Data blending allows users to get more analysis done in less time. Moreover, data blending in Tableau can empower teams to visualize said data to unlock better insights for business intelligence.
Yet the efficiency of these systems can be stymied by slowdowns in processing, whether it’s due to poor data orchestration or, oftentimes, delays in Tableau. Making Tableau run faster is typically a matter of implementing two improvements: Reducing heavy queries, and increasing data processing power.
Writing Better Queries
One advantage of data blending over SQL is that data sets can be joined without writing complex queries. When moving data to Tableau, however, heavy queries can cause the application to run slower than users want, leading to an overall delay in completing the data visualization task. One way to mitigate this is to simply write better queries, putting data in the right format to help it go faster.
Increasing Database Processing Power
On-premise data warehouses have historically been an expected necessity in data storage, analysis, and visualization. The often-cumbersome process of moving data from these stores to a visualization tool like Tableau had been written into the development time.
Now, with cloud data lakes such as Snowflake and Redshift, users can take advantage of the power of distributed data to gain access to insights more efficiently than with on-premise sources. This requires migrating data to the cloud using K3 for Amazon Redshift or K3 for Snowflake to handle the ETL process and create a faster process for integrating data into Tableau.
Data Orchestration with K3
Moving your data to a cloud storage environment like Snowflake or Redshift can make Tableau faster, but you need the right tools to do it. K3 ETL is the best tool to shift your data to the cloud to start taking advantage of faster processing times at every stage of your data orchestration journey.
Schedule a free demo below to see the advantages of data integration using K3: