A data analyst has set up a SQL query to run every four hours on a SQL endpoint, but the SQL endpoint is taking too long to start up with each run.
Which of the following changes can the data analyst make to reduce the start-up time for the endpoint while managing costs?
Using a Serverless SQL endpoint can reduce the start-up time for the endpoint while managing costs. Serverless SQL endpoints automatically scale resources based on the workload, ensuring efficient utilization without needing manual intervention. This approach eliminates the need to manage cluster size or scaling values directly and helps minimize start-up delays by leveraging pre-existing resources.
A data engineering team has created a Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables. The microbatches are triggered every minute.
A data analyst has created a dashboard based on this gold-level data. The project stakeholders want to see the results in the dashboard updated within one minute or less of new data becoming available within the gold-level tables.
Which of the following cautions should the data analyst share prior to setting up the dashboard to complete this task?
The dashboard cannot be refreshed that quickly. Given the requirement to see updated results within one minute or less, the data analyst should caution about the potential limitations on the refresh rate of the dashboard itself. Many dashboard tools have restrictions or lag times in how quickly they can refresh data, which might not align with the rapid updates from the gold-level tables triggered every minute.
Which of the following approaches can be used to ingest data directly from cloud-based object storage?
To directly ingest data from cloud-based object storage, you can create an external table while specifying the object storage path to LOCATION. The LOCATION parameter allows you to point to an external storage location, such as AWS S3, where the data resides. This approach integrates external data with your database system, making it accessible for query and analysis without copying the data into the database's internal storage.
A data analyst wants to create a dashboard with three main sections: Development, Testing, and Production. They want all three sections on the same dashboard, but they want to clearly designate the sections using text on the dashboard.
Which of the following tools can the data analyst use to designate the Development, Testing, and Production sections using text?
To designate sections on a dashboard using text, the most straightforward and practical method is to use direct text written into the dashboard in editing mode. This allows the data analyst to clearly label each section (Development, Testing, and Production) without requiring additional tools or configurations. Separate endpoints, queries, or color palettes do not inherently fulfill the need for text-based section designation. While Markdown-based text boxes can be useful, it is more common to use direct text input in the dashboard environment.
A data analyst needs to use the Databricks Lakehouse Platform to quickly create SQL queries and data visualizations. It is a requirement that the compute resources in the platform can be made serverless, and it is expected that data visualizations can be placed within a dashboard.
Which of the following Databricks Lakehouse Platform services/capabilities meets all of these requirements?
Databricks SQL is designed for creating SQL queries and data visualizations and supports serverless compute resources. It also provides features for building dashboards, making it the most suitable choice for the requirements stated.