This post will help you with syncing your Twitter Ads data to Snowflake. By doing this you will be able to perform advanced analytics on a system that is designed for this kind of data payloads, like Snowflake. Alternatively, you can simplify the process of syncing data from Twitter Ads to Snowflake by using Blendo, where the whole process will be handled by Blendo and you can focus on what matters, the monitoring of your digital marketing spending and performance data. By analyzing the data produced by Twitter Ads you can launch campaigns tailored based on your business goals and effectively expand your product’s reputation to new audiences.
About Twitter Ads
Twitter Ads is a self-service advertising platform, announced in April 2013 by Twitter. The ads launched within this platform belong to one of the following categories:
- Promoted Trends. A sponsored topic on the top of trending news section which is supposed to be one of the most discussed topics at the given time.
- Promoted Accounts. Accounts that are put at top of the suggested accounts box. They are usually a way for brands to gain more followers.
- Promoted Tweets. Tweets that shown first in the search results of related topics.
Access your data on Twitter Ads
The first step in loading your Twitter Ads data to any kind of data warehouse solution is to access your data and start extracting it.
By using the Ads API program businesses can create, run and manage ad campaigns programmatically on Twitter. A big part of the API is also a rich reporting system that helps you tailor your campaigns by selecting different targeting options and placement parameters. You can also retrieve detailed statistics on the performance of your campaigns by generating reporting or historical backfills.
Using this API, a user can retrieve details associated with the current account regarding the following resources:
- Lineitem Apps & Lineitems
- Promoted Accounts & Promoted tweets reference
- Scheduled promoted tweets reference
- Funding Instruments
- Media Creatives
- Targeting Criteria
- Account Media
- Scheduled/Promoted/Organic/Draft Tweets
Various reports can also be fetched as long as they are valid combinations between an entity and segmentation types, such as:
- Reach Campaigns Report
- Reach Funding Instruments Report
- Auction Insights Report
In addition to the above, the things that you have to keep in mind when dealing with the Twitter API, are:
- Rate limits. There is no restriction for concurrent API calls. There is a restriction for API calls per endpoint in 15-minute windows. However, in general limits are generous for most endpoints and should not impede use cases.
- Authentication. You authenticate on Twitter Ads using OAuth.
- Pagination. There is a pagination ability for retrieving data in some resources, with a page count that varies from 200 to 1000 depending on the specific resource endpoint. There is also a sorting method for retrieving data in some resources.
Transform and prepare your Twitter Ads data for Snowflake
After you have accessed your data on Twitter Ads, you will have to transform it based on two main factors,
- The limitations of the database that the data will be loaded onto
- The type of analysis that you plan to perform
Each system has specific limitations on the data types and data structures that it supports. If for example you want to push data into Google BigQuery, then you can send nested data like JSON directly.
Of course, when you are dealing with tabular data stores, like Microsoft SQL Server, this is not an option. Instead, you will have to flatten out your data, just as in the case of JSON, before loading into the database.
Also, you have to choose the right data types. Again, depending on the system that you will send the data to and the data types that the API exposes to you, you will have to make the right choices. These choices are important because they can limit the expressivity of your queries and limit your analysts on what they can do directly out of the database.
Data in Snowflake is organized around tables with a well-defined set of columns with each one having a specific data type.
Snowflake supports a rich set of data types. It is worth mentioning that a number of semi-structured data types is also supported. With Snowflake, is possible to load directly data in JSON, Avro, ORC, Parquet, or XML format. Hierarchical data is treated as a first-class citizen, similar to what Google BigQuery offers.
There is also one notable common data type that is not supported by Snowflake. LOB or large object data type is not supported, instead, you should use a BINARY or VARCHAR type instead. But these types are not that useful for data warehouse use cases.
A typical strategy for loading data from Twitter Ads to Snowflake is to create a schema where you will map each API endpoint to a table.
Each key inside the Twitter Ads API endpoint response should be mapped to a column of that table and you should ensure the right conversion to a Snowflake data type.
Of course, you will need to ensure that as the data types from the Twitter Ads API might change, you will adapt your database tables accordingly, there’s no such thing as automatic data type casting.
After you have a complete and well-defined data model or schema for Snowflake, you can move forward and start loading your data into the database.
Load data from Twitter Ads to Snowflake
Usually, data is loaded into Snowflake in a bulk way, using the COPY INTO command. Files containing the data, usually in JSON format, are stored in a local file system or in Amazon S3 buckets. Then a COPY INTO command is invoked on the Snowflake instance and data is copied into the data warehouse.
The files can be pushed into Snowflake using the PUT command, into a staging environment before the COPY command is invoked.
Another alternative is to upload the data directly into a service like Amazon S3 from where Snowflake can access the data directly.
Updating your Twitter Ads data on Snowflake
As you will be generating more data on Twitter Ads, you will need to update your older data on Snowflake. This includes new records together with updates to older records that for any reason have been updated on Twitter Ads.
You will need to periodically check Twitter Ads for new data and repeat the process that has been described previously while updating your currently available data if needed. Updating an already existing row on a Snowflake table is achieved by creating UPDATE statements.
Another issue that you need to take care of is the identification and removal of any duplicate records on your database. Either because Twitter Ads does not have a mechanism to identify new and updated records or because of errors on your data pipelines, duplicate records might be introduced to your database.
In general, ensuring the quality of the data that is inserted into your database is a big and difficult issue.
The best way to load data from Twitter Ads to Snowflake
So far we just scraped the surface of what can be done with Snowflake and how to load data into it. The way to proceed relies heavily on the data you want to load, from which service they are coming from and the requirements of your use case.
Things can get even more complicated if you want to integrate data coming from different sources. A possible alternative, instead of writing, hosting and maintaining a flexible data infrastructure, is to use a product like Blendo that can handle this kind of problems automatically for you.
Blendo integrates with multiple sources or services like databases, CRM, email campaigns, analytics and more. Quickly and safely move all your data from Twitter Ads to Snowflake and start generating insights from your data.
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