The hottest Cloud Computing Substack posts right now

And their main takeaways
Category
Top Technology Topics

AWS CEO Matt Garman Talks Amazon’s Big Bets in AI Chips, Reasoning, and Nuclear Energy

Big Technology • 4753 implied HN points • 03 Dec 24
🕹 Technology AI Development Cloud Computing Nuclear Energy Partnerships
  1. Amazon is focusing heavily on AI and has introduced new AI chips, reasoning tools, and a large AI training cluster to enhance their cloud services. They want customers to have more options and better performance for their AI needs.
  2. AWS believes in providing choices to customers instead of pushing one single solution. They aim to support various AI models for different use cases, which gives developers flexibility in how they build their applications.
  3. For energy solutions, Amazon is investing in nuclear energy. They see it as a clean and important part of the future energy mix, especially as demand for energy continues to grow.

Modellion

davidj.substack • 71 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.

Postgres in a box

benn.substack • 690 implied HN points • 06 Dec 24
🕹 Technology Databases Cloud Computing Software AI Data Management
  1. Software has changed from being sold in boxes in stores to being bought as subscriptions online. This makes it easier and cheaper for businesses to manage.
  2. The new trend is separating storage from computing in databases. This lets companies save money by only paying for the data they actually use and the calculations they perform.
  3. There's a push towards making data from different sources easily accessible, so you can use various tools without being trapped in one system. This could streamline how businesses work with their data.

PyIceberg: Current State and Roadmap

Ju Data Engineering Newsletter • 396 implied HN points • 28 Oct 24
🕹 Technology Data Engineering Software Development Big Data Open Source Cloud Computing
  1. Improving the user interface is crucial for more teams to use Iceberg, especially those that use Python for their data work.
  2. PyIceberg, which is a Python implementation, is evolving quickly and currently supports various catalog and file system types.
  3. While PyIceberg makes it easy to read and write data, it has some limitations, especially compared to using Iceberg with Spark, like handling deletes and managing metadata.

Iceberg + Single Node Engines

Ju Data Engineering Newsletter • 515 implied HN points • 17 Oct 24
🕹 Technology Data Engineering Cloud Computing Big Data Software Development Data Management
  1. The use of Iceberg allows for separate storage and compute, making it easier to connect single-node engines to the data pipeline without needing extra steps.
  2. There are different approaches to integrating single-node engines, including running all processes in one worker or handling each transformation with separate workers.
  3. Partitioning data can improve efficiency by allowing independent processing of smaller chunks, which reduces the limitations of memory and speeds up data handling.
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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.

The Overview Of Apache Spark

VuTrinh. • 879 implied HN points • 07 Sep 24
🕹 Technology Data processing Software Engineering Distributed Systems Open Source Cloud Computing
  1. Apache Spark is a powerful tool for processing large amounts of data quickly. It does this by using many computers to work on the data at the same time.
  2. A Spark application has different parts, like a driver that directs processing and executors that do the work. This helps organize tasks and manage workloads efficiently.
  3. The main data unit in Spark is called RDD, which stands for Resilient Distributed Dataset. RDDs are important because they make data processing flexible and help recover data if something goes wrong.

I spent 6 hours learning how Apache Spark plans the execution for us

VuTrinh. • 659 implied HN points • 10 Sep 24
🕹 Technology Data science Software Engineering Big Data Cloud Computing Machine Learning
  1. Apache Spark uses a system called Catalyst to plan and optimize how data is processed. This system helps make sure that queries run as efficiently as possible.
  2. In Spark 3, a feature called Adaptive Query Execution (AQE) was added. It allows the tool to change its plans while a query is running, based on real-time data information.
  3. Airbnb uses this AQE feature to improve how they handle large amounts of data. This lets them dynamically adjust the way data is processed, which leads to better performance.

The Weekly Kaitchup #64

The Kaitchup – AI on a Budget • 59 implied HN points • 25 Oct 24
🕹 Technology AI Machine Learning Software Data science Cloud Computing
  1. Qwen2.5 models have been improved and now come in a 4-bit version, making them efficient for different hardware. They perform better than previous models on many tasks.
  2. Google's SynthID tool can add invisible watermarks to AI-generated text, helping to identify it without changing the text's quality. This could become a standard practice to distinguish AI text from human writing.
  3. Cohere has launched Aya Expanse, new multilingual models that outperform many existing models. They took two years to develop, involving thousands of researchers, enhancing language support and performance.

I spent 5 hours learning how Google manages terabytes of metadata for BigQuery.

VuTrinh. • 399 implied HN points • 17 Sep 24
🕹 Technology Data Engineering Cloud Computing Database Systems Big Data
  1. Metadata is really important because it helps organize and access data efficiently. It tells systems where files are and which ones can be ignored during processing.
  2. Google's BigQuery uses a unique system to manage metadata that allows for quick access and analysis of huge datasets. Instead of putting metadata with the data, it keeps them separate but organized in a smart way.
  3. The way BigQuery handles metadata improves performance by making sure that only the relevant information is accessed when running queries. This helps save time and resources, especially with very large data sets.

Kubernetes for Data Engineers

VuTrinh. • 859 implied HN points • 03 Sep 24
🕹 Technology Data Engineering Cloud Computing DevOps Software Development Infrastructure
  1. Kubernetes is a powerful tool for managing containers, which are bundles of apps and their dependencies. It helps you run and scale many containers across different servers smoothly.
  2. Understanding how Kubernetes works is key. It compares the actual state of your application with the desired state to make adjustments, ensuring everything runs as expected.
  3. To start with Kubernetes, begin small and simple. Use local tools for practice, and learn step-by-step to avoid feeling overwhelmed by its many components.

Big Tech x Generative AI Q3 '24 Update (Part 2)

Tanay’s Newsletter • 44 implied HN points • 11 Nov 24
🕹 Technology AI Software Consumer Tech Cloud Computing Open Source
  1. Meta is focusing on open-source AI with the Llama models, claiming they are the most cost-effective and customizable option for developers. They are set to release even better versions soon.
  2. Microsoft’s AI business is booming, especially through their Azure Cloud, with expected revenue surpassing $10 billion. They are integrating AI across many of their products, driving impressive growth.
  3. Both companies are seeing success in using AI to enhance user engagement and advertising effectiveness. Meta has increased user time on their platforms, while Microsoft's AI tools are helping businesses save time and improve efficiency.

I spent 5 hours learning how Google lets us build a Lakehouse.

VuTrinh. • 139 implied HN points • 24 Sep 24
🕹 Technology Cloud Computing Data Engineering Software Development Information Storage
  1. Google's BigLake allows users to access and manage data across different storage solutions like BigQuery and object storage. This makes it easier to work with big data without needing to move it around.
  2. The Storage API enhances BigQuery by letting external tools like Apache Spark and Trino directly access its stored data, speeding up the data processing and analysis.
  3. BigLake tables offer strong security features and better performance for querying open-source data formats, making it a more robust option for businesses that need efficient data management.

Resilient Cyber Newsletter #15

Resilient Cyber • 119 implied HN points • 24 Sep 24
🕹 Technology Cybersecurity AI Security Software Development Cloud Computing Digital Transformation
  1. Some software vendors are creating security problems by delivering buggy products. Customers should demand better security from their suppliers during purchase.
  2. As companies rush to adopt AI, many are overlooking crucial security measures, which poses a big risk for future incidents.
  3. Supporting open source software maintainers is vital because many of them are unpaid. Companies should invest in the projects they rely on to ensure their continued health and security.

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

VuTrinh. • 279 implied HN points • 14 Sep 24
🕹 Technology Data Engineering Big Data Cloud Computing Data Management 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.

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.

How do we run Kafka 100% on the object storage?

VuTrinh. • 519 implied HN points • 27 Aug 24
🕹 Technology Software Cloud Computing Data Engineering
  1. AutoMQ enables Kafka to run entirely on object storage, which improves efficiency and scalability. This design removes the need for tightly-coupled compute and storage, allowing more flexible resource management.
  2. AutoMQ uses a unique caching system to handle data, which helps maintain fast performance for both recent and historical data. It has separate caches for immediate and long-term data needs, enhancing read and write speeds.
  3. Reliability in AutoMQ is ensured through a Write Ahead Log system using AWS EBS, which helps recover data after crashes. This setup allows for fast failover and data persistence, so no messages get lost.

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.

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.

I spent 4 hours learning Apache Iceberg. Here's what I found.

VuTrinh. • 799 implied HN points • 10 Aug 24
🕹 Technology Data Engineering Software Development Database Management Big Data Cloud Computing
  1. Apache Iceberg is a table format that helps manage data in a data lake. It makes it easier to organize files and allows users to interact with data without worrying about how it's stored.
  2. Iceberg has a three-layer architecture: data, metadata, and catalog, which work together to track and manage the actual data and its details. This structure allows for efficient querying and data operations.
  3. One cool feature of Iceberg is its ability to time travel, meaning you can access previous versions of your data. This lets you see changes and retrieve earlier data as needed.

I spent 7 hours diving deep into Apache Iceberg

VuTrinh. • 339 implied HN points • 31 Aug 24
🕹 Technology Data Engineering Software Development Cloud Computing Big Data Database Management
  1. Apache Iceberg organizes data into a data layer and a metadata layer, making it easier to manage large datasets. The data layer holds the actual records, while the metadata layer keeps track of those records and their changes.
  2. Iceberg's manifest files help improve read performance by storing statistics for multiple data files in one place. This means the reader can access all needed statistics without opening each individual data file.
  3. Hidden partitioning in Iceberg allows users to filter data without needing extra columns, saving space. It records transformations on columns instead, helping streamline queries and manage data efficiently.

Diving Deep into LinkedIn's Data Infrastructure: My 6-Hour Learning & Key Takeaways

VuTrinh. • 299 implied HN points • 03 Aug 24
🕹 Technology Data Engineering Software Architecture Databases Distributed Systems Cloud Computing
  1. LinkedIn's data infrastructure is organized into three main tiers: data, service, and display. This setup helps the system to scale easily without moving data around.
  2. Voldemort is LinkedIn's key-value store that efficiently handles high-traffic queries and allows easy scaling by adding new nodes without downtime.
  3. Databus is a change data capture system that keeps LinkedIn's databases synchronized across applications, allowing for quick updates and consistent data flow.

Practical Data Engineering using AWS Cloud Technologies

VuTrinh. • 339 implied HN points • 23 Jul 24
🕹 Technology Cloud Computing Data Engineering Software Development Information Systems
  1. AWS offers a variety of tools for data engineering like S3, Lambda, and Step Functions, which can help anyone build scalable projects. These tools are often underused compared to newer options but are still very effective.
  2. Services like SNS and SQS can help manage data flow and processing. SNS allows for publishing messages while SQS aids in handling high event volumes asynchronously.
  3. Using AWS for data engineering is often simpler than switching to modern tools. It's easier to add new AWS services to your existing workflow than to migrate to something completely new.

"Here's the sad state of the cloud"

Cloud Irregular • 3696 implied HN points • 22 Jan 24
🕹 Technology Cloud Computing Artificial Intelligence Tech industry Tech Culture Data Privacy
  1. The cloud landscape is shifting from big hyperscalers to more specialized services like standalone databases and DIY cloud-in-a-box.
  2. Using tools like Nightshade to protect art from being exploited by AI may not be the best strategy, focusing on creating original, high-quality art is key.
  3. Google, despite criticism, remains a significant player in the tech industry, seen as a symbol of intellectual prowess and innovation.

Take on the Kubernetes Resume Challenge

Cloud Irregular • 3104 implied HN points • 14 Feb 24
🕹 Technology Cloud Computing Programming Training Community
  1. The Cloud Resume Challenge community is launching a Kubernetes Challenge throughout March to help individuals build their Kubernetes skills by deploying a basic e-commerce website.
  2. The challenge focuses on learning the operations of a K8s cluster such as configuration, scaling, monitoring, and persistence, offering guidance to prevent going off track.
  3. Participants will work through the challenge together over 4 weeks in the CRC Discord server, with special incentives for those who complete it.

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.

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.

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.

How ADR Addresses Gaps in the Detection & Response Landscape

Resilient Cyber • 99 implied HN points • 20 Aug 24
🕹 Technology Cybersecurity Software Application Security Data Protection Cloud Computing
  1. Application Detection & Response (ADR) is becoming important because attackers are increasingly targeting application vulnerabilities. This shift means we need better tools that focus specifically on applications.
  2. Modern software systems are complex, making it hard for traditional security tools to catch real threats. That's why understanding how these systems interact can help identify harmful behavior more effectively.
  3. There’s a big push to find and fix security issues early in the development process. However, this focus on early detection often misses what's actually happening in real-life applications, making runtime security like ADR crucial.

Back to First Principles | Productiv CTO Ashish Aggarwal

Dev Interrupted • 28 implied HN points • 29 Oct 24
🕹 Technology Software Development Cloud Computing Startup Culture Developer Productivity Artificial Intelligence
  1. Developers have 'bad days' when tools fail, processes are messy, or team communication is weak. Senior devs often feel frustrated with organization problems, while junior ones may take failures personally.
  2. The term 'zombiecorn' describes startups worth over $1 billion that struggle to grow and find their market. They often have high spending, depend heavily on funding, and face challenges with customer growth.
  3. Google is working on an AI called Project Jarvis that could take control of your browser to do tasks. But there's concern it might make Google's other services, like Search and Maps, less reliable.

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.

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.

Google deepens AI integration across operations

philsiarri • 22 implied HN points • 31 Oct 24
🕹 Technology Artificial Intelligence Cloud Computing Software Development Product Management Digital marketing
  1. Google is using a lot of AI in its work, with over a quarter of new code created by AI and checked by engineers. This shows how much they're relying on technology to improve their services.
  2. The company's earnings are strong, with significant revenue from both Google Services and Google Cloud. AI features are helping to boost sales and attract new customers.
  3. Google's new AI tools are changing how people search online and are driving more ad revenue on platforms like YouTube, which is now making over $50 billion from ads and subscriptions.

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.

In search for ideal CDP for B2B SaaS

nonamevc • 24 implied HN points • 10 Nov 24
🕹 Technology SaaS Data Management Cloud Computing Digital Infrastructure
  1. Customer Data Platforms (CDPs) are becoming important for B2B SaaS companies by helping them unify data from different sources. This makes it easier for teams to work together and drive better marketing and sales efforts.
  2. There are two main types of CDPs: packaged and composable. Packaged CDPs are more like ready-made solutions, while composable CDPs allow for customization to better fit a company's specific needs.
  3. B2B companies might not need a standalone CDP as many existing tools are starting to include features traditionally offered by CDPs. This means businesses can often get what they need from tools they are already using.