The hottest Cloud Computing Substack posts right now

And their main takeaways
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Top Technology Topics

Apache Kafka - Producer

VuTrinh. • 199 implied HN points • 20 Jul 24
🕹 Technology Data Engineering Software Development Cloud Computing Distributed Systems Real-Time Processing
  1. Kafka producers are responsible for sending messages to servers. They prepare the messages, choose where to send them, and then actually send them to the Kafka brokers.
  2. There are different ways to send messages: fire-and-forget, synchronous, and asynchronous. Each method has its pros and cons, depending on whether you want speed or reliability.
  3. Producers can control message acknowledgment with the 'acks' parameter to determine when a message is considered successfully sent. This parameter affects data safety, with options that range from no acknowledgment to full confirmation from all replicas.

The Rise of Amazon's Trainium 2

Mule’s Musings • 288 implied HN points • 04 Nov 24
🕹 Technology Semiconductors AI Infrastructure Cloud Computing
  1. Amazon is significantly increasing its investments in technology infrastructure, particularly for AI services, showing a strong commitment to compete in the generative AI space.
  2. The success of Amazon's new custom silicon, Trainium 2, could be larger than expected as demand from AI applications grows rapidly.
  3. Trainium 2 represents Amazon's serious entry into the market for training AI models, positioning it as a competitor against established players like Nvidia.

Amazon Anthropic: Poison Pill or Empire Strikes Back

SemiAnalysis • 6667 implied HN points • 02 Oct 23
🕹 Technology AI Cloud Computing Machine Learning Artificial Intelligence
  1. Amazon and Anthropic signed a significant deal, with Amazon investing in Anthropic, which could impact the future of AI infrastructure.
  2. Amazon has faced challenges in generative AI due to lack of direct access to data and issues with internal model development.
  3. The collaboration between Anthropic and Amazon could accelerate Anthropic's ability to build foundation models but also poses risks and challenges.

The History and Evolution of Open Table Formats - Part II

Practical Data Engineering Substack • 79 implied HN points • 18 Aug 24
🕹 Technology Data Management Software Development Open Source Cloud Computing Database Systems
  1. The evolution of open table formats has improved how we manage data by introducing log-oriented designs. These designs help us keep track of data changes and make data management more efficient.
  2. Modern open table formats like Apache Hudi and Delta Lake offer database-like features on data lakes, ensuring data integrity and allowing for easier updates and querying.
  3. New projects are working on creating a unified table format that can work with different technologies. This means that in the future, switching between data formats could be simpler and more streamlined.

Microsoft & Google: "You'll have no real computers, and you'll be happy"

The Lunduke Journal of Technology • 6893 implied HN points • 26 Apr 23
🕹 Technology Tech Trends Cloud Computing
  1. Big tech companies are promoting the idea of using less capable computers and remote desktop-ing into central servers.
  2. Microsoft is pushing Windows 365 Frontline where users connect to a remote Windows 11 desktop provided by Microsoft.
  3. Google is providing low-power Chromebooks to employees and encouraging the use of Google Cloudtop for desktop software, eliminating the need for powerful computers.
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GroupBy #42: Paypal - Scaling Kafka

VuTrinh. • 219 implied HN points • 02 Jul 24
🕹 Technology Data Engineering Software Development Cloud Computing Big Data Infrastructure
  1. PayPal operates a massive Kafka system with over 85 clusters and handles around 1.3 trillion messages daily. They manage data growth by using multiple geographical data centers for efficiency.
  2. To improve user experience and security, PayPal developed tools like the Kafka Config Service for easier broker management and added access control lists to restrict who can connect to their Kafka clusters.
  3. PayPal focuses on automation and monitoring, implementing systems to quickly patch vulnerabilities and manage topics, while also optimizing metrics to quickly identify issues with their Kafka platform.

4 Trillion Events Daily at LinkedIn

VuTrinh. • 319 implied HN points • 08 Jun 24
🕹 Technology Data Engineering Real-Time Processing Machine Learning Software Development Cloud Computing
  1. LinkedIn processes around 4 trillion events every day, using Apache Beam to unify their streaming and batch data processing. This helps them run pipelines more efficiently and save development time.
  2. By switching to Apache Beam, LinkedIn significantly improved their performance metrics. For example, one pipeline's processing time went from over 7 hours to just 25 minutes.
  3. Their anti-abuse systems became much faster with Beam, reducing the time taken to identify abusive actions from a day to just 5 minutes. This increase in efficiency greatly enhances user safety and experience.

TPUv5e: The New Benchmark in Cost-Efficient Inference and Training for <200B Parameter Models

SemiAnalysis • 6263 implied HN points • 01 Sep 23
🕹 Technology Performance Cost efficiency Cloud Computing
  1. Google's TPUv5e offers a cost advantage for training and inferring models with under 200 billion parameters compared to AI chips from other companies.
  2. TPUv5e and TPUv5 prioritize efficiency and low power consumption over peak performance, with a focus on minimizing total cost of ownership.
  3. Google's TPUv5e system features high bandwidth communication between chips, linear cost scaling, and efficient software tools for ease of use.

Modellion

davidj.substack • 179 implied HN points • 25 Nov 24
🕹 Technology Data architecture Big Data Data Modeling Database Management Cloud Computing
  1. Medallion architecture is not just about data modeling but represents a high-level structure for organizing data processes. It helps in visualizing data flow in a project.
  2. The architecture has three main layers: Bronze deals with cleaning and preparing data, Silver creates a structured data model, and Gold is about making data easy to access and use.
  3. The terms Bronze, Silver, and Gold may sound appealing to non-technical users but could be more accurately described. Renaming these layers could better reflect their actual roles in data handling.

Apache Kafka - Consumer

VuTrinh. • 119 implied HN points • 27 Jul 24
🕹 Technology Data Engineering Software Development Information Systems Data processing Cloud Computing
  1. Kafka uses a pull model for consumers, allowing them to control the message retrieval rate. This helps consumers manage workloads without being overwhelmed.
  2. Consumer groups in Kafka let multiple consumers share the load of reading from topics, but each partition is only read by one consumer at a time for efficient processing.
  3. Kafka handles rebalancing when consumers join or leave a group. This can be done eagerly, stopping all consumers, or cooperatively, allowing ongoing consumption from unaffected partitions.

How Twitter processes 4 billion events in real-time daily

VuTrinh. • 339 implied HN points • 25 May 24
🕹 Technology Data Engineering Real-Time Processing Cloud Computing Data architecture Big Data
  1. Twitter processes an incredible 400 billion events daily, using a mix of technologies for handling large data flows. They built special tools to ensure they can keep up with all this information in real-time.
  2. After facing challenges with their old setup, Twitter switched to a new architecture that simplified operations. This new system allows them to handle data much faster and more efficiently.
  3. With the new system, Twitter achieved lower latency and fewer errors in data processing. This means they can get more accurate results and better manage their resources than before.

Gambling with language models

Rain Clouds • 51 implied HN points • 31 Dec 24
🕹 Technology Machine Learning Cloud Computing Data science Financial Analysis Investing
  1. Using AI models, like ModernBert, can help in predicting which stocks might perform better based on financial reports and market data. This means you can get insights without needing to be a finance expert.
  2. The project combines cloud computing with machine learning, making it easier to process large amounts of financial data quickly. This is important for anyone looking to analyze stocks more efficiently.
  3. While the model can make predictions, it's important to remember that investing in stocks always carries risks. Just because a model suggests a stock might do well, it doesn't guarantee success.

GroupBy #43: Uber | Kafka - The Tiered Storage

VuTrinh. • 139 implied HN points • 09 Jul 24
🕹 Technology Data Engineering Software Development Cloud Computing Information Systems Big Data
  1. Uber recently introduced Kafka Tiered Storage, which allows storage and compute resources to work separately. This means you can add storage without needing to upgrade processing power.
  2. The tiered storage system has two parts: local storage for fast access and remote storage for long-term data. This setup helps manage data efficiently and keeps the local storage less cluttered.
  3. When you need older data, it can be accessed directly from the remote storage, allowing faster performance for applications that need quick access to recent messages.

How does Uber handle petabytes of Spark shuffle data every day?

VuTrinh. • 159 implied HN points • 22 Jun 24
🕹 Technology Data Engineering Big Data Cloud Computing Software Development Distributed Systems
  1. Uber uses a Remote Shuffle Service (RSS) to handle large amounts of Spark shuffle data more efficiently. This means data is sent to a remote server instead of being saved on local disks during processing.
  2. By changing how data is transferred, the new system helps reduce failures and improve the lifespan of hardware. Now, servers can handle more jobs without crashing and SSDs last longer.
  3. RSS also streamlines the process for the reduce tasks, as they now only need to pull data from one server instead of multiple ones. This saves time and resources, making everything run smoother.

The Hadoop Distributed File System

VuTrinh. • 259 implied HN points • 18 May 24
🕹 Technology Data Storage Cloud Computing Big Data Software Architecture
  1. Hadoop Distributed File System (HDFS) is great for managing large amounts of data across many servers. It ensures data is stored reliably and can be accessed quickly.
  2. HDFS uses a NameNode that keeps track of where data is stored and multiple DataNodes that hold actual data copies. This design helps with data management and availability.
  3. Replication is key in HDFS, as it keeps multiple copies of data across different nodes to prevent loss. This makes HDFS robust even if some servers fail.

Resilient Cyber Newsletter #10

Resilient Cyber • 39 implied HN points • 20 Aug 24
🕹 Technology Cybersecurity AI Software Development Vulnerability Management Cloud Computing
  1. Security tool sprawl is increasing in organizations, with many now using 70 to 90 different tools, making it harder to manage effectively.
  2. AI can speed up fixing coding vulnerabilities, but many AI-generated codes can be insecure, requiring careful checking by developers.
  3. Understanding systems and processes is key to tackling the complexities of cybersecurity, rather than blaming external forces for challenges in job applications.

Authorization in microservice architecture, P4: Deploy in production

Hung's Notes • 79 implied HN points • 18 Jul 24
🕹 Technology Microservices Software Development Authorization Cloud Computing IT Infrastructure
  1. Migrating authorization logic from an old system to a new one can take a long time and requires careful planning to avoid errors.
  2. Each part of a business can manage its own authorization rules, making it easier for them to control access based on their specific needs.
  3. As systems grow, it's important to keep improving and adapting to new challenges, like optimizing runtime decisions and better analyzing access logs.

sqlmesh init duckdb

davidj.substack • 71 implied HN points • 03 Dec 24
🕹 Technology Data science Software Development APIs Analytics Cloud Computing
  1. There's a new public repository called bluesky-data where people can collaborate and follow along with its development. It's easy to get started by setting it up on your local machine.
  2. Using sqlmesh with the Bluesky data can provide real-time data availability, while also allowing for a more complete view of the data in a batch processing style. This means you can get both immediate updates and historical data.
  3. It's better to start with dlt and then initialize sqlmesh within that project. This way, you can efficiently manage large datasets without needing to compute everything each time.

sqlmesh migrate

davidj.substack • 47 implied HN points • 20 Dec 24
🕹 Technology Software Data Engineering Programming Cloud Computing Analytics
  1. If you're using dbt to run analytics, switching to sqlmesh is a good idea. It offers more features and is easy to learn while still being compatible with dbt.
  2. sqlmesh helps manage data environments and is more comprehensive in handling analytics tasks compared to dbt. It's simpler to transition from dbt to sqlmesh than from older methods like stored procedures.
  3. When using sqlmesh, think about where to run it and how to store its state. You have choices like using a different database or a cloud service, which can save you money and hassle.

Data Science Weekly - Issue 549

Data Science Weekly Newsletter • 159 implied HN points • 31 May 24
🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Cloud Computing Software Development
  1. Mediocre machine learning can be very risky for businesses, as it may lead to significant financial losses. Companies need to ensure their ML products are reliable and efficient.
  2. Understanding logistic regression can be made easier by using predicted probabilities. This approach helps in clearly presenting data analysis results, especially to those who may not be familiar with technical terms.
  3. Data quality management is becoming essential in today's data-driven world. It's important to keep track of how data is tested and monitored to maintain trust and accuracy in business decisions.

sqlmesh plan

davidj.substack • 59 implied HN points • 10 Dec 24
🕹 Technology Software Data Management Cloud Computing Analytics Development
  1. Virtual data environments in SQLMesh let you test changes without affecting the main data. This means you can quickly see how something would work before actually doing it.
  2. Using snapshots, you can create different versions of data models easily. Each version is linked to a unique fingerprint, so they don't mess with each other.
  3. Creating and managing development environments is much easier now. With just a command, you can set up a new environment that looks just like production, making development smoother.

Resilient Cyber Newsletter #1

Resilient Cyber • 119 implied HN points • 18 Jun 24
🕹 Technology Cybersecurity Artificial Intelligence Software Development Data Privacy Cloud Computing
  1. The SEC's case against SolarWinds could change how Chief Information Security Officers are viewed in the industry, potentially discouraging talented people from taking on these roles.
  2. Organizations need to actively prepare for cyberattacks through tabletop exercises, which can help teams respond better during real security incidents.
  3. Microsoft's cybersecurity issues have raised concerns regarding national security, highlighting the need for stronger security practices and accountability in tech companies.

What Are Non-Human Identities and Why Do They Matter?

Resilient Cyber • 159 implied HN points • 28 May 24
🕹 Technology Cybersecurity Identity Management Cloud Computing Data Breaches Software Development
  1. Non-Human Identities (NHIs) are the machine-based accounts used in businesses, often outnumbering human accounts significantly. They include things like service accounts and API keys, which are essential for modern tech operations.
  2. NHIs are a major security risk since they can have lots of permissions and are often left unmonitored. This makes them a target for hackers looking to exploit weak points in security systems.
  3. It’s important for companies to have strong governance around NHIs. Without proper controls, these machine identities can lead to security gaps and make it easier for attackers to gain access to systems.

Arm at Amazon

The Chip Letter • 2184 implied HN points • 18 Jul 23
🕹 Technology Cloud Computing
  1. Arm has found a place in the biggest cloud at Amazon.
  2. The importance of power efficiency in datacenters favors Arm designs due to lower power consumption.
  3. Arm has faced challenges in entering the server market, with various attempts by partners falling short over the past decade.

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.

Illumina Sequencers On The Internet

ASeq Newsletter • 65 implied HN points • 05 Dec 24
🕹 Technology Internet Security Data Privacy Cloud Computing
  1. Many Illumina sequencers are publicly accessible on the internet, which is a security risk. It's important to check if your sequencer is securely configured.
  2. About 15% of the sequencers tested had no user management enabled, allowing potentially unauthorized access. This means someone could view or even modify the data without permission.
  3. Most of the exposed instruments were located in the US, including instances at UCSD. It's crucial for owners to ensure their devices are not left vulnerable online.

Authorization in microservice architecture, P1: The motivation to change

Hung's Notes • 59 implied HN points • 18 Jul 24
🕹 Technology Software Development Cloud Computing Microservices Access Control Identity Management
  1. Authorization is a crucial part of managing digital evidence, and it needs to be efficient to handle many users and lots of data. Complex systems can find it hard to keep permissions clear.
  2. Current access control models like Role-Based Access Control (RBAC) and Discretionary Access Control (DAC) can get too complicated when managing many users and permissions. This can lead to messy code and performance issues.
  3. As organizations grow, they must decide how to structure their authorization logic, whether to centralize it in one team or spread it across many. Both choices have their own challenges in consistency and maintenance.

Preparing for Microsoft Security Copilot

Rod’s Blog • 496 implied HN points • 03 Jan 24
🕹 Technology Cybersecurity Artificial Intelligence Cloud Computing Training Monitoring
  1. Before adopting Microsoft Security Copilot, assess your current security situation by understanding assets, risks, vulnerabilities, and compliance requirements.
  2. Plan your integration strategy by deciding on which features to use, aligning with prerequisites such as licenses, and identifying user roles.
  3. Train your staff and stakeholders on how to use Microsoft Security Copilot, educate them about its benefits and challenges, and equip them with skills to operate and troubleshoot the service.

GroupBy #38: Modernizing Uber’s Batch Data Infrastructure with Google Cloud Platform, Apache Iceberg - What Is It

VuTrinh. • 119 implied HN points • 04 Jun 24
🕹 Technology Data Engineering Cloud Computing Data Infrastructure Machine Learning Open Source
  1. Uber is upgrading its data system by moving from its huge Hadoop setup to Google Cloud Platform for better efficiency and performance.
  2. Apache Iceberg is an important tool for managing data efficiently, and it can help create a more organized data environment.
  3. Building data products requires a strong foundation in data engineering, which includes understanding the tools and processes involved.

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.

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.

GroupBy #36: Agoda- How We Solve Load Balancing Challenges in Apache Kafka, How to reduce your Snowflake cost

VuTrinh. • 139 implied HN points • 21 May 24
🕹 Technology Data Engineering Software Development Cloud Computing Infrastructure Cost Optimization
  1. Working on pet projects is fun, but it's important to have clear learning goals to actually gain knowledge from them.
  2. When using tools like Spark or Airflow, always ask what problem they solve to understand their value better.
  3. To make your projects more effective, think like a user and check if they get what they need from your data systems.

What I Learned This Week #6

Eventually Consistent • 59 implied HN points • 01 Jul 24
🕹 Technology Data Management Concurrency Cloud Computing
  1. Data partitioning helps manage query loads by distributing large datasets across multiple disks and processors. Considerations include rebalancing for even distribution, distributed query execution, and dealing with hot spots.
  2. Partitioning secondary indexes can be done locally or globally, with tradeoffs between keeping related data together versus faster lookups for certain queries. Routing queries in distributed systems may use coordination services or gossip protocols for efficiency.
  3. Transactions provide a way to manage concurrency and software failures by ensuring operations either fully succeed or fully fail. AWS Lambda uses worker models for task execution and Rust Atomics for memory ordering control across threads.

Catalog of Catalogs

davidj.substack • 59 implied HN points • 14 Nov 24
🕹 Technology Data science Software Development Information Systems Data Engineering Cloud Computing
  1. Data tools create metadata, which is important for understanding what's happening in data management. Every tool involved in data processing generates information about itself, making it a catalog.
  2. Not all catalogs are for people. Some are meant for systems to optimize data processing and querying. These system catalogs help improve efficiency behind the scenes.
  3. To make data more accessible, catalogs should be integrated into the tools users already work with. This way, data engineers and analysts can easily find the information they need without getting overwhelmed by unnecessary data.

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.

Big Tech and Generative AI Q3 '24 Update

Tanay’s Newsletter • 63 implied HN points • 04 Nov 24
🕹 Technology AI Cloud Computing Data Analysis Software Development Tech Innovation
  1. Amazon is making big strides in AI by providing tools for developers and creating custom chips. They are seeing huge interest in their AI services, which are growing fast despite lower profit margins.
  2. Google is using AI to improve its search capabilities and has rolled out new features to enhance user experience. Their AI models, called Gemini, are being adopted widely across their products and they are investing significantly in infrastructure.
  3. Apple has launched its AI system, Apple Intelligence, focusing on privacy and enhancing the user experience of their products. Although they're investing in AI, their spending is still lower compared to competitors, but they plan to increase their efforts.

An AWS For Sequencing?

ASeq Newsletter • 58 implied HN points • 16 Nov 24
🕹 Technology Bioinformatics Data Analysis Cloud Computing Software Development Genetics
  1. Bioinformatics companies often struggle to succeed on their own, but some are finding unique ways to add value by providing analysis of sequencing data from external service providers.
  2. Just like how companies can use AWS for their server needs, the idea is to create an AWS-like platform specifically for DNA sequencing, making services easier and more accessible.
  3. Building a platform for sequencing could lower barriers for businesses and encourage new applications in the field, opening up more opportunities for innovation.

Data Science Weekly - Issue 538

Data Science Weekly Newsletter • 199 implied HN points • 14 Mar 24
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Cloud Computing
  1. Serverless computing can handle big tasks without limits, but it also brings challenges like managing large uploads effectively.
  2. Art careers can be influenced by the reputation of institutions, with established artists facing less access to elite spaces early on compared to newcomers.
  3. Learning about LLM evaluation metrics can help improve understanding and performance when working with large language models.