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Job Processing System at Motive

Job Processing System at Motive

Introduction
Go to the profile of Padmaja Y
Padmaja Y
How We Improved the Performance of Our Reports

How We Improved the Performance of Our Reports

At Motive, we collect gigabytes of data daily from our vehicle gateways, asset gateways and camera offerings. There are a multitude of ways…
Go to the profile of Mikk Tammark
Mikk Tammark
Syncing data from PostgreSQL to Snowflake with Debezium CDC pipelines

Syncing data from PostgreSQL to Snowflake with Debezium CDC pipelines

In this blog post, we’ll talk about how the Data Platform team at Motive built the Debezium change data capture (CDC) pipelines to sync…
Go to the profile of Tianyao Zhang
Tianyao Zhang
Reducing API Latency: Our Journey Toward Improved Performance

Reducing API Latency: Our Journey Toward Improved Performance

How we at Motive optimized our system performance and reliability by reducing latency and API error rates.
Go to the profile of Gopal Gupta
Gopal Gupta
Why you should start a technical reading group

Why you should start a technical reading group

A recount of our experience with starting a technical reading group at Motive.
Go to the profile of Duy Tran
Duy Tran
Power Take-Off: Processing AUX Signals to Account for Productive Idling

Power Take-Off: Processing AUX Signals to Account for Productive Idling

How our PTO feature distinguishes vehicles’ idle time (engine on, no motion) from productive idle time (engine on to power equipment).
Go to the profile of N. Vijay Kumar
N. Vijay Kumar
How We Reduced DB Load With Our Language Agnostic Point Query Cache

How We Reduced DB Load With Our Language Agnostic Point Query Cache

We arrived at a caching solution that reduced the daily query load on our hardest hit tables from ~100M calls to ~5M calls.
Go to the profile of Gautam Roy
Gautam Roy
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