October 2024 Summaries

Hex is introducing a new visual experience for exploring data, making it easy to slice, dice, and visualize information. The platform aims to bring together everyone with data, including power users like data scientists, analysts, and engineers, as well as other professionals who want to better understand their world. Key features include the Explore UI, no-code joins, integration of AI for natural language queries, conditional notifications, and support for semantic models from dbt MetricFlow and LookML.

Tristan Handy, CEO and co-founder of dbt, reflects on the growth and evolution of the Data Build Tool (dbt) at the Coalesce conference, highlighting its transition from open-source origins to a broader commercial product offering. Handy discusses the strategic choice to commercialize "statefulness" in dbt, avoiding significant licensing changes, and contrasts this with the open-source version, which serves as an entry point for users who do not yet need enterprise-level features. He also explores the potential impact of Apache Iceberg on data processing workflows, suggesting it could shift the industry back toward Extract-Transform-Load (ETL) methodologies. Handy emphasizes the importance of integration within the data stack and contemplates the nature of value capture and ROI in the modern data ecosystem. He suggests that data functions might be more effective if decentralized back to individual business lines, a departure from the centralized data teams that have emerged over the past decade.

HubSpot, with its 8,000 employees, relies on three analytics engineering teams to build data trust and ensure that everyone has the necessary information for making smart decisions. Tony Avino's team is responsible for managing HubSpot’s product data, which is used across various teams. A strong data culture requires leadership commitment, data literacy, appropriate tooling, and data accessibility across the organization. Analytics engineers streamline time-to-insight by transforming raw data into certified assets that can be consumed by other teams. Measuring ROI for analytics engineering includes surveying customers on ease of use and tracking time-to-insight. Treating deliverables as products helps drive adoption and ensures long-term management. The value chain concept clarifies roles, responsibilities, and handoff points in the data process from ingestion to insight. Data lineage tools can help identify unmanaged assets, while governance is crucial for maintaining data hygiene and managing costs. Advocating for change at a grassroots level can drive improvements in data literacy, tooling, and culture within an organization.