The hottest Cloud Computing Substack posts right now

And their main takeaways
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Top Technology Topics

Microsoft builds the bomb

benn.substack • 1508 implied HN points • 26 May 23
🕹 Technology Data Management Cloud Computing Software Development Artificial Intelligence
  1. The modern data stack aimed to revolutionize how technology is built and sold, focusing on modularity and specialized tools.
  2. Microsoft introduced Fabric as an all-in-one data and analytics platform to address the issue of fragmentation in the modern data stack.
  3. Fabric from Microsoft presents a unified solution but may risk limiting choice and innovation in the data industry.

The Tech Buffet #20: How To deploy a Cloud Function That Summarizes Youtube Videos

The Tech Buffet • 139 implied HN points • 11 Mar 24
🕹 Technology Cloud Computing Machine Learning DevOps Serverless Infrastructure
  1. Cloud Functions are a serverless way to run your code on Google Cloud without managing servers. You pay only for what you use, making it cost-effective.
  2. You can build a Cloud Function to summarize YouTube videos by extracting their transcripts and using AI to create concise summaries. This is done using Python libraries like youtube-transcript-api and langchain.
  3. Testing your Cloud Function locally is a great way to ensure it works before deploying it. You can use tools like Postman to check the API responses easily.

Is AI Capex Worth the Money? (ft. Goldman Sachs)

Enterprise AI Trends • 337 implied HN points • 11 Jul 24
🕹 Technology AI Cloud Computing Data science Enterprise Software Investment
  1. AI spending is still worth it because it can help big cloud providers move data to their services. This could open up a big opportunity for revenue, making the investment seem less risky.
  2. Most of the useful AI work happens behind the scenes and isn't visible to the public. This means many people might underestimate how much AI is actually helping businesses already.
  3. Companies are really committed to using generative AI and are treating it as a top priority. This commitment means we'll likely see more successful projects in the future.

GroupBy #37: Composable data management at Meta, How Uber Accomplishes Job Counting At Scale

VuTrinh. • 59 implied HN points • 28 May 24
🕹 Technology Data Engineering Software Development Data processing Cloud Computing Open Source
  1. When learning something new, it's good to start by asking yourself why you want to learn it. This helps set clear goals and expectations.
  2. Focusing on one topic at a time can make learning easier. Instead of spreading your time thin, dive deep into one subject.
  3. It's okay to feel stuck sometimes while learning. Just keep pushing through, relax, and remember that learning is a journey that takes time.

Scaling Distributed Systems with the Scatter-Gather Pattern

Engineering At Scale • 60 implied HN points • 15 Feb 25
🕹 Technology Distributed Systems Microservices Cloud Computing Software Design System Architecture
  1. The Scatter-Gather pattern helps speed up data retrieval by splitting requests to multiple servers at once, rather than one after the other. This makes systems respond faster, especially when lots of data is needed.
  2. Using this pattern can improve system efficiency by preventing wasted time waiting for responses from each service. This means the system can handle more requests at once.
  3. However, implementing Scatter-Gather can be tricky. It requires careful handling of errors and managing different data sources to ensure the information is accurate and reliable.
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NIST's "Strategies for Integration of Software Supply Chain Security in DevSecOps CI/CD Pipelines"

Resilient Cyber • 159 implied HN points • 13 Feb 24
🕹 Technology Cybersecurity Software Development Cloud Computing DevOps Data security
  1. Software supply chain attacks are on the rise, so companies need to protect their processes from potential risks. Understanding these threats is key for organizations that rely on software.
  2. NIST provides guidelines to help organizations improve their software security in DevSecOps environments. By following their advice, companies can ensure that their software development processes are safe from compromise.
  3. Implementing zero-trust principles and automating security checks during software development can greatly reduce the risk of attacks. This means controlling access and regularly checking for vulnerabilities throughout the development cycle.

Why did Databricks build the Photon engine?

VuTrinh. • 99 implied HN points • 06 Apr 24
🕹 Technology Data Engineering Software Development Cloud Computing Database Systems Big Data
  1. Databricks created the Photon engine to simplify data management by combining the benefits of data lakes and data warehouses into one system called the Lakehouse. This makes it easier and cheaper for companies to manage their data all in one place.
  2. Photon is designed to handle various types of raw data and is built with a vectorized approach instead of the traditional Java methods. This means it can work faster and better with different kinds of data without getting bogged down.
  3. To ensure that existing customers using Apache Spark can easily switch to Photon, the new engine is integrated with Spark’s system. This allows users to continue running their current processes while benefiting from the speed of Photon.

Lean Data Engineering with Dagster and DuckDB

The Data Jargon Newsletter • 158 implied HN points • 05 Mar 24
🕹 Technology Data Engineering Data Management Software Tools Cloud Computing 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.

Data Science Weekly - Issue 522

Data Science Weekly Newsletter • 259 implied HN points • 23 Nov 23
🕹 Technology Data science Artificial Intelligence Machine Learning Software Development Cloud Computing
  1. This newsletter shares weekly interesting links and updates in data science, AI, and machine learning. It's a great way to stay informed about new developments in these fields.
  2. There's a focus on practical tools and techniques for improving data science work, like using cloud processing for large datasets and methods for fine-tuning AI models effectively.
  3. The newsletter also highlights job opportunities and resources for those looking to enter or advance in the data science industry. It's beneficial for anyone looking to grow their career in this area.

Monolithic vs MicroServices.

Software Ninja Handbook • 3 HN points • 12 Sep 24
🕹 Technology Software Development Architecture Cloud Computing System Design Agile methodologies
  1. Monolithic applications have a single codebase, which makes them easier to manage for smaller projects, but harder to debug as they grow. Everything is tightly connected, so a problem in one part can affect the whole system.
  2. Microservices break down applications into smaller, independent services that can be developed and deployed separately. This allows teams to work faster and use different technologies for different parts of the application.
  3. Choosing between monolithic and microservices depends on factors like project size and team structure. Monoliths are good for small projects while microservices are better for larger, complex systems that need flexibility and scalability.

I spent 3 hours figuring out how BigQuery inserts, deletes and updates data internally. Here's what I found.

VuTrinh. • 139 implied HN points • 17 Feb 24
🕹 Technology Data Engineering Cloud Computing Database Management Data Analysis Software Development
  1. BigQuery manages data using immutable files, meaning once data is written, it cannot be changed. This helps in processing data efficiently and maintains data consistency.
  2. When you perform actions like insert, delete, or update, BigQuery creates new files instead of changing existing ones. This approach helps in features like time travel, which lets you view past states of data.
  3. BigQuery uses a system called storage sets to handle operations. These sets help ensure processes are performed atomically and consistently, maintaining data integrity during changes.

GroupBy #35: The Netflix Data Engineering Stack, Atlassian - Evolve the data platform with a Deployment Capability

VuTrinh. • 59 implied HN points • 14 May 24
🕹 Technology Data Engineering Software Development Cloud Computing Data architecture Programming
  1. Netflix has a strong data engineering stack that supports both batch and streaming data pipelines. It focuses on building flexible and efficient data architectures.
  2. Atlassian has revamped its data platform to include a new deployment capability inspired by technologies like Kubernetes. This helps streamline their data management processes.
  3. Migrating from dbt Cloud can teach valuable lessons about data development. Companies should explore different options and learn from their migration journeys.

Import AI 319: Sovereign AI; Facebook's weights leak on torrent networks; Google might have made a better optimizer than Adam!

Import AI • 439 implied HN points • 06 Mar 23
🕹 Technology AI Research Cloud Computing Data security Partnerships Innovation
  1. Google researchers achieved promising results by scaling a Vision Transformer to 22B parameters, showcasing improved alignment to human visual perception.
  2. Google introduced a potentially better optimizer called Lion, showing outstanding performance across various models and tasks, including setting a new high score on ImageNet.
  3. A shift toward sovereign AI systems is emerging globally, driven by the need for countries to develop their own AI capabilities to enhance national security and economic competitiveness.

The Tech Buffet #21: Deploy A Production-Ready Streamlit App with Cloud Run and Cloud Build

The Tech Buffet • 99 implied HN points • 22 Mar 24
🕹 Technology Cloud Computing App Development DevOps CI/CD
  1. Cloud Run lets you deploy containerized applications without worrying about server management. You only pay when your code is actively running, making it a cost-effective option.
  2. Using Pulumi as an Infrastructure as Code tool simplifies the process of setting up and managing cloud resources. It allows you to deploy applications by writing code instead of manually configuring settings.
  3. Automating your deployment with Cloud Build ensures your app updates easily whenever you make code changes. This saves time and effort compared to manually deploying each time.

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

VuTrinh. • 159 implied HN points • 20 Jan 24
🕹 Technology Data Engineering Cloud Computing Architecture Big Data
  1. BigQuery uses SQL again after moving away from it, making data analysis fast and easy. Users can now analyze huge datasets quickly without complex coding.
  2. It separates storage and compute resources, allowing for better performance and flexibility. This means you can scale them independently, which is very efficient.
  3. Dremel's serverless architecture means you don’t need to manage servers. You just use SQL, and everything else is automatically handled for you.

A Closer Look Into Databricks's Photon Engine

VuTrinh. • 79 implied HN points • 13 Apr 24
🕹 Technology Software Data Engineering Database Systems Cloud Computing Artificial Intelligence
  1. Photon engine uses columnar data layout to manage memory efficiently, allowing it to process data in batches. This helps in speeding up data operations.
  2. It supports adaptive execution, which means the engine can change how it processes data based on the input. This can significantly improve performance, especially when data has many NULLs or inactive rows.
  3. Photon integrates with Databricks runtime and Spark SQL, allowing it to enhance existing workloads without completely replacing the old system, making transitions smoother.

GroupBy #34: Hybrid Transactional/Analytical Storage, From Predictive to Generative – How Michelangelo Accelerates Uber’s AI Journey

VuTrinh. • 59 implied HN points • 07 May 24
🕹 Technology Data Engineering Artificial Intelligence Machine Learning Data Management Cloud Computing
  1. Hybrid transactional/analytical storage combines different types of data processing. This helps companies like Uber manage their data more efficiently.
  2. The shift from predictive to generative AI is changing how companies use machine learning. Uber's Michelangelo platform shows how this new approach can improve AI applications.
  3. Data reliability and observability are important for businesses as their data grows. Companies need tools to quickly find and fix data issues to keep their operations running smoothly.

Issue #4 - The Five Minute History of Data

The Data Ecosystem • 59 implied HN points • 05 May 24
🕹 Technology Data science AI Analytics Data processing Cloud Computing
  1. Data is generated and used everywhere now, thanks to smart devices and cheaper storage. This means businesses can use data for many purposes, but not all those uses are helpful.
  2. Processing data has become much easier over the years. Small companies can now use tools to analyze data without needing a team of experts, although some guidance is still necessary.
  3. Analytics has shifted from just looking at past data to predicting future trends. This helps companies make better decisions, and AI is starting to take over some of these tasks.

From Dynamo to DynamoDB : Unveiling the Titans of Tech

Mindful Matrix • 119 implied HN points • 18 Feb 24
🕹 Technology Database Management Cloud Computing
  1. Dynamo and DynamoDB are two names often seen in databases, but they have significant differences. Dynamo set the foundation, and DynamoDB evolved into a practical, scalable, and reliable service.
  2. Key differences between Dynamo and DynamoDB include their Genesis, Consistency Model, Data Modeling, Operational Model, and Conflict Resolution approaches.
  3. Dynamo focuses on eventual consistency, while DynamoDB offers both eventual and strong consistency. Dynamo is a simple key-value store, while DynamoDB supports key-value and document data models.

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

VTEX’s Tech Blog • 99 implied HN points • 10 Mar 24
🕹 Technology Cloud Computing Software Engineering Data Analytics DevOps
  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.

Software Supply Chain Security in DevSecOps & CI/CD

Resilient Cyber • 259 implied HN points • 27 Sep 23
🕹 Technology Cybersecurity Software Development DevOps Cloud Computing Information Security
  1. Software supply chain attacks are increasing, making it essential for organizations to protect their software development processes. Companies are looking for ways to secure their software from these attacks.
  2. NIST has issued guidance to help organizations improve software supply chain security, especially in DevSecOps and CI/CD environments. Following NIST's recommendations can help mitigate risks and ensure safer software delivery.
  3. The complexity of modern software environments makes security challenging. It's important for organizations to implement strict security measures throughout the development lifecycle to prevent attacks and ensure the integrity of their software.

Data Science Weekly - Issue 510

Data Science Weekly Newsletter • 279 implied HN points • 31 Aug 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Cloud Computing
  1. Autonomous drones can now race at human champion levels using deep reinforcement learning. This shows how advanced technology can mimic skilled human behavior in competitive sports.
  2. Google is rapidly developing its AI capabilities and plans to surpass GPT-4 by a significant margin soon. This could lead to more powerful AI tools for various applications.
  3. Reinforced Self-Training (ReST) is a new method for improving language models by aligning their outputs with human preferences. It offers better translation quality and can be done efficiently with less data.

Who cares about AuthZ? We went to KubeCon!

Permit.io’s Substack • 79 implied HN points • 28 Mar 24
🕹 Technology Cloud Computing Software Development Cybersecurity Data Management Open Source
  1. Fine-grained authorization is becoming really important as more developers talk about it. People see that better security can happen with smooth developer experiences.
  2. The rise of cloud-native architecture and big data means we need better ways to manage authorization decisions. It helps reduce decision fatigue and improves security.
  3. Tools like Policy as Code and various authorization engines are helping different teams work together better. This can lead to faster and more efficient development processes.

A detailed guide to running dbt Core in Production in AWS on ECS

The Orchestra Data Leadership Newsletter • 79 implied HN points • 28 Mar 24
🕹 Technology Cloud Computing Data Engineering AWS CI/CD Orchestration
  1. A detailed guide to running dbt Core in production in AWS on ECS is outlined, focusing on achieving cost-effective and reliable execution.
  2. Running dbt in production is not highly compute-intensive, as it primarily serves as an orchestrator, making it more cost-efficient compared to running Python code that utilizes compute resources.
  3. By setting up dbt Core on ECS in AWS and using Orchestra, you can achieve a scalable, cost-effective solution for self-hosting dbt Core with full visibility and control.

What's a data migration?

Technically • 29 implied HN points • 12 Nov 24
🕹 Technology Data Management Software Development Cloud Computing Database Systems IT Infrastructure
  1. Data migration is the process of moving information from one place to another, like relocating files when changing devices. It involves transferring various types of data, such as documents and databases, to ensure everything is in the right spot.
  2. Migrations can be complex and risky, often causing errors or service disruptions if not done carefully. This makes it crucial for companies to have good planning and oversight to avoid losing important data or negatively affecting users.
  3. There are many reasons to migrate data, such as upgrading technology or meeting new security regulations. Companies often need to adapt to growth or changes in the market, which can lead to costly and lengthy migration projects.

Gsnowflake

benn.substack • 792 implied HN points • 07 Jul 23
🕹 Technology Data AI Cloud Computing Natural Language Processing
  1. Google is technically a database but differs from traditional databases in its structure and content.
  2. Snowflake is introducing features like Document AI that hint at a shift towards focusing on information retrieval rather than just data analysis.
  3. The market for an information database could potentially be larger and more accessible than traditional data warehouses, offering simpler access to basic facts and connections.

The Next Wave Of AI Computing

Startup Pirate by Alex Alexakis • 235 implied HN points • 10 Mar 23
🕹 Technology Edge Computing AI Chips Cloud Computing Machine Learning
  1. Artificial intelligence has come a long way since Alan Turing, with AI chips being a key component for advanced computations.
  2. Edge computing moves computing power closer to where data is generated, enabling faster responses for AI applications like self-driving cars.
  3. Axelera AI is focusing on AI chips for edge computing and advancing technology for applications like computer vision in the physical world.

If It Fits, Gemma Sits 💎

Sector 6 | The Newsletter of AIM • 99 implied HN points • 23 Feb 24
🕹 Technology AI Software Open Source Cloud Computing Innovation
  1. Google has integrated its new model, Gemini, into Google Workspace, showing its focus on developing AI tools for users.
  2. While Google has released a model called Gemma, it is not truly open-source, which raises questions about its commitment to the open-source community.
  3. This year, Google is heavily promoting its Gemini brand, including recent updates and changes to its existing AI products like Bard.

Import AI 352: Asteroids and AI policy; privacy-preserving AI benchmarks; and distributed inference

Import AI • 159 implied HN points • 11 Dec 23
🕹 Technology AI Policy Cloud Computing
  1. Preparing for potential asteroid impacts requires coordination, strategic planning, and societal engagement.
  2. Distributed systems like LinguaLinked challenge traditional AI infrastructure assumptions, enabling local governance of AI models.
  3. Privacy-preserving benchmarks like Hashmarks allow for secure evaluation of sensitive AI capabilities without revealing specific information.

The Tech Buffet #17: 9 Effective Techniques To Boost Retrieval Augmented Generation (RAG) Systems

The Tech Buffet • 139 implied HN points • 02 Jan 24
🕹 Technology Artificial Intelligence Natural Language Processing Data Management Software Development Cloud Computing
  1. Make sure the data you use for RAG systems is clean and accurate. If you start with bad data, you'll get bad results.
  2. Finding the right size for document chunks is important. Too small or too large can affect the quality of the information retrieved.
  3. Adding metadata to your documents can help organize search results and make them more relevant to what users are looking for.

DataFrame

davidj.substack • 35 implied HN points • 20 Feb 25
🕹 Technology Data science Machine Learning Programming Cloud Computing Open Source
  1. Polars Cloud allows for scaling across multiple machines, making it easier to handle large datasets than using just a single machine. This helps in processing data faster and more efficiently.
  2. Polars is simpler to use compared to Pandas and often performs better, especially when transforming data for machine learning tasks. It supports familiar methods that many users already know.
  3. Unlike SQL, which runs well on cloud services, using Pandas and R for large-scale transformations has been challenging. The new Polars Cloud aims to bridge this gap, providing more scalable solutions.

The massive DeepSeek affect

TP’s Substack • 37 implied HN points • 15 Feb 25
🕹 Technology AI Models Open Source Consumer Electronics Software Development Cloud Computing
  1. DeepSeek has gained huge popularity in China, surpassing major competitors and reaching 30 million daily active users. This shows that users really like its features.
  2. Chinese companies are rapidly integrating DeepSeek into their products, from smartphones to cars, suggesting that more devices will soon be using this powerful AI tool.
  3. The rise of DeepSeek is changing how people in China use AI and might even provide better search options compared to existing services like Baidu. It's a big deal for the tech industry there.

I spent another 8 hours understanding the design of Amazon Redshift. Here's what I found.

VuTrinh. • 79 implied HN points • 16 Mar 24
🕹 Technology Data Engineering Cloud Computing Database Systems Machine Learning Big Data
  1. Amazon Redshift is designed as a massively parallel processing data warehouse in the cloud, making it effective for handling large data sets efficiently. It changes how data is stored and queried compared to traditional systems.
  2. The system uses a unique compilation service that generates specific code for queries, which helps speed up processing by caching compiled code. This means Redshift can reuse code for similar queries, reducing wait times.
  3. Redshift also uses machine learning techniques to optimize operations, such as predicting resource needs and automatically adjusting performance settings. This allows it to scale effectively and maintain high performance during heavy workloads.

GroupBy #31: Migrating a Trillion Entries of Uber’s Ledger Data from DynamoDB to LedgerStore, Grab Experiment Decision Engine

VuTrinh. • 59 implied HN points • 16 Apr 24
🕹 Technology Data Engineering Machine Learning Software Development Cloud Computing
  1. Uber successfully migrated over a trillion entries of its ledger data to a new database called LedgerStore without causing disruptions. This shows how careful planning can make big data moves smooth.
  2. Airbnb has open-sourced a machine learning feature platform called Chronon, which helps manage data and makes it easier for engineers to work with different data sources. This promotes collaboration and innovation in the tech community.
  3. The GrabX Decision Engine boosts experimentation on online platforms by providing tools for better planning and analyzing experiments. This can lead to more informed decisions and improved outcomes in projects.

5 SIEM Capabilities for Detection Engineering

Detection at Scale • 59 implied HN points • 15 Apr 24
🕹 Technology Security Data Management Automation Cloud Computing Programming
  1. Detection Engineering involves moving from simply responding to alerts to enhancing the capabilities behind those alerts, leading to reduced fatigue for security teams.
  2. Key capabilities for supporting detection engineering include a robust data pipeline, scalable analytics with a security data lake, and embracing Detection as Code framework for sustainable security insights.
  3. Modern SIEM platforms should offer an API for automated workflows, BYOC deployment options for cost-effectiveness, and Infrastructure as Code capabilities for stable long-term management.