I need to replicate Oracle database into one way. Data in replica will be only for the read.
Due to some limitations I cannot use Oracle Streams, Golden Gate and other commercial solutions.
What other possibilities do we have to perform that?
Materialized views over database link might be one option.
Or, perhaps you could even consider exporting & importing data (using Data Pump utilities).
Related
Trying to understand if there is any such concept like this in Oracle Database.
Let's say I have two Databases, Database_A & Database_B
Database_A has schema_A, is there a way I can attach this schema to Database_B?
What I mean by this is if there is a job populating a TABLE_A in schema_A, I can see that read-only view in Database_B. We are trying to split a big Oracle database into two smaller databases and have a vast PL/SQL code, and trying to minimize the refactoring here.
Sharding might be what you're looking for. The schemas and tables will still logically exist on all databases, but you can arrange the data to be physically stored in specific databases. There might be a way to setup shardspaces, tablespaces, and user default tablespaces in a way where each schema's data is automatically stored in a specific database.
But I haven't actually used sharding. From what I've read, it seems to be designed for massive distributed OLTP systems, and it is likely complicated to administer. I'd guess this feature isn't worth the hassle unless you have petabytes of data.
We have transaction tables in Oracle and for reporting purposes we need this data transfered in real time to another flat Oracle table in another database. The performance of the report is great with table placed in this flat table.
Currently we are using golden gate for replication to the other database and using materialized view for this but due to some problems we need to switch to some other way of populating/maintaining this flat table. What options do we have?
It is a pretty basic requirement but the solutions I can see are for batch processing. Also if there are any other solutions you feel would better serve this purpose. Changing the target database to something other is also an option as there might be more such reports coming ahead.
We seem to have bit of a debate on a discussion point in our team.
We are working on a Data Warehouse in the Microsoft SQL Server 2012 platform. We have followed the Kimball Architecture to build this Data Warehouse.
Issue:
A reporting solution (built on SSRS), which sources data from this Warehouse, has significant performance issues when sourcing data from fact and dim tables. Some of our team members suggest that we extract data from facts and dims into a new set of tables using SSIS packages. This would mean we denormalise these tables into ‘Snapshot’ tables. In this way the we would not need to join these tables to create data sets within the reports. Data could be read out of these tables directly.
I do have my own worries about this; inconsistencies, maintenance of different data structures, duplication of data etc to name a few.
Question:
Would you consider creating snapshot tables (by denormalising facts and dim tables) for reporting tables a right approach?
Would like to hear your thoughts on this.
Cheers
Nithin
I don't think there is anything wrong with snapshot tables. The two most important aspects of a data warehouse are:
The data is correct.
The data is useful.
If your users are unable to extract the totals they require, in a reasonable timescale, they won't use the warehouse.
My own solution includes 3 snapshot tables. Like you, I was worried about inconsistencies. To address this we built an automated checking process. This sub-system executes a series of queries, stored on a network drive, once an hour. Any records returned by the queries are considered a fail. Fails are reported and immediately investigated by my ETL team. This sub-system ensures the snapshots and underlying facts are always aligned and consistent with each other. Drift is prevented.
That said, additional tables equals additional complexity. And that requires more time/effort to manage. Before introducing another layer to your warehouse, you should investigate why these queries are underperforming. If joins are to blame:
Are you using an inappropriate data type, for your P/F keys?
Are the FKeys indexed (some RDBMS do this by default, others do not)?
Have you looked at the execution plans, for the offending queries?
Is the join really to blame, or is it a filter applied to the dim table?
for raw cube performance my advice would be to always try to denormalize your tables and have one fact table and one table for each dimension (star schema).
If you are unsure if it will actually help you could start creating materialized views. These are kind of the best of both worlds, on the long run you should alter your etl.
In my previous job we only had flattened tables which worked quite well. Currenly we have a normalized schema but flatten it in the last step.
At work we are thinking to move from Oracle to a NoSQL database, so I have to make some test on Cassandra and MongoDB. I have to move a lot of tables to the NoSQL database the idea is to have the data synchronized between this two platforms.
So I create a simple procedure that make selects into the Oracle DB and insert into mongo. Some of my colleagues point that maybe there is an easier(and more professional) way to do it.
Anybody had this problem before? how do you solve it?
If your goal is to copy your existing structure from Oracle to a NoSQL database then you should probably reconsider your move in the first place. By doing that you are losing any of the benefits one sees from going to a non-relational data store.
A good first step would be to take a long look at your existing structure and determine how it can be modified to affect positive impact on your application. Additionally, consider a hybrid system at the same time. Cassandra is great for a lot of things, but if you need a relational system and already are using a lot of Oracle functionality, it likely makes sense for most of your database to stay in Oracle, while moving the pieces that require frequent writes and would benefit from a different structure to Mongo or Cassandra.
Once you've made the decisions about your structure, I would suggest writing scripts/programs/add a module to your existing app, to write the data in the new format to the new data store. That will give you the most fine-grained control over every step in the process, which in a large system-wide architectural change, I would want to have.
You can also consider using components of Hadoop ecosystem to perform this kind of (ETL) task .For that you need to model your Cassandra DB as per the requirements.
Steps could be to migrate your oracle table data to HDFS (using SQOOP preferably) and then writing Map-Reduce job to transform this data and insert into Cassandra Data Model .
I intend to use Oracle Streams for replication from Source to Target. The Target will be used mainly to run Reports. Earlier, all the reports used to run on the Source itself. Therefore, this arrangement gives better performance as all report queries are directed to a dedicated Target.
I would also like to denormalize the tables on the Target to achieve better reports performance. Can denormalization be done in conjunction with Streams replication ? I know that Oracle Streams allows us to write our own dequeue process. But is there a simple "GUI"-based way to achieve de-normalization on the fly ... as and when Streams replicated the data ? Any pointers would be very helpful.
I think the cleanest way to denormalize would be to leave the Streams replication intact (with 1->1 mappings of the tables) and create materialized views on the target tables that handle the transformations you need.
I think GUI interfaces to these types of transformations get cumbersome quickly as the logic gets more complicated, but if you really want a GUI solution you can look at Oracle Warehouse Builder. Once the GUI-driven design is complete within OWB, you can generate PL/SQL packages to perform the ETL.