The hottest Data processing Substack posts right now

And their main takeaways
Category
Top Technology Topics

California Judiciary cancelled its purchase of ChatGPT Plus

All-Source Intelligence Fusion • 782 implied HN points • 12 Jan 24
🕹 Technology Artificial Intelligence Government Startups Data processing Social media
  1. The California Judiciary cancelled its purchase of ChatGPT Plus after submitting a $4,080 purchase order on January 2nd.
  2. The procurement was intended for a proof of concept to see if ChatGPT could aid in website tasks, but was cancelled due to the lack of comparable quotes.
  3. Justice Guerrero announced plans for artificial intelligence at a Judicial Council meeting, focusing on developing model rules for state courts regarding AI usage.

Understanding Space Based Architecture for efficient Data Processing[System Design Sundays]

Technology Made Simple • 379 implied HN points • 12 Feb 24
🕹 Technology Data processing System Design AI Architecture
  1. Space-Based Architecture (SBA) distributes processing and storage across multiple servers, enhancing scalability and performance by leveraging in-memory data grids.
  2. The components of SBA include Processing Units (PU) for executing business logic, Virtualized Middleware for managing shared infrastructure, and data pumps for data marshaling.
  3. SBA offers benefits such as scalability, fault tolerance, and low-latency data access, but comes with challenges like complexity in design, debugging, and data security.

Sentinel-free schemas: a thought experiment

Minimal Modeling • 393 implied HN points • 20 Dec 23
🕹 Technology Data Management Database Design Data Integrity Data processing
  1. NULL values in databases create compatibility issues and add complexity to conditional operations
  2. Sentinel values, like empty strings or placeholders, are similar to NULL values and can lead to incorrect results
  3. Creating sentinel-free schemas involves separating attributes into individual tables and explicitly defining reasons for missing data

Prompt-Based Feature Engineering Part 1: Generative AI Generates Data

nick’s datastack • 1 HN point • 24 Apr 24
🕹 Technology AI Data Analysis Machine Learning Data Engineering Data processing
  1. Generative AI can generate data, impacting workflows and pipelines significantly.
  2. Using LLMs for prompt-based feature engineering can save time and effort compared to traditional methods like manual data searching and merging.
  3. While LLMs in data pipelines may feel magical, it's important to be cautious of potential inaccuracies due to the probabilistic nature of AI outputs.
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The future of Scala, Uber logs, and the use of Virtual Threads with PostgreSQL - JVM Weekly vol. 68

JVM Weekly • 78 implied HN points • 18 Jan 24
🕹 Technology Programming Languages Data processing
  1. The future of Scala is being discussed, evaluating its potential and evolution within the programming language landscape.
  2. Uber managed to significantly reduce logging costs by integrating the Compressed Log Processor (CLP) tool with the Log4j library.
  3. Implementing Virtual Threads, like in the case of PostgreSQL TPC-C benchmark using Java 21, can present challenges and unexpected issues that require careful handling.

A Guide to Optimising your Spark Application Performance (Part 1).

SwirlAI Newsletter • 432 implied HN points • 02 Jul 23
🕹 Technology Data processing Optimization Performance
  1. Understanding Spark architecture is crucial for optimizing performance and identifying bottlenecks.
  2. Differentiate between narrow and wide transformations in Spark, and be cautious of expensive shuffle operations.
  3. Utilize strategies like partitioning, bucketing, and caching to maximize parallelism and performance in Spark applications.

Learning the Language of Rain

Daoist Methodologies • 176 implied HN points • 17 Oct 23
🔬 Science Neural Networks Information Theory Data processing Entropy
  1. Huawei's Pangu AI model shows promise in weather prediction, outperforming some standard models in accuracy and speed.
  2. Google's Metnet models, using neural networks, excel in predicting weather based on images of rain clouds, showcasing novel ways to approach weather simulation.
  3. Neural networks are efficient in processing complex data, like rain cloud images, to extract detailed information and act as entropy sinks, providing insights into real-world phenomena simulation.

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.

🏞️ Knowledge Capital unleashed: Maximizing Enterprise Potential with AI Copilots

Work3 - The Future of Work • 157 implied HN points • 02 Aug 23
🕹 Technology AI Enterprise Data processing Digital Tools Privacy Concerns
  1. Enterprise Copilots are becoming a norm with AI assistants being built by various players to maximize company potential.
  2. Information is vital in organizations and tools like AI assistants can help capture, organize, and use it effectively.
  3. The evolution of Enterprise AI Assistants is expected to progress from basic tasks to executing actions, and companies like Microsoft are leading the way in developing these tools.

SAI Notes #01: Watermarks in Stream Processing, SQL Query order of Execution.

SwirlAI Newsletter • 255 implied HN points • 07 May 23
🕹 Technology Data processing Stream Processing Data Engineering Data Systems
  1. Watermarks in Stream Processing help handle event lateness and decide when to treat data as 'late data'.
  2. In SQL Query execution, the order is FROM and JOIN, WHERE, GROUP BY, HAVING, SELECT, ORDER BY, LIMIT.
  3. To optimize SQL Queries, reduce dataset sizes for joins and use subqueries for pre-filtering.

Dashing Data Viz - Issue 229

Dashing Data Viz • 176 implied HN points • 14 Mar 23
🕹 Technology Data Visualization Data processing Data Analysis Online Learning
  1. The newsletter shares articles and videos on data visualization, like creating gradient line charts in R and using Tableau for interactive dashboards.
  2. There are resources available for learning new skills in data visualization, such as an online course on Intro to R for Data Viz.
  3. The newsletter also highlights interesting projects like visualizing the first 5,000 digits of Pi and provides resources for further reading on topics like data hierarchy best practices.

Unix 1-liner using jq

CodeFaster • 108 implied HN points • 20 Jul 23
🕹 Technology Programming Unix JSON Data processing
  1. The Unix 1-liner using jq efficiently filters and extracts specific data from a JSON response.
  2. Creating a small script like get-all-accounts to gather data beforehand is crucial for this command to work effectively.
  3. The jq command simplifies data processing by breaking down the process into four transformations.

Building LLM application using RAG

Mindful Matrix • 1 HN point • 07 Apr 24
🕹 Technology AI Data processing Cloud Computing Machine Learning Applications
  1. LLMs have limitations like not being able to update with new information and struggling with domain-specific queries.
  2. RAG (Retrieval Augmented Generation) architecture helps ground LLMs by using custom knowledge bases for generating responses to queries.
  3. Building a simple LLM application using RAG involves steps like loading documents, splitting data, embedding/indexing, defining LLM models, and retrieval/augmentation/generation.

Giving GPT "Infinite" Knowledge

Sudo Apps • 121 HN points • 06 May 23
🕹 Technology Data processing Machine Learning AI Applications Real-Time Data
  1. Training Large Language Models (LLMs) with new data constantly is impractical due to the vast amount of information and privacy concerns.
  2. OpenAI's focus on improving LLMs in other ways instead of just increasing model size indicates the end of giant model era.
  3. Using tokens, embeddings, vector storage, and prompting can help provide LLMs with large amounts of data for better interpretation and understanding.

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.

Limits of the Event-Driven Orchestrator

Data People Etc. • 106 implied HN points • 03 Apr 23
🕹 Technology Data processing Stream Processing
  1. Event-driven orchestrators are not suitable for stream processing because they cannot handle tasks with definite starts and ends.
  2. Event-driven applications operate asynchronously by triggering tasks based on events like files appearing in a directory.
  3. Unlike stream processors, orchestrators like Airflow and Dagster do not have the ability to hold state, distribute tasks for parallel execution, or shuffle data between tasks.

Dancing About Network Architecture

Cybernetic Forests • 39 implied HN points • 03 Sep 23
🕹 Technology AI Artificial Intelligence Data processing
  1. Dancing often comments on the space it happens in, whether intentionally or not, showing a connection between movement and design.
  2. Information in digital systems is usually stripped of physical origins and context, leading to loss and ambiguity.
  3. Artificial Intelligence often operates in a disembodied way, overlooking the importance of incorporating embodied knowledge and experiences.

Building Identity Resolution on Snowflake Using Snowpark

Sonal’s Newsletter • 58 implied HN points • 19 Jun 23
🕹 Technology Data processing Machine Learning Software Development Open Source Cloud Computing
  1. Building ML pipelines in Snowpark requires using third-party libraries like scikit-learn for machine learning.
  2. Integrating specialized functionalities like graph processing in Snowpark may require additional support or custom solutions.
  3. Adapting a codebase from Apache Spark to Snowpark requires careful consideration and potential restructuring to maintain efficiency and avoid technical debt.

Performance Tuning Snowpark For Identity Resolution On Snowflake

Sonal’s Newsletter • 19 implied HN points • 29 Jul 23
🕹 Technology Data processing Database Management
  1. Performance tuning Snowpark on Snowflake can significantly reduce processing time, from half a day to half an hour.
  2. Utilizing the query profiler by Snowflake and making targeted optimizations can have a high impact on performance.
  3. Optimizations like converting UDTFs to UDFs, caching Dataframes, and using batch size annotations can further optimize Snowpark workflows.

You don't need Langchain; here's how to do Retrieval-Augmented Generation without it

Vigneshwarar’s Newsletter • 3 HN points • 18 Sep 23
🕹 Technology AI NLP Machine Learning Data processing Information Retrieval
  1. Retrieval-Augmented Generation (RAG) pipeline can be built without using trendy libraries like Langchain
  2. RAG technique involves retrieving related documents, combining them with language models, and generating accurate information
  3. RAG pipeline involves data preparation, chunking, vector store, retrieval/prompt preparation, and answer generation steps

RISC-V support for BFloat16

Fprox’s Substack • 3 HN points • 04 Sep 23
🕹 Technology Computer Science Hardware Programming Machine Learning Data processing
  1. Brain Float 16 (BFloat16) format provides a compromise between accuracy and cost suited for machine learning applications.
  2. RISC-V is introducing support for BFloat16 format through scalar and vector extensions to improve efficiency in machine learning tasks.
  3. The new BFloat16 extensions in RISC-V have passed Architecture Review and are designed to be fully IEEE-754 compliant for numerical reproducibility.

How I Cracked Homestuck's Alchemy with Stable Diffusion and GPT-4

Record Crash • 3 HN points • 16 Jun 23
🕹 Technology AI Image Generation Data processing Machine Learning APIs
  1. Homestuck's Alchemy involves combining items using different operations and can create various outcomes, like weapons, outfits, and more.
  2. Using Generative AI models like GPT-3 and GPT-4, along with stable diffusion, can help in automating the process of generating new Homestuck alchemy results.
  3. Building a pipeline with ChatGPT, image generation, and compositing tools can streamline the process of generating text descriptions and corresponding images for Homestuck alchemy creations.