The hottest Data Infrastructure Substack posts right now

And their main takeaways
Category
Top Technology Topics

GroupBy #38: Modernizing Uber’s Batch Data Infrastructure with Google Cloud Platform, Apache Iceberg - What Is It

VuTrinh. • 119 implied HN points • 04 Jun 24
🕹 Technology Data Engineering Cloud Computing Data Infrastructure Machine Learning Open Source
  1. Uber is upgrading its data system by moving from its huge Hadoop setup to Google Cloud Platform for better efficiency and performance.
  2. Apache Iceberg is an important tool for managing data efficiently, and it can help create a more organized data environment.
  3. Building data products requires a strong foundation in data engineering, which includes understanding the tools and processes involved.

The Data Team Playbook: 50+ Resources For High-Performing Data Teams

SeattleDataGuy’s Newsletter • 836 implied HN points • 14 Mar 24
🕹 Technology Data Infrastructure Talent Acquisition Communication Influence
  1. Starting a career as a data team manager involves challenges and new skills, with resources like books to aid in the transition.
  2. Assisting team members in their career growth involves sharing helpful articles, guides, and videos.
  3. Improving project management, team culture, and communication are key elements in running successful data teams.

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.
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Model commoditization and product moats

Democratizing Automation • 126 implied HN points • 13 Mar 24
🕹 Technology Artificial Intelligence Data Infrastructure Model Training Open Source Machine Learning
  1. Models like GPT4 have been replicated in many organizations, leading to a situation where moats are less significant in the language model space.
  2. The open LLM ecosystem is progressing, but there are challenges in data infrastructure and coordination, potentially leading to a gap between open and closed models.
  3. Despite some skepticism, Language Models have been consistently enhancing their reliability making them increasingly useful for various applications, with potential for new transformative uses.

Machine Learning at a Pegacorn

Gradient Flow • 199 implied HN points • 04 Aug 22
🕹 Technology Machine Learning Metaverse Data Infrastructure AI Ethics
  1. Major tech companies are investing in the Metaverse along with AI and cloud computing, based on 2022 coverage.
  2. In the podcast 'Data Exchange', topics like data infrastructure for computer vision and machine learning at Gong are discussed.
  3. Tree-based learners outperform neural network-based learners on tabular data, and Transformers are used to cluster papers from ICML 2022.

🔮 Launching State of the Future_The Worlds First Deep Tech Tracker #001

Let Us Face the Future • 218 implied HN points • 24 May 23
🕹 Technology Deep Tech Artificial Intelligence Quantum Computing Data Infrastructure Startups
  1. State of the Future is a deep tech tracker covering a wide range of technologies like computer vision, generative AI, and quantum hardware.
  2. The three main trends identified in the future include solving productivity paradox, the shift from software in digital world to real world, and having optimism for the future.
  3. Important news includes suppressing quantum errors, challenges faced by Amazon's drone delivery project, and closures of vertical farming startups due to high costs.

Embed Retrieve Win

Gradient Flow • 99 implied HN points • 29 Sep 22
🕹 Technology Machine Learning Data Infrastructure Generative models NLP AI Applications
  1. Embeddings are low-dimensional spaces that make AI applications faster and cheaper while maintaining quality.
  2. Vector databases are designed for vector embeddings and are becoming essential for modern search engines and recommendation systems.
  3. Generative models like diffusion models are gaining attention in the research community and offer great opportunities for exploration and innovative projects.

This once hot data trend from a few months ago got resuscitated by our industry’s data sweetheart

The Orchestra Data Leadership Newsletter • 19 implied HN points • 27 Oct 23
🕹 Technology Data Management Data Infrastructure Data Orchestration Software Tools
  1. Data Mesh is a decentralized approach to enterprise data management, focusing on distributed datasets and data ownership within domains.
  2. DBT Mesh is a set of features that allow multiple teams to work on dbt projects with less friction, enabling separate repositories and orchestration capabilities.
  3. Having separate dbt jobs run across projects on a schedule is limited, requiring external workflow orchestration tools for more flexibility.

Experimentation Tools; Surge in MLOps; 2021 Books

Gradient Flow • 39 implied HN points • 09 Dec 21
🕹 Technology Data Infrastructure Machine Learning AI Applications Books Podcasts
  1. Investors and engineers are focusing on ML infrastructure and MLOps, but experimentation tools need more attention to bridge the gap between data teams and product teams.
  2. Financial services industry is utilizing AI and NLP via no-code platforms to build and deploy applications.
  3. Recommendations of books include topics on cyberweapons, macroeconomics, venture capital, and predictive investment frameworks.

Gradient Flow #13: Machine Learning Platforms, Graphs, and AI Ethics

Gradient Flow • 19 implied HN points • 16 Jul 20
🕹 Technology AI Ethics Data Infrastructure Remote work
  1. Graph technologies are essential for various applications like search, recommendation systems, and fraud detection.
  2. Machine learning tools and infrastructure are evolving to cater to modern AI applications and ensure cost-effectiveness.
  3. AI ethics guidelines are vital, but practical enforcement mechanisms are lacking, impacting their effectiveness.

Data Science Weekly - Issue 347

Data Science Weekly Newsletter • 19 implied HN points • 16 Jul 20
🕹 Technology Data science Machine Learning Artificial Intelligence Statistical Methods Data Infrastructure
  1. Netflix is working on making its data usage more efficient. They have created a dashboard that helps their team understand data costs and trends better.
  2. Using meta-augmentation in machine learning can improve performance more than just changing the model. It's important to focus on enhancing the data we use.
  3. When building robots, the goal should be to assist humans, not replace them. This approach considers the future of robotics in various fields like transportation and healthcare.

OLTP vs OLAP: The Core of Data Miscommunication

Data Products • 2 HN points • 23 Jun 23
🕹 Technology Data Infrastructure Data science Data Systems Data Modeling Data architecture
  1. The difference between OLTP and OLAP systems can cause miscommunication among data producers and consumers.
  2. OLTP systems focus on serving end users quickly with specific product features, while OLAP systems handle complex analytics by scanning large amounts of data.
  3. Empathy and communication between OLTP and OLAP teams are crucial to building scalable data products.

Data Engineering: A Software Engineer’s Approach

realkinetic • 0 implied HN points • 25 Jan 24
🕹 Technology Software Engineering Data Engineering Cloud Computing Data Analytics Data Infrastructure
  1. The tech industry varies in its expectations of data engineers, leading to challenges in team performance and hiring.
  2. Companies today need to be data-driven, utilizing modern data stack tools, which necessitates a blend of data engineering and software engineering skills.
  3. Data engineering benefits from adopting software engineering principles like treating systems as products, clear communication, and implementing CI/CD pipelines.

The Rise of the Data Product Manager

The Orchestra Data Leadership Newsletter • 0 implied HN points • 17 Nov 23
🕹 Technology Data Management Product Management Data Tools Data Infrastructure Marketplaces
  1. The role of Data Product Manager is gaining importance in the data industry, with a focus on delivering value and advocating for data to drive business outcomes.
  2. Tools like Fivetran, dbt, Snowflake, and platforms like Orchestra are simplifying data team setups and enabling Product Managers with less technical skills to handle data initiatives effectively.
  3. Federated teams, marketplace functionalities by Databricks and Snowflake, and the evolving concept of data quality and productization are shaping the field of data management towards a more product-led approach.