The hottest Data Warehousing Substack posts right now

And their main takeaways
Category
Top Technology Topics

Streaming in real life

davidj.substack • 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.

Leaving product analytics

timo's substack • 314 implied HN points • 05 Jun 23
🕹 Technology Analytics Product Data Warehousing
  1. Product analytics tools like Amplitude, Mixpanel, and Heap are evolving to offer new features like marketing attribution and user experience analytics.
  2. New players in the market like Kubit are focusing on providing product analytics directly on cloud data warehouses.
  3. The future of analytics is moving towards event analytics, opening up new possibilities and challenges for businesses.
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The Modern Data Stack is Dead… Long Live the Modern Data Stack - Part 1

davidj.substack • 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 Iceberg that sank the Snowflake?

Brick by Brick • 9 implied HN points • 01 Mar 24
🕹 Technology Data Warehousing Open Source Computing
  1. Snowflake's stock dropped significantly after the announcement of CEO Frank Slootman's retirement, with a key concern being the impact of Apache Iceberg on moving data out of Snowflake.
  2. Apache Iceberg is a powerful technology that allows for the efficient migration of data out of Snowflake to other systems for processing, causing revenue loss in both storage and compute for Snowflake.
  3. The paradigm shift towards technologies like Iceberg takes time in enterprise settings but can have a significant impact, highlighting the importance of capturing the compute dollars in data processing.

Microsoft Fabric Mirroring: Revolutionising Data Access and Real-Time Insights

Data Plumbers • 2 HN points • 01 Apr 24
🕹 Technology Data Analytics Data Warehousing Database Management Data Integration
  1. Microsoft Fabric Mirroring is a transformative technology that revolutionizes data access and real-time insights in organizations.
  2. Mirroring enables universal access to various databases, real-time data replication, and granular control over data ingestion into Microsoft Fabric's Data Warehousing experience.
  3. With Mirroring, organizations can achieve zero-ETL insights, leverage the innovative capabilities of Fabric's OneLake repository, and bridge the gap between data and action for swift adaptation and success.

Why has managing email in PLG B2B SaaS become such a tedious job?

nonamevc • 3 HN points • 05 Feb 24
🕹 Technology Data Management Pricing Models Data Warehousing
  1. There are multiple types of emails involved in PLG B2B SaaS, including transactional, product-related, and marketing campaigns.
  2. Challenges in managing email in PLG B2B SaaS include the need for orchestration, tedious content management, and high data volume.
  3. Companies like Inflection and Humanic offer new perspectives and solutions to address the complexities of managing email in PLG B2B SaaS.

Mastering the B2B Analytics Stack from Founding to Growth stage: A Comprehensive Guide

nonamevc • 6 HN points • 22 Mar 23
🕹 Technology Analytics SaaS CRM Data Warehousing Business Intelligence
  1. Consider the timing and readiness of your organization before implementing new tools in the B2B analytics stack.
  2. In the founding stage, focus on qualitative data, understanding customer needs, and building a customer profile.
  3. During the growth stage, invest in sophisticated analytics tools, like data warehouses and experimentation platforms, to effectively manage growing data.