The hottest Data Management Substack posts right now

And their main takeaways
Category
Top Technology Topics

Microsoft builds the bomb

benn.substack • 1508 implied HN points • 26 May 23
🕹 Technology Data Management Cloud Computing Software Development Artificial Intelligence
  1. The modern data stack aimed to revolutionize how technology is built and sold, focusing on modularity and specialized tools.
  2. Microsoft introduced Fabric as an all-in-one data and analytics platform to address the issue of fragmentation in the modern data stack.
  3. Fabric from Microsoft presents a unified solution but may risk limiting choice and innovation in the data industry.

The Trade Desk

Business Breakdowns • 334 implied HN points • 09 Jan 24
💼 Business Advertising Technology Data Management Market Dynamics Company growth
  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 Software Development Cybersecurity Data Management Programming Languages Microservices
  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 Cybersecurity Software Data Management Vulnerability Management Open Source
  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 AI Development Software Tools Data Management Generative AI Cloud Computing
  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.
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Taming the Unstructured Beast: Data Tools for Unleashing Generative AI

Gradient Flow • 139 implied HN points • 04 Apr 24
🕹 Technology AI Data Management Data Tools Generative AI
  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 AI Data Management Applications
  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 Artificial Intelligence Public Relations 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 E-commerce Cloud Computing Data Management System Architecture Performance optimization
  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 Engineering Data Management Value Creation Marketing
  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.

Getting to 4,000 Cybersecurity Vendors

The Security Industry • 26 implied HN points • 10 Dec 24
🕹 Technology Cybersecurity AI Security Data Management Industry Trends
  1. The number of cybersecurity vendors has increased significantly, from around 467 in 2003 to over 4,000 today. This shows how important cybersecurity has become over the years.
  2. Many early cybersecurity companies have disappeared, each with its own story, which highlights the changing landscape in the industry.
  3. There is a new wave of AI-focused security companies emerging, indicating trends and advancements in cybersecurity solutions.

Improving Security Data Lake Efficiency with Log Filtering

Detection at Scale • 119 implied HN points • 08 Apr 24
🕹 Technology Security Data Management Cost Optimization Performance optimization
  1. Security teams can optimize SIEM costs and improve data management by filtering logs effectively before they are ingested into the system. Filtering can enhance security data lake efficiency, reducing unnecessary costs and improving overall data quality.
  2. Starting with clear intentions and asking key questions about data value, cost constraints, and threat visibility can help in creating a comprehensive and cost-efficient log filtering program.
  3. Filtering at various stages - source, in transit, and within the SIEM itself - allows security teams to reduce storage costs, optimize performance, improve data quality, and enhance the relevance of collected logs.

What does Alteryx do?

Technically • 12 implied HN points • 07 Jan 25
🕹 Technology Data Tools Analytics Software Business Intelligence Data Management
  1. Alteryx is a tool that helps teams make sense of messy data without needing to code. It allows people to clean and analyze their data easily.
  2. Many companies have limited access to specialized data teams, which makes tools like Alteryx important for non-technical users.
  3. Alteryx started with a simple workflow builder for data cleaning but has grown to include many other analytics tools over time.

Lean Data Engineering with Dagster and DuckDB

The Data Jargon Newsletter • 158 implied HN points • 05 Mar 24
🕹 Technology Data Engineering Data Management Software Tools Cloud Computing Data Analytics
  1. Data lakes can be convenient but often lead to problems when trying to manage the data effectively. Keeping things simple with familiar tools can help make the data more useful.
  2. Using Dagster and DuckDB allows you to process data efficiently without complicated setups. You can do key tasks like aggregation and data cleaning right in your data flow.
  3. It's important to consider memory limits and choose the right file formats, like Parquet, for better processing. This way, you can keep your data pipeline running smoothly and avoid needless costs.

RAG Glossary

The Tech Buffet • 179 implied HN points • 21 Jan 24
🕹 Technology Machine Learning AI Research Software Development Data Management Programming
  1. Retrieval Augmented Generation (RAG) helps AI answer questions and generate content. It combines searching through documents with generating relevant answers.
  2. Using RAG can be tricky, especially in production environments. Adjustments may be needed to improve reliability and performance.
  3. Different indexing methods can optimize how RAG retrieves information. This can make it more efficient and effective in finding the right data.

ChinAI #252: The Top 10 Events of Internet Governance in China from 2023

ChinAI Newsletter • 157 implied HN points • 29 Jan 24
🕹 Technology Internet Governance AI Regulations Cybersecurity Data Management
  1. National Data Administration in China started coordinating data infrastructure construction in 2023.
  2. China took significant actions in internet governance, such as fines on financial platforms and AI-generated content regulations.
  3. Important events included new regulations on cyberviolence management and the first AI text-to-image infringement case in China.

Leveraging an AI Security Framework

Resilient Cyber • 79 implied HN points • 11 Apr 24
🕹 Technology AI Security Data Management Risk Assessment Software Development Cybersecurity
  1. The Databricks AI Security Framework (DASF) helps identify and manage risks in AI systems. It's important for security experts and AI developers to know how to keep AI safe while still allowing innovation.
  2. Data operations have the highest number of security risks, like data poisoning and poor access controls. If the raw data is compromised, it can affect the entire AI system.
  3. Different stages of AI development, like model training and deployment, have unique risks to watch for, such as model theft and prompt injection attacks. Understanding these risks helps keep AI applications secure.

GroupBy #34: Hybrid Transactional/Analytical Storage, From Predictive to Generative – How Michelangelo Accelerates Uber’s AI Journey

VuTrinh. • 59 implied HN points • 07 May 24
🕹 Technology Data Engineering Artificial Intelligence Machine Learning Data Management Cloud Computing
  1. Hybrid transactional/analytical storage combines different types of data processing. This helps companies like Uber manage their data more efficiently.
  2. The shift from predictive to generative AI is changing how companies use machine learning. Uber's Michelangelo platform shows how this new approach can improve AI applications.
  3. Data reliability and observability are important for businesses as their data grows. Companies need tools to quickly find and fix data issues to keep their operations running smoothly.

You just bought Snowflake. What next? Your Top 5 Priorities

The Orchestra Data Leadership Newsletter • 59 implied HN points • 29 Apr 24
🕹 Technology Data Management Data Infrastructure Data Modeling Data Governance
  1. Ensure rock-solid infrastructure for your Snowflake implementation to prevent pipeline failures and maintain data quality.
  2. Set clear expectations and prioritize projects to manage scope and quality, fostering trust and collaboration.
  3. Start thinking of data as a product during the Snowflake implementation to minimize costs, stabilize usage, and accelerate trust in the data team.

People are worried about Large Language Models

Honest but Curious • 1 HN point • 23 Sep 24
🕹 Technology AI Regulation Innovation Cybersecurity Data Management
  1. Many people in Silicon Valley are concerned that large language models (LLMs) could be a serious danger to humanity, leading to calls for regulation. California is currently considering a bill to create safety standards for LLMs.
  2. There is some debate about how well current benchmarks assess the capabilities of LLMs, with some arguing that these models are still not truly ready to replace human intelligence in work. This shows that having a great score on tests doesn’t necessarily mean practical usefulness.
  3. Israel's recent attack on Hezbollah's pager system demonstrates the complexities of security and technology. It involved creating specialized devices rather than hacking existing ones, emphasizing the need for careful vetting when purchasing hardware.

Who cares about AuthZ? We went to KubeCon!

Permit.io’s Substack • 79 implied HN points • 28 Mar 24
🕹 Technology Cloud Computing Software Development Cybersecurity Data Management Open Source
  1. Fine-grained authorization is becoming really important as more developers talk about it. People see that better security can happen with smooth developer experiences.
  2. The rise of cloud-native architecture and big data means we need better ways to manage authorization decisions. It helps reduce decision fatigue and improves security.
  3. Tools like Policy as Code and various authorization engines are helping different teams work together better. This can lead to faster and more efficient development processes.

In search for ideal CDP for B2B SaaS

nonamevc • 24 implied HN points • 10 Nov 24
🕹 Technology SaaS Data Management Cloud Computing Digital Infrastructure
  1. Customer Data Platforms (CDPs) are becoming important for B2B SaaS companies by helping them unify data from different sources. This makes it easier for teams to work together and drive better marketing and sales efforts.
  2. There are two main types of CDPs: packaged and composable. Packaged CDPs are more like ready-made solutions, while composable CDPs allow for customization to better fit a company's specific needs.
  3. B2B companies might not need a standalone CDP as many existing tools are starting to include features traditionally offered by CDPs. This means businesses can often get what they need from tools they are already using.

What's a data migration?

Technically • 29 implied HN points • 12 Nov 24
🕹 Technology Data Management Software Development Cloud Computing Database Systems IT Infrastructure
  1. Data migration is the process of moving information from one place to another, like relocating files when changing devices. It involves transferring various types of data, such as documents and databases, to ensure everything is in the right spot.
  2. Migrations can be complex and risky, often causing errors or service disruptions if not done carefully. This makes it crucial for companies to have good planning and oversight to avoid losing important data or negatively affecting users.
  3. There are many reasons to migrate data, such as upgrading technology or meeting new security regulations. Companies often need to adapt to growth or changes in the market, which can lead to costly and lengthy migration projects.

Maximizing the Potential of Large Language Models

Gradient Flow • 359 implied HN points • 09 Mar 23
🕹 Technology Artificial Intelligence Data Management Data science Natural Language Processing
  1. Language models need a three-pronged strategy of tuning, prompting, and rewarding to unlock their full potential.
  2. Fine-tuning pre-trained models is a common practice to tailor models for specific tasks and domains.
  3. Teams require simple and versatile tools to create custom models efficiently and effectively.

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.

The Data-Conscious Software Engineer

Data Products • 3 implied HN points • 28 Jan 25
🕹 Technology Data science Software Engineering Data Management Data products Machine Learning
  1. Data teams need to learn best practices from software engineering, but that's not enough. They also need engineers who understand how data works and can work well with them.
  2. Collaboration between data teams and software engineers is really important for success. If they don't communicate well, they can struggle to implement necessary changes and solve issues together.
  3. The idea of a 'data-conscious software engineer' is becoming essential. These engineers understand the value of data and can help improve how both teams work together, making both sides more efficient.

Decoding Apple's AI Ambitions

Gradient Flow • 219 implied HN points • 29 Jun 23
🕹 Technology Artificial Intelligence Machine Learning Data processing AI Applications Data Management
  1. Apple's AI focus is on Machine Learning and Computer Vision with emerging areas like Robotics and Speech Recognition, aiming to enhance services like Siri.
  2. Apple shows active interest in AI areas like Generative AI and large language models through their job postings, emphasizing deep learning skills.
  3. Apple's AI strategy integrates hardware and software to provide personalized experiences, leveraging silicon chips, Neural Engine, and fine-grained data for future AI applications.

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.

StackAware and Mithril Security: making private AI a reality

Deploy Securely • 117 implied HN points • 12 Jan 24
🕹 Technology AI Cybersecurity Privacy Data Management Artificial Intelligence
  1. Mithril Security offers tools for securing sensitive AI deployments.
  2. StackAware assists companies in managing risks related to cybersecurity, compliance, and privacy in AI deployments.
  3. Partnership between StackAware and Mithril Security combines expertise in AI threats and confidential AI for secure deployments.

Python Lists: A closer look, part 11

Mostly Python • 628 implied HN points • 30 Mar 23
🕹 Technology Programming Data Management Development Python Classes
  1. Copying a list in Python can lead to unexpected behavior if the items in the list are mutable objects.
  2. To create a true copy of a list with mutable objects, use the deepcopy() function from the copy module.
  3. When working with Python lists, consider the nature of the items in the list to decide between using list[:], list.copy(), or deepcopy().

Challenges In Adopting Retrieval-Augmented Generation Solutions

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 01 Apr 24
🕹 Technology AI Development Language Models Data Management User Experience Privacy Concerns Software Engineering
  1. Retrieval-Augmented Generation (RAG) uses contextual learning to improve responses and reduce errors, making it useful for Generative AI.
  2. RAG systems are easier to maintain and less technical, which helps keep them updated with changing needs.
  3. However, RAG can have shortcomings like poor retrieval strategies and issues with data privacy, leading to incomplete or incorrect answers.

Sort is now available on the AWS Marketplace!

Database Engineering by Sort • 23 implied HN points • 28 Oct 24
💼 Business Data Management Software Development E-commerce
  1. Sort is now on the AWS Marketplace, making it easier for businesses to manage data changes. This means users can quickly add Sort to their systems.
  2. Sort helps streamline data change management with a simple process for proposing and approving changes. It makes it easy for teams to fix errors or update records without hassle.
  3. Every data change is logged by Sort, creating a clear history of what changes were made and why. This feature ensures full transparency and helps maintain high data quality.

The Analytics Requirements Document

Sarah's Newsletter • 359 implied HN points • 27 Oct 22
💼 Business Analytics Product Launch Contracts Data Management Team Collaboration
  1. Analytics should be a first-class citizen in crafting product launches to avoid wasted time and ensure measurable success.
  2. Utilize detailed agreements like Product Requirements Documents (PRD) and Analytics Requirements Documents (ARD) to align teams, outline goals, data criteria, assumptions, and finalize expectations.
  3. Involving analytics early in the product evolution lifecycle is crucial for gathering and analyzing data effectively, helping in decision-making, and ensuring alignment across technical and business teams.

Overview of the AI landscape

Software Engineering Tidbits • 98 implied HN points • 22 Jan 24
🕹 Technology AI Machine Learning Data Management Tooling
  1. Large Language Models (LLMs) are key in AI applications like OpenAI's ChatGPT and Anthropic's Claude.
  2. Vector databases and embeddings help understand word associations, with tools like Pinecone and the Embedding Projector by TensorFlow.
  3. Tooling in AI is advancing, with Vellum for versioning prompts and Not Diamond for routing prompts for optimal model response.