# v1.1.0 Streaming analytics enhancements
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Version 1.1.0
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## Overview
Joule’s latest release offers businesses a comprehensive solution to accelerate use case development to generate value while minimising risk. The platform leverages dynamic ML models, metrics, reference data, and observability to provide real-time actions and insights.
With Joule, businesses can streamline their development efforts and make informed decisions based on data-driven insights. Joule’s intuitive development platform and user-focused design make it easy for businesses to leverage the power of data and maximise their potential.
## Features
### Predictive Processor
* JPMML model initialisation using local file and remote S3 stores
* Dynamic model refresh using model update notifications
* Offline prediction auditing that enables explainability, drift monitoring and model retraining
### Avro support
* Ability to process avro records for inbound and outbound events
* Complex data types supported using custom mapping
* Schema registry support
### Minio S3 Transport
* OOTB multi cloud S3 support
* Publish and consume events and insights to/from hybrid hosted S3 buckets
* Drive pipeline processing using S3 bucket notifications
* Consumer supports following file formats: PARQUET, CSV, ARROW, ORC
* Keep reference data up to date using external systems
### Reference Data
* Apply external data within stream processing tasks
* In-memory reference data elements kept up-to-date using source change notifications
* Support for key value and S3 stores
* Reference data file loader utility
### Rest Consumer APIs
* File consuming endpoint that enable ease of integration to upstream systems
* Joule event consumer endpoint to provide the ability to chain Joule processors within a cloud environment
### File Watcher Consumer
* File watcher that consumes and processes target files
* Supported formats; Parquet, Json, CSV, ORC and Arrow IPC
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## Enhancements
### Kafka
* Confluent schema registry support for outbound events
* Message partition support
* Confluent and RedPanda support
### Enricher processor
* Query optimisation
* SQL, OQL, and Key value enrichment support
### Transports
* Improved exception handling to fail on startup
* Strict ordering
### Apache Arrow
* Integrated and leveraged to process file efficiently and of various file formats
* Large file processing support
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## Optimisations
* Processing optimisations that reduce both memory and CPU utilisation while increasing event throughput.
* StreamEvent smart shallow cloning logic to reduce overall memory footprint while providing key data isolation
* StreamEvent change tracking switch to reduce memory overhead
## Upgrades
* Javalin 5.6.3
* Kafka 3.6.0
* Avro 1.11.3
* DuckDB 0.9.2
## Bug Fixes
StreamEventCSVDeserializer
* Fixed fields from holding only string values to correctly defined data types
* Allowed for custom date format to be provided
StreamEventJSONDeserializer
* Can now read an array of Json StreamEvent objects
### JVM Configuration Additions
* Require ‘--add-opens=java.base/java.nio=ALL-UNNAMED’ to be added to the java CLI due to Apache Arrow requirements
* Applying the G1 GC regionalized and generational garbage collector to improved memory usage
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