Learn how to use ‘insert into snowflake’ to add data to your Snowflake tables efficiently. This guide covers essential syntax, provides clear examples, and shares practical tips to enhance data insertion. Whether single or multiple rows, structured or JSON data, you’ll gain the knowledge to perform ‘insert into’ operations with confidence.

Key Takeaways

  • The INSERT INTO command in Snowflake is critical for populating tables with data, supporting the insertion of single or multiple rows with a command syntax that includes the target table and values.

  • Snowflake accommodates JSON data through the VARIANT data type and uses the PARSE_JSON function to insert JSON formatted strings into VARIANT columns, optimizing storage and query performance.

  • Performance can be optimized in Snowflake by using multi-row inserts to reduce network latency and transaction overhead, and employing Snowpipe for efficient, automated ingestion of large volumes of data from cloud storage.

Table of Contents

Understanding the Basics of Snowflake's INSERT INTO Command

In the realm of data management in Snowflake, mastering the INSERT INTO command is as fundamental as learning the alphabet before writing a novel. The command is used to populate table rows with data, effectively updating the table by adding one or more rows. The syntax of the INSERT INTO command involves specifying a target table, defining the values to insert, which can include literal values, the DEFAULT keyword, or NULL, thereby creating a solid foundation for data insertion into a Snowflake table.

Consider the INSERT INTO command in Snowflake as a master key, unlocking a multitude of operations within your Snowflake database. Not only can it be used for inserting data into a table, but it’s also instrumental in defining the structure and order of the inserted data, making it a vital tool in any data engineer’s toolbox. Having covered the basics, we can now delve deeper into the syntax and parameters of this command.

Syntax and Parameters

Mastering the INSERT INTO command begins with comprehending its basic syntax. The command begins with specifying the target table where rows will be inserted, followed by an optional list of target columns, and the VALUES clause with the data to be inserted. This might seem straightforward, but there’s more to it than meets the eye. For instance, you can specify which columns to insert data into by including the column names in the INSERT statement. This is extremely useful for matching values to the intended columns and avoiding dependencies on the column order.

There are times when you might want to insert multiple rows into a Snowflake table. In such cases, the VALUES clause can include multiple sets of corresponding values, each set enclosed in parentheses and separated by commas. However, it’s important to note that there’s a limitation of a maximum of 16,384 rows in a single INSERT command, and the number of target columns must match the number of values in each set. Armed with this knowledge, we can now examine the intricacies of data types and column matching.

Data Types and Column Matching

Like a well-orchestrated symphony, each individual component in a Snowflake table plays a specific part. Matching values with the data types of the respective columns in the INSERT INTO statement is critical to prevent type errors and maintain data integrity. In multi-row inserts, the data type of the first row acts as a template, requiring consistent data types across all rows for successful insertions. This means that inconsistent data types across rows in multi-row inserts lead to errors, even if the column types could theoretically coerce the values to the correct type.

When it comes to semi-structured data like JSON, there are additional considerations. Native JSON types such as strings and numbers are handled efficiently in storage and query performance in VARIANT columns. However, non-native data types like dates and timestamps may be less efficient and consume more space if not stored as their equivalent relational data types.

Having covered the fundamentals, we can now shift our focus to practical applications of the INSERT INTO command.

Inserting Single and Multiple Rows in Snowflake Tables

The flexibility of Snowflake shines through when inserting data into tables. Whether you’re dealing with a single row or multiple rows, Snowflake has got you covered. The platform supports the insertion of both single and multiple rows into a table using the INSERT INTO command. To insert multiple rows into a Snowflake table, the VALUES clause is extended to include multiple sets of values, with each representing a distinct row. This allows multiple rows to be inserted by enclosing various sets of values in parentheses and separating these sets with commas within a single INSERT statement.

This ability to insert multiple rows simultaneously is a game-changer for data engineers, providing a way to optimize data insertion operations and save valuable time. Let’s examine the specifics of inserting both single and multiple rows more closely.

Inserting a Single Row

When it comes to inserting a single row into a Snowflake table, the process is straightforward. You use the INSERT INTO statement in conjunction with the VALUES clause to specify the values for the corresponding columns. The syntax for inserting a single row includes:

  • The INSERT INTO statement

  • The table name

  • Potentially the column names

  • The VALUES clause with the values to be inserted enclosed in parentheses.

For example, if you want to create table ‘customer’ with columns (cid, customer_name, mobile, city, ordername) and insert a row into it, you would use the INSERT INTO command as follows: INSERT INTO customer (cid, customer_name, mobile, city, ordername) VALUES (1, ‘Joker’, 9099090909, ‘Washington’, ‘colors’);. This command adds a new customer to the table in a single operation, showcasing the simplicity and effectiveness of the INSERT INTO command.

Inserting Multiple Rows

The power of the INSERT INTO command in Snowflake really shines when inserting multiple rows. You can extend the VALUES clause to include multiple sets of values, each enclosed in parentheses and separated by commas. However, it’s important to note that the VALUES clause has a limit of 16,384 rows in a single INSERT INTO statement. For data loads exceeding this limit, using the COPY INTO command is recommended.

You can also utilize a SELECT statement to insert multiple rows into a table, facilitating the insertion from one or more source tables by specifying the desired values. For instance, an example of multi-row insert is the statement INSERT INTO customer (cid, customer_name, mobile, city, ordername) VALUES (2, ‘Thomas Shelby’, 8797899999, ‘Chicago’, ‘Gun’), (3, ‘Arthur Shelby’, 7657575657, ‘Austin’, ‘Shotgun’), (4, ‘John Shelby’, 77868768768, ‘Chicago’, ‘Table’); which adds three distinct rows to the ‘customer’ table. This method allows you to insert rows efficiently and quickly.

This demonstrates how the INSERT INTO command provides a powerful tool for managing and manipulating data in Snowflake.

Working with JSON Data in Snowflake

In the realm of big data, JSON has emerged as a popular format for semi-structured data, and Snowflake handles it with aplomb. The VARIANT data type in Snowflake is designed to store semi-structured data formats, including JSON. Semi-structured data such as JSON can be inserted into Snowflake tables by using VARIABLE columns. To convert a string into a VARIANT type, allowing insertion of JSON data into columns that support this format, Snowflake uses the PARSE_JSON function.

This ability to handle JSON data provides a significant advantage in dealing with modern data formats, allowing for greater flexibility and efficiency in data management. Let’s further explore how Snowflake handles the insertion of JSON values and manages VARIANT columns.

Inserting JSON Values

When it comes to inserting JSON values into a JSON column in Snowflake, the process involves a few additional steps. The JSON values must be enclosed in a single quoted string within the VALUES clause. To convert a JSON-formatted string into a format suitable for Snowflake’s VARIANT data type column, the PARSE_JSON function is utilized.

For example, if you want to insert JSON data into a Snowflake table, you would use the INSERT INTO command with the PARSE_JSON function as follows: INSERT INTO json_table (json_column) VALUES (PARSE_JSON(‘{“key”:”value”}’));. This command demonstrates how Snowflake’s robust handling of JSON data provides a powerful tool for data engineers.

Handling VARIANT Columns

VARIANT columns in Snowflake are versatile, capable of storing various data formats including all the columns of data types such as:

  • JSON
  • Avro
  • ORC
  • Parquet

directly, without the need to describe the hierarchical structure of the data explicitly. The VARIANT data type in Snowflake can store a value of any other type, including OBJECT and ARRAY, up to a maximum size of 16 MB of uncompressed data.

To convert a string containing valid JSON into a VARIANT type, Snowflake uses the PARSE_JSON function. This allows the JSON object to be stored in a VARIANT column. It’s worth noting that if the input string is ‘null’, it is interpreted as a JSON null value, resulting in a VARIANT value containing null, rather than SQL NULL.

Understanding these nuanced differences is crucial when working with JSON data in Snowflake.

Leveraging the OVERWRITE Clause in Snowflake's INSERT INTO Command

The next milestone in mastering data management in Snowflake is understanding the role of the OVERWRITE clause in the INSERT INTO command. This clause is used to replace existing data in a table. When the OVERWRITE clause is used, it signals Snowflake to truncate the target table before inserting new records, effectively clearing all previous data. This feature can be especially useful in scenarios such as refreshing tables with new data while preventing duplicate entries or data conflicts.

What’s also notable is that using the OVERWRITE option does not modify existing access control privileges on the target table, ensuring that permissions remain unchanged. This combination of power and flexibility transforms the OVERWRITE clause into a potent tool in your data management toolkit.

The Role of the OVERWRITE Clause

The OVERWRITE clause serves a crucial role in managing data in Snowflake. When used with the INSERT INTO command, it replaces the existing table data with newly inserted rows. This is achieved by truncating the target table, effectively removing all existing data before inserting new values.

What’s reassuring is that using the OVERWRITE parameter doesn’t affect the access control privileges of the target table, ensuring that permissions remain intact. This makes the OVERWRITE clause a powerful tool for data management, providing control and flexibility while ensuring security.

Options and Use Cases for the OVERWRITE Clause

The OVERWRITE clause in Snowflake’s INSERT INTO command is not just an option; it’s a versatile tool with a multitude of use cases. If not specified, Snowflake does not truncate the target table before the insert operation. When insert statements with the OVERWRITE clause are processed, they are executed within the current transaction scope. This eliminates the necessity for DDL statements to commit a transaction..

Moreover, the OVERWRITE option can be combined with a SELECT statement to insert the result of the query into the target table after truncation. An example of using the OVERWRITE clause is to rebuild a table with updated data from another table, effectively replacing the old data.

Comprehending these options and use cases can assist you in effectively leveraging the power of the OVERWRITE clause.

Practical Examples of Using INSERT INTO in Snowflake

The true power of any command is realized when put into practice. In this section, we’ll investigate practical examples of employing the INSERT INTO command in Snowflake. These examples showcase the versatility of the command, its use in a variety of scenarios, and how it can be optimized for efficiency. Adjusting parameters such as ‘PARAM_QUERY_ROWSET_SIZE’ in MicroStrategy’s ODBC configuration can enhance the performance of bulk insert operations.

In-house testing has confirmed that setting ‘PARAM_QUERY_ROWSET_SIZE’ to larger values, such as 1 MB, leads to more significant performance improvements during bulk inserts. To effectively adjust this parameter for Snowflake in MicroStrategy, add the configuration under the ODBCConfig.ini file, and restart the MicroStrategy I-Server to apply the changes. Now, let’s examine some specific examples of using the INSERT INTO command.

Inserting Data into Multiple Tables

One of the practical scenarios where the INSERT INTO command shines is when inserting data into multiple tables. Snowflake supports multi-table inserts by updating multiple tables and inserting rows with values derived from a query. This is achieved by specifying multiple INTO clauses in a single INSERT statement.

Unconditional multi-table inserts can be done with the ‘INSERT ALL’ syntax, which applies each row from the subquery to every INTO clause in the INSERT statement. On the other hand, conditional multi-table inserts allow rows to be directed into different tables based on specific ‘WHEN’ conditions. This flexibility allows you to manage data across multiple tables efficiently.

Working with Subqueries and CTEs

Subqueries and Common Table Expressions (CTEs) can be used to insert data from complex or derived sources, such as joins, aggregations, or transformations, into a Snowflake table, offering a powerful and flexible way to load data. CTEs, especially recursive ones, increase modularity and simplify maintenance by allowing the creation of a temporary result set that can be easily referenced within an INSERT INTO command.

Recursive CTEs are used for representing hierarchical data effectively, processing each level of hierarchy in sequence. The result set from a recursive CTE can be directly used in the INSERT INTO command, thus facilitating structured data loading including hierarchical data.

Subqueries, on the other hand, provide a means to specify the source of the values for the target table, ensuring dynamic data insertion based on complex source queries.

Optimizing Performance and Best Practices

Having covered the intricacies of using the INSERT INTO command in Snowflake, we can now shift our focus towards performance optimization and best practices. Using multi-row inserts can lead to faster data insertion by reducing network round trips and transaction overhead in Snowflake. It is essential to follow best practices for maximizing efficiency when inserting data into Snowflake. Adhering to these practices can greatly improve overall performance..

For large volume data loads, Snowpipe is recommended for automatic ingestion from cloud storage, which is more efficient than manual INSERT commands. Let’s explore these strategies in more depth.

Multi-Row Inserts for Improved Performance

Multi-row inserts in Snowflake significantly enhance performance by minimizing network round trips and the overhead associated with transactions. Performance and security for multi-row inserts into Snowflake can be further improved through the use of prepared statements with parameterized queries.

For multi-row insert operations to execute successfully without errors, all inserted values must have consistent data types across each row. Snowflake does not impose a set limit on the number of rows that can be inserted in a single operation, allowing for the insertion of large volumes, potentially millions or billions of rows.

Utilizing Snowpipe for High Volume Data

For handling large volume data loads, Snowpipe is an excellent tool in Snowflake. It enables automatic ingestion from cloud storage, which is more efficient than manual INSERT commands. Snowpipe supports automated data loads by leveraging event notifications from cloud storage services, which inform Snowpipe of the arrival of new data files to load.

Snowpipe uses Snowflake-supplied compute resources for data loading, eliminating the need for a user-specified warehouse during the process. Data files in Snowpipe are queued in a single sequence for each pipe object, generally loaded in the order they are staged, although the exact load order cannot be guaranteed due to multiple processes pulling files from the queue.

Summary and How Integrate.io Can Help

Data management in Snowflake can be a breeze when you master the INSERT INTO command. From understanding its basic syntax to utilizing it for inserting single and multiple rows, handling JSON data, leveraging the OVERWRITE clause, and applying practical examples, this command is a powerful tool in your data engineering arsenal. Moreover, by adhering to best practices and using strategies like multi-row inserts and Snowpipe, you can optimize data insertion operations, making your data management tasks more efficient and effective.

As a data engineer, your mission is to manage and manipulate data to generate value for your organization. Mastering tools like the INSERT INTO command in Snowflake is essential in this mission. With the knowledge and insights provided in this blog post, you’re now well-equipped to harness the power of the INSERT INTO command and take your data management skills to new heights.

Ready to elevate your data management within Snowflake to new heights? Integrate.io offers a powerful, user-friendly platform designed to streamline and optimize your data workflows. From seamless integration of structured and JSON data to efficient batch processing and real-time data ingestion, Integrate.io provides the tools and support needed to make your data insertion tasks simpler and more effective. Harness the full potential of your data with Integrate.io's robust data integration capabilities, and take the first step towards mastering data management in Snowflake. Contact us today to learn how Integrate.io can transform your data strategy.

Frequently Asked Questions

How do you manually insert data into a Snowflake table?

You can manually insert data into a Snowflake table by specifying the values for each column in the same order as the table's columns. For example, you would use the "INSERT INTO" command followed by the values to be inserted.

What is the insert function in Snowflake?

The INSERT command in Snowflake is the primary method for adding data to tables. It involves specifying the target table and the values to insert, with a straightforward syntax that's particularly useful for small-scale data insertion tasks.

Can you insert into a view Snowflake?

Yes, you can insert data into a view in Snowflake using the INSERT INTO command in order to add one or more rows of data into the table. Snowflake does provide this capability (date not included).

How can I insert multiple rows into a Snowflake table?

To insert multiple rows into a Snowflake table, simply extend the VALUES clause to include multiple sets of values, each enclosed in parentheses and separated by commas. Keep in mind that this should be done within a single INSERT statement.

How does Snowflake handle JSON data?

Snowflake handles JSON data using the VARIANT data type and the PARSE_JSON function to convert JSON-formatted strings into a format compatible with Snowflake's VARIANT data type column. This allows for efficient storage and querying of JSON data.