<p dir=”ltr” style=”line-height: 1.38; margin-top: 12pt; margin-bottom: 12pt;”><span style=”font-size: 11pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;”>Power BI developers often encounter challenges when handling large datasets due to limitations in memory and data model size. Pro licenses allow a maximum dataset size of 1 GB, while Premium capacities extend this but come with significant costs. Performance issues arise during data refreshes or when using complex calculations with large datasets. </span><span style=”font-size: 11pt; font-family: Arial,sans-serif; color: #1155cc; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: underline; -webkit-text-decoration-skip: none; text-decoration-skip-ink: none; vertical-align: baseline; white-space: pre-wrap;”>Power BI developer</span><span style=”font-size: 11pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;”> must optimize data models by using aggregations, reducing columns, or filtering unnecessary data. While effective, these techniques can lead to data granularity loss, impacting the depth of insights. Balancing performance with detailed reporting becomes a significant challenge for Power BI developers working with large data.</span></p>
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