We are using Delta lake table with 50 columns and few million rows in Azure environment. We have updated few rows over the last one month but did not maintained the value in "Updatedate" column available in table . Now we have to update the column with date on which the row is updated. Do we have any better way to capture the datetime when the row is updated from the delta table log. Does the Delta table maintains Change Data Capture(CDC) for each row.
CDF with Databricks Delta
To have the CDF feature available on a table, you must first enable the feature on said table. Below is an example of enabling CDF for the bronze table at table creation. You can also enable CDF on a table as an update to the table. In addition, you can enable CDF on a cluster for all tables created by the cluster.
For more information you can refer this link1 and link2
Related
I have an external hive table, partitioned on the date column. Each date has multiple AB test experiment’s data.
I need to create a job that drops experiments which have ended more than 6 months ago.
Since dropping data in an external hive partitioned table, drops the entire partition. In this case, data for one whole date. Is there a way to only drop part of a date?
Keep history data onto a partitioned table
Team,
I have scenario here - I have 2 tables - one is non partitioned and another one is partitioned table partition on one date field.
Have loaded the data from non-partitioned tables to a partitioned table and I have set the below parameter to load onto partition table.
write.partitionBy(“date”) \
.format(“orc”) \
.mode(“overwrite”) \
.saveAsTable(“schema.table1”)
Now both table count match which has 3 years of data. Which is as expected.
now I have refreshed only latest one year of data and try to load the partitioned table but it got loaded only with 1 year data, where as I need all 3 years data in the partitioned table.
What am I missing here.. I have to refresh only 1 year of data and load it to the partition table and keep building history.
Kindly suggest. Thanks
write.partitionBy(“date”)
.format(“orc”)
.mode(“overwrite”)
.saveAsTable(“schema.table1”)
Need to keep history with latest data refresh on each day basis.
How to rename a TABLE in Big query using StandardSQL or LegacySQL.
I'm trying with StandardSQL but it is giving following error,
RENAME TABLE dataset.old_table_name TO dataset.new_table_name;
Statement not supported: RenameStatement at [1:1]
Does it mean there is no any method(SQL QUERY) Which can rename a table?
I just want to change from non-partition table to partition-table
You can achieve this in two steps process
Step 1 - Export your table to Google Cloud Storage
Step 2 - Load file from GCS back to GBQ into new table with partitioned column
Both are free of charge
Still, have in mind some limitatins of partitioned tables - like number of partitions for example - it is 4000 per table as of today - https://cloud.google.com/bigquery/quotas#partitioned_tables
Currently it is not possible to rename table in Bigquery as explained in this document. You will have to create another table by following the steps given by Mikhail. Notice there is still some charge from table storage, but it is minimal. See this doc for detail information.
You can use the below query, it will create a new table with distinct records from old table with partition on given column.
create or replace table `dataset.new_table` PARTITION BY DATE(date_time_column) as select distinct * from `dataset.old_table`
I have an external hive table which is partitioned on load_date (DD-MM-YYYY). however the very first period lets say 01-01-2000 has all the data from 1980 till 2000. How can I further create partitions on year for the previous data while keeping the existing data (data for load date greater than 01-01-2000) still available
First load the data of '01-01-2000' into a table and create a dynamic partition table partitioned by data '01-01-2000'. This might solve your problem.
I have a scenario as described below need to create a SSIS Package for that.
I have 3 COLUMNS in source table which needs to be entered in destination table.
But all these columns has to be looked up in the look up table of destination database and then enter their ID's in the destination column.
For example
Source table has 3 columns with values
idnum static type timedimension geography modified date
1 price daydate france 8/12/2015
2 RetailpRICE WEEK ITALY 9/12/2014
I want a package which looks up the column values with the matchin ID and populates in the destination table...
I know we can use the LOOKUP transform to update the data for one single column in destination table what about the other columns which I need to insert along with the lookup insertion.
How can I achieve this ? Also is there a way to pull only the recent data from the source table using modified date column values
Use a different lookup for each lookup table that you need to reference to get the Ids. So if each of your columns that you want IDs for gets its ID from a different table, then you need to use three lookups, one after the other, until you have all three IDs.