InfluxData Unveils InfluxDB Edge Data Replication
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InfluxData Unveils InfluxDB Edge Data Replication

By CIOReview | Saturday, July 2, 2022

New capabilities combine the cloud's power with the IoT edge's precision to maximize time-series data processing.

FREMONT, CA: The data are accessed from another site if the system fails due to faulty hardware, virus, or another issue. Data replication allows large-scale data sharing among systems and distributes network load among multisite systems by making data available on several hosts or data centers. Edge Data Replication, a new capability for centralized business insights in globally distributed environments, was unveiled by InfluxData, the provider of the leading time-series platform InfluxDB. Edge Data Replication allows developers to gather, store, and analyze high-precision time-series data locally in InfluxDB while replicating all or parts of it to InfluxDB Cloud. 

"Data has gravity, and much of it is created far from clouds and data centers. This pulls applications out to the edge, where the data is born and produces its greatest value. Unfortunately, most databases don't acknowledge or respect these emerging hybrid edge-cloud environments. Edge Data Replication sets a new benchmark for time series platforms by combining the power of cloud with the precision of the edge – a best of both worlds solution that's key to tomorrow's distributed applications." says Rick Spencer, Vice President of Products, InfluxData.

InfluxData Edge Data Replication leverages the capabilities of InfluxDB at the edge and in the cloud to provide the industry's data replication solution, allowing users to:

Integrate edge and cloud DataOps: Connect edge and cloud workloads to automatically mirror data in real-time from InfluxDB edge sources to InfluxDB Cloud instances.

Reduce cloud ingress and egress costs: Filter and aggregate data from InfluxDB smartly at the edge before replicating it to InfluxDB Cloud. Data downsampling using Edge Data Replication results in faster processing and lower transfer costs.

Before transferring data, transform it: Dynamically add context to edge data required for cloud processing. Cloud computing may necessitate additional dimensions for querying or feature engineering.