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Instead of running three separate queries for WHERE col1 = ‘a’, WHERE col1 = ‘b’, and WHERE col1=’c’, simply start your analysis process by running a query against your source table and output all the records into a cached destination table. For example, let’s say you plan to run an analysis across three three types of records: a, b, and c. Take advantage of this in your analysis phase by first running a very wide scoped query that contains all the data you plan to analyze. Once data is read, it will be reused for about 24 hours. During this phase, you’ll want to take advantage of BigQuery’s caching.īigQuery writes all query results to a table which is either explicitly identified by the user (a destination table) or is a temporary, cached results table. Often, before we’re ready to build a dashboard, we use Tableau to do interactive analysis and “play” with the data in order to develop an understanding of it before trying to make it consumable by business users. You can then turn queries back on when you are ready to see the result. In this case, you can instruct Tableau to turn off queries while you build the view. If you are creating a dense data view, the queries might be time consuming and significantly degrade system performance.
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When building a dashboard, as you place fields on a shelf, Tableau generates the view by automatically querying the data source. With this live connection, you may also want to consider turning off automatic updates.
TABLEAU PREP BIGQUERY UPDATE
Incremental updates do not support update or delete actions to records that have already been processed – changing these requires a full reload of the extract. incrementally add data according to a periodic cycle) this should be used as a tactical, rather than long term, solution. While they can be used to collect and aggregate data over time (i.e. There is one important point to make about data extracts – they are not a replacement for data that might be in Google BigQuery, rather a complement. You should default into setting your Tableau connection against BigQuery as “Live” unless you have a specific reason to extract the data.įor an overview of what some of those reasons might be, read this excellent post by Tableau Zen Master, Jonathan Drummey where he outlines several use cases where extracts provide benefit. When connecting to BigQuery from Tableau, you will want to take advantage of BigQuery’s ability to process large datasets and only bring results across the network.