The hottest Analytics Substack posts right now

And their main takeaways
Category
Top Technology Topics

GroupBy #41: Uber’s Batch Data Infrastructure with Google Cloud Platform

VuTrinh. • 99 implied HN points • 25 Jun 24
🕹 Technology Data Engineering Cloud Computing Machine Learning Infrastructure Analytics
  1. Uber is moving its huge amount of data to Google Cloud to keep up with its growth. They want a smooth transition that won't disrupt current users.
  2. They are using existing technologies to make sure the change is easy. This includes tools that will help keep data safe and accessible during the move.
  3. Managing costs is a big concern for Uber. They plan to track and control spending carefully as they switch to cloud services.

Data Science Weekly - Issue 543

Data Science Weekly Newsletter • 219 implied HN points • 19 Apr 24
🕹 Technology Data science Machine Learning AI Analytics Data Engineering
  1. Statistical ideas have a big impact on the world. Learning about important papers can help us understand how statistics shape modern research and decision-making.
  2. Machine Learning teams have different roles that face unique challenges. Understanding these personas can help leaders support their teams better.
  3. Using vector embeddings can greatly improve search experiences in apps. They simplify processes that previously seemed too complex and highlight their usefulness in technology.

Breaking and Non-Breaking Changes

davidj.substack • 47 implied HN points • 11 Dec 24
🕹 Technology Software Development Data Engineering Analytics
  1. When making changes to data models, it's important to identify if they are breaking or non-breaking changes. Breaking changes affect downstream models, while non-breaking changes do not.
  2. SQLMesh automatically analyzes changes to understand their impact on other models. This helps developers avoid manual tracking and reduces the chances of errors.
  3. New features in SQLMesh will allow for more precise tracking of changes at the column level. This means less unnecessary work when something minor is modified.

Procella - The query engine at YouTube

VuTrinh. • 79 implied HN points • 29 Jun 24
🕹 Technology Data Engineering Cloud Computing Database Systems Analytics
  1. YouTube built Procella to combine different data processing needs into one powerful SQL query engine. This means they can handle many tasks, like analytics and reporting, without needing separate systems for each task.
  2. Procella is designed for high performance and scalability by keeping computing and storage separate. This makes it faster and more efficient, allowing for quick data access and analysis.
  3. The engine uses clever techniques to reduce delays and improve response times, even when many users are querying at once. It constantly optimizes and adapts, making sure users get their data as quickly as possible.

Indian IT’s GenAI Lag

Sector 6 | The Newsletter of AIM • 439 implied HN points • 14 Jan 24
🕹 Technology AI IT Business Analytics Software
  1. Indian IT companies like Infosys and TCS have shown strong financial performance, but they lack confidence in generating revenue from generative AI.
  2. In contrast, Accenture is making notable progress with generative AI, securing significant investments and showcasing strong growth.
  3. Many Indian IT firms are reducing new hiring and focusing more on training current employees, highlighting an emphasis on automation and upskilling rather than bringing on fresh talent.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Inside Netflix's $150 Million Bet On Christmas Day NFL Games

Huddle Up • 31 implied HN points • 23 Dec 24
🎾 Sports NFL Media Viewership Analytics Investments
  1. Netflix is investing $150 million in NFL games on Christmas Day. This is a big move as they try to attract more viewers.
  2. NBA viewership has dropped a lot recently, down over 25% compared to last year. This decline is making people worry about the sport's popularity.
  3. Despite NBA's falling ratings, they signed a massive $76 billion media rights deal. This shows that there's still a strong market interest in basketball, even if viewership is down.

Data Science Weekly - Issue 526

Data Science Weekly Newsletter • 419 implied HN points • 22 Dec 23
🕹 Technology Data science AI Machine Learning Analytics Software Development
  1. Generative AI is changing how we work with tools, improving the Human-Tool Interface. This can help us use technology in ways we never could before.
  2. Support Vector Machines (SVMs) can be very effective for prediction tasks, often outperforming other models in error rates. However, they aren’t as commonly used, possibly due to their complexity.
  3. Deep multimodal fusion is useful in surgical training. It helps classify feedback from experienced surgeons to trainees by combining different types of data like text, audio, and video.

The State of Data Testing in 2024: Why We Still Have Broken Windows

Inside Data by Mikkel Dengsøe • 49 implied HN points • 18 Nov 24
🕹 Technology Data Quality Analytics Business Intelligence Software Development
  1. Data teams are overwhelmed by too many alerts from test failures. This leads to important issues being overlooked.
  2. It's crucial to focus on the right tests that have significant business impact rather than just mechanical tests. This means deeper insights into the data are needed.
  3. Sharing the responsibility for data quality across teams can improve the situation. When everyone understands their role, issues are resolved faster.

Championship Sunday was a strange watch for Vikings fans

Purple Insider • 294 implied HN points • 29 Jan 24
🎾 Sports Football Draft Analytics Quarterbacks Strategy
  1. Sunday's games were strange for Vikings fans to watch from a unique perspective.
  2. Building a championship team can involve having an all-time great quarterback, hitting on many draft picks, or building a strong supporting cast around an affordable quarterback.
  3. Success in the NFL requires making bold decisions and it's challenging to win even with a great team.

Data: the new oil or a new byproduct?

Topsoil • 511 implied HN points • 30 Jun 23
🕹 Technology Data Agriculture Framework Digitization Analytics
  1. Data in agriculture is essential for advancements like Generative AI, automation, and precision agriculture.
  2. Challenges in farm digitization include issues like connectivity, interoperability, data quality, trust, and incentives.
  3. Farmers derive value from data through decision-making, enabling technologies, sharing with advisors, compliance, and future income opportunities.

Data Science Weekly - Issue 542

Data Science Weekly Newsletter • 139 implied HN points • 12 Apr 24
🕹 Technology Data science AI Machine Learning Programming Analytics
  1. This newsletter provides links and updates about data science, AI, and machine learning. It's a helpful resource for anyone wanting to stay informed in this field.
  2. One article teaches how to handle real questions using Python, which is great for people wanting practical coding skills. Another discusses techniques to make sure AI outputs stay on task.
  3. The newsletter also features resources and courses to help people learn and improve their skills in data science and related areas. It's a good place to find learning opportunities.

Issue #2 - The Data Ecosystem: Where do you even start?

The Data Ecosystem • 119 implied HN points • 21 Apr 24
🕹 Technology Data science Machine Learning Data Engineering Data Governance Analytics
  1. Data can be really complicated, and it's easy to miss how everything connects. People often focus on their own area and forget about the bigger picture of the data ecosystem.
  2. Chief Data Officers (CDOs) are important but can only do so much to fix data issues. They deal with many challenges, including limited power, lack of experience, and politics within the organization.
  3. To improve in the data field, we need to recognize the gaps in our knowledge, prioritize what to focus on, and continuously educate ourselves in both our own areas and related data domains.

Celebrating An Anniversary: Three Years of Writing About Analytics - Issue 154

Data Analysis Journal • 452 implied HN points • 26 Jul 23
🕹 Technology Analytics Data science Newsletter Writing Career development
  1. The author reflects on three years of writing a newsletter about analytics, thanking supporters and subscribers.
  2. The author's newsletter aims to document their journey, bridge the gap between academics and industry, and encourage classic data analysis.
  3. The author shares insights on their writing strategy, the power of being small and independent, and future plans for the newsletter.

GroupBy #39: 2000+ DBT models in airflow; Serverless Jupyter Notebooks at Meta

VuTrinh. • 59 implied HN points • 11 Jun 24
🕹 Technology Data Engineering Software Development Cloud Computing Analytics Data science
  1. Meta has developed a serverless Jupyter Notebook platform that runs directly in web browsers, making data analysis more accessible.
  2. Airflow is being used to manage over 2000 DBT models, which helps teams create and maintain their own data models effectively.
  3. Building a data platform from scratch can be a valuable learning experience, revealing important lessons about data structure and management.

How to think about social effectiveness to protect creative budgets

The Social Juice • 24 implied HN points • 20 Dec 24
💼 Business Marketing Social media Strategy Branding Analytics
  1. When discussing social media success, it's important to focus on effectiveness, not just creativity. You need to tell a complete story about how your ideas impact the brand and business.
  2. To have a strong approach, measure three key areas: channel effects, brand effects, and commercial effects. This way, you can show not only how many people saw your content but also how it influenced brand awareness and sales.
  3. Always establish clear success measures before starting a project. This helps everyone understand how to evaluate success and can lead to better budget support in the future.

Data Science Weekly - Issue 521

Data Science Weekly Newsletter • 339 implied HN points • 17 Nov 23
🕹 Technology Data science Machine Learning AI Programming Analytics
  1. JAX is becoming popular for its speed and capabilities, and learning it may be essential for those familiar with PyTorch. It does have a steeper learning curve, but there are resources to help ease the transition.
  2. The demand for GPUs is skyrocketing, driven by various market factors. Understanding these dynamics can help anticipate the future of technology and resource availability in industries reliant on powerful computing.
  3. Freelancing in data science can lead to an overwhelming number of job offers. Tips on finding clients on platforms like Upwork and LinkedIn can help navigate this new freelance landscape.

Data Science Weekly - Issue 524

Data Science Weekly Newsletter • 299 implied HN points • 08 Dec 23
🕹 Technology Data science AI Machine Learning Data Engineering Analytics
  1. Data engineering is evolving with new design patterns that help improve efficiency in handling data. A new book dives into these patterns and their importance.
  2. Machine learning is being used to understand and control the movement of silicon atoms in materials, which could lead to advancements in technology like better electronics.
  3. A new model called PoseGPT can estimate 3D human poses from images and text, linking physical movements to broader concepts about humans, showing the capabilities of large language models.

Why Today Is The Perfect Time to Learn Data | Seattle Data Guy

Data Analysis Journal • 373 implied HN points • 25 Oct 23
🕹 Technology Data science Analytics Data Engineering Learning Resources Career Advice
  1. Learning data is more accessible and better now than in the past years.
  2. For transitioning into data engineering, focus on SQL, programming, data warehouse, and data pipelines.
  3. Analysts should focus on understanding the business problem, building maintainable systems, and following a data analytics process.

Spotify, Revolut and the rise of the Super Sub

Department of Product • 393 implied HN points • 22 Jun 23
💼 Business Tech Subscription Analytics Product Launch Market Trends
  1. Some tech companies are experimenting with higher-priced subscription tiers to offer new features to exclusive users.
  2. Revenue generation is a key focus for many product teams, leading to new pricing strategies.
  3. Pricing experiments like launching super premium subscriptions are worth monitoring for trends in the industry.

How The Los Angeles Dodgers Used Analytics To Become MLB's Most Feared Team

Huddle Up • 46 implied HN points • 01 Nov 24
🎾 Sports Analytics Team Dynamics Baseball Investment Performance
  1. The Dodgers became very successful by investing a lot in analytics over ten years. This helped them make smart decisions that improved their performance.
  2. Their analytics-driven approach changed the team's fortune, turning them into MLB's second-most valuable team. It shows how important data can be in sports.
  3. Winning two World Series Championships in four years highlights the effectiveness of their strategies. It's a great example of how teamwork and strategy can lead to big successes.

Landing page improvements

André Casal's Substack • 19 implied HN points • 29 Jul 24
🕹 Technology Web Development User Experience Product Design Market Strategy Analytics
  1. Improving color contrast on a landing page helps make it more accessible for users. Clearer visuals can attract more visitors and keep them engaged.
  2. Adding logos and use-case sections to a landing page can help communicate what the product is about. It makes it easier for potential customers to understand if the product fits their needs.
  3. Getting feedback on a landing page and iterating on it is essential for creating a successful product. Regular updates based on user input help build trust and improve overall user experience.

📽 Webinar: How Convirza Scaled SLMs for Real-Time Call Analytics – Without Breaking the Bank

TheSequence • 126 implied HN points • 15 Nov 24
🕹 Technology AI Analytics Webinars Infrastructure Scalability
  1. Convirza found a way to analyze call data quickly and affordably. They combined many tools into one setup, making everything run smoother.
  2. Their response time for customers is now under two seconds, even when many people are using the service. This helps workers get the info they need fast.
  3. By switching to a new system, they reduced costs a lot. They no longer need expensive machines for each task, which keeps their expenses low while still providing accurate results.

What does Alteryx do?

Technically • 12 implied HN points • 07 Jan 25
🕹 Technology Data Tools Analytics Software Business Intelligence Data Management
  1. Alteryx is a tool that helps teams make sense of messy data without needing to code. It allows people to clean and analyze their data easily.
  2. Many companies have limited access to specialized data teams, which makes tools like Alteryx important for non-technical users.
  3. Alteryx started with a simple workflow builder for data cleaning but has grown to include many other analytics tools over time.

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.

Data Science Weekly - Issue 516

Data Science Weekly Newsletter • 299 implied HN points • 13 Oct 23
🕹 Technology Data science AI Machine Learning Data Engineering Analytics
  1. The newsletter is deciding whether to publish twice a week, but will stick to one issue for now to review feedback from readers.
  2. There's a focus on providing useful resources for data science, including articles and job opportunities in the field.
  3. New tools and methods in AI and data engineering are highlighted, addressing challenges like data integration and AI model training.

Data Science Weekly - Issue 511

Data Science Weekly Newsletter • 319 implied HN points • 07 Sep 23
🕹 Technology Data science AI Machine Learning Analytics Software Development
  1. AI startups can receive significant support through programs like AI Grant, offering up to $250,000 for development.
  2. Recent studies have shown that large language models can learn from just one example, which challenges previous beliefs about their efficiency.
  3. Using advanced tools like the Semantic Layer and LLMs can greatly improve data accuracy and speed for businesses, making analytics much easier.

Data Science Weekly - Issue 515

Data Science Weekly Newsletter • 299 implied HN points • 06 Oct 23
🕹 Technology Data science Artificial Intelligence Machine Learning Analytics Software Development
  1. There's a lot happening in data science right now. The team is considering adding a second newsletter each week to cover more exciting content.
  2. High-performing data scientists have specific traits that set them apart from others. Companies are researching these traits to help improve their teams.
  3. Art institutions can greatly benefit from data and analytics. Collaborating with leaders can help them use data to improve their operations and strategies.

Favorable Winds for AMD in the GenAI Chip Market

Gradient Flow • 139 implied HN points • 08 Feb 24
🕹 Technology Hardware AI Software Privacy Analytics
  1. AMD's hardware offers performance and efficiency gains for AI tasks, with specialized optimizations making them well-suited for training and inference in advanced AI scenarios.
  2. AMD has invested in mature and optimized open-source software like the ROCm stack, providing a critical foundation for maximizing the performance of their hardware in real-world AI applications.
  3. Market trends are aligning favorably for AMD, with shorter lead times improving chip availability, notable endorsements from industry leaders, and growing momentum indicating a strong position in the AI silicon landscape.

Data Science Weekly - Issue 520

Data Science Weekly Newsletter • 239 implied HN points • 10 Nov 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Analytics
  1. Data scientists share interesting links and news weekly about AI, machine learning, and data visualization. It's a great way to stay updated on trends and tools in the field.
  2. Learning about the basics of deep learning and mathematical foundations is important for anyone starting in machine learning. Understanding key concepts helps you tackle complex problems more effectively.
  3. There are many job opportunities in data science and related fields. Keeping an eye on openings can lead to exciting career advancements and collaborations.