The hottest Cloud Computing Substack posts right now

And their main takeaways
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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.

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.
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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.

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.

These 7 Software Engineering Skills Give You an Unfair Advantage

Brain Bytes • 119 implied HN points • 17 Jan 24
🕹 Technology Software Engineering Cybersecurity Automation Version Control Cloud Computing
  1. Thinking like a hacker helps in identifying and fixing security flaws before they are exploited, crucial in today's cybersecurity landscape.
  2. Understanding different devices through cross-platform critical thinking gives a competitive edge and promotes reusability of business logic.
  3. Scripting and automation for repetitive tasks enhances productivity by ensuring consistency, accuracy, and freeing up time for more complex work.

DevEx: Better than an ExDev (And your Ex)

Permit.io’s Substack • 19 implied HN points • 04 Jul 24
🕹 Technology Software Development Cybersecurity User Experience DevOps Cloud Computing
  1. Developer experience (DevEx) is really important because it helps developers focus on building great apps while also handling security tasks more smoothly.
  2. It's crucial to make security features easy to use so that everyone involved, from developers to non-technical users, can manage permissions and access without problems.
  3. A successful approach to DevEx considers the whole development process, ensuring security practices are integrated naturally into workflows from start to finish.

Data Science Weekly - Issue 484

Data Science Weekly Newsletter • 439 implied HN points • 02 Mar 23
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Cloud Computing
  1. Data scientists need the right tools and environment to do their jobs effectively. Organizations can help by improving their data science infrastructure.
  2. Understanding how to choose and advocate for important metrics is vital for product teams. This can lead to significant growth in user engagement.
  3. A/B testing is crucial in fraud detection to compare models and determine their effectiveness. It can provide valuable insights that improve model performance.

So... what is Stateless Architecture in Software Engineering[System Design Sundays]

Technology Made Simple • 199 implied HN points • 04 Jun 23
🕹 Technology Software Engineering Cloud Computing Architecture
  1. To understand stateless architecture, it's important to know the background of traditional client-server patterns and why moving towards stateless is beneficial.
  2. The concept of state in an application is crucial, and stateless architecture outsources state handling to more efficient systems like using cookies and shared instances for storing state.
  3. Stateless architecture simplifies state management, enhances client-side performance, and makes server scaling easier, aligning well with modern computing capabilities.

I spent 5 hours learning how ClickHouse built their internal data warehouse.

VuTrinh. • 1 HN point • 21 Sep 24
🕹 Technology Data Engineering Cloud Computing Database Management Data Warehousing
  1. ClickHouse built its internal data warehouse to better understand customer usage and improve its services. They collected data from multiple sources to gain valuable insights.
  2. They use tools like Airflow for scheduling and Superset for data visualization, making their data processing efficient. This setup allows them to handle large volumes of data daily.
  3. Over time, ClickHouse evolved its system by adding dbt for data transformation and improving user experiences with better SQL query tools. They also incorporated real-time data to enhance their reporting.

Defending CI/CD Environments - The NSA/CISA Way

Resilient Cyber • 299 implied HN points • 29 Jun 23
🕹 Technology Cybersecurity Software Development DevOps Cloud Computing Information Security
  1. CI/CD environments are crucial for the development and delivery of software, but they can also be targeted by hackers. It's important to secure these systems to prevent attacks.
  2. The NSA and CISA have released guidelines that offer best practices for protecting CI/CD pipelines. Using existing frameworks and tools can help improve security effectively.
  3. Transitioning to a Zero Trust model is recommended to enhance security in software development. This approach minimizes risks by ensuring that all access is restricted and monitored.

BigQuery processing engine: Shuffle

VuTrinh. • 119 implied HN points • 06 Jan 24
🕹 Technology Data Engineering Big Data Cloud Computing Data processing Analytics
  1. BigQuery uses a processing engine called Dremel, which takes inspiration from how MapReduce handles data. It improves how data is shuffled between workers for faster processing.
  2. Traditional approaches have issues like resource fragmentation and unpredictable scaling when dealing with huge data. Dremel solves this by managing shuffle storage separately from the worker, which helps in scaling and resource management.
  3. By separating the shuffle layer, Dremel reduces latency, improves fault tolerance, and allows for more flexible worker allocation during execution. This makes it easier to handle larger data sets efficiently.

LangGraph Cloud

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 02 Jul 24
🕹 Technology AI Software Development Cloud Computing NLP
  1. LangGraph Cloud is a new service that helps developers easily deploy and manage their LangGraph applications online.
  2. Agent applications can handle complex tasks automatically and use large language models to work efficiently, but they face challenges like high costs and the need for better control.
  3. LangGraph Studio provides a visual way to see how code flows in applications, helping users understand and debug their work without changing any code.

I spent 7 hours reading another paper to understand more about Snowflake's internal. Here's what I found.

VuTrinh. • 79 implied HN points • 02 Mar 24
🕹 Technology Data Engineering Cloud Computing System Architecture Database Design
  1. Snowflake has a unique design with three main layers: storage, virtual warehouse, and cloud service. This structure helps manage data efficiently and ensures high availability.
  2. The system uses a special ephemeral storage for temporary data during queries, which allows for quick access and less strain on the overall system. This helps with performance and reduces network load.
  3. Snowflake is designed for flexibility, allowing it to adapt resources based on customer needs and workloads. This elasticity helps provide better performance and efficiency.

GroupBy #29: Scaling AI/ML Infrastructure at Uber, The Sisyphean struggle and the new era of data infrastructure

VuTrinh. • 59 implied HN points • 02 Apr 24
🕹 Technology Data Engineering Machine Learning Infrastructure Software Development Cloud Computing
  1. Uber is focusing on building strong AI and machine learning infrastructure to keep up with the growing complexity of their models. This involves using both CPUs and GPUs for better efficiency.
  2. Data management is becoming crucial for companies like Netflix as they deal with massive amounts of production data. They are developing tools to effectively manage and optimize this data.
  3. The data streaming landscape is evolving, with new technologies emerging that make handling data easier and more efficient. This is changing how companies approach data infrastructure.

This well-known data company could be reversing the ETL to ELT shift

The Orchestra Data Leadership Newsletter • 79 implied HN points • 25 Feb 24
🕹 Technology Data Engineering Cloud Computing Data Integration ETL
  1. ETL (Extract-Transform-Load) and ELT (Extract-Load-Transform) have been key data engineering paradigms, but with the rise of the cloud, the need for in-transit data transformation has decreased.
  2. Fivetran, a widely known data company, is potentially shifting back to ETL methods by offering pre-built transformation features, effectively simplifying the data modeling process for users.
  3. There seems to be a trend towards a possible resurgence of ETL practices in the data industry, with companies like Fivetran potentially leading the way in providing ETL-like services within their platforms.

I spent 4 hours figuring out how BigQuery executes the SQL query internally. Here's what I found.

VuTrinh. • 79 implied HN points • 24 Feb 24
🕹 Technology Data Engineering Database Systems Cloud Computing Big Data Software Development
  1. BigQuery processes SQL queries by planning, optimizing, and executing them. It starts by validating the query and creating an efficient execution plan.
  2. The query execution uses a dynamic tree structure that adjusts based on data characteristics. This helps to manage different types of queries more effectively.
  3. Key components of BigQuery include the Query Master for planning, the Scheduler for assigning resources, and Worker Shards that carry out the actual computations.

GroupBy #28: Tableflow - The Stream/Table, Kafka/Iceberg Duality, Kafka tiered storage deep dive

VuTrinh. • 59 implied HN points • 26 Mar 24
🕹 Technology Data Engineering Software Development Machine Learning Cloud Computing Big Data
  1. Tableflow allows you to easily turn Apache Kafka topics into Iceberg tables, which could change how streaming data is managed.
  2. Kafka's new tiered storage feature helps separate compute and storage, making it easier to manage resources and keep systems running smoothly.
  3. Data governance is important but can be lackluster if it doesn't show clear business benefits, making us rethink its role in today's data landscape.

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.

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

VuTrinh. • 79 implied HN points • 10 Feb 24
🕹 Technology Data Engineering Cloud Computing Software Architecture Database Systems Data Analytics
  1. Snowflake separates storage and compute, allowing for flexible scaling and improved performance. This means that data storage can grow separately from computing power, making it easier to manage resources.
  2. Data can be stored in a cloud-based format that supports both structured and semi-structured data. This flexibility allows users to easily handle various data types without needing to define a strict schema.
  3. Snowflake implements unique optimization techniques, like data skipping and a push-based query execution model, which enhance performance and efficiency when processing large amounts of data.

Ask questions about your single cell data with natural language

LatchBio • 11 implied HN points • 12 Dec 24
🕹 Technology Biotech Data Analysis Machine Learning Cloud Computing
  1. Single cell sequencing helps scientists understand individual cells better. This technique is key for studying diseases and biological processes.
  2. Bench scientists need simple tools to analyze single cell data without needing extensive computational skills. This will help them work more independently and quickly.
  3. Providing scientists with easy access to their data will lead to new questions and insights in research. This can improve drug development and other important biological discoveries.

The stream processing model behind Google Cloud Dataflow

VuTrinh. • 39 implied HN points • 27 Apr 24
🕹 Technology Data processing Cloud Computing Big Data Software Engineering Stream Processing
  1. Google Cloud Dataflow is a service that helps process both streaming and batch data. It aims to ensure correct results quickly and cost-effectively, useful for businesses needing real-time insights.
  2. The Dataflow model separates the logical data processing from the engine that runs it. This allows users to choose how they want to process their data while still using the same fundamental tools.
  3. Windowing and triggers are important features in Dataflow. They help organize and manage how data is processed over time, allowing for better handling of events that come in at different times.

Data Science Weekly - Issue 503

Data Science Weekly Newsletter • 219 implied HN points • 14 Jul 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Cloud Computing
  1. Machine learning is making its way into finance, and researchers are identifying practical uses for it. This can help finance professionals learn new tools and statisticians find interesting financial problems to solve.
  2. AI platforms, like social media, are becoming crucial in our lives but can be confusing and unreliable. People are figuring out how to use these platforms effectively despite their unpredictability.
  3. Large language models are changing how data scientists work. These models can automate many tasks, allowing data scientists to focus on managing and assessing the AI's outputs.

Microservices vs. Monoliths: Why Startups Are Getting "Nano-Services" All Wrong

Tech Thoughts • 2 HN points • 08 Sep 24
🕹 Technology Software Development Startup Strategies System Architecture Cloud Computing
  1. Startups should avoid jumping into microservices too early. It's better to keep things simple with a basic structure while you're still figuring out your product.
  2. Creating too many tiny services, or 'nano-services', adds unnecessary complexity. This can slow you down and make it harder to manage your product.
  3. Focus on finding your product's market fit first. Once you have traction and need to scale, then it's time to consider adopting more complex systems like microservices.

Modernizing FedRAMP

Resilient Cyber • 139 implied HN points • 30 Oct 23
🕹 Technology Cloud Computing Cybersecurity Government Policy Regulatory Compliance Information Technology
  1. FedRAMP is being updated to make it easier for the government to use cloud services. The goal is to increase the number of authorized cloud providers and reduce the complicated process that currently exists.
  2. The memo emphasizes the use of automation and machine-readable formats to speed up compliance processes. This means that instead of relying on paper documents, they'll use technology to better manage security assessments.
  3. There's a push to allow more existing security certifications to count towards FedRAMP requirements. This could help smaller businesses enter the market and expand the options available for federal agencies.

Decentralized Cloud Computing

DeFi Education • 679 implied HN points • 31 May 22
🕹 Technology Cloud Computing Blockchain Decentralization Cryptography Digital innovation
  1. Decentralized cloud computing is changing how we store and process data. It allows users to control their own data without relying on big companies.
  2. This approach can lead to better security and privacy for users. It’s often seen as a more trustable alternative to centralized systems.
  3. As the market for tokens is evolving, exploring decentralized projects can unveil exciting new opportunities in tech and finance. Staying informed can help you find the next big thing.

Understanding Konfig's Opinionation

realkinetic • 19 implied HN points • 11 Jun 24
🕹 Technology Cloud Computing DevOps Tech stack
  1. Konfig is an opinionated platform that reduces the investment and total cost of ownership needed for an enterprise cloud platform and speeds up the delivery of new software products.
  2. Konfig promotes a structured platform with a focus on service-oriented architecture and domain-driven design, encouraging decoupling services and promoting durable teams.
  3. The platform enforces group-based access management, uses GitOps for infrastructure management, leverages managed services and serverless offerings, and provides an escape hatch for flexibility outside of its opinions.

Do Developers Dream of Google Zanzibar?

Permit.io’s Substack • 39 implied HN points • 12 Apr 24
🕹 Technology Open Source Software Development Access Control Cloud Computing
  1. Open-source licenses are changing, and companies are finding it hard to balance fairness and sustainability. This is an important topic in the tech community.
  2. Google Zanzibar is a powerful tool for managing user access and permissions across many applications. It has changed how developers think about authorization systems.
  3. Different authorization models exist, like RBAC and ABAC, but Google Zanzibar offers a simpler, more effective way to handle permissions, especially in large environments.

GroupBy #30: Uber- How LedgerStore Supports Trillions of Indexes, Composable Data Systems: Lessons from Apache Calcite Success

VuTrinh. • 39 implied HN points • 09 Apr 24
🕹 Technology Data Engineering Data Analysis Software Development Cloud Computing Machine Learning
  1. LedgerStore at Uber can handle trillions of indexes, making it a powerful tool for managing large-scale data efficiently.
  2. Apache Calcite helps build flexible data systems with strong query optimization features, which are vital for many data applications.
  3. Spotify's data platform plays a critical role in their operations, guiding how to build effective data systems in organizations.

Deep Dive: Akash Network

DeFi Education • 579 implied HN points • 05 Jun 22
🕹 Technology Cloud Computing Decentralization Marketplace Open Source Software Development
  1. Akash is a decentralized cloud computing platform that allows users to deploy applications easily. This gives people more control compared to traditional cloud services.
  2. It has a marketplace where buyers and sellers can exchange cloud computing resources. This makes it easier for users to find the services they need.
  3. Using Akash can be more cost-effective than popular centralized cloud providers like Amazon AWS or Google Cloud. This can save users money when they need cloud services.

Microsoft Sentinel SOC 101: Detecting and Mitigating Spear Phishing with Microsoft Sentinel

Rod’s Blog • 59 implied HN points • 12 Feb 24
🕹 Technology Cybersecurity Cloud Computing Data Analysis Automation Best Practices
  1. Spear phishing is a serious cyber-attack that targets specific individuals or organizations. Microsoft Sentinel's tools can help detect and prevent these types of threats.
  2. Microsoft Sentinel allows for the creation of custom analytics rules based on KQL queries to identify potential spear phishing activities. This helps in early detection of threats.
  3. Automation and playbooks in Microsoft Sentinel enable immediate responses like blocking URLs or initiating password resets upon detecting a spear phishing attempt.

The Future of Search and How You Can Shape It

Gradient Flow • 199 implied HN points • 23 Feb 23
🕹 Technology AI Search Engines Machine Learning Data Engineering Cloud Computing
  1. The blend of artificial intelligence and chatbot interfaces, like seen in ChatGPT, is transforming search applications, with startups emphasizing large language models for better search experiences.
  2. Expectations around user interactions with company websites are changing with the rise of chatbot-equipped search engines, requiring integration of AI and foundation models for improved responses incorporating text, images, videos, and audio.
  3. Data and AI teams are crucial in developing, testing, and maintaining next-generation search applications, with companies likely seeking more control over their data and the potential creation of custom models for enhanced privacy and innovation.

Jio Moment for Indian Hyperscalers?

Sector 6 | The Newsletter of AIM • 59 implied HN points • 08 Feb 24
🕹 Technology Cloud Computing Data Centers AI Development Telecommunications Business Growth
  1. Indian companies are growing their data center capacity rapidly, which poses challenges for major cloud service providers like AWS and Microsoft Azure. This means more options for businesses in India when it comes to cloud services.
  2. Government support and new data security rules are fueling the rise of hyperscale data centers in India. This shows a strong push towards more secure and accessible digital infrastructure.
  3. The growth in hyperscale capacity mirrors the earlier success of Jio in the telecom industry, suggesting India could play a big role in the global tech landscape with advances in AI and data services.