If you are streaming data into BigQuery using the Java `insertAll` method, it should be available almost immediately after insertion. You might run into some lag when trying to access the data through queries. This is likely because of consistency delays or other factors related to how your table is set up and partitioned.
In cases where your table is partitioned by ingestion time (_PARTITIONTIME), new rows may not be visible when applying a filter for specific dates. This is particularly true for fresh data. Even though BigQuery streaming inserts have low latency, brief periods can still occur before data can be queried.
Here's what you can do:
Verify that your table is partitioned and that you are querying the correct partition.
Consider running your query without the partition filter to check whether or not the data is there.
Make sure you use the INFORMATION_SCHEMA views to check for latency associated with the streaming buffer.
Review your inserting logic to confirm that there are no errors being surfaced (your logic is alright at the moment).
If you are looking for seamless and effortless data streaming as well as integration with BigQuery, you can take a look at the Windsor.ai ETL platform.
Here are the benefits that come with Windsor:
It automates the data workflows in such a way that human error is reduced and keeps your analytic data accurate and up to date.
Windsor allows scheduled refreshes of your data. It also provides data backfill.
The best part is that you can achieve automatic integration within 5 minutes.
Also, Windsor AI allows you to construct dashboards in real time using BI tools such as Looker or Power BI.
Windsor AI provides you with data updates as needed without having to deal with the streaming headaches. It also improves your BigQuery data integration.
Reach out to me if you need help with this!