The hottest Data Platforms Substack posts right now

And their main takeaways
Category
Top Technology Topics

Special Edition 🔥: Analytics As Applied Accounting | Lauren Balik

Data Analysis Journal • 687 implied HN points • 08 Jan 24
🕹 Technology Data Analytics BI Tools Data Platforms AI Privacy
  1. Becoming a data analyst or engineer through bootcamps is becoming less prevalent due to economic factors.
  2. Analytics leaders face challenges in setting boundaries and avoiding overlap with finance teams in accounting functions.
  3. Decentralized data team setups are generally more efficient, and the future may see more of this with changes in tax regulations.

Everyone is a CDP now

timo's substack • 157 implied HN points • 27 Nov 23
🕹 Technology Data Platforms Analytics Activation Integration
  1. The concept of a Customer Data Platform (CDP) is evolving with a focus on defining its functionality more clearly.
  2. There is a trend towards composable CDP solutions, allowing for flexibility but also potential complexity.
  3. The key value of a CDP lies in activation - using customer data to create targeted audiences for more efficient marketing strategies.

After the Modern Data Stack: Welcome back, Data platforms

timo's substack • 275 implied HN points • 16 Aug 23
🕹 Technology Data Platforms Evolution
  1. Data platforms are the next step after the Modern Data Stack, offering enhanced productivity, rapid iteration, and cost efficiency.
  2. The evolution of technology is not linear, but branches out in many directions, leading to multiple 'next' possibilities.
  3. New data platforms focus on integration, flexibility, and control, providing solutions for core issues like missing design, data quality, and integration challenges.

Modernity != Value

Sarah's Newsletter • 299 implied HN points • 19 Apr 22
🕹 Technology Data Analytics Data Tools Data Platforms Problem Solving
  1. Having modern tools doesn't guarantee providing value - it's more about how analytics teams use the tools to drive organizational change.
  2. The focus should be on delivering value to the organization rather than just building data platforms or using the most modern tools.
  3. Start simple with the minimum viable data stack and only add complexity when necessary - focus on solving real problems and evaluating tools based on problem-solving, maintenance, and scalability.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Data Automation and Orchestration Tools Range from Specialized LLM App Building Frameworks to General Purpose Data and Feature Platforms

The Strategy Deck • 0 implied HN points • 08 Aug 23
🕹 Technology Data Platforms
  1. Data automation and orchestration tools simplify data management tasks for ML applications.
  2. These tools combine data from various sources, clean and transform it for specific ML algorithms.
  3. The sector offers a broad range of tools, from ETL to specialized ML automation platforms, to cater to diverse data types and company needs.

Operational Use case Patterns for Apache Kafka and Flink — Part 1

Tributary Data • 0 implied HN points • 03 Jan 23
🕹 Technology Data Platforms Stream Processing Microservices Data Management
  1. Operational use cases with Kafka and Flink are crucial for business operations due to their message ordering, low latency, and exactly-once delivery guarantees.
  2. Using polyglot persistency with different data stores for read and write purposes can help solve the mismatch between write and read paths in microservices data management.
  3. Implementing a backend rate limiter using Flink as a Kafka consumer can help prevent exhausting an external system (e.g., a database) due to high message arrival rates from Kafka.