When this is not the case, monitoring, alerting, and logging are important to detect and track problems and understand system behavior. Successful execution can only occur if each service in the execution chain is functioning properly. When Databricks was founded, it only supported a single public cloud.
Workloads in the lakehouse typically integrate Databricks platform services and external cloud services, for example as data sources or targets. This blog will give you some insight as to how we collect and administer real-time metrics using our Lakehouse platform, and how we leverage multiple clouds to help recover from public cloud outages. This includes using “configuration as code” to avoid configuration drift, and “infrastructure as code” to automate the provisioning of all required lakehouse and cloud services. Automation can be used to simulate different failures or recreate scenarios that have caused failures in the past.Īutomating deployments and workloads for the lakehouse helps standardize these processes, eliminate human error, improve productivity, and provide greater repeatability. A reliable disaster recovery approach regularly tests how workloads fail and validates recovery procedures. Offers auditing and governance features to streamline. Provides the perfect foundation for a cost-effective, highly scalable lakehouse architecture. This is often referred to as horizontal scaling (number of nodes) and vertical scaling (size of nodes).Īn enterprise-wide disaster recovery strategy for most applications and systems requires an assessment of priorities, capabilities, limitations, and costs. Watch a live demo and learn how Delta Lake: Solves the challenges of traditional data lakes giving you better data reliability, support for advanced analytics and lower total cost of ownership. Once the peak is over, resources can be freed up and costs reduced accordingly. Adding new resources as needed must be easy, and only actual consumption should be charged for. For an organization to handle all of these workloads, it needs a scalable storage and compute platform. However, new projects, seasonal tasks, or advanced approaches such as model training (for churn, forecasting, and maintenance) create spikes in resource requirements. Standard ETL processes, business reports, and dashboards often have predictable resource requirements in terms of memory and compute. It must be actively managed to improve the quality of the final data sets so that the data serves as reliable and trustworthy information for business users. Data quality has many dimensions, including completeness, accuracy, validity, and consistency. Ideally, of course, Databricks would like. The focus is on designing applications to recover quickly and, in the best case, automatically.ĭata quality is fundamental to deriving accurate and meaningful insights from data. Using Lakehouse Federation, Databricks can now handle the query planning for this (and cache data as needed to keep the system performant).
For both the platform and the various workloads - such as streaming jobs, batch jobs, model training, and BI queries - failures must be anticipated and resilient solutions must be developed to increase reliability. In a highly distributed environment, outages can occur.
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