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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.

References

Apache Spark Configuration

Writing to Parquet Files in Spark

This is the correct answer because skew is a common situation that causes increased duration of the overall job. Skew occurs when some partitions have more data than others, resulting in uneven distribution of work among tasks and executors. Skew can be caused by various factors, such as skewed data distribution, improper partitioning strategy, or join operations with skewed keys. Skew can lead to performance issues such as long-running tasks, wasted resources, or even task failures due to memory or disk spills. Verified References: [Databricks Certified Data Engineer Professional], under “Performance Tuning” section; Databricks Documentation, under “Skew” section.

Granting a user 'Can Read' permissions on a notebook within Databricks allows them to view the notebook's content without the ability to execute or edit it. This level of permission ensures that the new team member can review the production logic for learning or auditing purposes without the risk of altering the notebook's code or affecting production data and workflows. This approach aligns with best practices for maintaining security and integrity in production environments, where strict access controls are essential to prevent unintended modifications.References: Databricks documentation on access control and permissions for notebooks within the workspace (https://docs.databricks.com/security/access-control/workspace-acl.html ).

This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with a name, an existing cluster id, and a notebook task. However, it does not specify any schedule or trigger for the job execution. Therefore, three new jobs with the same name and configuration will be created in the workspace, but none of them will be executed until they are manually triggered or scheduled. Verified References: [Databricks Certified Data Engineer Professional], under “Monitoring & Logging” section; [Databricks Documentation], under “Jobs API - Create” section.

This is the correct answer because it is the cluster configuration that will result in maximum performance for a job with at least one wide transformation. A wide transformation is a type of transformation that requires shuffling data across partitions, such as join, groupBy, or orderBy. Shuffling can be expensive and time-consuming, especially if there are too many or too few partitions. Therefore, it is important to choose a cluster configuration that can balance thetrade-off between parallelism and network overhead. In this case, having 8 VMs with 50 GB per executor and 20 cores per executor will create 8 partitions, each with enough memory and CPU resources to handle the shuffling efficiently. Having fewer VMs with more memory and cores per executor will create fewer partitions, which will reduce parallelism and increase the size of each shuffle block. Having more VMs with less memory and cores per executor will create more partitions, which will increase parallelism but also increase the network overhead and the number of shuffle files. Verified References: [Databricks Certified Data Engineer Professional], under “Performance Tuning” section; Databricks Documentation, under “Cluster configurations” section.

Delta Lake, built on top of Parquet, enhances query performance through data skipping, which is based on the statistics collected for each file in a table. For tables with a large number of columns, Delta Lake by default collects and stores statistics only for the first 32 columns. Thesestatistics include min/max values and null counts, which are used to optimize query execution by skipping irrelevant data files. When dealing with highly nested JSON structures, understanding this behavior is crucial for schema design, especially when determining which fields should be flattened or prioritized in the table structure to leverage data skipping efficiently for performance optimization.References: Databricks documentation on Delta Lake optimization techniques, including data skipping and statistics collection (https://docs.databricks.com/delta/optimizations/index.html ).

Comprehensive and Detailed In-Depth Explanation:

TheDatabricks CLIallows users to manage clusters using command-line commands. The correct command for creating a cluster follows a specific format.

Key Components in the Command:

Command Type:databricks compute create is the correct syntax for creating a new compute resource (cluster).

Runtime Version:'14.3.x-scala2.12' specifies the Databricks runtime to use.

Workers:--num-workers 5 sets the number of worker nodes to 5.

Node Type:--node-type-id i3.xlarge defines the hardware configuration.

Cluster Name:--cluster-name DataEngineer_cluster assigns a recognizable name to the cluster.

Evaluation of Options:

Option A (databricks clusters create ...)

Incorrect:databricks clusters createis not a valid commandin the Databricks CLI v0.205.

The correct CLI command for cluster creation is databricks compute create.

Option B (databricks clusters add ...)

Incorrect:databricks clusters addis not a valid CLI command.

Option C (databricks compute add ...)

Incorrect:databricks compute addis not a valid CLI command.

Option D (databricks compute create ...)

Correct:databricks compute create is the correct command for creating a cluster.

Conclusion:

The correct command to create a cluster with five workers, an i3.xlarge node type, and Databricks runtime 14.3.x-scala2.12 is:

databricks compute create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster

Thus, the correct answer isD.

References:

Databricks CLI Documentation

Delta Lake introducedDelta Universal Format (Delta UniForm), which allowsseamless interoperability between Delta Lake and Apache Iceberg. This means a Delta table can beconverted into an Iceberg table while maintaining Delta capabilities.

Explanation of Each Option:

(A) Require the analytics team to use a tool that supports Delta table

Incorrect:While Delta Lake is widely used, requiring the team to change toolsis not a flexible or scalable solution.

(B) Enable uniform on the transactions table to 'iceberg' so that the table can be read as an Iceberg table

Incorrect:

The uniform featuremust be enabled after conversion.

You cannotdirectly enable uniformwithout firstconverting the table.

(C) Create an Iceberg copy of the transactions Delta table which can be used by the analytics team

Incorrect:

Creating a separate Iceberg copy wouldduplicate storage and increase maintenance complexity.

This is not necessary whenDelta UniForm allows direct compatibility with Iceberg.

(D) Convert the transactions Delta table to Iceberg and enable uniform so that the table can be read as a Delta table

Correct:

The best approach is toconvert the existing Delta table to Icebergusing the DatabricksDelta to Iceberg migration tools.

After conversion,enabling uniform ensures the table remains accessible in both Delta and Iceberg formats.

Conclusion:

The best practice forinteroperability between Delta and Icebergis toconvert the Delta table to Iceberg and enable uniform, ensuringcross-compatibilitywithout data duplication.Thus,Option (D) is the correct answer.

References:

Delta UniForm for Apache Iceberg - Databricks Documentation

Convert Delta to Iceberg - Databricks

Converting the data to Delta Lake may not improve query performance on free text fields with high cardinality, such as the review column. This is because Delta Lake collects statistics on the minimum and maximum values of each column, which are not very useful for filtering or skipping data on free text fields. Moreover, Delta Lake collects statistics on the first 32 columns by default, which may not include the review column ifthe table has more columns. Therefore, the junior data engineer’s suggestion is not correct. A better approach would be to use a full-text search engine, such as Elasticsearch, to index and query the review column. Alternatively, you can use natural language processing techniques, such as tokenization, stemming, and lemmatization, to preprocess the review column and create a new column with normalized terms that can be used for filtering or skipping data. References:

Optimizations: https://docs.delta.io/latest/optimizations-oss.html

Full-text search with Elasticsearch: https://docs.databricks.com/data/data-sources/elasticsearch.html

Natural language processing: https://docs.databricks.com/applications/nlp/index.html