awareness: Snapchat is a popular tool for big-name brands to advertise because it works. With over 265 million daily active users, Snapchat can help you reach a sizable audience through amusing advertisements, eye-catching filters, or captivating stories.
- Connect with a Younger Demographic and Attract New Followers: This depends on your industry and target market (which is why it’s a good idea to conduct research first and develop your buyer personas). Given that 82 percent of all Snapchat users fall into this category and that they typically use the app for 30 minutes per day, it should be easy for you to target the appropriate audience.
- Influence Purchases: When they aren’t busy sending their friends a selfie taken with the most bizarre filter they could find, Snapchat users are likely to stumble upon new businesses. The ‘Discover’ icon on the app allows users to connect with brands, allowing them to view content produced by businesses using Snapchat for marketing. According to studies, Snapchat users are daily spending 35% more time in the Discover section and are 60% more likely to make impulsive purchases.
- Offers Another Way to Consume Content: You probably already have a variety of ways you produce content for your users, including blogs, eBooks, LinkedIn posts, and videos. There’s a good chance that your rivals are following suit.
What is Google BigQuery?
Google BigQuery is a highly scalable, serverless, multi-cloud Data Warehouse that uses a build-in query engine. It is a highly scalable serverless, fully-featured, fully manageable Data Warehouse that enables scalable analysis over petabytes of data. It is developed by Google and launched on 19th May 2010. It is designed such that it uses the processing power of Google’s infrastructure that makes a single SQL query to analyze petabytes of data in seconds.
BigQuery is also called SQL-based Data Warehouse as a Service (DWaaS) with zero infrastructure management. It is a serverless warehouse that does not require any upfront hardware provisioning or management. BigQuery runs SQL Queries and all requests are to be authenticated. Google provides a complete package to their users with Big Data loading features on Google Cloud Storage and connectivity with various Google apps like Apps Script. Google BigQuery has many built-in features like Machine Learning and AI Capabilities, Geospatial Analysis, and Business Intelligence.
BigQuery uses a Columnar Storage format that is optimized for analytical queries to store data. BigQuery displays data in tables, rows, and columns, with full database transaction semantics support (ACID). To ensure high availability, BigQuery storage is automatically replicated across multiple locations.
Key Features of Google BigQuery
- Flexible Scaling: You don’t have to explicitly tweak the cluster with BigQuery since computing resources are automatically adjusted according to the workload, and it can easily extend storage to Petabytes on demand. Patching, updates, computing, and storage resource scaling are all handled by Google BigQuery, making it a fully managed service.
- Storage: Google BigQuery leverages Google’s global storage system, Colossus, to store and optimize your data for free and with no downtime. To store data, Google BigQuery uses the opinionated Capacitor format in Colossus, which achieves various enhancements behind the scenes while burning a large amount of CPU/RAM, all without affecting query performance or imposing a bill limit.
- Programming Access: Google BigQuery may be easily accessed in applications using Rest API queries, Client Libraries such as Java, .Net, Python, the Command-Line Tool, or the GCP Console. It also includes query and database management tools.
- Federated Query: Google BigQuery employs a novel method for sending a query statement to an external database and receiving the results as a temporary table given the user’s data is stored in Bigtable, GCS, or Google Drive.
Why Connect Snapchat to BigQuery?
By operationalizing your data through the Snapchat to BigQuery integration, you can make the most of your time and concentrate on the things that are important to you. Connecting Snapchat to BigQuery enables operational reporting, improves performance by offloading queries, supports data governance initiatives, archives data for disaster recovery, and more. Additionally, the integration of Snapchat to BigQuery offers reliable, secure, and robust data movement.
Explore These Methods to Connect Snapchat to BigQuery
Snapchat ads are for every business, big or small. Snapchat ads can help you effectively reach your target audience. Google BigQuery offers exceptional performance because it can automatically scale up and down based on the volume of data and effectively analyses data.
When integrated, moving data from Snapchat to BigQuery could solve some of the biggest data problems for businesses. In this article, we have described two methods to achieve this:
Hevo Data, an Automated Data Pipeline, provides you a hassle-free solution to connect Snapchat to BigQuery 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 Snapchat but also enriching the data and transforming it into an analysis-ready form without having to write a single line of code.GET STARTED WITH HEVO FOR FREE
This method of integrating Snapchat to BigQuery would be time-consuming and somewhat tedious to implement. Users will have to write custom codes to enable Snapchat BigQuery migration. This method is suitable for users with a technical background.
Both the methods are explained below.
Connecting Snapchat to BigQuery
- Method 1: Using Hevo Data to Set Up Snapchat to BigQuery
- Method 2: Using Custom Code to Move Data from Snapchat to BigQuery
Method 1: Using Hevo Data to Set Up Snapchat to BigQuery
Hevo provides an Automated No-code Data Pipeline that helps you move your data from Snapchat to BigQuery. 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 Data Snapchat to BigQuery Migration can be done in the following 2 steps:
- Step 1: Configure Snapchat 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 Snapchat on the Select Source Type page.
- Step 1.4: Click + ADD SNAPCHAT ADS ACCOUNT on the page that asks you to configure your Snapchat ads account.
- Step 1.5: Use your Snapchat Ads account to log in.
- Step 1.6: To grant Hevo access to your Snapchat Ads data, click Continue.
- Step 1.7: Enter the information below in the Configure your Snapchat Ads Source page:
- Pipeline Name: A name for the Pipeline that is unique and does not exceed 255 characters.
- Select Organizations: The Snapchat company whose information you want to take in. You can choose from a variety of organizations.
- Select Ad Accounts: The account for Snapchat Ads whose data you want to download. You can choose several Ad Accounts.
- Historical Sync Duration: The time it takes for historical data to be synced with the Destination. 1 Year is the default value.
- 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 Google BigQuery 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 Google BigQuery from the Add Destination page.
- Step 2.4: Choose the BigQuery connection authentication method on the Configure your Google BigQuery Account page.
- Step 2.5: Choose one of these:
- Using a Service Account to connect:
- Service Account Key file, please attach.
- Note that Hevo only accepts key files in JSON format.
- Go to CONFIGURE GOOGLE BIGQUERY ACCOUNT and click it.
- Using a user account to connect:
- To add a Google BigQuery account, click +.
- Become a user with BigQuery Admin and Storage Admin permissions by logging in.
- To grant Hevo access to your data, click Allow.
- Using a Service Account to connect:
- Step 2.6: Set the following parameters on the Configure your Google BigQuery page:
- Destination Name: A unique name for your Destination.
- Project ID: The BigQuery Project ID that you were able to retrieve in Step 2 above and for which you had given permission in the previous steps.
- Dataset ID: Name of the dataset that you want to sync your data to, as retrieved in Step 3 above.
- GCS Bucket: To upload files to BigQuery, they must first be staged in the cloud storage bucket that was retrieved in Step 4 above.
- Enable Streaming Inserts: Enable this option to load data via a job according to a defined Pipeline schedule rather than streaming it to your BigQuery Destination as it comes in from the Source. To learn more, go to Near Real-time Data Loading Using Streaming. The setting cannot be changed later.
- Sanitize Table/Column Names: Activate this option to replace the spaces and non-alphanumeric characters in between the table and column names with underscores ( ). Name Sanitization is written.
- Step 2.5: Click Test Connection to test connectivity with the Amazon Redshift 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 Snapchat to BigQuery
To set up Snapchat BigQuery Integration, you first need to Connect Snapchat to CSV and CSV to BigQuery.
Snapchat to CSV
All of your metrics and insights for a specific ad account are available for export in two different types of reports in the form of a CSV. CSV also acts as a Snapchat BigQuery connector here. You could export:
For a specific period within that ad account, a custom report includes all of your reporting metrics. All of your campaigns, ad sets, and ads can be included in a custom report that you can pull. To export a personalized report:
- Step 1: Access Ads Manager by logging in.
- Step 2: From the dropdown menu in the top corner, choose an Advertising Account.
- Step 3: Choose “Manage Ads” from the menu by clicking the menu button in the top corner.
- Step 4: Choose “Campaigns,” “Ad Sets,” or “Ads.”
- Step 5: To edit the data that will be included in your CSV export, click “Columns.”
- Step 6: To select a configuration and export your CSV file, click “Download.”
- Step 7: Select “Export.” A document in excel format will be created using the current selection of columns, dates, and times.
Delivery Insight Reports
Delivery Insights is a report on ad performance that can assist you in determining who saw, engaged with, and responded to your advertisements. The effectiveness of your campaign can be improved by using audience insights from their demographics, locations, and interests to guide optimization. There are several ways to view Delivery Insights:
- When a campaign is launched or at any time after, you can export delivery insights at the level of the campaign, ad set, or ad.
- With Delivery Insights, you can create a performance report for the campaign, ad set, or ad level with a custom date range.
On-the-ground campaign, ad set, or ad level performance is provided by delivery insights, which also provide estimated audience data.
To View your insights:
- Step 1: Enter Ads Manager.
- Step 2: Choose an advertising account in the top right corner’s dropdown menu.
- Step 3: ‘Manage Ads‘ can be chosen by clicking the icon in the top corner.
- Step 4: Choose the “Campaigns,” “Ad Sets,” or “Ads” you wish to view insights for.
- Step 5: Select “View Insights”.
- Step 6: If necessary, modify the Attribution Window and Date Range.
Importing your insights:
- Step 1: Enter Ads Manager.
- Step 2:In the top right corner’s dropdown menu, choose an advertising account.
- Step 3: ‘Manage Ads’ can be chosen by clicking the icon in the top corner.
- Step 4: Opt for “Campaigns,” “Ad Sets,” or “Ads.”
- Step 5: You can view Delivery Insights for specific campaigns, ad sets, or ads by selecting them.
- Step 6: To export in bulk, click on Export.
- Step 7: From the dropdown, choose the desired metric breakdown.
- There are the following first-party choices:
- Age and Gender
- Only accessible to iOS 14 Opt-In users:
- Snapchat Lifestyle Category
- Region (US Only)
- DMA (US Only)
- Device Make
- There are the following first-party choices:
CSV to BigQuery
CSV is a Snapchat to BigQuery connector here. You can connect CSV to BigQuery in the following ways:
Using the Command Line Interface
The bq load command creates or updates a table and loads data in a single step.
E.g. Assuming you have a dataset named mydb and there exists a table named mytable in it.
bq load mydb.mytable mysource.txt name:string,count:integer
Explanation of the bq load command arguments:
datasetID: mydb tableID: mytable source: mysource.txt: [if necessary, include the full path to the file] schema: name:string, count:integer ..... [Repeat for all columns in the CSV to be mapped into Bigquery columns]
To check if the table has been populated, you can run the following command:
bq show mydb.mytable
Sample output will be:
Last modified Schema Total Rows Total Bytes Expiration ----------------- ------------------- ------------------- ------------ 22 Aug 15:31:00 |- name: string 352 6456 -- |- count: integer ....
The manual process has the obvious limitations of scalability, portability, and susceptibility to error.
Using BigQuery Web UI
You can make use of the simple Web UI of BigQuery and load CSV data using the following steps:
- Step 1: You can go to your Web console and click “Create table” and then “Create table from”.
- Step 2: Next, you can specify the CSV file, which will act as a source for your new table.
- Step 3: The “Source” dropdown will let you select amongst many sources like Cloud storage.
- Step 4: In “File format”, select CSV.
- Step 5: Then select a database and give your table a name.
- Step 6: You can either upload a sample JSON to specify the schema or leave the schema definition to “auto-detect”.
- Step 7: Some other configurable parameters are field delimiter/skip header rows/number of errors allowed/jagged rows etc.
- Step 8: Clicking on “Create Table” will now fetch your CSV, ascertain the schema, create the table and populate it with the CSV data.
Using Web API
A full discussion on the coding is beyond the scope of this article, but broadly speaking, your steps would be as follows:-
- Specify the source URL, dataset name, destination table name, etc.
- Initialize the client that will be used to send requests(can be reused for multiple requests).
- Specify the “Load Job configuration“, and make sure you do not miss essential format options.
- Load the table using API commands, this load job will block until the table is successfully created and loaded or an error occurs.
- Check if the job was completed or if there were some errors.
- Throw appropriate error messages, make changes, and retry the process.
This is the most configurable and flexible option, but also the most error-prone and susceptible to maintenance whenever the source or destination schema changes.
Your program will need some time-tested trials to mature.
This article explains the simple steps to Snapchat to BigQuery Integration. It also gives an overview of Bigquery and Snapchat.Visit our Website to Explore Hevo
Hevo Data 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 Snapchat and Cloud-based Data Warehouses likeSnowflake, Google BigQuery, etc. It will provide you with a hassle-free experience and make your work life much easier.
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