- Structured repositories optimized for analytical queries on transformed and curated data.
- Advantages: Excellent for complex analytical queries, business intelligence, and reporting on aggregated IoT data.
- Use Cases: Aggregated historical data analysis, trend analysis, business intelligence dashboards.
- Examples: Snowflake, Amazon accurate cleaned numbers list from frist database Redshift, Google BigQuery, Azure Synapse Analytics.
5. Hybrid Storage Approaches
- Description: Combining multiple storage solutions to leverage their individual strengths. For example, using a TSDB for hot data and a data lake for cold storage.
- Advantages: Optimal performance for diverse access patterns, cost optimization, flexibility.
- Use Cases: Common in large-scale IoT deployments to manage data across its lifecycle.
1. Design for Scalability from Day One
Anticipate exponential data growth and design ingestion and storage architectures that can scale horizontally without significant re-architecting.
2. Implement Robust Security Measures
- Encryption: Encrypt data in transit (TLS/SSL) and at rest (disk encryption, database encryption).
- Authentication and Authorization: Implement strong authentication for devices and users, and fine-grained access controls.
- Secure Device Management: Ensure secure firmware updates and device provisioning.
- Network Security: Utilize firewalls, VPNs, and network segmentation.
3. Prioritize Data Quality and Validation
Implement data validation at the ingestion lead qualification and follow-up point to ensure accuracy and consistency. Filter out erroneous or duplicate data to reduce storage and processing overhead.
Leverage Edge Computing
Process and analyze data at the edge whenever possible to reduce latency, conserve bandwidth, and improve responsiveness for critical applications.
5. Optimize for Cost
- Data Tiering: Implement multi-tier aero leads storage (hot, warm, cold) based on data access frequency.
- Data Compression: Employ efficient compression techniques to reduce storage footprint.
- Data Lifecycle Management: Define policies for data retention, archiving, and deletion.
- Filter and Aggregate at Source: Reduce the volume of data sent to the cloud by performing initial processing on the device or at the edge.