Extract
Streaming data from your operational data sources such as historians operating in an isolated network, or connecting to IT sources in your data center is as simple as a drag and drop task using DeepIQ
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Streaming data from your operational data sources such as historians operating in an isolated network, or connecting to IT sources in your data center is as simple as a drag and drop task using DeepIQ
Request a Demo
Ingest data from and write data to existing databases including legacy on-premise databases such as SAP and SQL Server or cloud native databases such as Snowflake, Azure CosmosDB, Google BigQuery, Google BigTable or AWS Aurora
Connect to streaming sources including Azure Event Hub, AWS Kinesis and Kafka, perform transformations and push data to downstream data layers including streaming and static sinks.
Connect to streaming sources including Azure Event Hub, AWS Kinesis and Kafka, perform transformations and push data to downstream data layers including streaming and static sinks.
Convert your Geospatial data sources including SHP files, GeoRasters and LAS files into analytic ready datasets. Convert disparate sources to a unified resolution and create high quality datasets with statistical algorithms.
DeepIQ Edge connectors are useful when your data source network is isolated from your cloud or on-premise digital platform. All DeepIQ Edge connectors are simple to deploy, support secure, fault tolerant data transfer and use a store and forward technology to handle intermittent loss of connectivity. With a control panel on DeepIQ Cloud server, you can orchestrate and monitor sophisticated data flows from your field networks.
Connect to your operational time
series data source including historians, PLCs and SCADA systems
Decompress, buffer and push
data from your field network to your cloud or on-premise platforms
Operate both in stream or batch
mode
Use clustered architecture for
robust and fault tolerant connectivity
Persist data to your favorite
data platform
Schedule periodic updates from your relational sources to your data platform, capturing only the most recent changes
Ingest large volumes of data by multithreading data reads from your sources
Execute sophisticated SQL queries on the edge to extract only data of interest
Change your connection properties to handle different types of relational sources
Multithread data pulls from your WITSML server to handle large number of data objects
Tune your query parameters and frequency for each data object based on your data needs
Handle all types of WITSML objects including logs and randomly growing objects
Use with any WITSML provider including Pason, Totco and Kongsberg
Pull data from your edge sources across multiple OT protocols including MQTT, OPC UA and OPC HDA
Browse the address space of compliant servers and subscribe to topics/namespaces of interest
Pull historic data when available based on your date range of interest
Support real time applications with capability to stream hundreds of thousands of tags per second
Get near real-time feeds from PI server after subscribing to tags of interest using PI Asset Framework (AF)
Get historic data at scale including interpolated and recorded values
Get continuous updates about changes to the underlying AF structure
Parallelize your requests to PI server to maximize throughput