A data engineer needs to conduct Exploratory Analysis on data residing in a database that is within the company's custom-defined network in the cloud. The data engineer is using SQL for this task.
Which type of SQL Warehouse will enable the data engineer to process large numbers of queries quickly and cost-effectively?
A data engineering team needs to integrate two data sources into Databricks:
Clickstream events: 5,000 events per second from an Apache Kafka topic
Customer master data: Only changed records every four hours from a Snowflake database
The solution must process clickstream data with latency under 30 seconds and prevent reprocessing customer master data that has not changed.
Which ingestion approach meets these requirements?
A data engineer is writing a script that is meant to ingest new data from cloud storage. In the event of the Schema change, the ingestion should fail. It should fail until the changes downstream source can be found and verified as intended changes.
Which command will meet the requirements?
Which of the following is hosted completely in the control plane of the classic Databricks architecture?
: The Databricks web application is the user interface that allows you to create and manage workspaces, clusters, notebooks, jobs, and other resources. It is hosted completely in the control plane of the classic Databricks architecture, which includes the backend services that Databricks manages in your Databricks account. The other options are part of the compute plane, which is where your data is processed by compute resources such as clusters. The compute plane is in your own cloud account and network.Reference:Databricks architecture overview,Security and Trust Center
A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following commands could the data engineering team use to access sales in PySpark?
The data engineering team can use thespark.tablemethod to access the Delta tablesalesin PySpark. This method returns a DataFrame representation of the Delta table, which can be used for further processing or testing.Thespark.tablemethod works for any table that is registered in the Hive metastore or the Spark catalog, regardless of the file format1.Alternatively, the data engineering team can also use theDeltaTable.forPathmethod to load the Delta table from its path2.Reference:1:SparkSession | PySpark 3.2.0 documentation2:Welcome to Delta Lake's Python documentation page --- delta-spark 2.4.0 documentation
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