September 2023 Summaries

Metaplane has introduced Data CI/CD tools for dbt Core users, providing more visibility into the impact of changes made to models and preventing potential issues before they occur. The integration includes two separate features: Impact Analysis, which shows the number of downstream tables and objects affected by a change, and Test Previews, which test proposed changes against production data on several data quality metrics. This feature is now available as part of Metaplane's dbt Core integration, allowing more teams to benefit from its capabilities.

Metaplane has released new dashboards and a menu designed to improve data observability by providing greater visibility into problems with your data at any given time. The platform integrates with various data storage systems, allowing users to monitor their data stack for errors. New features include a list of integrations, an open incidents view, offline monitors, sync issues, and a notification pane for updates. To use the new dashboard, existing Metaplane customers can log into their account, while new users need to create a free account, integrate a warehouse, add alerts, and set up monitors.

The RACI framework is used to distribute roles when managing data quality, with Responsible referring to those who own execution steps within a project, Accountable for those who own the results of the project, Consulted for those who will be pulled in as needed, and Informed for those who need to be aware of what's happening. The text provides examples of how these roles can apply to data quality management across various teams such as data engineers, analytics engineers, data analysts, business stakeholders, and business system administrators/software engineers.

Metaplane, a data quality monitoring tool, has introduced a new feature that allows users to backfill machine learning models with historical data from a .csv file. This eliminates the need for training time and enables immediate benefits of machine learning-based data quality monitors. The feature is particularly useful in scenarios such as warehouse migrations, dev vs prod environments, and monitoring child tables derived from parent tables. Users can start using this feature by logging into their Metaplane account and navigating to the desired monitor page.

Metaplane introduces a new monitoring page that consolidates all monitors across various sources for easy management and action-taking. This feature is beneficial for organizations with multiple data stores, different integration types, and diverse data quality metrics tracking needs. The Monitoring page allows users to sort and view monitors by type, search for specific monitors, add more monitors, take actions on multiple monitors at once, and see all incidents. To use this feature, a Metaplane account with an integrated data store and a few monitors is required.

Metaplane has been recognized as a G2 leader in Data Observability, ranking #1 in customer satisfaction with an average score of 4.8 out of 5 stars across 65 reviews and an NPS score of 90. The company's mission is to help data teams feel proactive, in full control, and confident in the data they use to leverage their organizations. Metaplane continually pushes boundaries by treating customers as design partners, establishing best practices that are then incorporated into the product. The company has built integrations with leading analytics and ETL tools while also focusing on programmatic aspects and an intuitive UI for broader business trust in data. Metaplane is committed to long-term alignment with its customers and making financial decisions that reflect the value they derive from the product.

Metaplane introduces its latest feature for Snowflake users, Snowflake Spend Monitoring. This tool allows businesses to monitor their daily total credit spend, a 30-day spend aggregation, and daily spend broken down by warehouse and user. The dashboard is powered by machine learning capabilities that can help detect abnormal spikes or drops in credit usage. Metaplane's Spend Analysis helps users capture upstream issues, confirm proper Snowflake configurations, set up regular optimization efforts, and improve the Total Cost of Ownership for their data stack. Apart from spend monitoring, Metaplane also offers data quality improvement features using machine learning to understand acceptable data quality metric thresholds.

Metaplane now allows users to configure the query timeout limit for their data warehouses on a source-by-source basis. This feature addresses the issue of occasional query timeouts during Metaplane's monitoring process, especially when dealing with complex data needs. Users can update the query timeout limit (in seconds) in the configuration options menu for each warehouse. This enhancement aims to make Metaplane more flexible and adaptable to various companies' unique monitoring requirements.