I am new to Informatica BDM.I have a use case in which I have to import the data incrementally (100 tables) from RDBMS into Hive on daily basis. Can someone please guide me with the best possible approach to achieve this?
Thanks,
Sumit
Hadoop is write onces read many (WORM) approach and the incremental load is not easy stuff. There are following guideline you can follow and validate your current requirement
If the table is a small/mid-size and not having too many records,
better to refresh the entire table
If the table is too big and incremental load has add/update/delete operation, you can think of staging the delta and perform a join operation to re-create data set.
For large table and large delta, you can create a version number for all the latest record and each delta may come to a new directory and a view should be created to get the latest version for further processing. This avoid heavy merge operation.
If the delete operation is not coming as change, then you also need to think how to act on it and in such case, you need to get the full refresh.
Related
Currently, there's a denormalized table inside a MySQL database that contains hundreds of columns and millions of records.
The original source of the data does not have any way to track the changes so the entire table is dropped and rebuilt every day by a CRON job.
Now, I would like to import this data into Elaticsearch. What is the best way to approach this? Should I use logstash to connect directly to the table and import it or is there a better way?
Exporting the data into JSON or similar is an expensive process since we're talking about gigabytes of data every time.
Also, should I drop the index in elastic as well or is there a way to make it recognize the changes?
In any case - I'd recommend using index templates to simplify index creation.
Now for the ingestion strategy, I see two possible options:
Rework your ETL process to do a merge instead of dropping and recreating the entire table. This would definitely be slower but would allow shipping only deltas to ES or any other data source.
As you've imagined yourself - you should be probably fine with Logstash using daily jobs. Create a daily index and drop the old one during the daily migration.
You could introduce buffers, such as Kafka to your infrastructure, but I feel that might be an overkill for your current use case.
I am new to Teradata & fortunately got a chance to work on both DDL-DML statements.
One thing I observed is Teradata is very slow when time comes to UPDATE the data in a table having large number of records.
The simplest way I found on the Google to perform this update is to write an INSERT-SELECT statement with a CASE on column holding values to be update with new values.
But what when this situation arrives in Data Warehouse environment, when we need to update multiple columns from a table holding millions of rows ?
Which would be the best approach to follow ?
INSERT-SELECT only OR MERGE-UPDATE OR MLOAD ?
Not sure if any of the above approach is not used for this UPDATE operation.
Thank you in advance!
At enterprise level, we expect volumes to be huge and updates are often part of some scheduled jobs/scripts.
With huge volume of data, Updates comes as a costly operation that involve risk of blocking table for some time in case the update fails (due to fallback journal). Although scripts are tested well, and failures seldom happen in production environments, it's always better to have data that needs to be updated loaded to a temporary table in required form and inserted back to same table after deleting matching records to maintain SCD-1 (Where we don't maintain history).
I would like to implement a synchronization between a source SQL base database and a target TripleStore.
However for matter of simplicity let say simply 2 databases. I wonder what approaches to use to have every change in the source database replicated in the target database. More specifically, I would like that each time some row changes in the source database that this can be seen by a process that will read the changes and populate the target database accordingly while applying some transformation in the middle.
I have seen suggestion around the mechanism of notification that can
be available in the database, or building tables such that changes can
be tracked (meaning doing it manually) and have the process polling it
at different intervals, or the usage of Logs (change data capture,
etc...)
I'm seriously puzzle about all of this. I wonder if anyone could give some guidance and explanation about the different approaches with respect to my objective. Meaning: name of methods and where to look.
My organization mostly uses: Postgres and Oracle database.
I have to take relational data and transform them in RDF so as to store them in a triplestore and keep that triplestore constantly synchronized with the data is the SQL Store.
Please,
Many thanks
PS:
A clarification between ETL and replication techniques as in Change Data capture, with respect to my overall objective would be appreciated.
Again i need to make sense of the subject, know what are the methods, so i can further start digging for myself. So far i have understood that CDC is the new way to go.
Assuming you can't use replication and you need to use some kind of ETL process to actually extract, transform and load all changes to the destination database, you could use insert, update and delete triggers to fill a (manually created) audit table. Columns GeneratedId, TableName, RowId, Action (insert, update, delete) and a boolean value to determine if your ETL process has already processed this change. Use that table to get all the changed rows in your database and transport them to the destination database. Then delete the processed rows from the audit table so that it doesn't grow too big. How often you have to run the ETL process depends on the amount of changes occurring in the source database.
Am working on a OWB project. My sources are tables partitioned monthly. I have a control_table that holds the last run date. I want to use OWB to fetch a whole partition (monthly) data into a landing table, enjoying the performance. Kindly assist in coming up with the way OWB treats partition as its source.
Thanks.
I don't think it's a good idea.
Just implement you logic in OWB. If partitions are available, OWB/ Oracle will use them to get any possible benefits.
If the partitions are not used, there should be a issue with the partitioning strategy. Try to correct the partitioning strategy.
Partitioning should be transparent.
I would like to write a MERGE statement in Vertica database.
I know it can't be used directly, and insert/update has to be
combined to get the desired effect.
The merge sentence looks like this:
MERGE INTO table c USING (select b.field1,field2 aeg from table a, table b
where a.field3='Y'
and a.field4=b.field4
group by b.field1) t
on (c.field1=t.field1)
WHEN MATCHED THEN
UPDATE
set c.UUS_NAIT=t.field2;
Would just like to see an example of MERGE being used as insert/update.
You really don't want to do an update in Vertica. Inserting is fine. Selects are fine. But I would highly recommend staying away from anything that updates or deletes.
The system is optimized for reading large amounts of data and for inserting large amounts of data. So since you want to do an operation that does 1 of the 2 I would advise against it.
As you stated, you can break apart the statement into an insert and an update.
What I would recommend, not knowing the details of what you want to do so this is subject to change:
1) Insert data from an outside source into a staging table.
2) Perform and INSERT-SELECT from that table into the table you desire using the criteria you are thinking about. Either using a join or in two statements with subqueries to the table you want to test against.
3) Truncate the staging table.
It seems convoluted I guess, but you really don't want to do UPDATE's. And if you think that is a hassle, please remember that what causes the hassle is what gives you your gains on SELECT statements.
If you want an example of a MERGE statement follow the link. That is the link to the Vertica documentation. Remember to follow the instructions clearly. You cannot write a Merge with WHEN NOT MATCHED followed and WHEN MATCHED. It has to follow the sequence as given in the usage description in the documentation (which is the other way round). But you can choose to omit one completely.
I'm not sure, if you are aware of the fact that in Vertica, data which is updated or deleted is not really removed from the table, but just marked as 'deleted'. This sort of data can be manually removed by running: SELECT PURGE_TABLE('schemaName.tableName');
You might need super user permissions to do that on that schema.
More about this can be read here: Vertica Documentation; Purge Data.
An example of this from Vertica's Website: Update and Insert Simultaneously using MERGE
I agree that Merge is supported in Vertica version 6.0. But if Vertica's AHM or epoch management settings are set to save a lot of history (deleted) data, it will slow down your updates. The update speeds might go from what is bad, to worse, to horrible.
What I generally do to get rid of deleted (old) data is run the purge on the table after updating the table. This has helped maintain the speed of the updates.
Merge is useful where you definitely need to run updates. Especially incremental daily updates which might update millions of rows.
Getting to your answer: I don't think Vertica supportes Subquery in Merge. You would get the following.
ERROR 0: Subquery in MERGE is not supported
When I had a similar use-case, I created a view using the sub-query and merged into the destination table using the newly created view as my source table. That should let you keep using MERGE operations in Vertica and regular PURGEs should let you keep your updates fast.
In fact merge also helps avoid duplicate entries during inserts or updates if you use the correct combination of fields in ON clause, which should ideally be a join on the primary keys.
I like geoff's answer in general. It seems counterintuitive, but you'll have better results creating a new table with the rows you want in it versus modifying an existing one.
That said, doing so would only be worth it once the table gets past a certain size, or past a certain number of UPDATEs. If you're talking about a table <1mil rows, I might chance it and do the updates in place, and then purge to get rid of tombstoned rows.
To be clear, Vertica is not well suited for single row updates but large bulk updates are much less of an issue. I would not recommend re-creating the entire table, I would look into strategies around recreating partitions or bulk updates from staging tables.