DATA

Time Series Forecasting with Machine Learning and Deep Learning

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27 March 2026 21h40

Modern Time Series Forecasting with Python — Industry-ready Machine Learning and Deep Learning time series analysis with PyTorch and PANDAS: https://
amzn.to/4eP5yYt v/ @PacktDataML 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼:
Apply ML and global models to improve forecasting accuracy through

Master Statistics and Data Science with This Comprehensive Guidebook

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27 March 2026 21h37

Get this incredible 448-page guidebook "The Art of Statistics: Learning from Data" at https://
amzn.to/4ts62LL (Over 3700 4- and 5-star reviews) #DataScience #DataScientist

Python Data Cleaning Snippets and Cookbook for Messy Data

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27 March 2026 21h32

Simple Practical Data Cleaning Python Code Snippets for messy data: https://
bit.ly/3hpthSD
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Data Cleaning Cookbook to prepare your data for analysis — PANDAS, NumPy, Matplotlib, scikit-learn,…: https://
amzn.to/4c6DAWf v/ @PacktDataML —————
#DataScience #DataScientist

Data Engineering Best Practices for Cloud Architecture and Cost Optimization

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27 March 2026 21h29

Data Engineering Best Practices — Architect robust and cost-effective data solutions in the cloud era: http://
amzn.to/3BkZ6df v/ @PacktDataML 𝓚𝓮𝔂 𝓕𝓮𝓪𝓽𝓾𝓻𝓮𝓼: Architect and engineer optimized data solutions in the cloud with best practices for performance and

→ View original post on X — @fogoros

Contextual SCADA Systems Transform Industrial Intelligence

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@fogoros

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27 March 2026 21h01

Traditional SCADA tells you 'pump failed.' Contextual systems explain 'pump is off because line is staged.' Same data, actionable intelligence.

→ View original post on X — @ingliguori

Generative BI: The New Era of Business Intelligence

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@ingliguori

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27 March 2026 19h25

Generative BI: The New Era of Business Intelligence. Learn more https://
go.kenovy.com/DwLd

→ View original post on X — @databricks

LLM-Based Sensitive Data Classification and Drift Detection

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@databricks

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27 March 2026 19h06

As schemas evolve, keeping sensitive data correctly labeled gets harder. At Databricks, LogSentinel uses LLMs on Databricks to classify columns, apply hierarchical and residency-aware labels, and continuously detect drift, creating tickets for violations. On 2,258 samples, it

→ View original post on X — @databricks

Database maintenance windows cause more disruption than hardware failures

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@databricks

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27 March 2026 17h12

Planned maintenance causes more database disruption than actual hardware failures.

Most databases get patched far more often than they experience outages, but every patch means a maintenance window, severed connections, and a cold cache that tanks performance for minutes after… pic.twitter.com/l1Mgm1PoZO
— Databricks (@databricks) 27 mars 2026

Planned maintenance causes more database disruption than actual hardware failures. Most databases get patched far more often than they experience outages, but every patch means a maintenance window, severed connections, and a cold cache that tanks performance for minutes after

→ View original post on X — @langchain

Kensho Multi-Agent Data Retrieval System on LangGraph

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@langchain

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27 March 2026 16h30

See how @Kensho built a multi-agent data retrieval system on LangGraph to give S&P Global customers a single entry point into verified financial data — equity research, macro, ESG, and more. The architecture consists of a central router + specialized Data Retrieval Agents, one