My source data is in Oracle and target data is in Teradata.Can you please provide me the easy and quick way to validate data .There are 900 tables.If possible can you provide syntax too
There is a product available known as the Teradata Gateway that works with Oracle and allows you to access Teradata in a "heterogeneous" manner. This may not be the most effective way to compare the data.
Ultimately what your requirements sound more process driven and to be done effectively would require the source data to be compared/validated as stage tables on the Teradata environment after your ETL/ELT process has completed.
Related
I want to build a Kafka Connector in order to retrieve records from a database at near real time. My database is the Oracle Database 11g Enterprise Edition Release 11.2.0.3.0 and the tables have millions of records. First of all, I would like to add the minimum load to my database using CDC. Secondly, I would like to retrieve records based on a LastUpdate field which has value after a certain date.
Searching at the site of confluent, the only open source connector that I found was the “Kafka Connect JDBC”. I think that this connector doesn’t have CDC mechanism and it isn’t possible to retrieve millions of records when the connector starts for the first time. The alternative solution that I thought is Debezium, but there is no Debezium Oracle Connector at the site of Confluent and I believe that it is at a beta version.
Which solution would you suggest? Is something wrong to my assumptions of Kafka Connect JDBC or Debezium Connector? Is there any other solution?
For query-based CDC which is less efficient, you can use the JDBC source connector.
For log-based CDC I am aware of a couple of options however, some of them require license:
1) Attunity Replicate that allows users to use a graphical interface to create real-time data pipelines from producer systems into Apache Kafka, without having to do any manual coding or scripting. I have been using Attunity Replicate for Oracle -> Kafka for a couple of years and was very satisfied.
2) Oracle GoldenGate that requires a license
3) Oracle Log Miner that does not require any license and is used by both Attunity and kafka-connect-oracle which is is a Kafka source connector for capturing all row based DML changes from an Oracle and streaming these changes to Kafka.Change data capture logic is based on Oracle LogMiner solution.
We have numerous customers using IBM's IIDR (info sphere Data Replication) product to replicate data from Oracle databases, (as well as Z mainframe, I-series, SQL Server, etc.) into Kafka.
Regardless of which of the sources used, data can be normalized into one of many formats in Kafka. An example of an included, selectable format is...
https://www.ibm.com/support/knowledgecenter/en/SSTRGZ_11.4.0/com.ibm.cdcdoc.cdckafka.doc/tasks/kcopauditavrosinglerow.html
The solution is highly scalable and has been measured to replicate changes into the 100,000's of rows per second.
We also have a proprietary ability to reconstitute data written in parallel to Kafka back into its original source order. So, despite data having been written to numerous partitions and topics , the original total order can be known. This functionality is known as the TCC (transactionally consistent consumer).
See the video and slides here...
https://kafka-summit.org/sessions/exactly-once-replication-database-kafka-cloud/
Daily, I query a few tables in SQL Developer, filtering to prior day activity, adding column to date stamp the data, then export to xlsx. Then I manually import each file to a MS SQL Server via SQL Server Import and Export Wizard. Takes many clicks, much waiting...
I'm essentially creating an archive in SQL Server, the application I'm querying overwrites data daily. I'm not a DBA of either database, I use the archived data to do validations and research.
It's tough to get my org to provide additional software, I've been trying to make this work via SQL Developer, SSMS Express ed, and other standard tools.
I'm looking to make this reasonably automated, either via scripts, scheduled tasks, etc. Appreciate suggestions that would work on my current situation, but if that isn't reasonable, and there's a very reasonable alternative, I can go back to the org to request software/access/assistance.
You can use SSIS to import the data directly from Oracle to SQL Server, unless you need the .xlsx files for another purpose. You can also export from Oracle to these, then load to SQL Server from these files if you do need the files. For the date stamp column, a Derived Column can be added within a Data Flow Task using the SSIS GETDATE() function for a timestamp in order to achieve the same result. This function returns a timestamp, and if only the date is necessary the (DT_DBDATE) function can cast it to a date data type that's compatible with this data type of SQL Server. Once you have the SSIS package configured, you can schedule in to run at regular intervals as a SQL Agent job. I'd also recommend installing the SSIS catalog (SSISDB) and using this the source to run the packages from. The following links shed more light on these areas.
SSIS
Connecting to Oracle from SSIS
Data Flow Task
Derived Column Transformation
Creating SQL Server Agent Jobs for SSIS packages
SSIS Catalog
Another option that you may consider (if it is supported in SQL Express) is using the BCP utility, which can be run from command line.
The BCP utility allows you to bulk copy the data from a delimited text file into a SQL Server table.
If you go this approach, things to consider:
Number of Columns in the source file need to match the number of columns in the destination
Data types must match (or be comparable)
Typically, empty strings will be converted to nulls, so you will need to consider if the columns are nullable.
(to name a few - if you want to delve deeper, you might also need to look at custom delimiters between fields and records. Don't forget, commas and line feeds are still valid characters in char type fields).
Anyhow, maybe it will work for you, maybe not. Sure, you might still have to deal with the exporting of the data from Oracle, but it might ease the pain getting the data in.
Have a read:
https://learn.microsoft.com/en-us/sql/tools/bcp-utility?view=sql-server-2017
Usually, I use a JDBC connection with some ETL tool to move data from one database(i.e Teradata) to another database(i.e Greenplum).
However, both of these databases comes with inbuilt utilities which can load/export huge amounts of data very fast, far faster than JDBC!. But the downside as far as I am aware of is that it can do so only to/from a file.
So, if I want to use them I have to Follow a process like-
Teradata ---(Fast Export)---> File ---(Gploader)---> Greenplum
I am wondering if it is possible to skip the File part and Combine the two utilities.
Teradata ---(FastExport & Gploader)--> Greenplum.
That way I can transfer huge amounts of data very quickly!
Yes, you most certainly can. Greenplum supports all kinds of external tables. One solution is to use an External Table that executes a command. That command can be a Java program that connects to Teradata to get data and uses the FastExport option.
I wrote the tool "gplink" to do just this. It automates the creation of Greenplum External Tables for JDBC sources.
Github:
https://github.com/pivotalguru/gplink
Teradata connection example:
https://github.com/pivotalguru/gplink/blob/master/connections/teradata.properties
And my blog:
http://www.pivotalguru.com/?page_id=982
I want to transfer tables data from SQL server to Informix and vice versa.
The transferring should be run scheduled and sometimes when the user make a specific action.
I do this operation through delete and insert transactions and it takes along long time through the web between 15 minute to 30 minute.
How to do this operation in easy way taking the performance in consideration?
Say I have
Vacation table in SQL Server and want to transfer all the updated data to the Vacation table in Informix.
and
Permission table in Informix and want to transfer all the updated data to the Permission table in SQL Server.
DISCLAIMER: I am not an SQL Server DBA. However, I have been an Informix DBA for over ten years and can make some recommendations as to its performance.
Disclaimer aside, it sounds like you already have a functional application, but the performance is a show-stopper and that is where you are mainly looking for advice.
There are some technical pieces of information that would be helpful to know, but in their absence, I'm going to make the following assumptions about your environment and application. Please comment or edit your question if I am wrong on any of these.
Database server versions. From the tags, it appears you are using SQL server 2012. However, I cannot determine the Informix server and version. I will assume you are running at least IDS 11.50 or greater.
How the data is being exchanged currently. Are you connecting directly from your .NET application to Informix? I would assume that is the case with SQL Server and will make the same assumption for your Informix connection as well.
Table structures. I assume you have proper indexing on the tables. On the Informix side, dbschema -d *dbname* -t *tablename* will give the basic schema.
If you haven't tried exporting data to CSV and as long as you don't have any compliance concerns doing this, I would suggest loading the data from a comma-delimited file. (Informix normally deals with pipe-delimited files, so you'll either need to adjust the delimiter on the SQL Server side to a pipe | or on the Informix import side). On the Informix end, this would be a
LOAD FROM 'source_file_from_sql_server' DELIMITER '|' INSERT INTO vacation (field1, field2, ..)
For reusability, I would recommend putting this in a stored procedure. Just wrap that load statement inside a BEGIN WORK; and COMMIT WORK; to keep your transactional integrity. Michał Niklas suggested some ways to track changes. If there is any correlation between the transfer of data to the vacation table in Informix and the permission table back in SQL Server, I would propose another option, which is adding a trigger to the vacation table so that you write all new values to a staging table.
With the import logic in a stored procedure, you can fire the import on demand:
EXECUTE PROCEDURE vacation_import();
You also mentioned the need to schedule the import, which can be accomplished with Informix's "dbcron". Using this feature, you'll create a scheduled task that executes vacation_import() periodically as well. If you haven't used this feature before, using OAT will be helpful. You will also want to do some housekeeping with the CSV files. This can be addressed with the system() call, which you can make from stored procedures in Informix.
Some ideas:
Add was_transferred column to source tables setting its default value to 0 (you can use 0/1 instead of false/true).
From source table select data with was_transferred=0.
After transferring data update selected source row, set its was_transferred to 1.
Make table syncro_info with fields like date_start and date_stop. If you discover that there is record with date_stop IS NULL it will mean that you are tranferring data. This will protect you against synchronizing data twice.
I have a spark cluster setup and tried both native scala and spark sql on my dataset and the setup seems to work for the most part. I have the following questions
From an ODBC/extenal connectivity to the cluster, what should i expect?
- the admin/developer shapes the data and persists/caches a few RDDs that will be exposed? (Thinking on the lines of hive tables)
- What would be the equivalent of connecting to a "Hive metastore" in spark/spark sql?
Is thinking along the lines of hive faulted?
My other question was
- when i issue hive queries, (and say create tables and such), it uses the same hive metastore as hadoop/hive
- Where do the tables get created when i issue sql queries using sqlcontext?
- If i persist the table, it is the same concept as persisting an RDD?
Appreciate your answers
Nithya
(this is written with spark 1.1 in mind, be aware that new features tend to be added quickly, some limitations mentioned below might very well disappear at some point in the future).
You can use Spark SQL with Hive syntax and connect to Hive metastore, which will result in your Spark SQL hive commands to be executed on the same data space as if they were executed through Hive directly.
To do that you simply need to instantiate a HiveContext as explained here and provide a hive-site.xml configuration file that specifies, among other things, where to find the Hive metastore.
The result of a SELECT statement is a SchemaRDD, which is an RDD of Row objects that has an associated schema. You can use it just like you use any RDD, including cache and persist and the effect is the same (the fact that the data comes from hive has not influence here).
If your hive command is creating data, e.g. "CREATE TABLE ... ", the corresponding table gets created in exactly the same place as with regular Hive, i.e. /var/lib/hive/warehouse by default.
Executing Hive SQL through Spark provides you with all the caching benefits of Spark: executing a 2nd SQL query on the same data set within the same spark context will typically be much faster than the first query.
Since Spark 1.1, it is possible to start the Thrift JDBC server, which is essentially an equivalent to HiveServer2 and thus allows you to execute SparkQL commands through a JDBC connection.
Note that not all Hive features are available (yet?), see details here.
Finally, you can also discard Hive syntax and metastore and execute SQL queries directly on CSV and Parquet files. My best guess is that this will become the preferred approach in the future, although at the moment the set of SQL features available like this is smaller than when using the Hive syntax.