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Data archiving and retention practices have become a necessary part of management in almost any well-run business today. Archival practices help in effective management of data and keep storage costs meticulously optimized. Retention ensures that data isn’t lost forever and is retrievable, even if it’s accidentally deleted or updated. This is especially significant in an environment like Databricks where operations are heavily reliant on the data being processed. In this blog post, we will delve into archiving and retention practices, particularly in SQL data within Databricks, and will discuss some best practices.
1. Understanding Archiving and Retention in SQL
In most organizations, SQL Server databases are growing at a fast rate, making data storage a challenge. Here, data archiving comes to the rescue; enabling organizations to move older data that is not frequently accessed, off to a safe place for long-term storage and retention.
For instance, SQL Server offers a feature called ‘Table Partitioning’ that enables smart data archiving. Here is how you set it up:
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-- Create a partition function CREATE PARTITION FUNCTION pf_name (data_type) AS RANGE LEFT FOR VALUES (boundary_value1, boundary_value2, ... boundary_valueN) -- Create a partition scheme CREATE PARTITION SCHEME ps_name AS PARTITION pf_name TO (file_group1, file_group2, ... file_groupN) |
2. Databricks and Data Retention
In Databricks, the data retention policy is set at the workspace level, thereby affecting all the data present in a particular workspace. Additionally, Databricks supports Delta Lake, which has functionalities like data versioning and time travel that aid in improving data retention.
Here is an example of a retention policy SQL code in Databricks:
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-- Set retention period for 7 days ALTER DATABASE dataBaseName SET RETENTION 7 |
3. Data Archiving and Retention Best Practices
Plan Ahead
Whether you are designing a data archiving policy or a retention plan, you need to have a clear understanding of your organization’s data needs. Do you need to keep a large amount of data readily accessible? How quick do you need data recovery to be? What are the compliance requirements? These are all questions you should know the answer to before you start planning.
Automate Archiving and Retention
Automating your data archive and retention processes cannot be overstressed. Why manually do task that you can automate? SQL operations include triggers that can automate this procedure. Below is a simple example:
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-- Create a trigger that moves data CREATE TRIGGER archive_trigger ON table_name AFTER INSERT, UPDATE AS BEGIN -- SQL statement END |
In conclusion, with the rapid increase in data storage, it’s now more important than ever to have an effective data archiving and retention policies in place. By following best practices and making efficient use of SQL and Databricks features, you can ensure that your data is well organized and well preserved.