davidj.substack

davidj.substack focuses on the exploration and application of data strategies, tools, and practices for organizational impact. It covers the evolution of the Modern Data Stack, the importance of semantic layers, data team dynamics, and tooling efficiencies to enhance data handling, analytics, and management.

Data Strategy and Analysis Data Team Management Modern Data Stack Technologies Semantic Layers in Data Analytics Data Quality and Governance Tooling and Automation in Data Management Productivity and Efficiency in Data Operations Real-time Data Processing

The hottest Substack posts of davidj.substack

And their main takeaways

What is a data product?

71 implied HN points • 15 Mar 24
🕹 Technology Data Products Interfaces Applications
  1. A data product can take various forms and be consumed in different ways, always requiring an interface for consumption.
  2. From raw data like CSV files to refined database tables, streams, JSON files, and ORM abstracted layers, all can be considered data products.
  3. BI tools, AI automation, and semantic layers play crucial roles in creating consumable data products for various industries, making data more refined and accessible.

We don’t need data contracts

71 implied HN points • 16 Feb 24
🕹 Technology Data Management Data Contracts Data Transformation
  1. Data teams face challenges when separated from product engineering, leading to loss of metadata and concerns about data quality. Data contracts can help address these issues by defining the nature, completeness, and format of shared data.
  2. Integrating data professionals within product teams can enhance understanding and usage of data, reducing the need for separate contracts. This approach allows for direct-to-consumer, organic data processes.
  3. Centralized data platform teams can establish common standards and infrastructure, enabling embedded data personnel in product teams to work efficiently. This collaborative model streamlines data transformation and enhances data accessibility.

Streaming in real life

47 implied HN points • 23 Feb 24
🕹 Technology Analytics Data Warehousing Real-Time Data
  1. Real-time data streaming from databases like MySQL to data warehouses such as Snowflake can significantly reduce analytics latency, making data processing faster and more efficient.
  2. Streamkap offers a cost-effective solution for streaming ETL, promising to be both faster and more affordable than traditional methods like Fivetran, providing a valuable option for data professionals.
  3. Implementing Streamkap in data architectures can lead to substantial improvements, such as reducing data update lag to under 5 minutes and delivering real-time analytics value for customers, showcasing the impact of cutting-edge data technology.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Semantic Stream

23 implied HN points • 29 Feb 24
🕹 Technology Querying Data Storage
  1. Consider how to use a semantic layer with streaming data to enhance efficiency and data processing.
  2. Streaming data warehouses handle storage differently than batch data warehouses, keeping fresh data in-memory and reducing compute cost.
  3. The semantic layer abstracts entities, attributes, and metrics, aiding in managing and optimizing queries on streaming data.

Standard Semantics

95 implied HN points • 01 Nov 23
🕹 Technology Data Modeling Semantic Layers APIs Standardization
  1. Having a standard interface for semantic layers is crucial to prevent failure and ensure compatibility among different layers.
  2. SQL APIs offered by semantic layers may not be truly SQL, leading to potential confusion and challenges in querying data.
  3. Supporting REST HTTP interfaces for semantic layers enables a broader range of use cases, including data applications for internal and external purposes.

The Modern Data Stack is Dead… Long Live the Modern Data Stack - Part 1

167 implied HN points • 19 Jul 23
🕹 Technology Data Stack Data Engineering Technology Tools Data Warehousing Business Intelligence
  1. The Modern Data Stack (MDS) community has grown significantly over the years with various meetups and events.
  2. Using tools like Snowflake, dbt, and Looker in the Modern Data Stack improves data capabilities and productivity.
  3. Although some criticize the Modern Data Stack and its imperfections, it has greatly enhanced data handling and analytics for many organizations.

The Modern Data Stack is Dead, Long Live the Modern Data Stack - Part 2

107 implied HN points • 26 Jul 23
🕹 Technology Data Stack Orchestration Consumption Semantic Layers
  1. The modern data stack is evolving with new tools and options for data architecture.
  2. Key trends include the focus on data ingestion and telemetry, improved orchestration tools, and advancements in compute engines.
  3. Data consumption is being enhanced through self-serve AI capabilities, BI tools, and free-form analyst tools, all sitting on a semantic layer.

Hard Goodbyes

143 implied HN points • 24 May 23
💼 Business Management Leadership Hiring Team Dynamics Company Culture
  1. Leaders may face difficult decisions when letting go of team members, even if it's the right thing to do.
  2. Communication and managing expectations are crucial in the process of letting go of team members.
  3. It is important to prioritize hiring the right people to avoid the challenging situation of having to let someone go.

Self vs Self

95 implied HN points • 07 Jun 23
🕹 Technology Engineering Team Management AI
  1. Individual Contributor roles in technology allow technically skilled individuals to advance without moving into management.
  2. Specialized IC roles, like Staff or Principal, are crucial for making better technical decisions and preventing engineering issues.
  3. Having fewer hard-to-hire line managers and more experienced ICs can lead to better support and scaling in technical teams.

Semantic Superiority - Part 2

107 implied HN points • 29 Mar 23
🕹 Technology Data Management Analytics Security APIs
  1. Semantic layers reduce repetitive code by providing a consistent framework for queries.
  2. Semantic layers enhance data security by controlling access and reducing accidental exposure of sensitive data.
  3. A semantic layer defines entities and structures, while a metrics layer is a subset that focuses mainly on defining data models.

Metricalypse Now

107 implied HN points • 15 Feb 23
🕹 Technology Data Management Analytics BI Tools Commercialization
  1. Two approaches to metrics layers: wide datasets without defined data models vs. defined data model for more powerful metrics.
  2. Importance of new semantic layer by dbt Labs acquiring Transform for a universal standalone analytics solution.
  3. Opportunity for data consumption vendors to integrate with new dbt semantic layer for a ubiquitous solution.

Semantic Superiority - Part 3

83 implied HN points • 05 Apr 23
🕹 Technology Semantic Layers BI Tools Open Source APIs
  1. Semantic layers are crucial for governance, security, accessibility, and developer experience benefits in data analytics.
  2. Standalone semantic layers offer more flexibility and serve multiple use cases compared to semantic layers built into BI tools.
  3. Different standalone semantic layer options like Cube, AtScale, dbt/MetricFlow, and Looker Modeller provide unique features and cater to varying needs in data modeling and analytics.

A Darker Truth

2 HN points • 07 Mar 24
🕹 Technology Artificial Intelligence Data Management Startups Workplace dynamics
  1. Text-to-semantic layer systems can work in enterprise but text-to-SQL ones won't due to technical deficiencies.
  2. Even with infinite resources, achieving a perfect text-to-SQL system may not be enough due to the importance of how data is perceived by stakeholders.
  3. Blame and humiliation dynamics in human interactions make text-to-semantic layer systems more viable than text-to-SQL systems in corporate settings.