Welcome back to our series on building a successful data ecosystem framework and comprehensive data strategy! If you are just joining us, be sure to check out the first post on data acquisition best practices. This week, the Blueprint team, continues with a discussion on near-real-time (NRT) and reverse ETL to enable teams to utilize data in the context of their roles.
The Lakehouse Optimizer
In the world of cloud services, it’s crucial to have a clear understanding of your consumption and expenses. This is where cost management and job optimization come into play. Without proper management and optimization, costs can spiral quickly. Blueprint understands the importance of cost management and job optimization and have developed the Lakehouse Optimizer, a valuable tool for your Databricks Lakehouse implementation.
The Lakehouse Optimizer delivers real-time insights into your Databricks clusters, jobs, and notebooks, giving your financial operations team complete transparency into Azure and AWS costs. This makes it easier for you to manage your expenses and optimize your data management tasks.
Managing Lakehouse Spend
Understanding the contributing factors behind escalating lakehouse costs can help financial operations teams make better decisions about how to allocate compute resources and manage costs.
1.
Processing jobs that still run but no longer serve the original need for resulting tables. Over time, data pipelines can become outdated and produce tables that are no longer useful. The continued processing of these jobs can lead to increased costs without any corresponding business value.
2.
Poorly written data pipelines that consume more compute resources than necessary to perform the task. This can happen due to inefficient code, unnecessary joins, or overly complex transformations. The Lakehouse Optimizer can help spot these issues, and Blueprint’s optimization services can refactor the pipelines to reduce the financial impact.
3.
Processing data at a frequency that is outside the demands of the business. Sometimes, businesses may believe that real-time processing is necessary when a periodic batch is sufficient. Over-processing data in real-time can be costly and unnecessary.
4 .
Running compute resources for longer than needed. This can happen when jobs are not optimized to finish quickly or when clusters are left running for longer than necessary. The Lakehouse Optimizer can identify these inefficiencies and help ensure that compute resources are used efficiently.
5.
Inefficient use of cloud storage. This can happen when data is stored in high-performance storage tiers when it’s not necessary. The Lakehouse Optimizer can help identify opportunities to move data to lower-cost storage tiers.
Understanding the reasons behind uncontrolled costs in lakehouse is crucial for financial operations teams to manage costs and allocate compute resources efficiently
Blueprint consistently helps organizations streamline their data management processes, improve performance, and minimize costs. Get in touch to learn about implementing the Lakehouse Optimizer and creating customized optimization strategies that suit your unique requirements.
