Data lakes are centralized repositories that allow organizations to store vast amounts of raw and processed data in their native format. This type of storage system can handle large volumes of structured, semi-structured, and unstructured data. The HiveMQ Enterprise Data Lake Extension makes it possible to:
Forward MQTT messages directly to the data lake without the need for additional infrastructure.
Supports any Data Lake infrastructure that is S3 or Azure Blob compatible - Databricks, Snowflake, etc.
Convert MQTT messages into Parquet table rows with column mappings.
Utilize HiveMQ Data Hub to weed out bad/invalid data and ensure data integrity and quality across your IoT deployment.
Use mappings to store only the MQTT message elements needed. This helps optimize storage capacity and querying while saving unnecessary data storage costs.
HiveMQ Data Lake Extension
The Data Lake Extension writes to popular object stores easily accessible from any data warehouse
Why HiveMQ and your Data Lake?
HiveMQ’s MQTT platform is the ideal choice for exploiting the investment in your data lake by combining IoT data with other data. Unlock the power of IoT with these key features:
Data Lake Agnostic
The HiveMQ Extension can be used to connect to a variety of data lakes and can support hybrid data lake environments - AWS S3, Azure Blob, Snowflake, Databricks, ...
Real-time Analytics
Analyzing IoT data provides real-time insights into device performance, user behavior, and environmental conditions for swift decision-making.
Predictive Analytics
Utilize data lake machine learning capabilities with real-time sensor data to build predictive models for pattern identification and trend analysis.
Historical Trend Analysis
Use the data lake to store and analyze new and historical IoT data to identify trends and patterns over time.
HiveMQ Extensions
HiveMQ extensions are plugins that provide seamless integration with streaming services, databases, data warehouses, and security services. There is a Custom SDK to build tailored extensions for specific integration needs.
Scalability
Extensions usage scales along with the rest of the cluster and each enterprise extension is designed and tested for use in a cluster.
Availability
Extensions run on each cluster node, so if a node exits the cluster the extension will be present on the replacement node.
Manageability
No separate nodes to manage. Easily manage cluster-wide configuration in a Kubernetes cluster with the HiveMQ Operator.
HiveMQ and Data Lake Resources
Top recommended resources to help you unlock the power of IoT with your data lake.
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