The hottest Data Analytics Substack posts right now

And their main takeaways
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Top Business Topics

Data Engineering: A Software Engineer’s Approach

realkinetic • 0 implied HN points • 25 Jan 24
🕹 Technology Data Analytics
  1. The tech industry varies in its expectations of data engineers, leading to challenges in team performance and hiring.
  2. Companies today need to be data-driven, utilizing modern data stack tools, which necessitates a blend of data engineering and software engineering skills.
  3. Data engineering benefits from adopting software engineering principles like treating systems as products, clear communication, and implementing CI/CD pipelines.

Implementing ETL on GCP

realkinetic • 0 implied HN points • 15 Jul 20
🕹 Technology Data Analytics
  1. ETL processes are vital for data analytics, involving extracting, transforming, and loading data for storage in a warehouse.
  2. GCP offers options like Data Fusion and Cloud Dataprep for implementing ETL pipelines, catering to varying technical skill levels and preferences.
  3. Alternative approaches on GCP for ETL include using services like Cloud Dataflow for more code-heavy processes or leveraging BigQuery for ELT if your team is SQL-focused.

Google Search Mania

Sector 6 | The Newsletter of AIM • 0 implied HN points • 02 Nov 23
🕹 Technology Data Analytics
  1. Google is always trying to improve its search engine with new technology like generative AI, which helps make searches smarter and more useful.
  2. The company recently had a good quarter, making 11% more money than last year, mostly from ads related to its search services.
  3. With over $59 billion from advertising, it's clear that Google's search engine is still a major driver of its profits.

Business (Un)Intelligence

Sector 6 | The Newsletter of AIM • 0 implied HN points • 26 Feb 23
💼 Business Data Analytics
  1. Many business intelligence tools haven’t evolved much and are falling behind modern trends and technologies.
  2. This lack of improvement is resulting in a decline in useful insights from these tools, leading to what's called 'business (un)intelligence.'
  3. Microsoft is performing well in this space, possibly attracting users away from competitors like Tableau due to its established ecosystem and offerings.

✅ Monday briefing: Waddscon on AI in PR, issues radar, Google Analytics switch, GPT3 Google docs, TikTok for text, IP overreach, British Gas fury, and more...

Wadds Inc. newsletter • 0 implied HN points • 06 Feb 23
🕹 Technology Data Analytics
  1. Waddscon is an event focused on how AI is changing public relations and marketing. It will feature experts discussing tools and the future of work.
  2. Google Analytics will switch to GA4 soon, and users need to set it up to avoid automatic changes. It's important to take control of your data tracking.
  3. A new app called Artifact is like TikTok but for text, helping users discover and read articles based on their interests.
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✅ Monday briefing: ESG opportunity, PR weak, bounce rate bounced, presenting data, hiding likes, considerate Twitter and more...

Wadds Inc. newsletter • 0 implied HN points • 10 May 21
💼 Business Data Analytics
  1. ESG stands for Environmental, Social, and Governance, and it's becoming important for public relations to focus on this. It helps businesses look at their impact on the planet and society.
  2. Bounce rate is replaced with engagement rate in Google Analytics. This new way of measuring shows how much users interact with your content, which is more helpful than just seeing if they leave a page.
  3. Instagram now lets users choose if they want to hide likes on their posts. This change is meant to help people feel less pressure about how many likes their content gets.

Finding Trends With Approximate Embedding Clustering

aspiring.dev • 0 implied HN points • 29 Apr 23
🕹 Technology Data Analytics
  1. Clustering similar data helps to identify trends and categories quickly. This is important for analyzing things like shopping habits or AI tasks.
  2. K-Means++ is a method that improves the speed and accuracy of finding cluster centers, which helps in managing data without needing too much preparation.
  3. Using approximate clustering techniques allows for faster processing of data and keeps up with changing trends, making it useful for things like tracking popular text-to-speech messages.

[in case you missed It] Data Science Weekly - Issue 459

Data Science Weekly Newsletter • 0 implied HN points • 11 Sep 22
🕹 Technology Data Analytics
  1. Organizations should work on improving their data quality because it directly impacts their success and competitive edge. Creating better data can lead to better decisions and outcomes.
  2. The modern data stack's activation layer is crucial for turning data into actionable insights. This allows companies to go beyond just looking at data and actually use it to improve their products and services.
  3. Using the right tools, like ONNX for model deployment, can help make machine learning models more portable and less tied to specific programming environments. This makes it easier to run models across different programming languages.

[in case you missed it] Data Science Weekly - Issue 378

Data Science Weekly Newsletter • 0 implied HN points • 21 Feb 21
🕹 Technology Data Analytics
  1. Creating robots that can think morally is similar to parenting. Teaching them right from wrong can be approached in the same way we teach children.
  2. Transformers are important in both language and image processing. Understanding how to use them can help with many tasks in data science.
  3. Building systems for data quality and observability is essential. By using tools like SQL, we can keep track of how our data changes and ensure it stays reliable.

[in case you missed it] Data Science Weekly - Issue 377

Data Science Weekly Newsletter • 0 implied HN points • 14 Feb 21
🕹 Technology Data Analytics
  1. Using Active Learning can save time and effort in machine learning. It allows models to learn with less labeled data by letting them ask questions about unclear data.
  2. There is a growing shift from Excel to Python in many industries. This change is driven by the need for more advanced data analysis and the capabilities Python offers.
  3. Understanding the importance of machine learning in healthcare is crucial. Innovations like AI systems that can identify smells may lead to new diagnostic tools and enhance medical practices.

[in case you missed it] Data Science Weekly - Issue 294

Data Science Weekly Newsletter • 0 implied HN points • 13 Jul 19
🕹 Technology Data Analytics
  1. A new poker bot has learned strategies to beat skilled players, showing the advancements in AI technology. This could change how games are played and studied.
  2. Generative adversarial networks, which are known for creating deepfakes, may have positive uses in medical fields like cancer diagnosis. Before, they were mainly seen in a negative light.
  3. San Francisco is trying to use AI to reduce bias in the prosecution process, aiming to make the legal system fairer. This could help in addressing racial discrimination in the courtroom.

GroupBy #9: FDAP stack, Iceberg and Hudi ACID Guarantees, Data Driven Management

VuTrinh. • 0 implied HN points • 14 Nov 23
🕹 Technology Data Analytics
  1. The FDAP stack is important in building reliable data systems. It helps to manage data more efficiently by using advanced technologies.
  2. Learning about data quality is crucial. It ensures that the information used for decision-making is accurate and trustworthy.
  3. Data-driven management is all about making decisions based on solid data insights. It helps businesses understand what works and what doesn't.

Unlocking Insights with Apache Spark: A Beginner's Guide to Data Aggregation

DataSketch’s Substack • 0 implied HN points • 03 Apr 24
🕹 Technology Data Analytics
  1. Apache Spark is a powerful tool for analyzing big data due to its speed and user-friendly features. It helps data engineers to work with large datasets effectively.
  2. Data aggregation involves summarizing data to understand trends better. It includes basic techniques like summing and averaging, grouping data by categories, and performing calculations on subsets.
  3. Windowing functions in Spark allow for advanced calculations, like running totals and growth rates, by looking at data relative to specific rows. This helps to analyze trends without losing the detail in the data.

Harnessing Data Architecture: Practical Models and SQL Solutions

DataSketch’s Substack • 0 implied HN points • 26 Mar 24
🕹 Technology Data Analytics
  1. Creating effective data models is crucial for businesses to organize and use their data efficiently.
  2. Different industries like eCommerce, healthcare, and retail have unique data needs that can be addressed with tailored database solutions.
  3. Understanding SQL and how to create tables and relationships helps in developing strong data architecture.

Is dbt Labs cannibalizing the modern data stack with dbt Cloud?

clkao@substack • 0 implied HN points • 18 Oct 24
🕹 Technology Data Analytics
  1. dbt Labs is expanding its features to create a more unified data platform. This means users won’t need multiple tools since dbt can handle many basic data needs.
  2. Applying software development practices to data workflows can be tricky. The way we test data is different, and adopting these practices hasn’t been easy for everyone.
  3. Recce is designed to improve the software development workflow for data. It helps users validate changes easily and ensures everyone understands what correctness means in the data context.

Precog and Bittensor: Redefining Crypto Market Intelligence

Coin Metrics' State of the Network • 0 implied HN points • 28 Jan 25
🔮 Crypto Data Analytics
  1. Bittensor is a decentralized network that rewards users for solving AI tasks. This way, the best performers get recognized and compensated for their work.
  2. Precog, built on Bittensor's infrastructure, allows users to compete in predicting crypto prices. Those who make accurate forecasts can earn rewards, making the process both competitive and engaging.
  3. The entire system uses blockchain technology to ensure fairness and transparency. This way, everyone involved can trust that rewards are distributed based on performance.