The hottest Data Storage Substack posts right now

And their main takeaways
Category
Top Technology Topics

I spent 8 hours learning Parquet. Here’s what I discovered

VuTrinh. • 1658 implied HN points • 24 Aug 24
🕹 Technology Data Engineering Data Storage Data processing Analytics
  1. Parquet is a special file format that organizes data in columns. This makes it easier and faster to access specific data when you don't need everything at once.
  2. The structure of Parquet involves grouping data into row groups and column chunks. This helps balance the performance of reading and writing data, allowing users to manage large datasets efficiently.
  3. Parquet uses smart techniques like dictionary and run-length encoding to save space. These methods reduce the amount of data stored and speed up the reading process by minimizing the data that needs to be scanned.

Synology DS923+ vs. FreeBSD w/ZFS

Blog System/5 • 827 implied HN points • 13 Dec 24
🕹 Technology Software Hardware Networking Data Storage Operating Systems
  1. Synology DS923+ and FreeBSD with ZFS offer different approaches for storage solutions. The DS923+ is a dedicated device designed for ease of use, while FreeBSD requires more manual setup and maintenance.
  2. The Synology system provides a friendly user interface and features like cloud backup options, while FreeBSD offers powerful command-line control but can be less user-friendly.
  3. Using the Synology NAS can give more peace of mind regarding data health and security due to its built-in features like encryption and monitoring alerts, compared to a DIY FreeBSD setup.

System Design: Feature Flag

The ZenMode • 42 implied HN points • 24 Jan 25
🕹 Technology System Design Software Development Data Storage API design
  1. Feature flags allow you to turn app features on or off without changing the code. This is like having a light switch for each feature, making it easy to manage them.
  2. Different types of feature flags help with various tasks, like rolling out incomplete features or testing new ideas with users. This way, you can learn what works best before a full launch.
  3. Building a feature flag system requires a control service, a way to store the flags, and an interface to access them in your app. This helps keep everything organized and responsive.

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.

Import AI 359: $1 billion gov supercomputer; Apple’s good synthetic data technique; and a thousand-year old data library

Import AI • 339 implied HN points • 05 Feb 24
🕹 Technology AI Research Supercomputers Data Storage Language Models
  1. Google uses LLM-powered bug fixing that is more efficient than human fixes, highlighting the impact of AI integration in speeding up processes.
  2. Yoshua Bengio suggests governments invest in supercomputers for AI development to stay ahead in monitoring tech giants, emphasizing the importance of AI investment in the public sector.
  3. Microsoft's Project Silica showcases a long-term storage solution using glass for archiving data, which is a unique and durable alternative to traditional methods.
  4. Apple's WRAP technique creates synthetic data effectively by rephrasing web articles, enhancing model performance and showcasing the value of incorporating synthetic data in training.
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What I Learned This Week #4

Eventually Consistent • 79 implied HN points • 16 Jun 24
🕹 Technology Data Storage Rust
  1. Storage engines are categorized into OLTP and OLAP, optimizing for different access patterns like low latency vs. high throughput respectively.
  2. Data structures meant for in-memory usage need encoding for network or disk storage to ensure platform independence and self-containment.
  3. When writing data to a file system, the OS buffers data in memory for performance, requiring explicit flushing to prevent the risk of data loss in case of system crashes.

SAI Notes #07: What is a Vector Database?

SwirlAI Newsletter • 412 implied HN points • 18 Jun 23
🕹 Technology Data Engineering Machine Learning Big Data Database Management Data Storage
  1. Vector Databases are essential for working with Vector Embeddings in Machine Learning applications.
  2. Partitioning and Bucketing are important concepts in Spark for efficient data storage and processing.
  3. Vector Databases have various real-life applications, from natural language processing to recommendation systems.

SAI #26: Partitioning and Bucketing in Spark (Part 1)

SwirlAI Newsletter • 373 implied HN points • 15 Apr 23
🕹 Technology Data Engineering Big Data Performance optimization Data Storage Data processing
  1. Partitioning and bucketing are two key data distribution techniques in Spark.
  2. Partitioning helps improve performance by allowing skipping reading the entire dataset when only a part is needed.
  3. Bucketing is beneficial for collocating data and avoiding shuffling in operations like joins and groupBys.

A Guide to Optimising your Spark Application Performance (Part 2)

SwirlAI Newsletter • 314 implied HN points • 06 Aug 23
🕹 Technology Programming Big Data Optimization Data Storage
  1. Choose the right file format for your data storage in Spark like Parquet or ORC for OLAP use cases.
  2. Understand and utilize encoding techniques like Run Length Encoding and Dictionary Encoding in Parquet for efficient data storage.
  3. Optimize Spark Executor Memory allocation and maximize the number of executors for improved application performance.

Altcoin Report - April 2023

Crypto is Easy • 216 implied HN points • 07 Apr 23
🔮 Crypto Data Storage
  1. Distributed data storage platforms offer ownership of data and infrastructure without the usual trade-offs.
  2. These platforms allow users to monetize their unused storage capacity and services, creating opportunities for cost savings and potential profits.
  3. The emergence of tokenized solutions like Filecoin, Arweave, STORJ, and Sia showcases a shift towards decentralized data storage networks in Web 3.0.

Biomemory - Enzymatic Data Storage

ASeq Newsletter • 14 implied HN points • 11 Dec 24
🕹 Technology Data Storage Startups Investments Biotechnology
  1. A French startup called Biomemory has raised $18 million for its new enzymatic data storage technology. This is surprising because other companies in the same field are struggling.
  2. Biomemory's first product includes a card that can encode data into DNA, specifically a message of 'Hello World!' using a unique encoding method. This method has some inefficiencies, as it uses more bases than necessary.
  3. The startup faces challenges with encoding data, particularly with homopolymers, which might complicate their technology. Future developments could look into improving these encoding issues.

RAG is more than just vectors

Tribal Knowledge • 11 HN points • 17 Jul 24
🕹 Technology AI/ML Data Storage APIs Data retrieval
  1. RAG provides context to an LLM by fetching data from various sources, not just vector databases. It can use any data store to enhance the language model's predictions.
  2. Context for an LLM can include system prompts, chat history, RAG, fine-tuning, and more. Any way to turn information into text can improve LLM performance.
  3. RAG can work with vectors, but it's not limited to them. By enabling the LLM to call functions, it can fetch data from a variety of sources beyond vectors, like relational or graph databases.

HCF EP 005: Cursor based pagination

Hasen Judi • 35 implied HN points • 04 Jan 25
🕹 Technology Software Development Web Development Data Storage Programming User Interface
  1. Cursor-based pagination lets you skip to the next set of results easily. It's better for large lists because it doesn't waste time reading and ignoring lots of entries.
  2. This method is more stable, as it remembers where you left off even if there are changes to the list. It's like using a bookmark to continue reading later.
  3. However, it has some downsides, like not being able to jump to a specific page directly, which might be less convenient for users wanting to skip ahead quickly.

You don't know this for sure: How BigQuery stores semi-structured data?

VuTrinh. • 59 implied HN points • 13 Jan 24
🕹 Technology Data Engineering Cloud Computing Big Data Data Storage Database Management
  1. BigQuery uses a method called definition and repetition level for efficient storage of nested and repeated data. This allows for reading specific parts of data without needing to access other related data.
  2. In columnar storage, data is organized by columns which can improve performance, especially for analytical queries, because only the needed columns are loaded.
  3. Using this method might increase file sizes due to redundancy, but it helps reduce the input/output operations needed when accessing nested fields.

The CHAT Stack, GPT-4, And The Near-Term Future Of Software

jonstokes.com • 237 implied HN points • 15 Mar 23
🕹 Technology Artificial Intelligence Software Development Machine Learning Data Storage API
  1. Developers will build apps on top of ChatGPT and similar models to create interactive and knowledgeable AI assistants
  2. The CHAT stack approach involves Context, History, API, and Token window, enabling how software applications will operate in the near future
  3. GPT-4 introduces an enlarged token window, improved control surfaces, and better ability to follow human instructions

Data Pipelines - Streams to Parquet

Bytewax • 19 implied HN points • 19 Dec 23
🕹 Technology Data Pipelines Stream Processing Data Storage Data processing
  1. One common use case for stream processing is transforming data into a format for different systems or needs.
  2. Bytewax is a Python stream processing framework that allows real-time data processing and customization.
  3. Bytewax enables creating custom connectors for data sources and sinks, making it versatile for various data processing tasks.

Semantic Stream

davidj.substack • 23 implied HN points • 29 Feb 24
🕹 Technology Querying Data Storage
  1. Consider how to use a semantic layer with streaming data to enhance efficiency and data processing.
  2. Streaming data warehouses handle storage differently than batch data warehouses, keeping fresh data in-memory and reducing compute cost.
  3. The semantic layer abstracts entities, attributes, and metrics, aiding in managing and optimizing queries on streaming data.

Why You’re Missing The Point Of Analytics

Three Data Point Thursday • 19 implied HN points • 05 Oct 23
🕹 Technology Data Analytics Data Management Data products Data Storage
  1. Analytics and Business Intelligence are about turning data into actionable insights, not just analyzing historical data.
  2. Separating data into 'hot' and 'cold' categories can lead to cost savings and less complexity in data management.
  3. Be cautious of the term 'data product' as it can have different meanings to different people, and ensure clarity in hiring, marketing, and tool usage.

Issue #84

Infra Weekly Newsletter • 4 implied HN points • 11 Mar 24
🕹 Technology Data Storage Cloud Security Programming Networking Development Tools
  1. EchoVault is a distributed data store using the RAFT consensus protocol and Go, providing various data structures.
  2. Microsoft's AI Team's exposure of 38TB data raises concerns on cloud security, emphasizing the need for stronger preventive measures.
  3. In the tech world, learning about RISC-V's importance to Java and tools like bpftop for optimizing eBPF performance can enhance your knowledge and skills.

Data in Rust

Cybernetic Forests • 19 implied HN points • 11 Apr 21
🕹 Technology Data Storage Art Music Programming Computing
  1. Tape was the first data storage medium, made of iron oxide with data inscribed by magnets, and tape art and music have explored its possibilities.
  2. Music on tape has influenced data on tape, with notable examples like Brian Eno and Delia Darbyshire using tape as a creative tool.
  3. Art, like music experimentation, serves as a space for safe exploration and where things can break, contributing to science and knowledge without being driven solely by profit or power.

OpenAI Seeding, Model Fingerprints & Log Probabilities

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 14 Nov 23
🕹 Technology AI Machine Learning NLP Data Storage Modeling
  1. The seed parameter helps in reproducing responses from an AI by combining it with the user prompt. This means if you want the same answer again, you need to use the same seed with the same question.
  2. System fingerprints are used to track changes in the AI model or environment. If the fingerprint changes, the responses might also change, so it’s important to keep track of this along with the seed.
  3. Log probabilities will be introduced to help understand which responses the AI is likely to give. This feature can be useful for improving things like search functions and suggestions.

Internal Storage Design of Modern Key-value Database Engines [Part 3]

Practical Data Engineering Substack • 0 implied HN points • 13 Aug 23
🕹 Technology Database Systems Data Storage Computer Science Software Engineering
  1. Compaction is an important process in key-value databases that helps combine and clean up data files. It removes old or unnecessary data and merges smaller files to make storage more efficient.
  2. Different compaction strategies exist, like Leveled and Size-Tiered Compaction, each with its own benefits and challenges. The choice of strategy depends on the database's read and write patterns.
  3. The RUM Conjecture explains the trade-offs in database optimization, balancing read, write, and space efficiency. Improving one aspect can worsen another, so it's key to find the right balance for specific needs.

Empires of the Cloud: Azure's Ascension

Sector 6 | The Newsletter of AIM • 0 implied HN points • 12 Jan 23
🕹 Technology Cloud Computing Data Storage Artificial Intelligence Networking Startups
  1. Microsoft is making big moves in the cloud space, especially with the recent acquisition of Fungible, a company that makes advanced data processing units.
  2. This move shows Microsoft is focusing on improving Azure's performance and efficiency, moving away from traditional data centers.
  3. They also plan to incorporate OpenAI's technology into their services, which could enhance their offerings in the market.

When Our Digital World Fades, Will We Too?

The Digital Anthropologist • 0 implied HN points • 16 Feb 24
🕹 Technology Digital Preservation Artificial Intelligence Data Storage Genetics Information Security
  1. Stone and paper may endure longer than digital storage. Our digital memories are fragile and could be lost in the future.
  2. Our current Digital Age might leave a gap in history for future historians and archaeologists to wonder about.
  3. Technological advancements may lead to storing information in DNA, potentially changing how future generations understand humanity.

tweetsearch - twitter search engine using thrudb and django

Thái | Hacker | Kỹ sư tin tặc • 0 implied HN points • 22 Apr 08
🕹 Technology Programming Web Development Data Storage
  1. Creating a Twitter search engine using Thrudb and Django was a successful venture that allowed for efficient query searches
  2. Thrudb, Django, and Python were praised for their capabilities in providing a strong technology platform for building innovative applications
  3. The tweetsearch project port from perl/cgi to python/django was possible thanks to late nights and a collaborative effort

Using the 🤗 Hub for model storage

machinelearninglibrarian • 0 implied HN points • 30 Dec 21
🕹 Technology Machine Learning Software Development Data Storage Open Source
  1. The 🤗 hub is a useful space for sharing and finding machine learning models. It's great for avoiding duplicate work and helps others use or adapt models easily.
  2. Using the huggingface_hub library can simplify working with models stored on the 🤗 hub. It allows for downloading, updating, and managing models more efficiently than using GitHub alone.
  3. You can also upload models directly to the 🤗 hub, making the process smoother after training. Additionally, creating revision branches for models helps manage different versions better.

From Vector Search to Entity Processing: Evolving Zettelgarden's Connection Engine

Nick Savage • 0 implied HN points • 02 Dec 24
🕹 Technology Artificial Intelligence Software Development Data Storage Knowledge Management Human-computer interaction
  1. Zettelgarden aims to help users discover connections between their notes, not just the recent ones. It wants to make sure older notes are just as visible and important as new ones.
  2. The project started with vector search, which had some challenges when dealing with longer notes. To overcome this, smaller chunks of text were used for better connections.
  3. Now, Zettelgarden is focusing on 'entity processing' to identify important people, places, and events within notes. This helps link related ideas more effectively.

Meta Introduces New AI Features Across Facebook, Instagram, Messenger, and WhatsApp

pocoai • 0 implied HN points • 07 Dec 23
🕹 Technology AI Data Storage Startups Cloud Computing Language Models
  1. Meta introduced over 20 new AI features across Facebook, Instagram, Messenger, and WhatsApp, enhancing user experiences.
  2. Google unveiled Gemini AI in three sizes - Nano, Pro, and Ultra, catering to various information types like text, code, audio, images, and video.
  3. Vast Data raised $118 million for its data storage platform tailored for AI workloads, aiming to expand its business reach globally.

này là cloud computing

Thái | Hacker | Kỹ sư tin tặc • 0 implied HN points • 02 Aug 09
🕹 Technology Cloud Computing Data Storage Infrastructure Security Startups
  1. Cloud computing trends take time to reach different regions; blogging, web 2.0, and now cloud computing are examples of such trends.
  2. The success of cloud computing services lies in cost-effectiveness and the ability to handle large amounts of data for many users.
  3. Developing a public cloud computing service requires a high level of expertise, infrastructure, and financial resources, making it a playground for top tech giants.

Privacy

Ingig • 0 implied HN points • 29 Sep 23
🕹 Technology Privacy Data Storage Encryption Cloud Services Cybersecurity
  1. Storing data locally using PLang can enhance privacy by reducing the risk of data leaks or breaches.
  2. By storing apps like writing, Excel, PowerPoint, etc., on your computer, you can access your data offline, ensure full sync between devices, and encrypt data for security.
  3. PLang offers privacy benefits like encrypted data storage, anonymous registration, and protection against widespread hacking incidents.