Unlock the potential of your Databricks environment with Zipher, a zero-touch solution for real-time workloads optimization. Zipher simplifies the complex process of managing Databricks clusters, ensuring your clusters are always running at peak efficiency with the lowest costs.

How does Zipher transform Databricks cluster management?

At the core of Zipher is our proprietary ML-based model that continuously learns and profiles your workloads. We leverage this model to enable efficient resource allocation by calibrating different aspects of your Databricks environment:

Zipher applies a dynamic approach that sets the optimal configuration per cluster, for every run of the job. This includes selecting the optimal settings in real-time, including worker & driver instance types, spark configurations and availability zones, while considering spot instance availability and termination probabilities.
In runtime, Zipher automatically scales the resources using the only spark-aware scaler that utilizes ML to actively learn and profile your workloads. This approach adjusts cluster resources in real-time and drives a continuous improvement that maximizes efficiency and reduces costs, Zipher tailors the orchestration and scheduling of your jobs, to ensure that all the jobs are performed in the most efficient manner and will be finalized on time to meet the SLA that you define.

What can Zipher deliver for you?

Cutting Databricks costs by 30-50%.
Enhancing performance with 20-40% fewer job failures.
Maintaining SLA-compliant performance, ensuring consistent and reliable outcomes.
Zero-touch solution. Enabling your data teams to focus on the business needs and strategic tasks.
Easy setup process. Zipher integrates seamlessly into your workflow, without any code changes.

Zipher - Automated Databricks OptimizationZipher

Zipher - Automated Databricks Optimization

Zipher

Zipher - Automated Databricks Optimization

Zipher

Zipher - Automated Databricks Optimization
Zipher