The hottest Data Management Substack posts right now

And their main takeaways
Category
Top Technology Topics

Iceberg + Single Node Engines

Ju Data Engineering Newsletter • 515 implied HN points • 17 Oct 24
🕹 Technology 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.

The World's Largest Search Doesn't Want You to Search

The Honest Broker • 45746 implied HN points • 19 Feb 25
🕹 Technology Data Management
  1. Search engines, especially Google, are moving away from their main job of helping people find information. Instead, they want to keep users on their platforms with AI results that don’t always give good answers.
  2. Google prioritizes its advertising and profitability over providing reliable search results. People often end up with low-quality information or ads instead of what they are really looking for.
  3. Many users are losing trust in Google and other big tech companies because they feel the platforms are not serving their needs. If this trend continues, it could lead to serious consequences for these companies.

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

VuTrinh. • 279 implied HN points • 14 Sep 24
🕹 Technology Data Management
  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.

Issue #14 - The Forgotten Guiding Role of Data Modelling

The Data Ecosystem • 659 implied HN points • 14 Jul 24
🕹 Technology Data Management
  1. Data modeling is like a blueprint for organizing information. It helps people and machines understand data, making it easier for businesses to make decisions.
  2. There are different types of data models, including conceptual, logical, and physical models. Each type serves a specific purpose and helps bridge business needs with data organization.
  3. Not having a structured data model can lead to confusion and problems. It's important for organizations to invest in good data modeling to improve data quality and business outcomes.

Issue #16 - The Data Quality Conundrum (Part 2 – Solving)

The Data Ecosystem • 439 implied HN points • 28 Jul 24
🕹 Technology Data Management
  1. Data quality isn't just a simple fix; it's a complex issue that requires a deep understanding of the entire data landscape. You can't just throw money at it and expect it to get better.
  2. It's crucial to identify and prioritize your most important data assets instead of trying to fix everything at once. Focusing on what truly matters will help you allocate resources effectively.
  3. Implementing tools for data quality is important but should come after you've set clear standards and strategies. Just using technology won’t solve problems if you don’t understand your data and its needs.
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Revenue Operations Pros Are Work Horses

beyondrevenueoperations • 39 implied HN points • 12 Oct 24
💼 Business Data Management
  1. Revenue Operations focuses on aligning sales, marketing, and customer support to boost overall revenue. This means all teams need to work together to improve the customer experience.
  2. Data accuracy and management are crucial in Revenue Operations. Keeping customer data clean helps everyone make better decisions and understand what drives sales.
  3. Ongoing support and training empower teams to succeed. Providing the right tools and resources ensures that all revenue-generating teams can perform at their best.

Understanding America's AI Action Plan

Contemplations on the Tree of Woe • 1696 implied HN points • 25 Jul 25
🕹 Technology Data Management
  1. The U.S. sees AI as crucial for winning against rivals, especially China. They believe having strong AI can help improve the economy and ensure national security.
  2. There is a push to make AI less regulated in the U.S. This is different from Europe, which is more cautious about AI rules and laws.
  3. The government wants to ensure AI promotes free speech and American values but faces challenges in making sure AI stays unbiased and reflects different viewpoints.

The History and Evolution of Open Table Formats - Part II

Practical Data Engineering Substack • 79 implied HN points • 18 Aug 24
🕹 Technology Data Management
  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.

Issue #12 – The Three Biggest Data Problems Companies Face

The Data Ecosystem • 239 implied HN points • 30 Jun 24
💼 Business Data Management
  1. Companies often struggle with a data operating model that doesn't connect well with their other teams. This leads to isolation among data specialists, making it hard to work effectively.
  2. Data models, which are important for understanding and using data correctly, are often overlooked. When organizations don’t reference these models, they can drift further away from their goals.
  3. Many data quality issues come from deeper problems within the organization, like poor data governance and inconsistent processes. Fixing just the visible data quality issues won't solve the bigger problems.

The National Parking Platform is a big, exciting deal and you should know about it

Odds and Ends of History • 1608 implied HN points • 22 May 25
🇺🇸 U.S. Politics Data Management
  1. The National Parking Platform (NPP) is a new data system that makes paying for parking easier by allowing any payment app to work with any car park. This means you won't have to download many apps just to park your car.
  2. This platform collects data from all car parks, which helps local authorities manage parking better and reduce traffic by making sure spaces are used efficiently.
  3. The NPP could lead to new ways of thinking about parking, like offering discounts for electric cars or using real-time data to help drivers find available spots before they arrive.

Hype cycles

Bite code! • 10520 implied HN points • 24 Jun 23
🕹 Technology Data Management
  1. XML was once believed to be the future, but turned out to create technical debt instead.
  2. Following every hype blindly in technology can lead to failed projects and waste of money.
  3. Using the right tool for the right job is crucial in software development, avoiding unnecessary complexity and costs.

Do we need the Lakehouse architecture?

VuTrinh. • 399 implied HN points • 20 Apr 24
🕹 Technology Data Management
  1. Lakehouse architecture combines the strengths of data lakes and data warehouses. It aims to solve the problems that arise from keeping these two systems separate.
  2. This new approach allows for better data management, including features like ACID transactions and efficient querying of big datasets. It enables real-time analytics on raw data without needing complex data movements.
  3. With the help of technologies like Delta Lake and similar systems, the Lakehouse can handle both structured and unstructured data efficiently, making it a promising solution for modern data needs.

Where AI wins.

The Uncertainty Mindset (soon to become tbd) • 199 implied HN points • 12 Jun 24
🕹 Technology Data Management
  1. AI is great at handling large amounts of data, analyzing it, and following specific rules. This is because it can process things faster and more consistently than humans.
  2. However, AI systems can't make meaning on their own; they need humans to help interpret complex data and decide what's important.
  3. The best use of AI is when it works alongside humans, each doing what they do best. This way, we can create workflows that are safe and effective.

27 Unique Dev Challenges: A Recent Study Explored the Top Challenges Faced by LLM Developers

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 20 Aug 24
🕹 Technology Data Management
  1. Developers face many challenges when working with large language models (LLMs), including issues with API calls and integrating them into existing systems.
  2. Common problems also involve managing large datasets and ensuring data privacy and security while using LLMs for tasks like text generation.
  3. Understanding unpredictable outputs from LLMs is essential, as it affects the reliability and performance of applications built with these models.

Issue #10 - The Data Lifecycle

The Data Ecosystem • 159 implied HN points • 16 Jun 24
🕹 Technology Data Management
  1. The data lifecycle includes all the steps from when data is created until it is no longer needed. This helps organizations understand how to manage and use their data effectively.
  2. Different people and companies might describe the data lifecycle in slightly different ways, which can be confusing. It's important to have a clear understanding of what each term means in context.
  3. Properly managing data involves stages like storage, analysis, and even disposal or archiving. This ensures data remains useful and complies with regulations.

Analytics Team: Strategic Partner or Service Org?

Elena's Growth Scoop • 1139 implied HN points • 30 Jun 23
💼 Business Data Management
  1. Having a data-driven culture is important for making informed decisions and connecting actions to business outcomes.
  2. In many companies, data is not well managed and can lead to frustration when trying to implement a data-driven culture too soon.
  3. Striking a balance and ensuring data accuracy is crucial before pushing for a data-driven culture.

Issue #9 - Clarifying Data Terminology

The Data Ecosystem • 159 implied HN points • 09 Jun 24
🕹 Technology Data Management
  1. Data can mean many things, from raw collections to curated evidence used in decisions. It's important to define what data means in each situation to avoid confusion.
  2. Poorly defined data terms can lead to problems in data literacy, collection, and management. This can create issues for organizations trying to use data effectively.
  3. Understanding different categories of data, like data types and processing stages, helps in managing and analyzing data better. Knowing these categories makes it easier to communicate and use data in an organization.

My Definition of Data Modeling (for today)

Joe Reis • 530 implied HN points • 20 Jan 24
🕹 Technology Data Management
  1. Data modeling has various definitions by different experts and serves to improve communication, provide utility, and solve problems.
  2. A data model is a structured representation that organizes data for both humans and machines to inform decision-making and facilitate actions.
  3. Data modeling is evolving to consider the needs of machines, different use cases, and a wider range of modeling approaches for various situations.

Issue #1 - We Need to Rethink Data

The Data Ecosystem • 259 implied HN points • 13 Apr 24
🕹 Technology Data Management
  1. The data industry is really complicated and often misunderstood. People usually talk about symptoms, like bad data quality, instead of getting to the real problems underneath.
  2. It's important to see the entire data ecosystem as connected, not just as separate parts. Understanding how these parts work together can help us find new opportunities and improve how we use data.
  3. This newsletter aims to break down complex data topics into simple ideas. It's like a cheat sheet for everything related to data, helping readers understand what each part is and why it matters.

In the race between humans and AIs, Data Governance might be the only lever we can pull, to charge for a fair win against the machines

The Diary of a #DataCitizen • 19 implied HN points • 28 Aug 24
🕹 Technology Data Management
  1. Data governance is important for keeping technology human-friendly. It helps us make sure that tech doesn't take over our lives.
  2. The rise of AI has changed the game, making data and AI governance even more crucial. We need to focus on using technology in ways that benefit everyone.
  3. Good tech creates real value for people. It's about how well technology works for the users, not just its shiny features or capabilities.

Learning from the Past: Comparing the Hype Cycles of Big Data and GenAI

Gradient Flow • 159 implied HN points • 02 May 24
🕹 Technology Data Management
  1. Adopt a measured approach to GenAI implementation by learning from past technology hype cycles like Big Data.
  2. Organizations should clearly define business problems before adopting GenAI to avoid misalignment and wasted resources.
  3. In navigating the GenAI landscape, prioritize data quality, governance, talent investment, and leveraging open-source solutions for successful adoption.

Is It Time to Say Goodbye to Data Engineers?

SeattleDataGuy’s Newsletter • 812 implied HN points • 06 Feb 25
🕹 Technology Data Management
  1. Data engineers are often seen as roadblocks, but cutting them out can lead to major problems later on. Without them, the data can become messy and unmanageable.
  2. Initially, removing data engineers may seem like a win because things move quickly. However, this speed can cause chaos as data quality suffers and standards break down.
  3. A solid data strategy needs structure and governance. Rushing without proper planning can lead to a situation where everything collapses under the weight of disorganization.

Best Practices in Retrieval Augmented Generation

Gradient Flow • 599 implied HN points • 19 Oct 23
🕹 Technology Data Management
  1. Retrieval Augmented Generation (RAG) enhances language models by integrating external knowledge sources for more accurate responses.
  2. Evaluating RAG systems requires meticulous component-wise and end-to-end assessments, with metrics like Retrieval_Score and Quality_Score being crucial.
  3. Data quality is pivotal for RAG systems as it directly impacts the accuracy and informativeness of the generated responses.

Speed Without Understanding - One of the Biggest Risks in Data Engineering

SeattleDataGuy’s Newsletter • 329 implied HN points • 30 Jun 25
🕹 Technology Data Management
  1. Speed in data engineering can be risky. Acting fast without fully understanding the consequences can lead to mistakes, like accidentally deleting important data.
  2. Every new tool or change can add complexity. If something breaks, it may cause confusion for others, so it’s important to think carefully about what you build.
  3. Having a mix of experienced and new team members is really helpful. It encourages sharing knowledge and can prevent big errors when someone leaves the team.

Postgres in a box

benn.substack • 920 implied HN points • 06 Dec 24
🕹 Technology 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.

What I Learned This Week #6

Eventually Consistent • 59 implied HN points • 01 Jul 24
🕹 Technology Data Management
  1. Data partitioning helps manage query loads by distributing large datasets across multiple disks and processors. Considerations include rebalancing for even distribution, distributed query execution, and dealing with hot spots.
  2. Partitioning secondary indexes can be done locally or globally, with tradeoffs between keeping related data together versus faster lookups for certain queries. Routing queries in distributed systems may use coordination services or gossip protocols for efficiency.
  3. Transactions provide a way to manage concurrency and software failures by ensuring operations either fully succeed or fully fail. AWS Lambda uses worker models for task execution and Rust Atomics for memory ordering control across threads.

Tech the Heck? - CloudTruth

Cloud Irregular • 2069 implied HN points • 19 Feb 24
🕹 Technology Data Management
  1. Explaining complex tech products in simple language is important for understanding and adoption.
  2. Developers may value different aspects of a tech product compared to business decision-makers, causing a mismatch in communication.
  3. CloudTruth focuses on managing crucial configuration data, highlighting the importance of precision in language and clear communication.

The Trade Desk

Business Breakdowns • 334 implied HN points • 09 Jan 24
💼 Business Data Management
  1. The Trade Desk helps ad agencies spend their budgets more effectively by providing a platform for optimizing programmatic advertising.
  2. The company focuses on building strong, recurring relationships with buy-side agencies, leading to a high customer retention rate.
  3. The Trade Desk functions as a data management platform, enabling efficient real-time bidding and liquidity in the digital advertising market.

Authorization in microservice architecture, P3: The Policy Language

Hung's Notes • 39 implied HN points • 18 Jul 24
🕹 Technology Data Management
  1. A Domain-Specific Language (DSL) helps create clear and precise authorization policies for microservices. It makes it easier for everyone involved, from developers to managers, to understand authorization rules.
  2. The new policy language is designed to overcome performance issues by allowing lazy loading and efficient management of large datasets. This means it doesn't grab unnecessary data upfront, speeding up processes.
  3. Using YAML instead of complex formats makes the policies more readable and easier for non-engineers to understand. This helps ensure that more people can participate in and review authorization rules effectively.

Death Knell of the NVD?

Resilient Cyber • 199 implied HN points • 11 Mar 24
🕹 Technology Data Management
  1. The NIST National Vulnerability Database (NVD) is an important source for understanding software vulnerabilities, but it is facing significant issues. Many vulnerabilities lack timely analysis and critical information.
  2. There is a need for better tagging and categorization of vulnerabilities, such as associating Common Vulnerability Enumeration (CVE) identifiers with specific products. Without this, organizations struggle to know what vulnerabilities affect their systems.
  3. Alternatives to the NVD like the Sonatype OSS Index and the Open-Source Vulnerabilities (OSV) Database are emerging, but they focus primarily on open-source software. The effectiveness and reliability of the NVD remain crucial for broader security practices.

LangSmith, LangGraph Cloud & LangGraph Studio

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 39 implied HN points • 15 Jul 24
🕹 Technology Data Management
  1. There's a shift in generative AI, moving away from just powerful models to more practical user applications. This includes a focus on using data better with tools that help manage these models.
  2. New tools like LangSmith and LangGraph are designed to help developers visualize and manage their AI applications easily. They allow users to see how their AI works and make changes without needing to code everything from scratch.
  3. We are now seeing a trend towards no-code solutions that make it easier for anyone to create and manage AI applications. This approach is making technology more accessible to people, regardless of their coding skills.

Taming the Unstructured Beast: Data Tools for Unleashing Generative AI

Gradient Flow • 139 implied HN points • 04 Apr 24
🕹 Technology Data Management
  1. Unstructured data processing is crucial for AI applications like GenAI and LLMs. Extracting and transforming data from various formats like HTML, PDF, and images is necessary to leverage unstructured data.
  2. Data preparation involves tasks like cleaning, standardization, and enrichment. This enhances data quality, making it more suitable for AI applications like Generative AI.
  3. Data utilization in AI integration includes retrieval, visualization, and model serving. Efficient querying, visualizing data trends, and seamless integration of data with AI models are key aspects of successful AI implementation.

GenAI and LLMs: Insights from TikTok and KPMG

Gradient Flow • 119 implied HN points • 18 Apr 24
🕹 Technology Data Management
  1. Large enterprises are shifting towards in-house AI application development using foundation models, impacting the industry by enabling cost savings and customization.
  2. AI adoption rates among U.S. businesses are rapidly growing, expected to almost double by Fall 2024, with a focus on technology and development applications.
  3. Companies like TikTok and KPMG are adopting GenAI in different ways – TikTok invests heavily in content creation, while KPMG focuses on integrating AI into audit and advisory services, showcasing diverse applications of GenAI.

✅ AI in PR shifts from experimentation to implementation

Wadds Inc. newsletter • 39 implied HN points • 08 Jul 24
🕹 Technology Data Management
  1. AI is becoming a key part of public relations, moving beyond trials to real use in daily tasks. This means teams are now figuring out how to best integrate AI tools into their work.
  2. AI offers significant benefits, like increased efficiency and productivity, but it requires a clear approach to adopt and adapt it effectively. Breaking down workflows is essential to understand where AI can help.
  3. The impact of AI on public relations is both a technology and a culture issue, meaning it's important for everyone in a team to learn and work together to make the most of these tools.

How VTEX improved the shopper experience with Amazon DynamoDB

VTEX’s Tech Blog • 119 implied HN points • 16 Apr 24
🕹 Technology Data Management
  1. VTEX improved their shopping cart system by switching from Amazon S3 to Amazon DynamoDB. This change was made to enhance speed and make the shopping experience better for users.
  2. They faced challenges because some shopping cart items were too large for DynamoDB's limits. To fix this, they reduced the data size and created a process to store bigger items separately in S3.
  3. After gradually migrating to DynamoDB, VTEX achieved a 30% reduction in shopping cart API latency. This helped their overall efficiency and improved customer satisfaction.

Long-Chain Marketing: How Data Engineering & Data Management Create Value For The Business

High ROI Data Science • 297 implied HN points • 10 Jan 24
💼 Business Data Management
  1. Understanding the long-chain in marketing is crucial for connecting business outcomes with data and metrics.
  2. Data engineering and knowledge management are essential for transforming data into valuable assets that can be monetized by the business.
  3. Long-chain marketing involves seeing marketing efforts as part of a longer sequence of actions that lead to business outcomes, rather than standalone events.

How to Keep Your Data Team From Becoming a Money Pit

SeattleDataGuy’s Newsletter • 282 implied HN points • 23 May 25
💼 Business Data Management
  1. It's important to focus on outcomes, not just outputs. Creating a lot of dashboards means nothing if they don't help people make better decisions.
  2. Making good data work requires engaging with stakeholders. Understanding what users actually need can lead to more effective solutions.
  3. Success in data teams means having clear ownership and goals. Projects can fail if no one knows who is responsible for them or what they should achieve.

For the Next Month: Going All-in on The Browser Company's Dia-AI

Brad DeLong's Grasping Reality • 176 implied HN points • 01 Aug 25
🕹 Technology Data Management
  1. The Dia Browser is a new tool that aims to combine AI with web browsing, helping users get more control and streamline their information processing.
  2. Large language models like ChatGPT can handle information overload by summarizing and organizing data, acting like advanced autocomplete systems that enhance productivity.
  3. While these technologies are powerful, they lack true understanding and reasoning, meaning users still play a crucial role in guiding their use effectively.