The hottest Database Management Substack posts right now

And their main takeaways
Category
Top Technology Topics

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.

How does Notion handle 200 billion data entities?

VuTrinh. • 519 implied HN points • 06 Aug 24
🕹 Technology Data Engineering Database Management Analytics Machine Learning
  1. Notion uses a flexible block system, letting users customize how they organize their notes and projects. Each block can be changed and moved around, making it easy to create what you need.
  2. To manage the huge amount of data, Notion shifted from a single database to a more complex setup with multiple shards and instances. This change helps them handle stronger user demands and analytics needs more efficiently.
  3. By creating an in-house data lake, Notion saved a lot of money and improved data processing speed. This new system allows them to quickly get data from their main database for analytics and support new features like AI.

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.

Embeddings are a good starting point for the AI curious app developer

Bryant’s Newsletter • 572 HN points • 17 Apr 24
🕹 Technology AI App Development Database Management Search Algorithms
  1. Vector embeddings are essential for search and recommendations, measuring similarity in various languages and providing efficiency in AI app development.
  2. Pgvector, a Postgres extension, is a powerful tool for storing and querying embeddings and combining standard SQL logic with embedding operations.
  3. Working with embeddings feels like regular code compared to more complex language models, offering a simpler and more deterministic approach to AI development.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

The CAP Theorem needed an update.

System Design Classroom • 359 implied HN points • 28 Apr 24
🕹 Technology Data Systems Computer Science Software Design Networking Database Management
  1. The CAP theorem says you can have consistency, availability, or partition tolerance, but only two at a time. This means systems have to make trade-offs depending on what they prioritize.
  2. The PACELC theorem expands on CAP by considering what happens during normal operation without network issues. It adds more options about choosing between latency and consistency.
  3. Real-world examples, like a multiplayer game leaderboard, show how these principles apply. You can have quick updates with potential outdated info or consistent scores that take longer to change.

Shift-Left Analytics

SUP! Hubert’s Substack • 50 implied HN points • 22 Nov 24
🕹 Technology Data Analytics Real-Time Processing Machine Learning Data Quality Database Management
  1. Shift-left analytics means doing analysis early in the data process. This helps in getting insights faster and making quick decisions.
  2. It focuses on checking data quality right away, so only reliable data is used. This leads to more accurate insights and avoids problems caused by bad data.
  3. Collaboration between teams is encouraged in this approach. By working together from the start, everyone can ensure their analyses are useful and aligned with business goals.

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.

I spent 3 hours figuring out how BigQuery inserts, deletes and updates data internally. Here's what I found.

VuTrinh. • 139 implied HN points • 17 Feb 24
🕹 Technology Data Engineering Cloud Computing Database Management Data Analysis Software Development
  1. BigQuery manages data using immutable files, meaning once data is written, it cannot be changed. This helps in processing data efficiently and maintains data consistency.
  2. When you perform actions like insert, delete, or update, BigQuery creates new files instead of changing existing ones. This approach helps in features like time travel, which lets you view past states of data.
  3. BigQuery uses a system called storage sets to handle operations. These sets help ensure processes are performed atomically and consistently, maintaining data integrity during changes.

From Dynamo to DynamoDB : Unveiling the Titans of Tech

Mindful Matrix • 119 implied HN points • 18 Feb 24
🕹 Technology Database Management Cloud Computing
  1. Dynamo and DynamoDB are two names often seen in databases, but they have significant differences. Dynamo set the foundation, and DynamoDB evolved into a practical, scalable, and reliable service.
  2. Key differences between Dynamo and DynamoDB include their Genesis, Consistency Model, Data Modeling, Operational Model, and Conflict Resolution approaches.
  3. Dynamo focuses on eventual consistency, while DynamoDB offers both eventual and strong consistency. Dynamo is a simple key-value store, while DynamoDB supports key-value and document data models.

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.

The Tech Buffet #22: Why You Should Consider Weaviate As Your Ultimate Vector Database

The Tech Buffet • 39 implied HN points • 23 Apr 24
🕹 Technology Machine Learning Data science Software Development Database Management AI Applications
  1. Weaviate is a powerful vector database that helps in creating advanced AI applications. It's useful for managing large amounts of data and performing semantic searches efficiently.
  2. When working with Weaviate, you can easily load and index data, allowing for quick access to information. This makes it easier to build systems that need to handle a lot of data quickly.
  3. Weaviate supports different search methods like vector search, keyword search, and hybrid search. This way, you can find the most relevant results based on your needs.

Global Incremental ID

Hung's Notes • 79 implied HN points • 13 Dec 23
🕹 Technology Data Structures Distributed Systems Software Engineering Database Management
  1. Global Incremental IDs are important for preventing ID collisions in distributed systems, especially during tasks like data backup and event ordering.
  2. UUID and Snowflake ID are two common types of global IDs, each with unique advantages and disadvantages. For instance, UUIDs are larger but widely used, while Snowflake IDs are smaller but more complex to generate.
  3. Different systems, like Sonyflake and Tinyid, offer specialized methods for generating IDs, helping to ensure performance and avoiding database bottlenecks.

Push it to the Limit EP. 2—GPT Engineer

Prompt’s Substack • 1 HN point • 13 Sep 24
🕹 Technology Software Development Programming Languages Web Development Artificial Intelligence Database Management
  1. Using GPT Engineer with Claude Sonnet 3.5 can help build complex web applications. The right prompts can generate backend logic and React components more effectively.
  2. Integrating a large database with many tables can be challenging. Using tools like Supabase and Claude to auto-generate code can simplify this process, especially for handling data and API calls.
  3. It's important to carefully manage UI changes and prompt adjustments. Even small updates can lead to unexpected results, so being specific in requests can help maintain stability while developing.

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.

GroupBy #32: Canva - Scaling to Count Billions, Ensuring Precision and Integrity: A Deep Dive into Uber’s Accounting Data Testing Strategies

VuTrinh. • 19 implied HN points • 23 Apr 24
🕹 Technology Data Engineering Software Development Cloud Computing Artificial Intelligence Database Management
  1. Canva's usage of creator content has skyrocketed, with data showing its growth doubling every 18 months. Managing the architecture to track this data is a significant challenge.
  2. Uber has developed strong testing and monitoring processes for its financial accounting data. This ensures accuracy and presents reliable external financial reports.
  3. With the rise of data lakehouses, utilizing tools like Apache Hudi and Paimon can enhance data storage and performance. These tools help build efficient and scalable data solutions.

How Shopify Balances Database Shards Without Downtime

Arpit’s Newsletter • 58 implied HN points • 01 Mar 23
🕹 Technology E-commerce Database Management Systems Design
  1. Shopify uses a distributed architecture with pods to handle a large number of shops sharing the same database.
  2. Shopify balances database shards without downtime by moving shops between pods using a tool called ghostferry.
  3. To ensure no downtime or data loss, Shopify follows three phases when moving a shop from one pod to another: batch copy, prepare for cutover, and cutover and updating the routing.

Reusing memory.

Software Bits Newsletter • 154 implied HN points • 15 Jul 23
🕹 Technology Database Management
  1. Vector databases store and manage high-dimensional vectors for tasks like similarity search.
  2. Simple changes like reusing memory can significantly improve performance in databases.
  3. Optimizations like object pooling and thread local memory can enhance performance further.

🤦‍♂️ Poor data cleansing & high costs sink 98% of machine learning projects

HackerPulse Dispatch • 5 implied HN points • 12 Nov 24
🕹 Technology Machine Learning Cybersecurity Data Analytics Software Development Database Management
  1. Most machine learning projects fail because of bad data cleaning and high costs. Companies are looking for better ways to manage their budgets.
  2. There are new security threats in programming, like malware hiding in code libraries. Developers need to check packages carefully before using them.
  3. Intel found a huge boost in performance for their Linux kernel from a tiny code change. This shows how small tweaks can lead to big improvements.

More on false links

Minimal Modeling • 101 implied HN points • 24 Jul 23
🕹 Technology Data Modeling Database Management
  1. In modeling, consider defining links based on specific sentence structures, like anchor, verb, anchor.
  2. Carefully distinguish between false links and actual links to avoid modeling mistakes.
  3. Identifying and managing different types of links can prevent confusion and improve database accuracy.

Performance Tuning Snowpark For Identity Resolution On Snowflake

Sonal’s Newsletter • 19 implied HN points • 29 Jul 23
🕹 Technology Data processing Performance Tuning 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.

Sort Feature Overview: Better Data Through Collaboration

Database Engineering by Sort • 15 implied HN points • 01 Mar 24
🕹 Technology Data Quality Collaboration Database Management Workflow Transparency
  1. Data quality is crucial for businesses as it influences customer experience, decision-making, and AI outcomes.
  2. Collaboration is key for improving data quality, as automated tools can only address a portion of data issues.
  3. Sort provides a platform for transparent collaboration on databases, allowing for public and private database sharing, issue tracking, proposing and reviewing database changes.

A Walk Through of the New Analyst Dashboard

The Security Industry • 15 implied HN points • 04 Mar 24
🕹 Technology Cybersecurity Data Analysis Database Management
  1. Version 6 of the Analyst Dashboard for cybersecurity industry research brings a dramatic update to user interface and introduces useful new tools.
  2. Knowing all cybersecurity product vendors is crucial for creating a comprehensive data tool, and manual categorization of vendors is currently necessary.
  3. By collecting data on vendors, answering specific questions about the cybersecurity industry becomes possible, like listing vendors in a certain city or sorting them by year founded.

Microsoft Fabric Mirroring: Revolutionising Data Access and Real-Time Insights

Data Plumbers • 2 HN points • 01 Apr 24
🕹 Technology Data Analytics Data Warehousing Database Management Data Integration
  1. Microsoft Fabric Mirroring is a transformative technology that revolutionizes data access and real-time insights in organizations.
  2. Mirroring enables universal access to various databases, real-time data replication, and granular control over data ingestion into Microsoft Fabric's Data Warehousing experience.
  3. With Mirroring, organizations can achieve zero-ETL insights, leverage the innovative capabilities of Fabric's OneLake repository, and bridge the gap between data and action for swift adaptation and success.

Vector Database: History and Basic Concept

The Beep • 2 HN points • 08 Feb 24
🕹 Technology Artificial Intelligence Database Management Data science Machine Learning Software Development
  1. Vector databases help store and manage embedding vectors effectively. This is important for improving how AI finds and retrieves information.
  2. The concept of vector databases has been around for a long time, dating back to the 1990s. They have evolved from early uses in semantic models to current advanced techniques.
  3. Various algorithms have been developed to convert digital items into vectors and to streamline searching within these vectors. This makes it easier for AI to understand and process data.

Malloy Data

Why Now • 5 implied HN points • 26 Oct 23
🕹 Technology Programming Database Management Data Analysis Software Development Programming Languages
  1. Malloy is a new query language for describing data relationships and transformations in SQL databases.
  2. Malloy compiles to SQL optimized for your database, has a semantic data model and query language, excels at reading and writing nested data sets, and handles complex queries seamlessly.
  3. Malloy also introduces a semantic layer similar to Looker, allowing for saving calculations like measures and defining dimensions to describe and transform data.

Why You Should (Still) Join Pinecone

Why You Should Join • 4 implied HN points • 04 Sep 23
🕹 Technology Artificial Intelligence Database Management Machine Learning Product Development Startup Growth
  1. Pinecone has seen significant growth and is actively hiring for various roles in different locations.
  2. Pinecone developed the first fully managed database for vectors, making working with vectors easy and efficient.
  3. Pinecone remains a market leader with a strong team, continuous product improvements, and a growing customer base.

Essential steps for optimizing a product catalog database

Database Engineering by Sort • 0 implied HN points • 14 Mar 24
🕹 Technology Database Management Data Analysis Collaboration Workflow Optimization Change Management
  1. Managing a product catalog database is challenging due to constantly changing data and unique attributes for each product
  2. Description tools like Sort enable database teams to provide important details like table names, hints for querying, and change logs
  3. Collaborate effectively on database improvements using features like inviting contributors, using data explorer to pinpoint errors, creating issues for fixes, and utilizing change requests in Sort

In-game Analytics Pipeline with Redpanda, ClickHouse, and Streamlit

Tributary Data • 0 implied HN points • 13 Mar 24
🕹 Technology Data Analytics Database Management Web Development
  1. In-game analytics provide insights into player behavior, helping developers make informed decisions to enhance gameplay experience and increase player retention.
  2. Redpanda, ClickHouse, and Streamlit form a robust analytics pipeline where Redpanda collects gameplay events, ClickHouse processes and organizes the data for analysis, and Streamlit enables visualization through a real-time leaderboard.
  3. By leveraging technologies like Apache Flink for preprocessing raw gameplay events, developers can further enhance insights into player behaviors and interactions to improve the gaming experience and retain players.

How to Design Software — Tags and Groups

Joseph Gefroh • 0 implied HN points • 22 Dec 16
🕹 Technology Software Design Database Management
  1. When designing software, consider implementing a tagging system for ordering, filtering, grouping, and organizing records based on properties.
  2. Using comma-separated strings in a single database column for tags is simple but leads to difficulties in querying, formatting errors, and length limitations.
  3. Storing tags in separate columns might seem organized, but it can complicate querying and checking for the existence of tags across multiple columns.

🎓 Making (reasonably) good decisions quickly; Using a third Entity instead of a Join Table

Become a Senior Engineer • 0 implied HN points • 14 Mar 24
🕹 Technology Decision-making Database Management APIs Programming
  1. Making decisions quickly is crucial for unblocking progress and enabling action, learning, and iteration.
  2. When dealing with complex decisions, prioritize understanding the problem, collaborating with your team, and utilizing prototyping for informed choices.
  3. Using a third entity instead of a join table in relational databases can better reflect domain logic and avoid compatibility issues with frameworks.

Polymath Engineer Weekly #86

Polymath Engineer Weekly • 0 implied HN points • 18 Mar 24
🕹 Technology Database Management Compression Furniture
  1. Databases can scale by implementing horizontal sharding tailored to unique architecture, allowing for smaller feature sets and specific optimizations.
  2. Analyzing Kafka's performance can involve tackling tail latency with eBPF by identifying areas causing queuing and delays, such as synchronized blocks.
  3. In the luxury watch industry, success factors can be revealed through comprehensive reports like the Morgan Stanley analysis, providing insights into market dynamics.