#databased

[databasedwowow]

Henrietta: Do you like being big spoon or little spoon? Akatsuki: I am not a spoon. I’m a knife.

[mediumdata]

Dear Microsoft Excel,

I am writing you today to clarify that my fucking data will always have fucking headers. What kind of fucking animal do you think I am that I am raw dogging data with no header row?

Are you fucking stupid?

Signed,

Medium Data

[bliiot-jerry]

ARMxy ARM based SBC with MongoDB Database for Energy Management Systems

Case Details

1. Solution Background

Under the construction of new power systems, energy management systems face:

Massive heterogeneous device integration (smart meters, PV inverters, etc.)

High-frequency data acquisition requirements (millisecond to second level)

Distributed edge computing node deployment demands

Multi-source data fusion analysis challenges

2. Technology Selection Rationale

1. Advantages of ARM Industrial Computers

Outstanding Energy Efficiency: Cortex-A series processors consume <15W, suitable for 7×24 operation

Environmental Adaptability: Wide-temperature design (-40℃~85℃), EMI resistance

Rich Interfaces: Supports industrial protocols like RS-485/Modbus/OPC UA

Cost Benefits: 40%-60% hardware cost reduction compared to x86 platforms

2. Core Value of MongoDB

Time Series Processing: Native time series collections enable efficient storage compression

Schema Flexibility: Dynamically adapts to device data format changes

Distributed Architecture: Supports three-tier data flow from edge to cloud

Geospatial Computing: Enables topology analysis for distributed energy stations

3. System Architecture Design

1. Overall Topology

Edge Layer → Regional Layer → Cloud Center │ │ │ Device Access Data Aggregation Intelligent Analysis │ │ │ ARM Nodes ARM Cluster x86 Cluster

2. Core Functional Modules

TierComponentTechnical ImplementationEdgeData AcquisitionIndustrial computer Modbus parsing      Local StorageMongoDB sharded clusterRegionalData CleansingAggregation pipeline preprocessing         Cache QueueChange Stream real-time streamingCloudDigital TwinSpatiotemporal data modeling       Predictive AnalyticsAggregation framework + ML integration

4. Key Implementation Considerations

1. Data Governance Strategy

Hierarchical Storage

Raw Data: 30-day retention at edge nodes

Feature Data: 1-year retention at regional centers

Aggregated Data: Permanent cloud storage

Quality Assurance

Schema Validation for format constraints

Oplog-based resume from interruption

NTP-synchronized timestamps

2. Performance Optimization

Storage Optimization

Automatic bucketing for time series collections

ZSTD compression (6:1 ratio)

TTL auto-expiration policies

Query Acceleration

Composite indexes (Device ID + Timestamp)

Pre-aggregated common metrics

Covered index optimization

3. High Availability Design

Edge Layer Disaster Recovery

3-node replica set (2 ARM + 1 arbiter)

Automatic failover (<30s)

Daily incremental snapshots

Cross-tier Synchronization

Change Stream monitoring

Idempotent write operations

Bandwidth-adaptive configuration

5. Typical Application Scenarios

1. Distributed PV Monitoring

Requirements: 2000+ string inverters, 80+ parameters/sec 15-minute power prediction

Highlights:

Edge node real-time irradiance-power conversion

60% storage savings with time series data

Geospatial analysis for O&M optimization

2. Smart Substation Management

Requirements: 500+ temperature/humidity sensors, millisecond-level alerts Equipment health assessment

Highlights:

Dynamic document storage for lifecycle data

Time series anomaly detection (3σ algorithm)

3D thermal visualization

6. Implementation Roadmap

Pilot Phase (1-2 months)

Deploy 3-node cluster at test sites

Validate data integrity & consistency

Stress testing (5,000 simulated devices)

Regional Expansion (3-6 months)

Develop edge node auto-configuration tools

Establish cross-regional data channels

Implement security hardening (SM cryptographic algorithms)

Full Deployment (6-12 months)

Build unified monitoring platform

Develop smart analytics model library

Obtain Classified Protection Level 3 certification

7. Risk Mitigation Strategies

Hardware Risks

Dual power redundancy

Hardware watchdog chips

SoC temperature monitoring

Data Risks

WiredTiger CRC validation

Journal persistence

Regular db.hashCheck()

Network Risks

VPN+TLS dual encryption

Traffic shaping policies

Network quality probes

This solution has been successfully deployed in a provincial grid company, achieving:

2 billion daily energy data transactions

Sub-200ms anomaly response

75% O&M cost reduction

Annual energy savings exceeding ¥12 million

The ARM+MongoDB architecture provides cost-effective, scalable, and real-time infrastructure support for energy industry digital transformation.

[jonathandesverneyusanews]

A Data-Based View of Legalized Cannabis Use

A Data-Based View of Legalized Cannabis Use

To cannabis watchers, 2020 was the pivot year in the long and inevitable march toward federal legalization of adult-use cannabis in the United States. Thirty-eight states currently allow medical marijuana, and 15 of these have made it legal for adults over 21 to consume cannabis freely. Most telling, in 2020 the people of South Dakota were the first to skip over “medical marijuana” and go…

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[detund]

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DU™️ #Repost @christophgruenberger ・・・ Go now follow💥💥💥 @theageofdata 💾💾💾 #databased #design 🔝🔝🔝 @detroitunderground https://www.instagram.com/p/CJhhKkNlJa7/?igshid=1o2dibdqdv0v0

[newsfind]

Data-Based Car Insurance Discounts : car insurance discounts

Data-Based Car Insurance Discounts : car insurance discounts

Ford is looking to offer consumers car insurance discounts based on the data that can come from connected cars. In order to provide car insurance discounts, Ford partnered with Nationwide to create usage-based insurance. The two companies are stating that discounts can be up to 40%, however, the exact number will differ case by case.

The car insurance discounts will be available in 39 states…

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[slimlastforskolins]

For the first time, families can get a data-based estimate at the time of admission -- ScienceDaily

For the first time, families can get a data-based estimate at the time of admission — ScienceDaily

“When is my baby going home?” is one of the first questions asked by families of infants admitted to the neonatal intensive care unit (NICU). Now clinicians have a data-based answer. Moderate to late preterm babies (born at gestational age of 32 to 36 weeks) who have no significant medical problems on admission are likely to be discharged at 36 weeks of postmenstrual age (gestational age plus age…

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