Bringup-labs
Bringup-labs @Bringup_labs ·
Searching through raw ROS bags is dead. 🛑 If you want faster dev cycles, basic search won't cut it anymore. The new standard for rosbag pipelines: 🔍 Condition-based filtering 🏷️ Combine metadata, tags & attributes ⚡️ Instant data pinpointing #ROS2 #Robotics #DataOHHI
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HighByte
HighByte @HighByteInc ·
🔮 What does the future of Industrial #DataOps look like? In a live interview with@arc_advisory, HighByte Chief Product Officer John Harrington sits down with Colin Masson to share his thoughts on this evolving approach to industrial data management. 🎙️In 15 minutes, theial DataOps is transforming IT/OT convergence - Key challenges organizations face in scaling data architectures - HighByte’s approach to industrial data modeling - Use cases driving digital transformation - What’s next for autonomous AI and edge‑to‑cloud integration Watch the full interview here. �bit.ly/4bJlYRKycUs
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Michael Olakunle | Data Management & Entry Special
Michael Olakunle | Data Management & Entry Special @michael262s2x ·
Most businesses have a "Data Literacy" problem. 📉 A spreadsheet of numbers is useless if your team can’t read the story behind them. I use my background in English to translate messy data into 99.9% accurate reports. Don't just collect data. Communicate it. �� #DataOps #Excede
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Panki
Panki @Panki_96 ·
Replying to @ARDEMinc
@ARDEMinc AI extract → rules validate → exceptions → HITL verify → audit log. That closed loop is the only safe way to scale AI data capture without creating faster, more expensive errors. ARDEM gets it right. Solid approach! 🔄 #DataQuality #DataOps #HITL
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Tee🕊️
Tee🕊️ @TrishRosie ·
Data Tip: Validation is your best friend. Don't let 'messy data' happen in the first place. Set up Data Validation (dropdowns/date ranges) before your team starts entering info. It’s easier to prevent a mistake than to clean 1,000 rows later. #ExcelTips #DataOps
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Ally
Ally @AllyJenks8 ·
Fivetran renewal coming up? Good time to ask: what am I actually getting for this and what's it costing me at scale? #DataOps #ETL
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Panki
Panki @Panki_96 ·
Replying to @ChrisBergh
@ChrisBergh Love the instant anomaly visibility in the open-source monitors — volume spikes and freshness issues flagged so clearly. This is exactly the kind of practical observability teams actually use. Diving into the demo now! 🔥 #DataQuality #DataOps
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Michael Ni 倪孟堅
Michael Ni 倪孟堅 @mikeni ·
Replying to @mikeni
2/5 Old world: when temperatures drifted, teams had to manually read PDFs, cross‑reference manufacturer specs, and coordinate via email between store staff, clinical teams, and ops. It worked occasionally—but at scale, it was slow, error‑prone, and maddening. #DataOps #HealthTech
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Syncari
Syncari @syncari ·
With Syncari Synapse for Microsoft Excel, you can sync data in real time across OneDrive and SharePoint, making Excel part of your unified, governed data foundation. Clean, consistent, AI-ready data, with no manual work required. #Syncari #DataOps #MDM #AI #Excel Learn more 👇
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SahlOps
SahlOps @sahlops1 ·
Stop wasting hours on manual copy-paste. SahlOps automates web scraping and turns websites into structured, business-ready data. Better inputs. Faster execution. Cleaner outputs. #WebScraping #Automation #DataOps
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Neha
Neha @nehaa_DQ ·
Replying to @ChrisBergh
@ChrisBergh DataOps isn’t optional anymore—it’s the backbone of reliable analytics. 🚀 Strong data quality + observability = faster insights, better decisions, and scalable growth. #DataOps #DataQuality
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