InfluxData, creator of the leading time series platform InfluxDB, announced Edge Data Replication, a new capability for centralized business insights in widely distributed environments. Edge Data Replication enables developers to collect, store and analyze high-precision time series data in InfluxDB at the edge, while replicating all or subsets of this data into InfluxDB Cloud. With Edge Data Replication, InfluxData delivers on both platform breadth and simplicity, scaling time series data processing in a new way that embraces the precision of the edge and the power of the cloud.
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Modern applications are highly distributed across architectures and produce enormous volumes of time series data at the edge. Effectively managing the data in these applications and associated assets is a time-consuming, resource-intensive process, and developers often don’t have the capabilities or budgets to manage hundreds or thousands of data pipelines. InfluxDB Edge Data Replication changes the way developers and operators process time series data across distributed environments using a new edge database configuration that durably queues and mirrors select datasets to the cloud. The availability of Edge Data Replication marks a critical first step in InfluxData’s journey to solve key time series data integration and orchestration challenges for customers building distributed Industrial and Internet of Things (IoT) applications in energy, manufacturing, aerospace, and other high-tech industries.
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“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,” said Rick Spencer, Vice President of Products, InfluxData. “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.”
According to Gartner®, “Enterprises are now employing machine learning models and computing at the edge in order to preprocess data rather than just migrating data to the cloud. Rather than one-way archiving, early adopters are doing multidirectional synchronization of data between edge, core data center and public cloud. Two-way synchronization allows enterprises to make use of the elastic nature of the compute infrastructure found among cloud service providers. Furthermore, with the upcoming expansion of edge computing, there will be a growing need for solutions that enable data workflow among edge, storage and multiple public cloud providers.”*