⚙️ Exingenieros de Snowflake crean Tower para un punto ciego en ingeniería de datos
Asistentes de IA generan código, pero ejecutarlo con fiabilidad es otro reto.
https://thenewstack.io/tower-python-data-pipelines/
#DataEngineering #DataPipeline #Python #RoxsRoss
If your pipeline today is “export → clean in Excel → upload to DB → analyze → export → format in Excel again”, v1.2.X offers an alternative: clean with SQL, analyze with stats, and use AI to automate the final mile of reporting: matasoft.hr/qtrendcontro...
#DataPipeline #AI #SQL #Statistics
What is Data Engineering? Tips, Tools, & Why It Matters
Learn how data pipelines, integration, and scalable tools transform raw data into insights for analytics, BI, and ML.
Explore more: www.hitechanalytics.com/blog/what-is...
#DataEngineering #DataPipeline #DataIntegration #ETLpipelines
Expliquer un pipeline de données ? Comparez-le à une chaîne de montage : données brutes -> transformation -> produit fini. Pour RH : un bon engineer anticipe et documente. #DataEngineering #DataPipeline #DataWorkflow #DataEngineer #IngénierieDeLaDonnée https://www.linkedin.com/posts/gabriel-chandesris_dataengineering-datapipeline-dataworkflow-activity-7434584660539719682-V61C
Expliquer un pipeline de données ? Comparez-le à une chaîne de montage : données brutes -> transformation -> produit fini. Pour RH : un bon engineer anticipe et documente. #DataEngineering #DataPipeline #DataWorkflow #DataEngineer #IngénierieDeLaDonnée https://www.linkedin.com/posts/gabriel-chandesris_dataengineering-datapipeline-dataworkflow-activity-7434584660539719682-V61C
Expliquer un pipeline de données ? Comparez-le à une chaîne de montage : données brutes -> transformation -> produit fini. Pour RH : un bon engineer anticipe et documente. #DataEngineering #DataPipeline #DataWorkflow #DataEngineer #IngénierieDeLaDonnée
www.linkedin.com/posts/gabrie...
Transform raw photons from giant telescopes into cosmic discoveries with POTPyRI - a Python pipeline that turns terabytes of sky images into calibrated, science-ready data for hunting transients and variables
https://github.com/CIERA-Transients/POTPyRI
#Astronomy #DataPipeline #Transients
Rico: Alpha-stage data pipeline for the Argus Optical Array and Evryscope, managing transient alerts and light curves from cutting-edge wide-field astronomical surveys
https://github.com/argus-hdps/rico
#TransientAstronomy #SurveyAstronomy #DataPipeline
I built one pipeline four times. The winner wasn’t the fastest tool; it was the one that failed loudly, stayed debuggable, and didn’t punish ops. #datapipeline
🔧 Esta simple brecha de infraestructura frena la productividad de la IA
Las empresas invierten mucho en IA, pero una brecha infraestructural limita su rendimi
thenewstack.io/this-simple-infrastructu...
#AIInfrastructure #DataPipeline #MLOps #RoxsRoss
Why dumping raw metrics into an Al is a disaster, and how to engineer a reliable reporting system.
open.substack.com/pub/kylepaul...
#AI #DataPipeline #Workflow #PromptEngineering
Explore the top data engineering partners in India specializing in real-time analytics solutions.
medium.com/p/699918818c...
#DataEngineering #RealTimeAnalytics #BigData #DataPipeline #CloudData #StreamingData #AI #MachineLearning #DataAnalytics #DigitalTransformation #IndianTech #EnterpriseData
What is a Data Pipeline?
A data pipeline automates the movement, transformation, and delivery of data for real-time and batch analytics. It improves data quality, speeds insights, and supports smarter business decisions.
Read more: bit.ly/4aYVxby
#DataPipeline #ETL #DataAnalytics #MachineLearning
The Role of Data Engineering in Modern Business Intelligence
www.ekascloud.com/our-blog/the...
#DataEngineering #BusinessIntelligence #DataAnalytics
#DataPipeline #BigData #ETL #DataStrategy
#ModernBI #TechInnovation #DataDrivenDecisionMaking
#DigitalTransformation #Ekascloud
For my "real estate agent for renters" app (need a name, taking suggestions, I built a 7 stage data pipeline with claude code to extract estimates sunlight hours for apt complexes. #buildinpublic #poc #sunlight #datapipeline
Building a Self-Healing Data Pipeline That Fixes Its Own Python Errors
How I built a self-healing pipeline that automatically fixes bad CSVs, schema changes, and weird delimiters.
Telegram AI Digest
#ai #datapipeline #news
Построение самовосстанавливающегося конвейера данных, который исправляет собственные ошибки Python
Как я построил самовосстанавливающийся конвейер, который автоматически исправляет плохие CSV-файлы, изменения схемы и странные разделители.
Telegram ИИ Дайджест
#ai #datapipeline #news
Your agency bills $8,000 for a "custom" Shopify pipeline. 42 hours. Then Client B needs "the same but with Facebook Ads." Another 55 hours. Client C? QuickBooks integration—your dev copies 70% of the code but bills 68 hours anyway. This is the Drainpipe Economy. Your team rebuilds the same 15 solutions, pretending each is unique. --- There are exactly 27 patterns covering 94% of "custom" requests. Your developers know this. They copy-paste internally while charging clients full price. This works until Client A talks to Client B at a conference, or your best dev leaves with all the tribal knowledge. I've seen $32,000 refunds hit overnight. DataPipe Agency Pro is that platform—white-labeled. Deploy the 27 patterns with clicks. Your branding everywhere. Charge $1,500-$3,000/month per client for "your agency's data infrastructure" while keeping 85% margins. Monday morning: New client needs analytics. Instead of weeks of development, you check boxes in your dashboard and click deploy. 12 minutes. Bill $2,250/month. --- "But what if clients find out it's not custom?" Do they care if their CRM is custom? Their accounting software? No. They care if it works. Your best clients want reliable solutions, not unique snowflakes. When you sell "our agency's platform" instead of "custom code," they trust you more. You're no longer a contractor—you're a platform provider. --- The math: 5 clients at $2,000/month = $120,000/year. Custom development costs $80,000 in dev time. Net: $40,000. With DataPipe: Same $120,000 revenue. Cost: 10 hours/month junior dev time = $1,500. Net: $118,500. That difference isn't profit. It's oxygen. It's the space to actually lead your agency instead of fighting fires. --- STOP THE BLEEDING → https://eyedolise.github.io/datapipe Your developers are already copy-pasting. Your clients are already paying for reused solutions. The only difference is whether you're getting paid for value or getting the $32,000 refund email at 2 AM.
THE DRAINPIPE ECONOMY: WHY AGENCIES LOSE $32,000 PER CLIENT
#AgencyOwner #SaaS #RecurringRevenue #DataPipeline #TechAgency
eyedolise.github.io/datapipe/
Your agency bills $8,000 for a "custom" Shopify pipeline. 42 hours. Then Client B needs "the same but with Facebook Ads." Another 55 hours. This is the Drainpipe Economy. Your team rebuilds the same 15 solutions, pretending each is unique. --- There are exactly 27 patterns covering 94% of "custom" requests. Your developers know this. They copy-paste internally while charging clients full price. This works until Client A talks to Client B at a conference, or your best dev leaves with all the tribal knowledge. I've seen $32,000 refunds hit overnight. --- One Austin agency escaped. They built their own internal platform. Same core, slight variations per client. Their margins went from 30% to 85%. Their developers stopped burning out. Their clients paid more for the "platform" than for "custom." Because "enterprise infrastructure" beats "duct tape code" every time. --- DataPipe Agency Pro is that platform—white-labeled. Deploy the 27 patterns with clicks. Your branding everywhere. Charge $1,500-$3,000/month per client for "your agency's data infrastructure" while keeping 85% margins. Monday morning: New client needs analytics. Instead of weeks of development, you check boxes in your dashboard and click deploy. 12 minutes. Bill $2,250/month. --- "But what if clients find out it's not custom?" Do they care if their CRM is custom? Their accounting software? No. They care if it works. Your best clients want reliable solutions, not unique snowflakes. When you sell "our agency's platform" instead of "custom code," they trust you more. You're no longer a contractor—you're a platform provider. --- The math: 5 clients at $2,000/month = $120,000/year. Custom development costs $80,000 in dev time. Net: $40,000. With DataPipe: Same $120,000 revenue. Cost: 10 hours/month junior dev time = $1,500. Net: $118,500. That difference isn't profit. It's oxygen. It's the space to actually lead your agency instead of fighting fires. --- STOP THE BLEEDING!
THE DRAINPIPE ECONOMY: WHY AGENCIES LOSE $32,000 PER CLIENT
#AgencyOwner #SaaS #RecurringRevenue #DataPipeline #TechAgency
eyedolise.github.io/datapipe/
Simply drag&drop Minitab's MPX file into Origin 2026 to extract all data, output, and metadata
Learn more and download a free trial at www.originlab.com/2026
#originlab #OriginPro #OriginPro2026 #Minitab #DataConnector #DataImport #ImportData #DataPipeline #ETL #DataAnalysis #DataVisualization
Prophecy accelerates data pipeline construction with quick-start AI agents #Technology #SoftwareandApps #Other #DataPipeline #AI #Automation
🚀 New Repo! SQL Server 2025’s JSON functions (JSON_ARRAYAGG, JSON_OBJECT, OPENJSON) make relational data API‑ready straight from T‑SQL.
From tables → JSON → APIs, no middleware required.
github.com/kimberly-eme...
#SQLServer2025 #JSON #DataPipeline #APIFriendly #ModernData
The choice between Python and R often depends on the data science pipeline stage. R might be preferred for deep statistical analysis, while Python shines in data preparation, model deployment, and integration into larger software systems. #DataPipeline 4/6
The latest update for #Graylog includes "How to Speed Up #IncidentResponse With Guided Remediation" and "What Is a #DataPipeline".
#monitoring #logging https://opsmtrs.com/2FicYrB
The latest update for #Fivetran includes "#Datapipeline state management: An underappreciated challenge" and "How data products bring order and governance to data management".
#Integration #DataAnalytics #ETL https://opsmtrs.com/3tEuYBf
The latest update for #Graylog includes "What Is a #DataPipeline" and "MCP Explained: Conversational #AI for Graylog".
#monitoring #logging https://opsmtrs.com/2FicYrB
rsyslog will (most probably) soon speak YAML.
Not a revolution — just joining the languages the rest of the stack already uses.
Simple stuff in YAML, complex logic still in RainerScript.
And yes, you can mix both.
Think: easy setup for containers and cloud, full power for those who like […]
AI coding transforms data engineering: How dltHub's open-source Python library helps developers create data pipelines for AI in minutes - buff.ly/2nU8zPm #python #data #datapipeline #libraries #programmers
AI Native Data Pipeline - What Do We Need?
CocoIndex is a next generation data pipeline built for AI-native workloads. It can handle unstructured, multimodal, and dynamic data and open system, at scale.
Telegram AI Digest
#ai #datapipeline #news
Нативная конвейер обработки данных для ИИ - Что нам нужно?
CocoIndex — это конвейер данных нового поколения, созданный для рабочих нагрузок, ориентированных на искусственный интеллект. Он может обрабатывать неструктурированные, мультимодальные и динами…
Telegram ИИ Дайджест
#ai #datapipeline #news
🚀 Myth-buster: rsyslog isn’t just a “legacy syslogd”.
It’s a full-blown ETL engine for modern data pipelines — ingesting from files, journals, syslog, Kafka; transforming with RainerScript, mmnormalize, GeoIP, PII redaction; and delivering to Elasticsearch […]
[Original post on mastodon.social]
Software speaks 💻💬 through data pipelines.
They’re what let your apps share info and work together behind the scenes.
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#techgurutori #techtermstuesday #datascience #datapipeline #datadriven
