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To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?
This is the correct answer because it addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed. The situation is that an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added, due to new requirements from a customer-facing application. By configuring a new table with all the requisite fields and new names and using this as the source for the customer-facing application, the data engineering team can meet the new requirements without affecting other teams that rely on the existing table schema and name. By creating a view that maintains the original data schema and table name by aliasing select fields from the new table, the data engineering team can also avoid duplicating data or creating additional tables that need to be managed. Verified Reference: [Databricks Certified Data Engineer Professional], under ''Lakehouse'' section; Databricks Documentation, under ''CREATE VIEW'' section.
The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity. Each user should be able to execute workloads against their assigned clusters at any time of the day.
Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.
https://learn.microsoft.com/en-us/azure/databricks/security/auth-authz/access-control/cluster-acl
https://docs.databricks.com/en/security/auth-authz/access-control/cluster-acl.html
A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays:
Which limitation will the team face while diagnosing this problem?
When adding new fields to a Delta table's schema, these fields will not be retrospectively applied to historical records that were ingested before the schema change. Consequently, while the team can use the new metadata fields to investigate transient processing delays moving forward, they will be unable to apply this diagnostic approach to past data that lacks these fields.
:
Databricks documentation on Delta Lake schema management: https://docs.databricks.com/delta/delta-batch.html#schema-management
All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG
There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion. However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?
Partitioning the data by the topic field allows the company to apply different access control policies and retention policies for different topics. For example, the company can use theTable Access Controlfeature to grant or revoke permissions to the registration topic based on user roles or groups. The company can also use theDELETEcommand to remove records from the registration topic that are older than 14 days, while keeping the records from other topics indefinitely. Partitioning by the topic field also improves the performance of queries that filter by the topic field, as they can skip reading irrelevant partitions.Reference:
Table Access Control: https://docs.databricks.com/security/access-control/table-acls/index.html
DELETE: https://docs.databricks.com/delta/delta-update.html#delete-from-a-table
A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize and Auto-Compaction cannot be used.
Which strategy will yield the best performance without shuffling data?
For this scenario where a one-TB JSON dataset needs to be converted into Parquet format without employing Delta Lake's auto-sizing features, the goal is to avoid unnecessary data shuffles and yet ensure optimal file sizes for the output Parquet files. Here's a breakdown of why option A is most suitable:
Setting maxPartitionBytes: The spark.sql.files.maxPartitionBytes configuration controls the size of blocks that Spark reads from the data source (in this case, the JSON files) but also influences the output size of files when data is written without repartition or coalesce operations. Setting this parameter to 512 MB directly addresses the requirement to manage the output file size effectively.
Data Ingestion and Processing:
Ingesting Data: Load the JSON dataset into a DataFrame.
Applying Transformations: Perform any required narrow transformations that do not involve shuffling data (like filtering or adding new columns).
Writing to Parquet: Directly write the transformed DataFrame to Parquet files. The setting for maxPartitionBytes ensures that each part-file is approximately 512 MB, meeting the requirement for part-file size without additional steps to repartition or coalesce the data.
Performance Consideration: This approach is optimal because:
It avoids the overhead of shuffling data, which can be significant, especially with large datasets.
It directly ties the read/write operations to a configuration that matches the target output size, making it efficient in terms of both computation and I/O operations.
Alternative Options Analysis:
Option B and D: Involves repartitioning, which would trigger a shuffle of the data, contradicting the requirement to avoid shuffling for performance reasons.
Option C: Uses coalesce, which is less intensive than repartition but can still lead to uneven partition sizes and does not directly control the output file size as effectively as setting maxPartitionBytes.
Option E: Setting shuffle partitions to 512 doesn't directly control the output file size for writing to Parquet and could lead to smaller files depending on the dataset's partitioning post-transformations.
Reference
Apache Spark Configuration
Writing to Parquet Files in Spark
