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)
Related
I'm working on an SSIS project that pulls data form Excel and loads to Oracle Database every month. I plan to pull data from Excel file and load to Oracle stage table. I will be using a merge statement because the data that gets loaded each month is a rolling 12 month list and the data can change, so need to be able to INSERT when records don't match or UPDATE when they do. My control flow looks like this: Truncate Stage Table (to clear out table from last package run)---> DATA FLOW from Excel to Stage Table---> Merge to Target Table in Oracle.
My problem is that the data in the source Excel file doesn't have any unique columns to select a primary key or a composite key, as it is a possibility (although very unlikely) that a new record could have the exact same information. I am unable to utilize the "generated always as identity" because my SSIS package needs to truncate at the beginning of each job to clear out the Stage Table. This would generate the same ID numbers in the new load and create problems in the Target Table.
Any suggestions as to how I can get around this problem?
Welcome to SO and ETL. Instead of using a staging table, in SSIS use two sources: Excel file and existing production table. Sort both inputs and then perform a merge join on the unique identifier. From there, use a derived column transformation to add a new column called 'Action' which will mark a row as either an INSERT/UPDATE/DELETE based on whether the join key is NULL. So:
NULL from file means DELETE (not in file, in database)
NULL from database means INSERT (in file, not in database)
Not NULL for both means UPDATE (in file, in database)
From there, use a conditional split to split rows to either a OLE DB Destination (INSERT), or SQL Command (UPDATE or DELETE). You can now remove the stage environment and MERGE command from your process. This has the added benefit of removing the ETL load from the SQL Server, assuming SSIS is running on a separate server.
Note: The sort transformation has the option to remove duplicates.
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 have a huge schema containing billions of records, I want to purge data older than 13 months from it and maintain it as a backup in such a way that it can be recovered again whenever required.
Which is the best way to do it in SQL - can we create a separate copy of this schema and add a delete trigger on all tables so that when trigger fires, purged data gets inserted to this new schema?
Will there be only one record per delete statement if we use triggers? Or all records will be inserted?
Can we somehow use bulk copy?
I would suggest this is a perfect use case for the Stretch Database feature in SQL Server 2016.
More info: https://msdn.microsoft.com/en-gb/library/dn935011.aspx
The cold data can be moved to the cloud with your given date criteria without any applications or users being aware of it when querying the database. No backups required and very easy to setup.
There is no need for triggers, you can use job running every day, that will put outdated data into archive tables.
The best way I guess is to create a copy of current schema. In main part - delete all that is older then 13 months, in archive part - delete all for last 13 month.
Than create SP (or any SPs) that will collect data - put it into archive and delete it from main table. Put this is into daily running job.
The cleanest and fastest way to do this (with billions of rows) is to create a partitioned table probably based on a date column by month. Moving data in a given partition is a meta operation and is extremely fast (if the partition setup and its function is set up properly.) I have managed 300GB tables using partitioning and it has been very effective. Be careful with the partition function so dates at each edge are handled correctly.
Some of the other proposed solutions involve deleting millions of rows which could take a long, long time to execute. Model the different solutions using profiler and/or extended events to see which is the most efficient.
I agree with the above to not create a trigger. Triggers fire with every insert/update/delete making them very slow.
You may be best served with a data archive stored procedure.
Consider using multiple databases. The current database that has your current data. Then an archive or multiple archive databases where you move your records out from your current database to with some sort of say nightly or monthly stored procedure process that moves the data over.
You can use the exact same schema as your production system.
If the data is already in the database no need for a Bulk Copy. From there you can backup your archive database so it is off the sql server. Restore the database if needed to make the data available again. This is much faster and more manageable than bulk copy.
According to Microsoft's documentation on Stretch DB (found here - https://learn.microsoft.com/en-us/azure/sql-server-stretch-database/), you can't update or delete rows that have been migrated to cold storage or rows that are eligible for migration.
So while Stretch DB does look like a capable technology for archive, the implementation in SQL 2016 does not appear to support archive and purge.
I have a database1 which has more than 500 tables and I have database2 which also has the same number of tables and in both the databases the name of tables are same.. some of the tables have different table definitions, for example a table reports in database1 has 9 columns and the table reports in database2 has 10.
I want to copy all the data from database1 to database2 and it should overwrite the same data and append the columns if structure does not match. I have tried the import export wizard in SQL Server 2008 but it gives an error when it comes to the last step of copying rows. I don't have the screen shot of that error right now, it is my office PC. It says that error inserting into the readonly column xyz, some times it says that vs_isbroken, for the read only column error as I mentioned a enabled the identity insert but it did not help..
Please help me. It is an opportunity in my office for me.
SSIS and SQL Server 2008 Wizards can be finicky tools.
If you get a "can't insert into column ABC", then it could be one of the following:
Inserting into a PK column -> when setting up the mappings, you need to indicate to overwrite the value
Inserting into a column with a smaller range -> for example from nvarchar(256) into nvarchar(50)
Inserting into a calculated column (pointed out by #Nick.McDermaid)
You could also get issues with referential integrity if your database uses this (most do).
If you're going to do this more often, then I suggest you build an SSIS package instead of using the wizard tooling. This way you will see warnings on all sorts of issues like the ones I've described above. You can then run your package on demand.
Another suggestion I would make, is that you insert DB1 into "stage" tables in DB2. These tables should have no relational integrity and will allow you to break the process into several steps as follows.
Stage the data from DB1 into DB2
Produce reports/queries on issues pertinent to your database/rules
Merge the data from stage tables into target tables using SQL
That last step is where you can use merge statements, or simple insert/updates depending on a key match. Using SQL here in the local database is then able to use set theory to manage the overlap of the two sets and figure out what is new or to be updated.
SSIS "can" do this, but you will not be able to do a bulk update using SSIS, whereas with SQL you can. SSIS would do what is known as RBAR (row by agonizing row), something slow and to be avoided.
I suggest you inform your seniors that this will take a little longer to ensure it is reliable and the results reportable. Then work step by step, reporting on each stages completion.
Another two small suggestions:
Create _Archive tables of each of the stage tables and add a Tstamp column to each. Merge into these after the stage step which will allow you to quickly see when which rows were introduced into DB2
After stage and before the SQL merge step, create indexes on your stage tables. This will improve the merge performance
Drop those Indexes after each merge, this will increase the bulk insert Performance
Basic on Staging (response to question clarification):
Links:
http://www.codeproject.com/Articles/173918/How-to-Create-your-First-SQL-Server-Integration-Se
http://www.jasonstrate.com/tag/31daysssis/
http://blogs.msdn.com/b/andreasderuiter/archive/2012/12/05/designing-an-etl-process-with-ssis-two-approaches-to-extracting-and-transforming-data.aspx
Staging is the act of moving data from one place to another without any checks.
First you need to create the target tables, the schema should match the source tables.
Open up BIDS and create a new Project and in it a new SSIS package.
In the package, create a connection for the source server and another for the destination.
Then create a data flow step, in the step create a data source for each table you want to copy from.
Connect each source to a new data destination and set the appropriate connection and table.
When done, save and do a test run.
Before the data flow step, you might like to add a SQL step that will truncate all the target tables.
If you're open to using tools then what about using something like Red Gate Sql Compare and Red Gate SQL Data Compare?
First I would use data compare to manage the schema differences, add the new columns you want to your destination database (database2) from the source (database1). Then with data compare you match the contents of the tables any columns it can't match based on names you specify how to handle. Then you can pick and choose what data you want to copy from your destination. So you'll see what data is new and what's different (you can delete data in the destination that's not in the source or ignore it). You can either have the tool do the work or create you a script to run when you want.
There's a 15 day trial if you want to experiment.
Seems like maybe you are looking for Replication technology as is offered by SQL Server Replication.
Well, if i understood your requirement correctly, you need to make database2 a replica of database1. Why not take a full backup of database1 and restore it as database2? Your database2 will be exactly what database1 is at the time of backup.
I am new to SSIS.I got the task have according to the scenario as explained.
Scenario:
I have two databases A and B on different machines and have around 25 tables and 20 columns with relationships and dependencies. My task is to create a database C with selected no of tables and in each table I don't require all the columns but selected some. Conditions to be met are that the relationships should be intact and created automatically in new database.
What I have done:
I have created a package using the transfer SQL Server object task to transfer the tables and relationships.
then I have manually edited the columns that are not required
and then I transferred the data using the data source and destination
My question is: can I achieve all these things in one package? Also after I have transferred the data how can I schedule the package to just transfer the recently inserted rows in the database to the new database?
Please help me
thanks in advance
You can schedule the package by using a SQL Server Agent Job - one of the options for a job step is run SSIS package.
With regard to transferring new rows, I would either:
Track your current "position" in another table, assumes you have either an ascending key or a time stamp column - load the current position into an SSIS variable, use this variable in the WHERE statement of your data source queries.
Transfer all data across into "dump" copies of each table (no relationships/keys etc required just the same schema) & use a T-SQL MERGE statement to load new rows in, then truncate "dump" tables.
Hope this makes sense - its a bit difficult to get across in writing.