Extract data from websites in LLM ready JSON or CSV format. Crawl or Scrape entire website with Website Crawler
An article discussing the importance of time series databases and data visualization tools like Grafana for managing and interpreting streams of data in various applications.
The author mentions several time series databases (TSDs) and visualization tools, focusing on their features, advantages, and some limitations. The article also provides an example of a Building Management and Control (BMaC) project that uses InfluxDB and Grafana for data visualization.
| Database | Description | Notable Features |
|-------------------|-------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------|
| InfluxDB | Partially open source, with version 3 being an edge data collector. | Shard-based storage, compaction levels, time series index, optional retention. |
| Apache Kudu | Column-based database optimized for multidimensional OLAP workloads. | Part of the Apache Hadoop ecosystem. |
| Prometheus | Developed at SoundCloud for metrics monitoring. | Written in Go, similar to InfluxDB v1 and v2. |
| RRDTool | All-in-one package with a circular buffer TSD that also does graphing. | Language bindings for various programming languages. |
| Graphite | Similar to RRDTool but uses a Django web-based application to render graphs. | Web-based graphing. |
| TimescaleDB | Extends PostgreSQL, supporting typical SQL queries with TSD functionality and optimizations. | Supports all typical SQL queries. |
The article also discusses Grafana as a popular tool for creating dashboards to visualize time series data, mentioning its compatibility with multiple TSDs and SQL databases. It concludes by highlighting the importance of understanding one's specific needs before choosing a TSD and visualization solution.
The author mentions several time series databases (TSDs) and visualization tools, focusing on their features, advantages, and some limitations. The article also provides an example of a Building Management and Control (BMaC) project that uses InfluxDB and Grafana for data visualization.
| Database | Description | Notable Features |
|-------------------|-------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------|
| InfluxDB | Partially open source, with version 3 being an edge data collector. | Shard-based storage, compaction levels, time series index, optional retention. |
| Apache Kudu | Column-based database optimized for multidimensional OLAP workloads. | Part of the Apache Hadoop ecosystem. |
| Prometheus | Developed at SoundCloud for metrics monitoring. | Written in Go, similar to InfluxDB v1 and v2. |
| RRDTool | All-in-one package with a circular buffer TSD that also does graphing. | Language bindings for various programming languages. |
| Graphite | Similar to RRDTool but uses a Django web-based application to render graphs. | Web-based graphing. |
| TimescaleDB | Extends PostgreSQL, supporting typical SQL queries with TSD functionality and optimizations. | Supports all typical SQL queries. |
The article also discusses Grafana as a popular tool for creating dashboards to visualize time series data, mentioning its compatibility with multiple TSDs and SQL databases. It concludes by highlighting the importance of understanding one's specific needs before choosing a TSD and visualization solution.
This video course introduces DuckDB, an open-source database for data analytics in Python. It covers creating databases from files (Parquet, CSV, JSON), querying with SQL and the Python API, concurrent access, and integration with pandas and Polars.
A guide to building a front-end data application using Taipy, comparing it to Streamlit and Gradio, and providing a step-by-step implementation of a sales performance dashboard.
An article discussing the role of data orchestrators in managing complex data workflows, their evolution, and various tools available for orchestration.
Keboola MCP Server enables AI-powered data pipeline creation and management. It allows users to build, ship, and govern data workflows using natural language and AI assistants, integrating with tools like Claude and Cursor. It's free to use, with costs based on standard Keboola usage.
PhD student Sarah Alnegheimish is developing Orion, an open-source, user-friendly machine learning framework for detecting anomalies in large-scale industrial and operational settings. She focuses on making machine learning systems accessible, transparent, and trustworthy, and is exploring repurposing pre-trained models for anomaly detection.
Regeneron Pharmaceuticals is acquiring 23andMe for $256 million, gaining access to the genetic data of around 15 million customers. This raises data privacy concerns, despite assurances from Regeneron to honor existing privacy practices. The sale also brings up questions about potential compensation for customers if the data leads to profitable medications.
This article discusses the importance of integrating responsible AI practices with security measures, particularly within organizations like Grammarly. It emphasizes treating responsible AI as a product principle, securing the AI supply chain, and the interconnectedness of responsible AI and security. It also touches on the future of AI customization and control.
---
The LinkedIn article, “Leading With Trust: When Responsible AI and Security Collide,” by Grammarly’s CISO Sacha Faust, argues that responsible AI isn’t just an ethical or compliance issue, but a critical security imperative.
**Key takeaways:**
* **Responsible AI as a Product Principle:** Organizations should integrate responsible AI into product design, asking questions about values alignment, employee enablement, and proactive risk identification.
* **Secure the AI Supply Chain:** Organizations must trace AI model origins, evaluate vendors, and control key components (moderation, data governance, deployment) to mitigate risks.
* **Blur the Lines:** Responsible AI and AI security are intertwined – security ensures systems *work* as intended, while responsible AI ensures they *should* behave a certain way.
* **Certification & Transparency:** Frameworks like ISO/IEC 42001:2023 can signal commitment to AI governance and build trust.
* **Future Focus: Customization vs. Control:** Leaders need to address policies and safeguards for increasingly customized and autonomous AI systems, balancing freedom with oversight.
---
The LinkedIn article, “Leading With Trust: When Responsible AI and Security Collide,” by Grammarly’s CISO Sacha Faust, argues that responsible AI isn’t just an ethical or compliance issue, but a critical security imperative.
**Key takeaways:**
* **Responsible AI as a Product Principle:** Organizations should integrate responsible AI into product design, asking questions about values alignment, employee enablement, and proactive risk identification.
* **Secure the AI Supply Chain:** Organizations must trace AI model origins, evaluate vendors, and control key components (moderation, data governance, deployment) to mitigate risks.
* **Blur the Lines:** Responsible AI and AI security are intertwined – security ensures systems *work* as intended, while responsible AI ensures they *should* behave a certain way.
* **Certification & Transparency:** Frameworks like ISO/IEC 42001:2023 can signal commitment to AI governance and build trust.
* **Future Focus: Customization vs. Control:** Leaders need to address policies and safeguards for increasingly customized and autonomous AI systems, balancing freedom with oversight.
A user with a Russian IP address attempted to log into NLRB systems shortly after DOGE gained access, raising concerns about potential foreign intelligence operations. A whistleblower alleges DOGE exfiltrated data and disabled security monitoring, and received threats after raising concerns internally.
