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.
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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).
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.
We have different set of data into different systems like Hadoop, Cassandra, MongoDB. But our analytic team want to get the stitched data from different systems. For example customer information with demographic will be in one system, their transactions will be in another system. Analytic should able to query to get data like from US users what was the volume of transaction. We need to develop an application to provide ease way to interact with different system. What is the best way to do?
Another requirement:
If we want to provide their custom workspace in a system like MongoDB, they can easily place with it. What is the best strategy to pull data from one system to another system on demand?
Any pointer or common architecture used to solve this kind of problem will be really helpful.
I see two questions here:
How can I consolidate data from different systems into one system?
How can I create some data in Mongo for people to experiment with?
Here we go ... =)
I would pick one system and target that for consolidation. In other words, between Hadoop, Cassandra and MongoDB, which one does your team have the most experience with? Which one do you find easiest to query with? Which one do you have set up to scale well?
Each one has pros and cons to scale, storage and queryability.
I would pick one and then pump all data to that system. At a recent job, that ended up being MongoDB. It was easy to move data to Mongo and it had by far the best query language. It also had a great community and setting up nodes was easier than Hadoop, etc.
Once you have solved (1), you can trim your data set and create a scaled down sandbox for people to run ad-hoc queries against. That would be my approach. You don't want to support the entire data set, because it would likely be too expensive and complicated.
If you were doing this in a relational database, I would say just run a
select top 1000 * from [table]
query on each table and use that data for people to play with.
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 am writing an ETL (in python with a mongodb backend) and was wondering : what kind of standard functions and tools an ETL should have to be called an ETL ?
This ETL will be as general purpose as possible, with a scriptable and modular approach. Mostly it will be used to keep different databases in sync, and to import/export datasets in different formats (xml and csv) I don't need any multidimensional tools, but it is a possibility that it'll needed later.
Let's think of the ETL use cases for a moment.
Extract.
Read databases through a generic DB-API adapter.
Read flat files through a similar adapter.
Read spreadsheets through a similar adapter.
Cleanse.
Arbitrary rules
Filter and reject
Replace
Add columns of data
Profile Data.
Statistical frequency tables.
Transform (see cleanse, they're two use cases with the same implementation)
Do dimensional conformance lookups.
Replace values, or add values.
Aggregate.
At any point in the pipeline
Load.
Or prepare a flat-file and run the DB product's loader.
Further, there are some additional requirements that aren't single use cases.
Each individual operation has to be a separate process that can be connected in a Unix pipeline, with individual records flowing from process to process. This uses all the CPU resources.
You need some kind of time-based scheduler for places that have trouble reasoning out their ETL preconditions.
You need an event-based schedule for places that can figure out the preconditions for ETL processing steps.
Note. Since ETL is I/O bound, multiple threads does you little good. Since each process runs for a long time -- especially if you have thousands of rows of data to process -- the overhead of "heavyweight" processes doesn't hurt.
Here's a random list, in no particular order:
Connect to a wide range of sources, including all the major relational databases.
Handle non-relational data sources like text files, Excel, XML, etc.
Allow multiple sources to be mapped into a single target.
Provide a tool to help map from source to target fields.
Offer a framework for injecting transformations at will.
Programmable API for writing complex transformations.
Optimize load process for speed.
Automatic / heuristic mapping of column names. E.g simple string mappings:
DB1: customerId
DB2: customer_id
I find a lot of the work I (have) done in DTS / SSIS could've been automatically generated.
not necessarily "required functionality", but would keep a lot of your users very happy indeed.