Challenge
Over 100,000 real time data per day were being captured from the IOT devices in different locations. Keeping consistency, faster and processing those data organization wise was a challange.
Storing survey data, measuring the compensation and categorizing organizations for certifications was a heavy task.
Not every onboarding IOT was sending the same format of data. Filtering those defected data was a bit challenging there.
Over 100,000 real time data per day were being captured from the IOT devices in different locations. Keeping consistency, faster and processing those data organization wise was a challange.
Storing survey data, measuring the compensation and categorizing organizations for certifications was a heavy task.
Not every onboarding IOT was sending the same format of data. Filtering those defected data was a bit challenging there.
Solution
- Real-Time Data Capture with the Snapshot Listener in the Python SDK: This is a best fitted way to record data in real-time for this project. This eliminates the requirement for continuous polling and keeps the application current.
- Utilized indexed queries and enhance data retrieval processes by focusing queries on particular timeframes or data types, minimizing excessive data transfers. Execute query batching to prevent overwhelming our system with a multitude of small queries.
- Before entering incoming data into the database, filter and preprocess it (e.g., perform transformations, eliminate irrelevant data fields, etc.).
- To increase access speeds, make sure data is stored in partitions according to time or place.
- Implemented a data validation layer to check for common defects in the data (e.g., missing values, out-of-range values, or corrupted data). This was done by applying validation rules:
- Range checks for numerical values filtered negative water consumption values.
- Type checks for data consistency.
- Range checks for numerical values filtered negative water consumption values.
- Real-Time Data Capture with the Snapshot Listener in the Python SDK: This is a best fitted way to record data in real-time for this project. This eliminates the requirement for continuous polling and keeps the application current.
- Utilized indexed queries and enhance data retrieval processes by focusing queries on particular timeframes or data types, minimizing excessive data transfers. Execute query batching to prevent overwhelming our system with a multitude of small queries.
- Before entering incoming data into the database, filter and preprocess it (e.g., perform transformations, eliminate irrelevant data fields, etc.).
- To increase access speeds, make sure data is stored in partitions according to time or place.
- Implemented a data validation layer to check for common defects in the data (e.g., missing values, out-of-range values, or corrupted data). This was done by applying validation rules:
- Range checks for numerical values filtered negative water consumption values.
- Type checks for data consistency.
- Range checks for numerical values filtered negative water consumption values.
Results
Increased efficiency
- 40% improvement in operational processes
Cost savings
- Reduced operational costs by 30%.
Growth metrics
- Enabled clients to scale to 50%. more users without performance issues.
Time saved
- Reduced manual data entry time by 70%.
Increased efficiency
- 40% improvement in operational processes
Cost savings
- Reduced operational costs by 30%.
Growth metrics
- Enabled clients to scale to 50%. more users without performance issues.
Time saved
- Reduced manual data entry time by 70%.
