Unlocking insights through time: Time travel in Fabric Warehouse (Public Preview)
As data volumes continue to grow in today’s rapidly evolving world of Artificial Intelligence, it is crucial to reflect on historical data. It empowers businesses to derive valuable insights that aid in making well-informed decisions for the future. Preserving multiple historical data versions not only incurs significant costs but also presents challenges in upholding data integrity, resulting in a notable impact on query performance. So, we are thrilled to announce the preview of ability to query the historical data through time travel at the T-SQL statement level which helps unlock the evolution of data over time.
What is time travel?
Traditionally, when data in a table is updated or modified, it’s overwritten, posing challenges for historical data analysis. This often resulted in the creation of multiple data copies, leading to increased storage expenses and reduced query efficiency. Also, specifying the same timestamp for each table within the query not only added complexity to the queries but also increased the likelihood of errors and maintenance overhead impacting both performance and scalability.
Fabric warehouse offers the capability to time travel at the T-SQL statement level, which enables querying and analyzing data as it existed at different points in time in the past by specifying the timestamp just once within the query.
How to time travel?
The Fabric warehouse retains historical versions of tables for a period of seven calendar days. This retention allows for querying the tables as they existed at any point within the retention timeframe.
Within a SELECT statement, using the OPTION clause the timestamp can be specified only once for the entire query. The results of the query will reflect the state of data AS OF the timestamp specified in the query. For complex queries that involve multiple tables such as joins, stored procedures, or views, the timestamp is applied just once for the entire query instead of specifying the same timestamp for each table within the same query. This ensures that the entire query is executed with reference to the specified timestamp, maintaining the uniformity and integrity of the data throughout the query execution.
When to time travel?
The ability to time travel is beneficial in the following scenarios.
Stable reporting: Frequent execution of extract, transform, and load (ETL) jobs is essential to keep up with the ever-changing data landscape. The ability to time travel supports this goal by ensuring data integrity while providing the flexibility to generate reports based on the query results that are returned as of a past point in time, such as the previous evening, while background processing is ongoing. ETL activities can run concurrently while the same table is queried as of a prior point-in-time.
Historical trend analysis: Time travel simplifies the analysis of historical data, helping uncover valuable trends and patterns through querying data across various past time frames all while adhering to the Fabric foundational principle of one copy of data thus minimizing storage costs. This facilitates predictive analysis by allowing experimentation with historical datasets and the training of predictive models. It enables anticipating future trends and helps to make well-informed, data-driven decisions.
Troubleshooting: Time travel provides a swift, efficient, and cost-effective troubleshooting capability by offering a simplified method for analysis and comparison through querying. This streamlines the process of quickly identifying the root cause, enabling businesses to rebound with ease.
Performance Analysis: Time travel can help analyze the performance of warehouse queries overtime. This helps identify the performance trends based on which the queries can be optimized.
Audit and compliance: Time travel streamlines auditing and compliance procedures by empowering auditors to navigate through data history. This not only helps to remain compliant with regulations but also helps enhance assurance and transparency.
Machine learning models: Time travel helps in reproducing the results of machine learning models by facilitating analysis of historical data and simulating real-world scenarios. This enhances the overall reliability of the models so that accurate data driven decisions can be made.
By utilizing the ability to time travel, businesses can unlock valuable insights from the historical data that helps enable innovation and strategic planning leading to accelerated business growth. Come embrace the power of time travel in Fabric warehouse at the T-SQL statement level and pave the way for the future where past informs about the present and helps shapes the future.
