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Thenational network of Delta Dental companies protects more smiles than any other insurance company. Have a question about coverage or looking for dental insurance? Connect with your Delta Dental to learn more.

We give our nearly 1.8 million members the power to use and manage their dental benefits easily. Through innovative plans, a large dentist network, and expert customer service, we make dental care accessible, affordable, and simple, leading to a healthier smile and life. And through our foundation and corporate social responsibility initiatives, we are active in the community, expanding access to quality dental care and giving to organizations that support our mission of improving the oral health of the communities we serve. Creating healthy Colorado smiles is what drives us.

Delta Dental of Colorado and VSP Vision Care are partnering to bring best-in-class vision benefits to complement our dental benefits. That means flexible, quality benefits you can sink your teeth into. Clear benefits. Smart savings.

Delta Dental Patient Direct is not an insurance plan. It's a dental savings plan that provides members with significant savings on certain dental procedures. No maximums, no waiting periods, no annual deductible, and no claims to file.

Options for groups of two to 99 enrolled employees that allow you to select a plan that fits the unique needs of your business. Bundle dental and vision to cover the needs of all your employees at a price that works with your budget.

If you're not familiar with our website and secure portals, visit our resource library to learn about the administration of your dental benefits plan, the navigation of the our website, and the capabilities of the our portals.

We sell several plans for Connect for Health Colorado, Colorado's health care insurance marketplace for individuals and small groups. We also sell individual plans directly at deltadentalcoversme.com. If you need assistance finding an individual plan on this platform, you can use our Help on Demand service to connect with a broker.

Delta Dental of Colorado is not just the state's leading dental benefits company; we are also a nonprofit with a mission to improve the oral health of the communities we serve. We want our members to understand that strong connection between oral and overall health to make preventive dental care a priority. The oral health resources on The Wellness Connection can help you and your family find information to help you understand and manage your oral health. We even have tools to help parents get their kids to love brushing and flossing!

Copyright document.write(new Date().getFullYear()); Delta Dental of Colorado. All Rights Reserved. Delta Dental of Colorado is a part of Delta Dental Plans Association. Through our national network of Delta Dental companies, we offer dental coverage in all 50 states, Puerto Rico and other U.S. territories

You can remove data that matches a predicate from a Delta table. For instance, in a table named people10m or a path at /tmp/delta/people-10m, to delete all rows corresponding to people with a value in the birthDate column from before 1955, you can run the following:

You can update data that matches a predicate in a Delta table. For example, in a table named people10m or a path at /tmp/delta/people-10m, to change an abbreviation in the gender column from M or F to Male or Female, you can run the following:

You can upsert data from a source table, view, or DataFrame into a target Delta table by using the MERGE SQL operation. Delta Lake supports inserts, updates and deletes in MERGE, and it supports extended syntax beyond the SQL standards to facilitate advanced use cases.

Delta Lake merge operations typically require two passes over the source data. If your source data contains nondeterministic expressions, multiple passes on the source data can produce different rows causing incorrect results. Some common examples of nondeterministic expressions include the current_date and current_timestamp functions. In Delta Lake 2.2 and above this issue is solved by automatically materializing the source data as part of the merge command, so that the source data is deterministic in multiple passes. In Delta Lake 2.1 and below if you cannot avoid using non-deterministic functions, consider saving the source data to storage, for example as a temporary Delta table. Caching the source data may not address this issue, as cache invalidation can cause the source data to be recomputed partially or completely (for example when a cluster loses some of it executors when scaling down).

You can use the WHEN NOT MATCHED BY SOURCE clause to UPDATE or DELETE records in the target table that do not have corresponding records in the source table. We recommend adding an optional conditional clause to avoid fully rewriting the target table.

whenMatched clauses can have at most one update and one delete action. The update action in merge only updates the specified columns (similar to the update operation) of the matched target row. The delete action deletes the matched row.

Each whenMatched clause can have an optional condition. If this clause condition exists, the update or delete action is executed for any matching source-target row pair only when the clause condition is true.

whenNotMatched clauses can have only the insert action. The new row is generated based on the specified column and corresponding expressions. You do not need to specify all the columns in the target table. For unspecified target columns, NULL is inserted.

Each whenNotMatched clause can have an optional condition. If the clause condition is present, a source row is inserted only if that condition is true for that row. Otherwise, the source column is ignored.

Each whenNotMatchedBySource clause can have an optional condition. If the clause condition is present, a target row is modified only if that condition is true for that row. Otherwise, the target row is left unchanged.

merge automatically validates that the schema of the data generated by insert and update expressions are compatible with the schema of the table. It uses the following rules to determine whether the merge operation is compatible:

For all actions, if the data type generated by the expressions producing the target columns are different from the corresponding columns in the target Delta table, merge tries to cast them to the types in the table.

A column in the target table is not present in the source table. The target schema is left unchanged; the values in the additional target column are either left unchanged (for UPDATE) or set to NULL (for INSERT).

In Delta 2.3 and above, columns present in the source table can be specified by name in insert or update actions. In Delta 2.2 and below, only INSERT * or UPDATE SET * actions can be used for schema evolution with merge.

The table schema is changed to (key, value, new_value). Existing records with matches are updated with the value and new_value in the source. New rows are inserted with the schema (key, value, new_value).

The table schema is changed to (key, old_value, new_value). Existing records with matches are updated with the new_value in the source leaving old_value unchanged. New records are inserted with the specified key, new_value, and NULL for the old_value.

The table schema is changed to (key, old_value, new_value). Existing records with matches are updated with the new_value in the source leaving old_value unchanged, and unmatched records have NULL entered for new_value.

The table schema is changed to (key, old_value, new_value). New records are inserted with the specified key, new_value, and NULL for the old_value. Existing records have NULL entered for new_value leaving old_value unchanged. See note (1).

Delta MERGE INTO supports resolving struct fields by name and evolving schemas for arrays of structs. With schema evolution enabled, target table schemas will evolve for arrays of structs, which also works with any nested structs inside of arrays.

In Delta 2.3 and above, struct fields present in the source table can be specified by name in insert or update commands. In Delta 2.2 and below, only INSERT * or UPDATE SET * commands can be used for schema evolution with merge.

Reduce the search space for matches: By default, the merge operation searches the entire Delta table to find matches in the source table. One way to speed up merge is to reduce the search space by adding known constraints in the match condition. For example, suppose you have a table that is partitioned by country and date and you want to use merge to update information for the last day and a specific country. Adding the condition

will make the query faster as it looks for matches only in the relevant partitions. Furthermore, it will also reduce the chances of conflicts with other concurrent operations. See Concurrency control for more details.

Compact files: If the data is stored in many small files, reading the data to search for matches can become slow. You can compact small files into larger files to improve read throughput. See Compact files for details.

Control the shuffle partitions for writes: The merge operation shuffles data multiple times to compute and write the updated data. The number of tasks used to shuffle is controlled by the Spark session configuration spark.sql.shuffle.partitions. Setting this parameter not only controls the parallelism but also determines the number of output files. Increasing the value increases parallelism but also generates a larger number of smaller data files.

A common ETL use case is to collect logs into Delta table by appending them to a table. However, often the sources can generate duplicate log records and downstream deduplication steps are needed to take care of them. With merge, you can avoid inserting the duplicate records.

The dataset containing the new logs needs to be deduplicated within itself. By the SQL semantics of merge, it matches and deduplicates the new data with the existing data in the table, but if there is duplicate data within the new dataset, it is inserted. Hence, deduplicate the new data before merging into the table.

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