7 Best Practices for Optimizing Databricks
In today’s world, data is being generated at an exponential rate. To process this data, companies need robust platforms that can scale to meet their requirements. Databricks is one such platform that provides a managed spark service, allowing organizations to scale their big data processing capabilities without having to worry about the underlying infrastructure. In this blog, we will explore some of the best practices for optimizing Databricks.
Databricks vs Amazon EMR and GCP Dataproc
Databricks is different from other platforms like Amazon EMR and GCP Dataproc. It is a SaaS platform but you can run the actual engine (“compute”) on your CSP in your own VPC. Databricks manages clusters for you instead of using dynamic allocation. It also has its own scheduler, notebook solution, and data viewer, making it a one-stop solution for big data processing needs.
7 Best Practices for Databricks Optimization
Like other platforms, Databricks comes with its own Big Data optimization challenges. Follow these guidelines to prevent waste and unnecessary expenses in your Databricks platform:
- Turn off clusters that are not in use and enable auto-termination: Databricks allows you to turn off clusters that are not in use. This can save you a lot of money in terms of infrastructure costs. You can also enable auto-termination to automatically terminate clusters after a specified period of inactivity.
- Share clusters between different groups: Databricks allows you to share clusters between different groups. This means that you can allocate resources to different groups as required. This can be useful if you have teams that have different data processing requirements.
- Track costs: It is essential to keep track of your costs when using Databricks. This will help you ensure that you are not overspending on infrastructure costs. Databricks provides auditing features that allow you to monitor your usage and spend.
- Consistently audit: Regular auditing of your Databricks usage is essential. This will help you identify which teams or users are spending the most and take corrective measures as required. You can also track the usage of active DBUs (Databricks Units), which is a measure of computational resources used by Databricks.
- Enable spot instances: Databricks supports spot instances, which are unused EC2 instances that are available at a discounted price. This can help you save money on infrastructure costs.
- Use photon acceleration: Databricks supports photon acceleration, which is a feature that speeds up SQL queries using vectorized execution. This feature is only effective if you are using the Spark SQL API.
- Use Granulate: Granulate optimizes Apache Spark clusters and Java. This can help you optimize your cluster’s performance and reduce infrastructure costs.
Databricks is an excellent platform for big data processing needs. However, optimizing Databricks usage is essential to ensure that you are not overspending on infrastructure costs. By following the best practices outlined in this blog and using Granulate’s Big Data solutions, you can optimize your Databricks usage and save costs.