I'm trying to update a record based on ID field in Hive table using informtica cloud data integration. But the problem is Instead of updating the existing record it is creating new record. Can anyone please suggest better approach
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I have a table created in production with incorrect data.My rec_srt_dt and rec_end_dt columns have been loaded wrongly. rec_srt_dt is sys_dt and now I have modified the query to load the data properly. My question is how do I have to handle the existing data present in production table and how to add new changes to that data?
My source table is oracle, Using Spark for the transformations and the target table is in AWS.
Kindly help me in this.
While importing data from sql server or any RDBMS database to hadoop using Sqoop, we can get newly appended records or modified records using incremental append or last modified or some free form queries.
Is there anyway we can identify deleted records? Considering when record is deleted it will not exist in sql table.
One workaround is to load full table using Sqoop and compare with previous table in hive.
Is there any other best way to do?
No, you can not get deleted records using sqoop.
A better workaround could be:
Create a boolean field status(default true) in your SQL Server table.
Whenever you need to delete that record don't delete just update with marking status false.
If you are using last-modified increment import, you will get this changed data in HDFS.
Later (after sqqop import) you can delete all these records with status false.
If you are syncing the entire partition or table then you can identify deleted records after sqoop import before merging them using full join with existing target partition or table. Records existing in target table/partition which do not exist in imported data are those deleted on source database since last sync.
Incremental sqooping does not handle deleted records out of the box. There are two approach you may want to consider.
Please look at this post.
I am trying to have a setup in Pentaho where :
My source data is in MySQL DB and target database is Amazon redshift.
I want to have incremental loads on Redshift database table, based on the last updated timestamp from MySQL DB table.
Primary key is student ID.
Can I implement this using update/insert in Pentaho ?
Insert/Update step in Pentaho Data Integration serves the purpose of inserting the row if it doesn't exist in the destination table or updating it if it's already there. It has nothing to do with incremental loads, but if your loads should be inserting or updating the record based on some Change Data Capture mechanism then this is the right step at the end of the process.
For example you could go one of two ways:
If you have a CDC then limit the data at Table Input for MySQL since you already know the last time a record has been modified (last load)
If you don't have a CDC and you are comparing entire tables then go for joining the sets to produce rows that has changed and then perform a load (slower solution)
I'm using Spark Streaming and Hive. I want to insert or update data into exist table in hive using SparkSQL. But, I don't know check whether data existed. Please suggest me this question!
Can anyone please tell me the process of incremental updates in Hive.
Actually i would like to understand with the help of Hive, how do we manage the master data table.
If everyday there are new set of records coming to get inserted into hive table.
In the new set of records there could be new keys, there could be existing keys which need an update.
I found some information at the link.
Can any one please guide me.