May 2024 Summaries

The problem faced by many organizations is that their good data is always one request away. To solve this, Census Datasets offers a way to join multiple datasets together quickly and easily, without requiring significant coding expertise. The process involves setting up datasets from SQL queries, assigning relationships between them, building calculated columns, and using these to create target audiences. These audiences can then be synced to LinkedIn Ads for targeted campaigns and to Salesforce for empowering sales teams with better data. Throughout the process, Census Datasets leverages the power of the organization's data warehouse and minimizes code requirements, making it a user-friendly solution for data transformation.

Datasets` is a new feature in Census that aims to reduce `data stress` by providing a shared, singular view of trusted data, simplifying data exploration and modeling. It addresses the causes of `data stress`, including incomplete or siloed data, lack of collaboration, and poor data quality. Datasets provide a more powerful way for businesses to expand and enrich shared data definitions, enabling collaboration between data teams and business teams, improving data quality through features like Dataset Explorer, Entity Resolution, and Data Transformation. It also enables business teams to get closer to their actual data, reducing back-and-forth on data requests, and provides capabilities to transform and govern data, empowering data teams to overcome data silos and quality issues.

Census Embedded is a platform that enables companies to ingest data from their customers' data warehouses or export data to their customers' business applications, using the company's best-in-class Reverse ETL. This allows product and engineering teams to rapidly ship and maintain customer-facing integrations natively within their own SaaS platform, saving time and resources on developing native integrations. The new feature "Embeddable Components" allows any Census customer to add the power of sync creation UI directly into their application, freeing them from handling all the scenarios inherent in 30+ source and 200+ destination APIs. This enables companies to fulfill customer needs faster, improve customer experiences, and product agility, while surpassing the limitations of traditional Integration Platform-as-a-Service (iPaaS) solutions that fall short in data reliability and scalability.

An ETL pipeline is a process that extracts raw, unstructured data from various sources, transforms it into a standardized format suitable for analysis or business needs, and loads it into a target system designed for storing vast amounts of information. The three main processes involved in an ETL pipeline are extract, transform, and load, which work together to bridge the gap between siloed data and actionable insights. By moving valuable data from point A to point B, ETL pipelines enable businesses to make informed decisions based on accurate and unified data, improve data quality, increase efficiency, provide analytics for decision-making, scale to accommodate growing needs, and offer benefits such as data integration, improved data quality, higher efficiency, analytics for decision-making, scalability, creating reference data, extracting and standardizing data, validating data, transforming data, staging data, loading data, scheduling future processing, and activating valuable customer data. ETL pipelines differ from other solutions like data pipelines, ELT, and Reverse ETL in terms of their focus on preparing data for analysis and loading it into a target system, prioritizing data quality, and offering flexibility in the transformation process. Reverse ETL, a subset of ETL, allows businesses to move valuable data back into operational systems, enhancing personalization at scale, boosting operational efficiency, enabling real-time decision-making, and providing a single source of customer truth.