Spark pipelines, built and run without the specialist queue
Create, schedule, watch, and tune Spark jobs from one interface, without dropping into spark-submit and a cluster console to do it.
Orchestrate operations for optimum scalability & efficiency with kubernetes integration
Build Spark pipelines with a drag-and-drop interface
See how your Spark pipelines are actually performing
Reduce costs & enhance efficiency by getting optimization recommendations pipelines.
Build Spark pipelines without writing Spark
Building a Spark pipeline usually means a specialist, a ticket, and a wait. Here it means dragging steps onto a canvas and connecting them.
Design a complex Spark pipeline on the canvas and deploy it from the same place.
Whether it is batch processing, or real time analytics on large scale data, Transformer’ intuitive design and deployment feature ensures meeting of data processing needs swiftly and efficiently.
Transformer: AI-Powered Visual Spark Pipeline Builder
Design, optimize, and deploy production Spark pipelines on a drag-and-drop canvas with automatic code generation and multi-cluster execution.
Scaling Spark with ease: Kubernetes powered efficiency
Transformer runs on Kubernetes. A pipeline that outgrows its cluster gets more pods, rather than a redesign.
Because it runs on Kubernetes, a workload that needs more capacity gets more pods, and gives them back when the job finishes.
Transformer and Kubernetes together handle terabytes and petabytes on the same pipeline definition. Volume becomes a capacity question rather than a rewrite.
Fuelling rapid Spark job development & execution
Transformer covers the whole pipeline lifecycle, from first draft through to the production run.
It enables data engineers to swiftly craft intricate Spark pipelines, eliminating the need for time-consuming manual configurations and coding.
Fast to build only helps if the output is right. Pipelines are validated before they are allowed to run.
Whether adapting to changing data sources, scaling up for increased demand, or implementing real-time analytics, Transformer is the agile companion.
360 degree visibility to ensure smooth running of data processes
You can see inside a running Spark job: the stages, the tasks, and where the time actually goes.
Transformer isn't just about monitoring; it's about ensuring the data operations run flawlessly.
Implementing SMART (SLAs, Monitoring, Actions, Rules, Traceability) framework, it provides a 360-degree view of Spark pipelines, proactively detecting and addressing potential issues before they impact data delivery, helping in maintaining data quality and reliability.
Maximize efficiency, minimize costs with intelligent recommendation engine: Intellisense
Intellisense is an intelligent companion, constantly analyzing pipeline executions to provide optimization recommendations.
Execution metrics feed back into the pipeline, so partitioning and resource allocation can be tuned against what really ran instead of what was expected.
The numbers it surfaces are the ones you would tune against: skew, spill, stage duration, and executor use.
With Intellisense, data engineering operations become smarter, more efficient, and cost-effective.
Works with the rest of the platform.
Move data in at any cadence: scheduled batch, on demand, or change data capture that streams every insert and update as it happens.
Read moreConnect any two systems without a custom project. Build the flow on a visual canvas, test it before it goes live, and run it with approvals attached.
Read moreTake a model from data preparation to production in one place. AutoML and experiment tracking, one-click deployment as a versioned API, drift monitoring after go-live.
Read moreBring a pipeline you have been putting off.
See Transformer build a Spark pipeline, run it on Kubernetes, and return optimization recommendations, live, on your stack.