Databricks Databricks-Certified-Professional-Data-Engineer Exam With Confidence Using Practice Dumps

return spark.readStream.table("bronze")

return spark.readStream.load("bronze")

return spark.read.option("readChangeFeed", "true").table ("bronze")

The Tungsten encoding used by Databricks is optimized for storing string data; newly-added native support for querying JSON strings means that string types are always most efficient.

Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.

Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads.

Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.

Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.

It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.

It marks a column as small enough to store in memory on all executors, allowing a broadcast join.

It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.

It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.

It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.