Microsoft Fabric Updates Blog

Announcing the General availability of time travel and 30 days of data retention in Fabric Warehouse

In the rapidly evolving world of generative artificial intelligence, historical data plays a significant role in influencing the decision-making process and shaping organizational strategies. Data retention within data warehouse refers to the practice of preserving and managing previous iterations of the data encompassing any inserts, updates or deletes made to the warehouse for a specified period. As a quick response to the valuable customer feedback, we are thrilled to extend the data retention period to 30 calendar days from 7 calendar days and announce the General availability of Time travel at the T-SQL Statement level.

Unlocking the potential of increased retention

Extending the data retention period to 30 calendar days opens new avenues for exploration, by leveraging Fabric warehouse features – Time travel at T-SQL statement level, Time travel with table clone, and restore in place.

  1. Enhanced Analytical depth: Increase in retention helps analyze trends, patterns and discrepancies with greater precision and context. This helps make strategic decisions with a greater level of confidence based on a more holistic understanding of their data landscape.
  2. Improved Predictive Capabilities: Historical data is the backbone of predictive analytics, helping organizations to anticipate future trends and proactively address challenges. Extension of retention period enables organizations to build more enhanced predictive models, foresee market trends and help them stay ahead of the curve.
  3. Regulatory Compliance and Risk Mitigation: In highly regulated industries, extended data retention enables in maintaining compliance with regulations and facilitates establishing the audit trails. This helps to mitigate risks associated with non-compliance.
  4. Fabric warehouse recovery: Extending the data retention to 30 days ensures that in the form of restore points the entire Fabric warehouse history is preserved for that duration, while also expanding the total number of system-generated restore points from 42 to 180 available at any given time. In scenarios such as failed release or data corruption, these restore points can be utilized to restore the Fabric warehouse that corresponds to the most stable state prior to the incident. In addition, users are empowered to create any number of user-defined restore points which are retained for 30 calendar days.

In the data-centric world, data retention is pivotal in modern data management enabling organizations to utilize historical data effectively for analysis, reporting, compliance, and strategic decision-making. By the utilization of data retention, businesses can extract valuable insights from the past to drive success both now and in the future. In today’s rapidly evolving digital landscape, adaptability is crucial. Our decision to extend the data retention period from 7 to 30 calendar days underscores our commitment to agility and customer-centricity. Come embrace the data retention of 30 calendar days to obtain deeper insights and stay ahead of the curve.

Susiję tinklaraščio įrašai

Announcing the General availability of time travel and 30 days of data retention in Fabric Warehouse

lapkričio 4, 2024 – Salil Kanade

AI is transforming data warehousing, making complex analytics more accessible and efficient. With tools like Copilot for Data Warehouse and AI Skill, Microsoft Fabric offers two powerful, complementary resources that serve both data developers and business users. This blog explores how these tools differ, when to use each, and how they can work together to … Continue reading “Data Warehouse: Copilot & AI Skill”

spalio 31, 2024 – Jovan Popovic

Fabric Data Warehouse is a modern data warehouse optimized for analytical data models, primarily focused on the smaller numeric, datetime, and string types that are suitable for analytics. For the textual data, Fabric DW supports the VARCHAR type that can store up to 8KB of text, which is suitable for most of the textual values … Continue reading “Announcing public preview of VARCHAR(MAX) and VARBINARY(MAX) types in Fabric Data Warehouse”