DeepIQ

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

Ingest

Structured Databases

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

Ingest

Streaming Sources

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.

Ingest

Geospatial Data

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.

Ingest

Edge Connectors

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

Relational Ingestion Edge Connector

Capture Slow Changing Dimensions

Schedule periodic updates from your relational sources to your data platform, capturing only the most recent changes

Scale

Ingest large volumes of data by multithreading data reads from your sources

Transform

Execute sophisticated SQL queries on the edge to extract only data of interest

Extend

Change your connection properties to handle different types of relational sources

WITSML Connector

Scale

Multithread data pulls from your WITSML server to handle large number of data objects

Configure

Tune your query parameters and frequency for each data object based on your data needs

Support

Handle all types of WITSML objects including logs and randomly growing objects

Connect

Use with any WITSML provider including Pason, Totco and Kongsberg

Timeseries Ingestion Edge Connector

Support

Pull data from your edge sources across multiple OT protocols including MQTT, OPC UA and OPC HDA

Browse

Browse the address space of compliant servers and subscribe to topics/namespaces of interest

Update

Pull historic data when available based on your date range of interest

Real-time

Support real time applications with capability to stream hundreds of thousands of tags per second

OSI PI Edge Connector

Subscribe

Get near real-time feeds from PI server after subscribing to tags of interest using PI Asset Framework (AF)

Pull

Get historic data at scale including interpolated and recorded values

Structure

Get continuous updates about changes to the underlying AF structure

Parallelize

Parallelize your requests to PI server to maximize throughput