Winter '23 — what's new for Stitch?

By Thibaut Gourdel
screen shot of Stitch web page for adding data source integrations with logos for Facebook, Google, LinkedIn, Bing, Snapchat and TikTok adsscreen shot of Stitch web page for adding data source integrations with logos for Facebook, Google, LinkedIn, Bing, Snapchat and TikTok ads

The Stitch team has released many new capabilities in the last few months, and we're excited to share all the new features and improvements with you. In this blog post, we'll take a closer look at what's new in this latest release and how it can benefit you. 

Here's a quick overview of what's new in Winter '23 for Stitch: 

  1. New marketing data sources! 
  2. History mode for Snowflake 
  3. Role-based access management 
  4. Enhanced pipeline monitoring 

Let's dive into each of these new capabilities in more detail.

New marketing sources

The field of digital marketing is constantly evolving and we are thrilled to unveil our new connectors in beta for: TikTok ads and Snapchat ads. In addition beta connectors, connectors for Twitter ads and Pinterest ads will be available in the near future. 

What’s more, we released a new connector late last year for Google Analytics 4 to support the next generation of web analytics.

screenshot showing Stitch ad connectors with logos for Facebook, Google Ads, LinkedIn, SnapChat, and TikTokscreenshot showing Stitch ad connectors with logos for Facebook, Google Ads, LinkedIn, SnapChat, and TikTok

History mode for Snowflake

When synchronizing records from systems such as business applications into databases or data warehouses, a change in the schema or in a dimension can cause the replication process to lose important historical data in the destination. 

The history mode allows Stitch users to keep track of historical data in the warehouse — when there is a change in the source — for historical analysis or auditing purposes. 

Different methodologies exist to keep track of historical data when there is a change to a dimension. Typically referred to as a Slowing Changing Dimension (SCD), this dimension stores and manages both current and historical data over time in a data warehouse. 

Stitch's new history mode for Snowflake implements SCD type 2, which enables users to track changes as version records with current flag and active dates among other metadata. 

Role-based access management 

When organizations grow, a growing number of team members need to set up and manage data pipelines — by accessing the same Stitch account. To minimize the risk of improper manipulations and security breaches, any administrative-related product area (billing settings, plan choice, user management etc.) should be restricted to admin users. 

Role-based access control provides greater account security by restricting the actions a given user can take within Stitch. Whereas previously everyone was an admin, in this new release, a user can now be assigned one of two roles:  

  • Admin: Can take administrative actions, like changing the plan or adding team members
  • General User: Can manage pipeline configurations but cannot take administrative actions

screenshot showing the Admin and General user access options for Stitchscreenshot showing the Admin and General user access options for Stitch

Pipeline Monitoring 

Data engineering and analytics teams need to continuously deliver reliable data. Upstream pipeline issues such as system outages, underlying schema changes, and human errors affect the freshness of metrics and can halt downstream processes entirely. In causing data downtime, these issues costs organizations time and money. Today, data teams learn about data downtime reactively — and often when the data is urgently needed. This puts teams in firefighting mode and undermines trust in both the team and the data. Pipeline observability capabilities are key to ensure all issues are immediately and proactively identified and communicated to the affected stakeholders. 

As part of those capabilities, Stitch will offer data pipeline monitoring by displaying key metrics on data ingestion around:  

  • Data volume (track massive spikes and drops)  
  • Data freshness (latency)  
  • Schema changes/drifts  

screenshot of Stitch data showing rows of data loaded over time, represented by a blue bar graph that showing 267,843,593 rows of data for that billing periodscreenshot of Stitch data showing rows of data loaded over time, represented by a blue bar graph that showing 267,843,593 rows of data for that billing period

Stitchdata.com screenshot showing a dot and line graph representing load latency over time with a 31-day average of 1 hour and 14 minutesStitchdata.com screenshot showing a dot and line graph representing load latency over time with a 31-day average of 1 hour and 14 minutes

Stay tuned for more information on our journey for better data observability.

In addition to these major new features, various other enhancements have been implemented to improve overall performance and user experience. You can test them out yourself by starting a free trial or booking a demo today.

We hope that you'll find these new capabilities to be a valuable addition to manage your data pipelines with Stitch. If you have any questions or feedback, please don't hesitate to reach out to our support team.