The hottest Data Analytics Substack posts right now

And their main takeaways
Category
Top Business Topics

The Satellite Detection Edition

Why is this interesting? • 965 implied HN points • 24 Feb 26
🕹 Technology Data Analytics
  1. Commercial trackers, not government sensors, were the first to find the tiny Mozhayets‑6 satellite, showing that private teams now play a leading role in space detection.
  2. Very small, faint satellites can hide by riding with larger craft or matching orbital planes, and states are experimenting with designs that make craft harder to track.
  3. Space awareness is now a commercial product sold to militaries, insurers, and investors, so early warnings may come from subscribers or data engineers rather than traditional command centers.

Uber’s Big Data Revolution: From MySQL to Hadoop and Beyond

VuTrinh. • 279 implied HN points • 14 Sep 24
🕹 Technology Data Analytics
  1. Uber evolved from simple data management with MySQL to a more complex system using Hadoop to handle huge amounts of data efficiently.
  2. They faced challenges with data reliability and latency, which slowed down their ability to make quick decisions.
  3. Uber introduced a system called Hudi that allowed for faster updates and better data management, helping them keep their data fresh and accurate.

How did Discord evolve to handle trillions of data points

VuTrinh. • 399 implied HN points • 20 Aug 24
🕹 Technology Data Analytics
  1. Discord started with its own tool called Derived to manage data, but it found this system limited as it grew. They needed a better way to handle complex data tasks.
  2. They switched to using popular tools like Dagster and dbt. This helped them automate and better manage their data processes.
  3. With the new setup, Discord can now make changes quickly and safely, which improves how they analyze and use their vast amounts of data.

Did Las Vegas get too greedy?

Silver Bulletin • 740 implied HN points • 21 Dec 25
💼 Business Data Analytics
  1. Visitor numbers and room revenues are falling even with discounted prices, marking the biggest year-over-year drop since COVID and lower average daily rates after inflation.
  2. High-roller gaming like baccarat is holding up, but middle-class gambling and spending are down as blackjack, roulette and slots see lower play and customers wager less.
  3. Casinos have tightened odds and monetized many services to boost short-term profits, but those data-driven tactics risk alienating ordinary visitors and eroding repeat business over time.

Issue #16 - The Data Quality Conundrum (Part 2 – Solving)

The Data Ecosystem • 439 implied HN points • 28 Jul 24
🕹 Technology Data Analytics
  1. Data quality isn't just a simple fix; it's a complex issue that requires a deep understanding of the entire data landscape. You can't just throw money at it and expect it to get better.
  2. It's crucial to identify and prioritize your most important data assets instead of trying to fix everything at once. Focusing on what truly matters will help you allocate resources effectively.
  3. Implementing tools for data quality is important but should come after you've set clear standards and strategies. Just using technology won’t solve problems if you don’t understand your data and its needs.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Snowflake vs Databricks Is the Wrong Debate

SeattleDataGuy’s Newsletter • 541 implied HN points • 12 Dec 25
🕹 Technology Data Analytics
  1. Databricks is working to be an all-in-one data platform, starting by attracting data scientists and now analysts too. They want to be seen as a solution that can fit everyone's data needs.
  2. Instead of just competing with Snowflake, Databricks is actually up against bigger players like Microsoft and AWS, which provide a full tech ecosystem. Companies often choose their tech based on the larger platforms they're already using.
  3. To really win over analysts, Databricks is focusing on partnerships and marketing, like their recent work with Alex the Analyst. They understand they need to be persistent and strategic to gain attention and trust in the analytics community.

Boom Times For ChatGPT

Big Technology • 4003 implied HN points • 07 Feb 25
🕹 Technology Data Analytics
  1. ChatGPT is seeing a big surge in usage after some slow months. It’s now doing much better than its competitors.
  2. Recent data shows ChatGPT has reached a key turning point in its growth. This is a positive shift that many are noticing.
  3. The chatbot now attracts more users and interest, making it a front-runner in the AI space. Its popularity is on the rise.

Excel Forever

Net Interest • 42 implied HN points • 06 Feb 26
💰 Finance Data Analytics
  1. AI assistants can rapidly build serviceable financial models inside Excel by pulling public data and automating forecasts, showing how much routine analyst work can be automated.
  2. Excel remains the central workspace for finance because it’s a shared language that lets analysts inject judgment, so AI that integrates with Excel is more useful than tools that try to replace it.
  3. Advances in AI (bigger context windows and better reasoning) put pressure on legacy market-intelligence vendors and valuations, though complex cases and human judgment still matter.

High-performance engineering managers

Elevate • 1113 implied HN points • 09 Jan 24
💼 Business Data Analytics
  1. Effective managers have key traits that significantly impact employee performance, happiness, and retention, as proven by Google's Project Oxygen.
  2. Soft skills like coaching, communication, and support are more valued than technical expertise by employees, emphasizing the importance of emotional intelligence in management.
  3. Using rigorous people analytics, organizations can identify and develop high-impact management behaviors specific to their unique culture, leading to improved leadership and employee satisfaction.

GroupBy #44: Meta | The Data Stack

VuTrinh. • 119 implied HN points • 16 Jul 24
🕹 Technology Data Analytics
  1. Meta uses a complex data warehouse to manage millions of tables and keeps data only as long as it's needed. Data is organized into namespaces for efficient querying.
  2. They built tools like iData for data discovery and Scuba for real-time analytics. These tools help engineers find and analyze data quickly.
  3. Data engineers at Meta develop pipelines mainly with SQL and Python, using internal tools for orchestration and monitoring to ensure everything runs smoothly.

Special Edition 🔥: Analytics As Applied Accounting | Lauren Balik

Data Analysis Journal • 687 implied HN points • 08 Jan 24
🕹 Technology Data Analytics
  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.

Issue #6 - Hey Data, Show Me The Money!!!

The Data Ecosystem • 119 implied HN points • 19 May 24
💼 Business Data Analytics
  1. Investing in data is a strategic move, not just about spending money. It's important to align data efforts with business goals to see real value.
  2. When pitching for data investment, focus on the benefits it will bring. Clear communication of value can help rebuild trust with leadership.
  3. Measuring the success of data investments through defined KPIs is essential. This helps in making future improvement and investment decisions.

Earth Observation Use Cases

Space Ambition • 119 implied HN points • 17 May 24
🕹 Technology Data Analytics
  1. Earth observation is key for weather and climate studies. It helps scientists track weather patterns and understand climate change using data from satellites.
  2. Satellites are important for monitoring natural and human-made disasters. They provide real-time data that helps in managing disaster response and understanding impacts.
  3. Remote sensing data supports various sectors like finance, ecology, and infrastructure. It aids in resource management, economic predictions, and assessing environmental changes.

A metrics layer is like your New Year's resolution…

HyperArc • 39 implied HN points • 11 Jul 24
🕹 Technology Data Analytics
  1. A metrics layer helps standardize how companies measure data, making it easier for everyone to understand what is important. It can automate calculations, like rolling averages, which saves time and reduces confusion.
  2. Traditional business intelligence tools often lose useful underlying information, which makes it hard to understand how certain metrics were created. More context is needed to ensure decisions are well-informed and based on complete data.
  3. HyperArc offers a solution by capturing the team's insights and reasoning during analysis. It helps keep track of not just the final metrics, but also the thought process behind them, making it easier to revisit and understand decisions in the future.

Results, Actually

benn.substack • 639 implied HN points • 27 Dec 24
💼 Business Data Analytics
  1. Data-driven companies get a lot of attention, but many people still prefer investing in companies led by experienced individuals. This shows that experience holds significant value in business decisions.
  2. People like to be seen as unique or contrarian, but they often know what others like. This means that even when choosing something different, they still have a sense of the mainstream.
  3. There’s a funny perspective on what robots are, with younger generations seeing different meanings in technology compared to older ones. What one generation sees as a robot, another might just see as a gadget.

Data is actually not a great VC-backed business

Five Links (and three graphs) by Auren Hoffman • 202 implied HN points • 27 Jul 25
💼 Business Data Analytics
  1. Data companies are not a good fit for venture capital because they grow slowly and don't need large amounts of funding. They can be profitable but don't usually scale quickly like software companies do.
  2. The number of hedge funds and other businesses buying data is actually declining, and despite expectations, AI hasn't significantly changed this trend.
  3. The best data companies are often private and attract interest from private equity firms rather than venture capital. They offer steady profits but not the explosive growth that VCs typically look for.

Wrapping the Year for the Cyber 150

The Security Industry • 25 implied HN points • 03 Jan 26
🕹 Technology Data Analytics
  1. A data-centered ranking of mid-size cyber firms (50–500 employees) surfaces the fastest-growing vendors and is a practical starting point for investors.
  2. Most of the listed companies kept expanding—121 grew in the past year—and the group attracted heavy venture funding, with 39 firms raising over $4B in 2025 and $11.5B raised in total.
  3. Some firms graduated out of the mid-size category by exceeding 500 employees, while 29 companies saw headcount declines in 2025, often because they were acquired.

Lean Data Engineering with Dagster and DuckDB

The Data Jargon Newsletter • 158 implied HN points • 05 Mar 24
🕹 Technology Data Analytics
  1. Data lakes can be convenient but often lead to problems when trying to manage the data effectively. Keeping things simple with familiar tools can help make the data more useful.
  2. Using Dagster and DuckDB allows you to process data efficiently without complicated setups. You can do key tasks like aggregation and data cleaning right in your data flow.
  3. It's important to consider memory limits and choose the right file formats, like Parquet, for better processing. This way, you can keep your data pipeline running smoothly and avoid needless costs.

Underutilized.

Cybernetic Forests • 279 implied HN points • 05 Nov 23
🕹 Technology Data Analytics
  1. Generative AI is essentially a new form of Big Data, emphasizing pattern analysis to automate processes.
  2. The expansion of data is essential for the existence of generative AI tools, demonstrating a rebranding of data analytics into AI.
  3. The tech industry's focus on data monetization and predictive analytics has led to virtual interactions that distance us from real human connection and community.

Swarm Deserves a Redemption Arc

Mind Meld • 294 implied HN points • 09 Apr 23
🕹 Technology Data Analytics
  1. Swarm is more than just a check-in app, it reflects deeper aspects of people's lives and desires.
  2. Foursquare pivoted to a successful B2B platform leveraging its vast location data.
  3. There's potential for Swarm to evolve into a more meaningful social networking platform based on users' check-in history and preferences.

Analytics should be an Assembly Line

Data Thoughts • 3 HN points • 10 Sep 24
💼 Business Data Analytics
  1. Analytics should be handled like an assembly line to make it more efficient and accessible. This means creating standard processes to measure and track important business metrics.
  2. Most companies need to focus on basic descriptive analytics, which involves identifying and measuring key metrics. These metrics will help businesses understand what drives their success.
  3. Having well-defined metrics is essential before deeper analysis can happen. Insights from data come from well-measured processes, allowing teams to explore and understand their business better.

VTEX scales to 150 million metrics using Amazon Managed Service for Prometheus

VTEX’s Tech Blog • 99 implied HN points • 10 Mar 24
🕹 Technology Data Analytics
  1. VTEX successfully scaled its monitoring system to handle 150 million metrics using Amazon's Managed Service for Prometheus. This helped them keep track of their numerous services efficiently.
  2. By adopting this system, VTEX cut its observability expenses by about 41%. This shows that smart choices in technology can save money.
  3. The new architecture allows VTEX to respond to problems faster and reduces the chances of system failures. It increased the reliability of their metrics, making everyday operations smoother.

Introducing post stats in the Substack app

Substack • 658 implied HN points • 26 Jun 24
🕹 Technology Data Analytics
  1. Substack now has a feature that shows writers detailed statistics about their posts. This helps creators see how well their posts are doing and where new subscribers are coming from.
  2. There is a new Discussion tab that makes it easier for writers to engage with comments and interactions on their posts. This way, they can manage conversations in one place without searching through notifications.
  3. The Substack app is driving a lot of new subscriptions. The app helps users discover content and connects writers to their audience more effectively.

Eventify everything - Data modeling for event data

timo's substack • 117 implied HN points • 06 Feb 24
🕹 Technology Data Analytics
  1. Data modeling for event data involves handling various source data and supporting diverse analysis use cases.
  2. Event data modeling can be organized into layers, from raw source data to consumption-ready data for analytics tools.
  3. Qualifying events to activities in event data modeling helps improve data usability and user experience in analytics tools.

Top Links 625 Decomposing economic surprise. When threats work and when they don't. Is quantum the new AI? And the Secret Leviathan.

Chartbook • 300 implied HN points • 21 Jan 25
💰 Finance Data Analytics
  1. The Bloomberg Economic Surprise Index for the US shows how unexpected events in the economy can change predictions. It's important to pay attention to these surprises to get a better understanding of the current economic climate.
  2. Understanding when threats are effective or not can help in managing situations better. Knowing the right time to take action can make a big difference in outcomes.
  3. Quantum technology is being compared to AI as a new frontier in innovation. It's exciting to think about how these technologies might change our future.

Data Science Weekly - Issue 502

Data Science Weekly Newsletter • 319 implied HN points • 07 Jul 23
🕹 Technology Data Analytics
  1. Generative design is making strides in drug discovery, but there are still challenges to address for better outcomes.
  2. The UK government is investing in a Foundation Model Taskforce to harness AI for societal benefits and safety.
  3. Keeping updated with developments in data science, such as new models and applications, is essential for professionals in the field.

Data at Depth Newsletter 9: More Engagement, Better Titles, Your Own Custom GPT

Data at Depth • 79 implied HN points • 21 Mar 24
🕹 Technology Data Analytics
  1. The newsletter shares the creator's journey, including an increase in followers on Medium and steady Substack subscribers.
  2. The author discusses their recent creative projects and articles, reflecting on the title creation process.
  3. Readers can access a 7-day free trial to explore the full post archives of the Data at Depth newsletter.

Why Start-up CEOs no longer require a full-blown data team

The Orchestra Data Leadership Newsletter • 79 implied HN points • 18 Mar 24
💼 Business Data Analytics
  1. CEOs are moving away from hiring full data teams and are opting for small consultancies to set up their data stack, reducing risk and cost.
  2. One-person data teams in startups face overwhelming responsibilities, leading to chaos and potentially costly decisions.
  3. New technologies like Orchestra help single-person data teams maintain visibility and orchestration without expensive tools, accelerating the data value businesses receive.

What is beyond event data?

timo's substack • 196 implied HN points • 18 Jul 23
🕹 Technology Data Analytics
  1. Activities are crucial for adding a business layer to event data.
  2. An event must have meaning, defined by a name that describes the action triggered.
  3. Implementing an activity layer helps in structuring and understanding event data in a more meaningful way.

THE WAY OF WAYS

group by 1 • 196 implied HN points • 18 Aug 23
🕹 Technology Data Analytics
  1. The Modern Data Stack evolved but faced challenges of cost, complexity, and sprawl.
  2. MDS led to more focus on product analytics and consolidation of data systems.
  3. There is still a need for innovation in data modeling to address complexity and drive value.

I spent another 6 hours understanding the design principles of Snowflake. Here's what I found

VuTrinh. • 79 implied HN points • 10 Feb 24
🕹 Technology Data Analytics
  1. Snowflake separates storage and compute, allowing for flexible scaling and improved performance. This means that data storage can grow separately from computing power, making it easier to manage resources.
  2. Data can be stored in a cloud-based format that supports both structured and semi-structured data. This flexibility allows users to easily handle various data types without needing to define a strict schema.
  3. Snowflake implements unique optimization techniques, like data skipping and a push-based query execution model, which enhance performance and efficiency when processing large amounts of data.

Leading Panther's Next Chapter as CTO

Detection at Scale • 79 implied HN points • 05 Feb 24
🕹 Technology Data Analytics
  1. Transitioning from CEO to CTO to lead Panther's technical team, allowing more focus on delivering security outcomes via the product.
  2. Introduction of the concept of Detection Engineering, emphasizing reliability, scalability, and automation in security practices.
  3. Adapting Panther's approach to evolving security needs, enhancing code-driven detection for broader use and improving correlation, analytics, and visualization capabilities.

Flow State Data Engineering: I want it to be normal

Sung’s Substack • 139 implied HN points • 14 Mar 23
🕹 Technology Data Analytics
  1. Data engineering involves many tedious tasks and manual checks, hindering the ability to reach a state of flow
  2. Software engineers have smoother workflows and better tools compared to data engineers, allowing them to focus on their work and enjoy the process
  3. There is potential to improve the data engineering workflow by implementing real-time monitoring, interactive previews, and streamlined processes to enhance the experience