Native integrations for orchestration, monitoring, cataloging, and AI services.
Rising data platform costs are among the biggest challenges enterprises face today. Yeedu provides a transformative solution that drastically reduces expenses and eliminates this critical barrier, empowering organizations to focus on driving innovation and achieving their goals.
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Traditional data platforms charge based on cloud resource consumption, such as the number of cores and hours used. The more you consume, the more they profit - leaving the problem of high cloud costs unaddressed. Using Yeedu turns their inertia into your benefit.
We are thrilled to explore the vast possibilities that Yeedu introduces to our enterprise. With its cloud-agnostic compute management plane, Yeedu enables us to scale our computational tasks to the required dimensions efficiently. This capability allows us to optimize our cloud compute costs effectively compared to other available tools in the market, while consistently maintaining high performance standards.
Based on what we see in production we anticipate up to 65% annual cost savings with Yeedu.
Yeedu is the cost optimization engine data platforms have been missing.
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Yeedu offers a tiered licensing model, with monthly fees starting at $2,000. Pricing increases with higher usage tiers, ensuring scalability and cost efficiency.
Yeedu supports open-source Apache Spark, and your PySpark and Scala jobs can be migrated “As-Is” to Yeedu. It also supports Python 3+.
No, Yeedu supports open-source Apache Spark. Jobs can be written in PySpark or Scala and migrated to other platforms as needed, ensuring flexibility.
Yeedu has helped enterprises cut costs by an average of 60%. To evaluate potential savings, you can start by onboarding a sample workload.
Yes, Yeedu can run any Python job written in Python 3+, including those using popular modules like pandas.
Yeedu is a data platform that creates and runs data processing workloads like Databricks and Cloudera. However, Yeedu works well with Databricks and Cloudera's governance setup, so rather than a full workload migration, customers can start migrating high-cost workloads to Yeedu to maximize their cost savings. Over time, more workloads can be migrated as customers see value.
Yeedu supports Python, Scala, and Java jobs and provides optimal performance for Spark workloads. It is designed to enhance Spark application efficiency and cost savings.
No, there are no minimum thresholds. Yeedu delivers noticeable cost savings when a mix of low, medium, and heavy workloads are run on the platform.
Yeedu runs entirely within your cloud account, under your firewall. You do not need to export data outside your environment.
Existing Python, Scala, and Java jobs can be onboarded to Yeedu by uploading the code files. If your jobs are in notebook format, they can be easily migrated to Yeedu’s notebook editor and executed seamlessly.
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