What’s Ahead for 2025

We hope you’ve been able to protect your peace and surround yourself with solidarity in these times. At CTA, we’re digging in on the future, and we’re focused on the concrete steps we can take to empower the resistance. That means:

• We’ve got new, transparent pricing - we’re thrilled to announce our PAD+Voterfile pricing starting at just $5.5k per month. 

• We’re hiring - we have exciting initiatives to tackle and are looking for a number of new Proggers to help us accomplish big things. 

• We’re releasing new features! Check out the ProgLog below - but we’ve got a sweet new sync view that gives you transparency into sync schedules like never before. We’ve also partnered with MiG to release a speedy new sync to DDx!

• We’re planning for the features our partners need next. We are so grateful to our partners who participated in focus groups to help us prioritize our roadmap & begin construction. 

• We’re doubling down on PAD infrastructure - Analytics Hub, Kubernetes, and Replatforming - Oh My! CTA has a lot to share in our infrastructure evolution for 2025. 

2025 will be an ambitious year for CTA and for our partners. We couldn’t be more grateful to work hand-in-hand with all of you as we begin the next phase of our critical work together.

In solidarity, 

Kat

Improving User Experience: Analytics Hub

Hello! Welcome to 2025! Here at CTA, we are excited to share some information with you about our new method of delivering data to our partners (this could be you!) using Analytics Hub.
What is Analytics Hub? Analytics Hub is a Google Cloud service that makes it easy to share data across different projects. (Learn more about it here.)

When CTA runs data pipelines on behalf of our partners, we start by syncing data into an internal project so we can clean it and run tests - for example, to check for duplicate rows - before delivering the data to our partner organizations. Previously, we did this using materialized views, which are a type of data object in BigQuery in which a view periodically updates its table based on its SQL definition (as opposed to a standard view, which runs the SQL anytime the view is queried). Starting in 2025, we are now delivering all data via Analytics Hub.

How does Analytics Hub work? If you want to really get into the weeds, you can read Google’s documentation. Here’s a TLDR: CTA creates an Analytics Hub “listing” that points to our internal dataset containing partners’ data - for example, a dataset containing Facebook Marketing data for Cool Democracy Forever (a fictional org - for now). We then create a “subscription” for Cool Democracy Forever, which results in a “linked dataset” appearing in their project. 

Delivering data with Analytics Hub is fast and simple. A linked dataset is essentially a mirror into the data in the CTA internal project. This means there is no final step of syncing data into a partner’s BigQuery dataset - as soon as it is updated and validated in CTA’s project, partner organizations will immediately see those updates in their linked datasets.

Analytics Hub linked datasets greatly simplify access management and data governance. Materialized views require that views have authorizations configured in order to access the data from CTA’s internal project. On rare occasions, these authorizations could be disrupted, and they are a little tricky to manage, especially across a large number of data objects. With Analytics Hub, no additional access needs to be managed once we create a subscription to a dataset on behalf of a partner organization. This simplifies access control, makes it easier for CTA to monitor the data we deliver to our partners, and ensures that partners will have uninterrupted access to all of their data! 

Another benefit of Analytics Hub linked datasets is that they give publishers access to detailed analytics about their usage and billing. Even if a dataset is subscribed to by multiple projects, Analytics Hub provides a handy INFORMATION_SCHEMA table called SHARED_DATASET_USAGE. This table provides helpful usage info broken down by organization that can be queried right in BigQuery Studio, making it simple to understand usage patterns.

At CTA, we are always on the lookout for new technologies that make all of our lives easier and take the struggle out of data management. We are thrilled to bring our pipelines into the modern era using the benefits of Analytics Hub in Google Cloud Platform, and we look forward to telling you more about other upgrades!

Focus Groups

In planning for what comes next and to help us determine the new features that we’ll be releasing in 2025, CTA conducted several weeks of focus groups at the end of 2024, interviewing both existing PAD super users and prospective partners about their use of PAD, the data they need, and tools in the ecosystem. In these focus groups, we learned how our partner organizations make use of PAD as a centralized hub for their organization, analytics, and reporting. We also narrowed in on the pain points our partners experience in navigating the mass of data, tools, and platforms they use to run successful programs. Thanks to these conversations, we have a great sense of opportunities for CTA to make the ecosystem better!

We’d like to extend a huge “Thank You” to the organizations who participated in these focus groups, as well as those who reached out with interest. The insights and feedback shared during these interviews will have a direct impact on our roadmap and have already influenced many of our upcoming releases. We look forward to opening up more focus group time slots in the near future for organizations who were not able to participate at the end of last year—be on the lookout for updates from our team! And if you’re interested in staying in the loop please feel free to sign up to be notified of future sessions.

We're Hiring!

As we start the new year, CTA is thrilled to have the opportunity to expand our team to work on a range of new projects and to support our new and existing partners! Current open roles include:

• Technical Director and Technical Lead

• Senior Project Manager

• Product Manager

• Senior Software Engineer and Full Stack Engineer

• Director of Operations

All listings can be found on CTA’s jobs page. Please apply if you’re interested, and please help us spread the word. Help us continue to support the movement!

Join Our Team

ProgLog: Star Date 012025

Stay updated with the latest PAD releases. 

New Features and Syncs:

• Sync Schedule released in PAD UI - CTA Partners can now access a view in the PAD UI that gives up-to-date information on all the integrations and data being delivered to their projects by CTA. This new view will show partners the sync’s schedule, when a sync was last run, and when it’s scheduled next! PAD users can find this new view in the “Syncs” section of PAD UI. Check it out at cta.tools!

Infrastructure Updates

• Analytics Hub Migration - You can read more about this in our Technical write up above.

• Airbyte Cloud Migration - Increasing scale and reliability of our integration platform.

• dbt on Kubernetes

Moving Data Between Datasets in Bulk

Welcome to Notes from BenDesk: Ben is our resident Freshdesk captain and manager of all help@ inquiries. We're bringing you interesting inquiries from his inbox each month to help share learnings across our community.

Question of the Month: Now that the 2024 election cycle is behind us, we want to archive and move data from one dataset to another. Is there an easy way to transfer or snapshot data in bulk between datasets in BigQuery?

Answer: PAD offers several methods for creating snapshots and moving data in bulk between datasets. Depending on whether you're working with static tables, views, or materialized views, you have a few options available. These range from simple point-and-click features in the UI to running lightweight code. Below are a couple of methods to accomplish your needs.

Static Tables: If you're looking to copy multiple static tables, BigQuery's Data Transfer Service is a fast, straightforward point-and-click solution. Here's how it works:

1. While logged into BigQuery, select the dataset or table you want to transfer.

2. Click the “Copy" button.

3. Choose the Destination Project, Dataset, and Table.

4. Hit the "Copy" button at the bottom to initiate the transfer.

Once the job has been executed, you can further modify it to run once, on-demand, or on a schedule. Please note that this method is only available for static tables, not views or materialized views.

For more information on Data Transfer Service, refer to Google's documentation here.

Static Tables, Views, & Materialized Views: Datasets often include a mix of static tables, views, and materialized views. Unfortunately, BigQuery’s UI doesn’t reliably copy views as static tables. However, you can use procedural SQL to “loop” through each table in a dataset—whether it’s a static table, view, or materialized view—and create static snapshots of them in bulk!

For more information and sample code for using procedural SQL, refer to our knowledge base article here. 

What's CTA Reading + Watching

• The Other Significant Others: Reimagining Life with Friendship at the Center: From NPR's Rhaina Cohen, The Other Significant Others explores the stories of a variety of people who have oriented their lives around key friendships, rather than primarily around romantic partnership. It's endearing, fascinating, and inspirational in a time that community of all forms is so key. 

• The Mother-in-Law by Sally Hepworth: What starts with the murder of a family matriarch turns into a story of family, love, and the bonds between women. 

• The Inmate by Freida McFadden: This psychological thriller follows a nurse practitioner in a maximum-security prison as the secrets from her past slowly creep into her present.