In this article, we will explore three ways Qlik Talend Cloud enhances data quality for Databricks assets, helping customers maximize the value of data for their AI initiatives.
AI-driven success starts with date pregătite pentru AI — ensuring optimal models, strong trust, and maximum AI value. For AI-driven teams sourcing data from Databricks, accuracy, freshness, and trust are mission-critical. With Qlik and Databricks, organizations can rapidly build automated data pipelines for real-time data and transform data assets into trusted Data Products — ensuring AI-ready data that is accurate, reliable, and scalable. This blog explores three ways Qlik Talend Cloud enhances data quality for Databricks assets, helping customers maximize the value of data for their AI initiatives.
The Challenge: Ensuring data quality at scale for AI
Handling data quality at scale presents a substantial challenge. AI and analytics teams face increasing difficulties in maintaining data quality while managing complex, real-time, high-volume datasets. As datasets grow and AI projects evolve, transferring large volumes of data in and out of Databricks to assess data quality slows down these initiatives and raises compliance risks. Seamless, in-lake data quality checks are essential to optimizing costs, improving efficiency, and ensuring reliable AI-driven decisions.
The Solution: Scaling data quality processing with Databricks and Qlik
With Qlik Talend Cloud, data teams can efficiently create curated data products from Databricks and leverage the newly launched push-down data quality processing capability for Databricks. This means data quality is evaluated where the data resides — without unnecessary movement or added complexity.
Top 3 benefits of using Databricks + Qlik for Data Quality
Ensuring high-quality data at scale is critical for organizations looking to drive AI, machine learning, and advanced analytics.
Let’s look at the top 3 benefits:
1. High-performance data quality at scale
With Databricks and Qlik Talend Cloud, you can leverage the power of SQL to execute semantic and data quality checks efficiently within the Databricks environment, ensuring high performance across large datasets. The result of these quality checks can be seen through the Qlik Trust Score (as a percentage or a score out of 5) and individual field-level quality indicators. Additionally, pushdown data quality processing is also supported on continuously updated Delta Live Tables, enhancing data quality for real-time applications.
Data quality checks on a Databricks dataset with configurable processing modes
2. Maximizing Databricks value with Data Products
Transform Databricks assets into high-quality, domain-specific data products that drive AI, machine learning, and analytics use cases.
Data products created from Databricks assets and complete end-to-end lineage tracked
By developing domain-centric, easily consumable, reusable data products from Databricks assets, data teams can accelerate insights, enhance discoverability, and establish a foundation for reliable, high-impact data-driven decision making.
3. Enhanced data sovereignty and real-time processing
Data quality processing within Databricks eliminates unnecessary data movement outside the lakehouse, enhancing security, compliance, and sovereignty.
Conclusion
The synergy between Databricks and Qlik Talend Cloud offers a powerful solution for organizations to achieve AI-driven success. By leveraging Qlik‘s pushdown data quality processing directly within Databricks, data teams can run data quality at scale and build valuable data products — closing the gap between raw data and AI-ready insights.
Article source: https://community.qlik.com/.
For information about Qlik™, click here: qlik.com.
For specific and specialized solutions from QQinfo, click here: QQsolutions.
In order to be in touch with the latest news in the field, unique solutions explained, but also with our personal perspectives regarding the world of management, data and analytics, click here: QQblog !