DataHub overview

Documentation overview

The following sections will walk you through all the functionalities of Cumulocity IoT DataHub in detail.

For your convenience, here is an overview of the contents of this document:

DataHub at a glance

The Cumulocity IoT platform allows you to manage and monitor a variety of devices. The data emitted by these devices is stored in the Operational Store of Cumulocity IoT, with older data potentially being removed (based on data retention settings). In order to run an ad-hoc query against recent device data, Cumulocity IoT offers a REST API.

In addition to this simple ad-hoc querying, various use cases require more sophisticated analytical querying over the device data, potentially covering long periods of time. Cumulocity IoT DataHub is the tool designed for this purpose.

With Cumulocity IoT DataHub, you can connect existing tools and applications to Cumulocity IoT, such as:

• Business Intelligence/reporting tools (using ODBC, JDBC)

• Machine learning applications (mainly written in Python using ODBC)

• Arbitrary custom applications (using JDBC for Java applications, ODBC for .NET, Python, node.js, and others, or REST for Web applications)

• Other Software AG products like TrendMiner or Cumulocity IoT Machine Learning Workbench

The main features of the Cumulocity IoT DataHub application are:

• It allows you to use scalable and inexpensive storage by providing an easy-to-use data pipeline that extracts data from the Operational Store of Cumulocity IoT to a data lake for long-term archival and efficient analytical querying.
• It offers an SQL-based Query Interface for querying the data lake and enables you to connect arbitrary applications that support ODBC, JDBC, or REST protocols.

The following diagram illustrates the high-level concepts.

The central component of Cumulocity IoT DataHub is Dremio, a distributed SQL engine that is used for the two purposes mentioned above. It offers an SQL API which can be accessed via JDBC, ODBC, and REST. Dremio is in charge of Extract-Transform-Load (ETL) pipelines that:

• Periodically extract data from the Operational Store of Cumulocity IoT.
• Transform the data into a relational format.
• Persist the data as Apache Parquet files in the configured data lake.

When a user submits an SQL query, the query runs against data in the data lake. Thus, the Operational Store of Cumulocity IoT is not accessed during query processing; the Operational Store is only accessed by the regular ETL process which extracts the data. DataHub manages those ETL processes and ensures their execution in a periodic manner.

The table below summarizes the main terms used throughout this documentation.

Info: Google Cloud Storage (GCS) is currently not supported.

Design of offloading pipelines

Offloading refers to moving data from the Operational Store of Cumulocity IoT to a data lake in order to:

• Provide the data in a tabular/relational and condensed format which can be leveraged for efficient SQL-based querying.
• Build a low-cost long-term archive of data.
• Separate analytical workloads from operational workloads.

The starting point is one of the base Cumulocity IoT collections, such as the measurements collection, that is to be offloaded into the data lake. Once an offloading pipeline for this collection has been configured and started, a couple of actions take place.

Info: DataHub only supports offloading for the following base Cumulocity IoT collections: alarms, events, inventory, measurements. Offloading of other collections is currently not supported.

When an offloading job runs, the contents of the collection are offloaded. The document-based entities of the Operational Store of Cumulocity IoT are transformed into a relational format by flattening the entries and mapping them to relational rows.

Info: The mapping automatically extracts a standard set of attributes from each entity, such as time, source, id, and type. It transforms them into columns in the data lake table. Furthermore, it automatically transforms the contents of measurement fragments into columns of the table. Non-standard fields can also be offloaded to a limited extent. Section Configuring offloading jobs provides more details and examples for this transformation.

As a result of these extraction and transformation steps, the flattened data is stored in Parquet files in the data lake. Apache Parquet is a column-based storage format which enables compression and efficient data fetching. These Parquet files are managed in a folder structure based on a temporal hierarchy, because analytical queries commonly have a temporal background, e.g. compute the average oil pressure of last month. In order to ensure a compact layout of the Parquet files, DataHub also regularly runs a compaction algorithm over these files in the background. As the data is stored in a time-based hierarchical manner in the data lake, DataHub can efficiently prune partitions. In addition, queries can explicitly leverage this temporal structure to increase query performance.

Important: You must not modify the data lake contents as this will corrupt your offloading pipelines and neither data consistency nor completeness can be guaranteed any more.

The scheduler of DataHub runs the offloading pipelines in a periodic manner. The UI shows for each pipeline the corresponding schedule. Within each of these pipeline executions, newly arrived data is extracted from the Cumulocity IoT collection and transformed and stored in the same way as described above. These incremental offloading tasks are designed to ensure a loss-free and duplicate-free offloading from the collection. For example, if one offloading execution fails, the next execution will automatically pick up the increments the failed one should have processed.

For each offloading pipeline, a so-called target table is created in Dremio that points to the corresponding data folders in the data lake. When you run queries against the offloaded data, Dremio uses these target tables.