January 2022 Summaries

The debate around the definition of "data as a product" is ongoing, with various authors offering different perspectives. Some definitions focus on applying key product development principles to data projects, such as identifying unmet needs, agility, iterability, and reusability. Others emphasize providing data to stakeholders automatically or applying the principles of product thinking to create data products. A fourth perspective views data as a product as a shared mindset across the entire company that integrates all pieces of data, tools, and processes into one big data product. Ultimately, the best definition is one that encapsulates key product development principles and their application to data projects, recognizing that there is no single "right" answer and what matters most is finding processes and systems that help data teams advocate for the importance of data on a wider organizational level.

Census, a tool that helps with data use cases, has introduced new connectors across various categories including finance, marketing, product, sales, ecommerce, and customer success. These new connectors enable users to sync their data into tools like Anaplan, Chargify, TikTok, Microsoft Ads, LinkedIn Ads, Autopilot, ActiveCampaign, Pendo, Appcues, Delighted, Heap Analytics, Correlated, Shopify, Kustomer, and Gainsight, among others. The new connectors facilitate personalization, operational analytics, marketing automation, product usage data integration, sales trigger campaigns, ecommerce product recommendations, customer success support, and more. Additionally, Census now supports dbt 1.0, offers increased security for Snowflake on Google Cloud Platform, and invites users to vote on their most important integrations to help prioritize the next set of connectors.

Reverse ETL is crucial in today's data ecosystems because it enables businesses to transfer data from their data warehouses to other systems, making it simple for companies to activate the data they have gathered to provide better customer experiences, improve internal processes, and more. Reverse ETL is taking off due to its importance in providing real-time access to data, allowing companies to extract data from their data warehouses and make it available to other operational systems at the speed companies expect today. Unlike ELT, reverse ETL involves extracting data from a target system, transforming it, and making it available to other operational systems, primarily for operational purposes such as syncing data with customer-facing applications or partner systems.

This article presents four solutions to sync data from Snowflake to Segment: Cloud app source using AWS S3 as a staging location, HTTP tracking API source, Python Analytics library, and Reverse ETL with Census. The cloud app source solution uses AWS S3 as a temporary storage for the data before it is synced to Segment, while the HTTP tracking API source allows users to send requests directly from their servers to Segment's servers. The Python Analytics library provides a server-side controller code or stand-alone script to run regularly and sends requests to Segment's servers. The Reverse ETL with Census solution uses Census as an intermediary to sync data from Snowflake to Segment, providing a more robust long-term solution for users who need to sync data on a daily basis.