公式動画ピックアップ
AAPL
ADBE
ADSK
AIG
AMGN
AMZN
BABA
BAC
BL
BOX
C
CHGG
CLDR
COKE
COUP
CRM
CROX
DDOG
DELL
DIS
DOCU
DOMO
ESTC
F
FIVN
GILD
GRUB
GS
GSK
H
HD
HON
HPE
HSBC
IBM
INST
INTC
INTU
IRBT
JCOM
JNJ
JPM
LLY
LMT
M
MA
MCD
MDB
MGM
MMM
MSFT
MSI
NCR
NEM
NEWR
NFLX
NKE
NOW
NTNX
NVDA
NYT
OKTA
ORCL
PD
PG
PLAN
PS
RHT
RNG
SAP
SBUX
SHOP
SMAR
SPLK
SQ
TDOC
TEAM
TSLA
TWOU
TWTR
TXN
UA
UAL
UL
UTX
V
VEEV
VZ
WDAY
WFC
WK
WMT
WORK
YELP
ZEN
ZM
ZS
ZUO
公式動画＆関連する動画 [Demo: Real-Time Smart Meter Analytics with MQTT & MongoDB Atlas]
If you're building or modernizing a Meter Data Management System, this is the architecture worth understanding 🔗 https://mdb.link/FSZcUSfXr1o-smart-meters
Subscribe to MongoDB YouTube→ https://mdb.link/subscribe
By 2030, there'll be more than 3 billion smart meters in the field, each firing readings every 15 minutes. Keeping up with that volume — while solving for interoperability, data privacy, and query performance — is one of the hardest data engineering problems in the energy sector.
In this video, we walk through the architecture utilities are actually using to ingest, store, and analyze meter data in real time, using MQTT and MongoDB Atlas Time Series collections. From rolling averages and gap fill to extremes detection and tiered storage, you'll see how one platform handles the full workload without a separate warehouse or nightly batch job.
Key Takeaways
Architecture Overview: Setting up an MQTT front end with MongoDB Atlas time series back end.
Live Simulator Demo: Ingesting smart meter telemetry directly into Atlas collections.
Real-Time Anomaly Detection: Triggering event-driven alerts using MongoDB Change Streams.
Advanced Analytics Pipelines: Running rolling averages ($setWindowFields), gap filling ($densify, $fill), and extremes detection.
Geospatial Queries: Utilizing 2D sphere indexes and $geoNear for grid stability.
Storage Optimization: Comparing standard vs. time series compression ratios at fleet scale.
Chapters:
00:00:00 - Smart Meter Data Headaches & Challenges
00:02:00 - Architecture Overview: MQTT + MongoDB Atlas
00:03:48 - Live Demo: Simulator Ingest & Time Series Collections
00:04:08 - Real-Time Anomaly Detection with Change Streams
00:04:34 - Analytics Pipeline 1: Rolling Averages
00:04:58 - Analytics Pipeline 2: Gap Filling & Interpolation
00:05:30 - Analytics Pipeline 3: Extremes Detection
00:06:06 - Analytics Pipeline 4: Geospatial Grid Queries
00:06:33 - Storage Compression & Efficiency
#MongoDB #SmartMeters #EnergyTech #MQTT #MongoDBAtlas #MeterDataManagement #TimeSeriesData #UtilityTech #DataArchitecture #GridModernization
Visit Mongodb.com → https://mdb.link/MongoDB
Read the MongoDB Blog → https://mdb.link/Blog
Read the Developer Blog → https://mdb.link/developerblog
MongoDB for Developers YouTube Channel → https://www.youtube.com/@MongoDBDevelopers
164
5