);position:absolute;right:0;width:10px;top:calc(50% + 0px);transform:translateY(-50%)}}@media (min-width:1200px){.dqlabs-header nav ul>li>a{font-size:18px}}@media (min-width:992px) and (max-width:1365px){.dqlabs-header .logo img{max-height:32px}.dqlabs-header nav ul>li{font-size:14px;margin-right:25px}}@media (max-width:1199px){.dqlabs-header{padding:5px 0!important}.dqlabs-header .container{position:relative;display:flex;justify-content:space-between;align-items:center;padding:0 20px}.dqlabs-header .menu-container{width:auto!important}.dqlabs-header .logo{margin-top:2px}.dqlabs-header .logo img{max-height:32px}.dqlabs-header nav,.dqlabs-header .btns-right{display:none!important}}@media (max-width:679px){.dqlabs-header .logo img{max-height:35px}.dqlabs-mega-menu{border:unset}}.dqlabs-card{width:100%;box-shadow:0 1px 10px #74747421;border-radius:20px;background-color:var(--white)}.dqlabs-footer-links aside ul li a{color:#fff}.dqlabs-footer-links aside ul li{margin-bottom:10px;font-size:16px}.video-border{padding:25px;border-radius:25px;background-color:#f2f4f7}.video-border{padding:25px;border-radius:18px;background-color:#F5F6F9;border:1px solid #A1B0C4}div#eut-theme-wrapper{padding:123px 0 0 0}@media (max-width:480px){div#eut-theme-wrapper{padding:63px 0 0 0}}.initial-message-bubble{height:35px}body .popup{position:fixed;top:50%;left:50%;transform:translate(-50%,-50%);max-width:100%;background-color:#FFF;color:#fff;padding:50px 30px;box-shadow:0 4px 12px rgb(0 0 0/.6);flex-direction:column;z-index:1000;display:none;background-image:url(/wp-content/themes/corpus/images/popup/gartner-mq/dqlabs-named-a-visionary-bg.png);background-size:cover;border-radius:3px}body .popup-content-wrapper{display:flex;flex-direction:row;align-items:center;justify-content:center}body .popup img{width:43%;height:auto;object-fit:cover}body .popup .popup-content{width:60%}body 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in the 2025 Gartner® Magic Quadrant™ For Augmented Data Quality Solutions
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Optimize query performance, gain visibility into data consumption trends, and reduce compute costs with comprehensive usage observability.
Provides a detailed view of key query execution metrics, including total query count, and average, maximum, and minimum query execution times for each asset, offering a complete understanding of query performance.
By revealing high query execution times and inefficient queries, DQLabs empowers data teams to take necessary steps to control and optimize compute costs.
User and query volume highlight asset importance, enabling smarter prioritization of data quality management.