Spectro Cloud announced Palette EdgeAI to simplify how organizations deploy and manage AI workloads at scale across simple to complex edge locations, such as retail, healthcare, industrial automation, oil and gas, automotive/connected cars, and more.
StreamSets announced innovations that help companies efficiently build and continuously operate dataflows that span their data center and leading cloud platforms — AWS, Microsoft Azure and Google Cloud Platform.
New capabilities include data drift handling for cloud data stores for improved pipeline resiliency, continuous integration and delivery (CI/CD) automation that brings DevOps-style agility to dataflow pipelines, and the ability to centrally manage in-stream data protection policies for security and compliance.
These features build on StreamSets DataOps Platform’s rich catalog of cloud connectors, its cloud-native architecture for easy cross-platform deployment, and its ability to elastically scale dataflows via Kubernetes.
Features such as data drift handling and in-stream data protection are powered by StreamSets’ unique Intelligent Pipelines capability, which inspects and analyzes data in-flow, overcoming the lack of visibility common in traditional data integration and big data ingestion approaches.
A majority of StreamSets customers already use the StreamSets DataOps Platform for cloud dataflows, executing both “lift and shift” cloud migration projects that require peak throughput, and continuous real-time streaming of data.
“As our customers embark on their hybrid cloud journey, we see first-hand their struggle to orchestrate end-to-end management of data movement across a growing range of on-premises and cloud platforms,” said Arvind Prabhakar, CTO, StreamSets. “Our DataOps platform was architected as cloud-native from the start, allowing us to easily evolve with the market. Cloud drift-handling and CI/CD for dataflows are unique enhancements that help our customers on their journey from traditional to modern data integration based on DataOps.“
The expansion of data architectures into the cloud creates challenges for enterprises that still rely on traditional data integration software or single-purpose big data ingestion tools. Using these methods, pipelines take too long to build and deploy, and often rely on valuable, specialized developers. They are opaque, denying end-to-end visibility into pipeline performance to prevent failures or detect sensitive personal data in the dataflow. Finally, they are rigid, breaking whenever data drift occurs, such as when fields are added or changed or data platforms are upgraded.
With these new features, which began rolling out in late August, StreamSets DataOps Platform now offers:
- Development automation through a full-featured dataflow designer that includes “easy button” connectors for Amazon S3, Elastic MapReduce (EMR) and RedShift; Azure Data Lake Storage, HDInsight and Azure Databricks; Google DataProc and Snowflake
- Elastic scaling of cloud, multi-cloud and reverse hybrid cloud dataflows via Kubernetes
- New data drift handling, which automatically reflects updates to source schema in Amazon Athena, Azure SQL and Google BigQuery cloud data services
- A new CI/CD framework for automating frequent changes to dataflows through iterative design, test, validate and deployment steps
- New central governance of StreamSets Data Protector policies that detect and deal with sensitive data such as PII and PHI