I attached Tableau with Bigquery and was working on the Dash boards. Issue hear is Bigquery charges on the data a query picks everytime.
My table is 200GB data. When some one queries the dash board on Tableau, it runs on total query. Using any filters on the dashboard it runs again on the total table.
on 200GB data, if someone does 5 filters on different analysis, bigquery is calculating 200*5 = 1 TB (nearly). For one day on testing the analysis we were charged on a 30TB analysis. But table behind is 200GB only. Is there anyway I can restrict Tableau running on total data on Bigquery everytime there is any changes?
The extract in Tableau is indeed one valid strategy. But only when you are using a custom query. If you directly access the table it won't work as that will download 200Gb to your machine.
Other options to limit the amount of data are:
Not calling any columns that you don't need. Do this by hiding unused fields in Tableau. It will not include those fields in the query it sends to BigQuery. Otherwise it's a SELECT * and then you pay for the full 200Gb even if you don't use those fields.
Another option that we use a lot is partitioning our tables. For instance, a partition per day of data if you have a date field. Using TABLE_DATE_RANGE and TABLE_QUERY functions you can then smartly limit the amount of partitions and hence rows that Tableau will query. I usually hide the complexity of these table wildcard functions away in a view. And then I use the view in Tableau. Another option is to use a parameter in Tableau to control the TABLE_DATE_RANGE.
1) Right now I learning BQ + Tableau too. And I found that using "Extract" is must for BQ in Tableau. With this option you can also save time building dashboard. So my current pipeline is "Build query > Add it to Tableau > Make dashboard > Upload Dashboard to Tableau Online > Schedule update for Extract
2) You can send Custom Quota Request to Google and set up limits per project/per user.
3) If each of your query touching 200GB each time, consider to optimize these queries (Don't use SELECT *, use only dates you need, etc)
The best approach I found was to partition the table in BQ based on a date (day) field which has no timestamp. BQ allows you to partition a table by a day level field. The important thing here is that even though the field is day/date with no timestamp it should be a TIMESTAMP datatype in the BQ table. i.e. you will end up with a column in BQ with data looking like this:
2018-01-01 00:00:00.000 UTC
The reasons the field needs to be a TIMESTAMP datatype (even though there is no time in the data) is because when you create a viz in Tableau it will generate SQL to run against BQ and for the partitioned field to be utilised by the Tableau generated SQL it needs to be a TIMESTAMP datatype.
In Tableau, you should always filter on your partitioned field and BQ will only scan the rows within the ranges of the filter.
I tried partitioning on a DATE datatype and looked up the logs in GCP and saw that the entire table was being scanned. Changing to TIMESTAMP fixed this.
The thing about tableau and Big Query is that tableau calculates the filter values using your query ( live query ). What I have seen in my project logging is, it creates filters from your own query.
select 'Custom SQL Query'.filtered_column from ( your_actual_datasource_query ) as 'Custom SQL Query' group by 'Custom SQL Query'.filtered_column
Instead, try to create the tableau data source with incremental extracts and also try to have your query date partitioned ( Big Query only supports date partitioning) so that you can limit the data use.
Related
I have requirement, where data is indigested from the Azure IoT hub. Sample incoming data
{
"message":{
"deviceId": "abc-123",
"timestamp": "2022-05-08T00:00:00+00:00",
"kWh": 234.2
}
}
I have same column mapping in the Azure Data Explorer Table, kWh is always comes as incremental value not delta between two timestamps. Now I need to have another table which can have difference between last inserted kWh value and the current kWh.
It would be great help, if anyone have a suggestion or solution here.
I'm able to calculate the difference on the fly using the prev(). But I need to update the table while inserting the data into table.
As far as I know, there is no way to perform data manipulation on the fly and inject Azure IoT data to Azure Data explorer through JSON Mapping. However, I found a couple of approaches you can take to get the calculations you need. Both the approaches involve creation of secondary table to store the calculated data.
Approach 1
This is the closest approach I found which has on-fly data manipulation. For this to work you would need to create a function that calculates the difference of Kwh field for the latest entry. Once you have the function created, you can then bind it to the secondary(target) table using policy update and make it trigger for every new entry on your source table.
Refer the following resource, Ingest JSON records, which explains with an example of how to create a function and bind it to the target table. Here is a snapshot of the function the resource provides.
Note that you would have to create your own custom function that calculates the difference in kwh.
Approach 2
If you do not need a real time data manipulation need and your business have the leniency of a 1-minute delay, you can create a query something similar to below which calculates the temperature difference from source table (jsondata in my scenario) and writes it to target table (jsondiffdata)
.set-or-append jsondiffdata <| jsondata | serialize
| extend temperature = temperature - prev(temperature,1), humidity, timesent
Refer the following resource to get more information on how to Ingest from query. You can use Microsoft Power Automate to schedule this query trigger for every minute.
Please be cautious if you decide to go the second approach as it is uses serialization process which might prevent query parallelism in many scenarios. Please review this resource on Windows functions and identify a suitable query approach that is better optimized for your business needs.
I have a dashboard connected to a BigQuery Table, BI engine works as expected as I am using a calendar filter and my table is partitioned per date.
when I select a longer date range, BI engine stop working with this message "The table or data volume was larger than BI Engine supports at this time", that's fair.
Please notice, I am already filtering by a partition, but sometimes, I need to see the whole data
to solve that, I created a BI reservation, and I notice regardless of the size 1,2,4 GB the memory used is always 600MB? and I get the same message, I attached a screenshot here, is this by design?
Bug Report here: https://issuetracker.google.com/issues/150633500
turn out the error is not related to reservation, but to the fact that BI engine support only 500 partition, my table has more
https://cloud.google.com/bi-engine/docs/overview#limitations
the solution is instead of partition per day, I will use something like week or month
We are using Google data studio to create mobile analytic reports using google Firebase data, linking with BigQuery. We have a dataset created in the report which uses a query to pull data from BigQuery - SELECT * FROM table.events_* which returns 69.4 GB data (verified in validator). The problem is when we create report using this query in dataset, for each report we are charged for 'BigData Analysis' in Tebibytes, which is way too much. But when we calculate the pricing for query, it is not even $1 for the data that we use.
Not sure why the data is processed in tebibytes. Here are some details about the data table in BigData -
Table size: 246.69 MB
Number of columns: 57
Tried query with some filters as well, but still returning Tebibytes data for single report. Is reducing or filtering number of columns only way to restrict the data processing? what transactions comes under BigData Analysis? (other than query processing).
Your help is greatly appreciated. Thanks in advance
I am using SQL assistant and my data brings in snapshots from a huge database in the form of timestamps. Occasionally the snapshots bring in multiples per hour. The data is correct, multiple snapshots do happen from time to time within an hour, not always but it does happen.
I am bringing this into Spotfire and viewing by an hour and when more than one snapshot happens in the hour, the data shows as doubled.
I only want to display one per hour preferably the last(max) timestamp for the hour. Example; for the 7 am hour the data has a snapshot for 7:10 am and one for 7:55 am.
These are correct but I only want to display the last(max) timestamp, 7:55 am in this case. I can't figure the issue out in Spotfire so I am leaning towards a fix in SQL. How can I display only 1 for each hour?
You'd do this similarly to how you'd probably do it in SQL -- using a ranking/rownumber function.
The basic way Rank in Spotfire works is Rank(Order columns, order direction, partitioned columns, tie method)
You need to partition by the combination of Date and Hour, and then sort descending by your timestamp column.
So the code to identify the rows that you want to isolate should be something along the lines of:
Rank([TimestampColumn], "desc", Date([TimestampColumn]), Hour([TimestampColumn]), "ties.method=first")
What you do with it from here is going to depend on how you plan to use the data - for example, you can Limit Data Using Expression and set the code above = 1 which will limit your table accordingly (helpful if you don't want your users to accidentally forget to filter), or you can create a calculated column which turns it into a flag of some form like here:
If(Rank([TimestampColumn], "desc", Date([TimestampColumn]), Hour([TimestampColumn]), "ties.method=first") = 1, "Latest", "Duplicate")
Which allows your users to filter by this property. This way, they have the option to look at the extra rows.
Ultimately, though, if you want to only ever see these rows, and have no use for the earlier records, I'd probably do it in SQL, if you have that ability. This reduces the number of rows you have to load into your analytic.
Over the past few weeks, I've written a pipeline that picks up all the clickstream data that is being broadcasted from a website. The pipeline makes use of AWS in the following way: S3 > EC2 (for transforms) > Athena (scanning a clean, partitioned s3). New data comes into the pipeline every 24hour and this works great - my clickstream data is easily queriable. However, I now need to add some additional columns i.e. time spent on each page. This can be achieved by sorting by user ID, timestamp and then taking the difference between the timestamp column of row_n1 and row_n2. So my questions are:
1) How can I do this via an SQL query? I'm struggling to get it to work, but my thinking is that once I do I can trigger this query every 24hours to run on the new clickstream data that's coming into Athena.
2) Is this a reasonable way to add additional columns or new aggregate tables? for example, build a query that runs every 24hours on new data to append to a new table.
Ideally, I don't want to touch any of the source code that's been written to do the "core" ETL pipeline
for reference my table looks similar to the following (with the new column time spent on page) :
| userID | eventNum | Category| Time | ...... | timeSpentOnPage |
'103-1023' '3' 'View' '12-10-2019...' 3s
Thanks for any direction/advice that can be provided.
I'm not entirely sure what you are asking, and some example data and expected output would be helpful. For example, I don't quite understand what you mean by row_n and row_m.
I'm going to guess that you mean something like calculating the difference between the timestamps of consecutive rows. That can be achieved by a query like
SELECT
userID,
timestamp - LAG(timestamp, 1) OVER (PARTITION BY userID ORDER BY timestamp) AS timeSpentOnPage
FROM events
The LAG window function returns the value from a previous row (1 in this case means the previous row) in the window given by the window frame (in this case all rows with the same userID and sorted by timestamp). It's kind of like GROUP BY but for each row, if that makes sense.
It wouldn't quite give you the time spent on each page, some page views would look like they were very long when in fact there was just not any activity between them (say someone browsed some, went to lunch, and browsed some more – the last page view before lunch would look like it spanned the whole lunch).
There is no way to do the equivalent of UPDATE in Athena. The closest thing is doing a "CTAS" (Create Table AS) to create a new table (which with some automation can be turned into creating new partitions for existing tables).
If you provide some more information about your data I can revise this answer with other suggestions.