Databricks-Certified-Professional-Data-Engineer Exam Dumps : Databricks Certified Data Engineer Professional Exam

Partitioning a Delta Lake table is a strategy used to improve query performance by dividing the table into distinct segments based on the values of a specific column. This approach allows queries to scan only the relevant partitions, thereby reducing the amount of data read and enhancing performance.

Considerations for Choosing a Partition Column:

Cardinality: Columns with high cardinality (i.e., a large number of unique values) are generally poor choices for partitioning. High cardinality can lead to a large number of small partitions, which can degrade performance.

Query Patterns: The partition column should align with common query filters. If queries frequently filter data based on a particular column, partitioning by that column can be beneficial.

Partition Size: Each partition should ideally contain at least 1 GB of data. This ensures that partitions are neither too small (leading to too many partitions) nor too large (negating the benefits of partitioning).

Evaluation of Columns:

date:

Cardinality: Typically low, especially if data spans over days, months, or years.

Query Patterns: Many analytical queries filter data based on date ranges.

Partition Size: Likely to meet the 1 GB threshold per partition, depending on data volume.

user_id:

Cardinality: High, as each user has a unique ID.

Query Patterns: While some queries might filter by user_id, the high cardinality makes it unsuitable for partitioning.

Partition Size: Partitions could be too small, leading to inefficiencies.

post_id:

Cardinality: Extremely high, with each post having a unique ID.

Query Patterns: Unlikely to be used for filtering large datasets.

Partition Size: Each partition would be very small, resulting in a large number of partitions.

post_time:

Cardinality: High, especially if it includes exact timestamps.

Query Patterns: Queries might filter by time, but the high cardinality poses challenges.

Partition Size: Similar to user_id, partitions could be too small.

Conclusion:

Given the considerations, the date column is the most suitable candidate for partitioning. It has low cardinality, aligns with common query patterns, and is likely to result in appropriately sized partitions.

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 if the 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:

Full-text search with Elasticsearch:

Natural language processing:

 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 References: [Databricks Certified Data Engineer Professional], under “Lakehouse” section; Databricks Documentation, under “CREATE VIEW” section.