odbc | Connect to ODBC databases | SQL Database library

Happy to share that I got the solution for this approach. In order to get the files in sql format use the given code to extract DDL and DML Code.

The given code is for sample database dbc.

I may have found a solution using the links from @AlwaysLearning. The first sub actually answers my question to upload a file to a remote FILESTREAM SQL Server. The second sub downloads all uploaded files into a given directory.

I have two suggestions:

1. Instead of using a data conversion transformation, use a derived column that convert the DT_TEXT value to DT_STR before converting it to unicode:

You seem to have encountered a quirk in MySQL Connector/ODBC's handling of result sets from a stored procedure. For this example procedure:

I will try to give some tips to help you improve your package performance. You should start troubleshooting your package systematically to find the performance bottleneck.

Some provided links are related to SQL Server. No worries! The same rules are applied in all database management systems.

First, you should ensure that you have sufficient resources to load the data from the source server into the destination server.

Ensure that the available memory on the source, ETL, and destination servers can handle the amount of data you are trying to load. Besides, make sure that your network connection bandwidth is not decreasing the data transfer performance.

• How Does SSIS Manage Connection Traffic?

Make sure that the following hardware issues are not occurring in any of the servers:

1. Drive out of storage
2. Server is out of memory

Make sure that your machine is not running out of memory. You can simply use the Task Manager to identify the amount of available memory.

After checking the available resources, you should ensure that your data source is not a heap. At least it would be best if you had a primary key created on that table.

• Clustered Index vs. Heap in SQL Server
• SQL Server clustered tables vs. Heap tables

If your SQL Command contains any filtering, ordering, or Joins, you should create the indexes needed by those operations.

• Use WHERE, JOIN, ORDER BY, SELECT Column Order When Creating Indexes
• Is the WHERE-JOIN-ORDER-(SELECT) rule for index column order wrong?

Instead of using OLE DB source to connect to Oracle, try using the Microsoft Connector for Oracle (Previously known as Attunity connectors). Microsoft previously mentioned that it should provide faster performance than traditional OLE DB providers.

Use the Oracle connection manager rather than OLE DB connection manager.

To be honest, I am not sure if this component can make the data load faster since I didn't test it before

The last thing to try is to remove the ODBC destination and add any dummy task. For example, use a row count component.

Run the package; if the data is loaded faster, then loading data from Oracle is not decreasing the performance.

Now, let us start troubleshooting the SSIS package.

First, try to execute the package in 64-bit mode. You can change this from the package configuration. Make sure that the Run64bitRuntime property is set to True.

Using SSIS, data is loaded in memory while being transferred from source to destination. There are two properties in the data flow task that specifies how much data is transferred in memory buffers used by the SSIS pipelines.

Based on the following Integration Services performance tuning white paper:

DefaultMaxBufferRows – DefaultMaxBufferRows is a configurable setting of the SSIS Data Flow task that is automatically set at 10,000 records. SSIS multiplies the Estimated Row Size by the DefaultMaxBufferRows to get a rough sense of your dataset size per 10,000 records. You should not configure this setting without understanding how it relates to DefaultMaxBufferSize.

DefaultMaxBufferSize – DefaultMaxBufferSize is another configurable setting of the SSIS Data Flow task. The DefaultMaxBufferSize is automatically set to 10 MB by default. As you configure this setting, keep in mind that its upper bound is constrained by an internal SSIS parameter called MaxBufferSize which is set to 100 MB and can not be changed.

You should try to change those values and test your package performance each time you change them until the package performance increases.

You should make sure that the destination table does not have any constraints or triggers since they significantly decrease the data load performance; each batch inserted should be validated or preprocessed before storing it.

• Are SQL Server database triggers evil?
• The benefits, costs, and documentation of database constraints

Besides, the more you have indexes the lower is the data load performance.

ODBC destination has several custom properties that can affect the data load performance such as BatchSize (Rows per batch), TransactionSize (TransactionSize is only available in the advanced editor).

• ODBC connection is very slow

There are few different approaches:

1. Use the immediate arg in your DBI::dbExecute statement

As mentioned in the comment before I got an answer from another direction:

You have to use DataflowOverrides in the ODBC-Source in BIML. For my example you have to do something like this:

I found what I was looking for in odbc package documentation : direct execution.

The odbc package uses Prepared Statements to compile the query once and reuse it, allowing large or repeated queries to be more efficient. However, prepared statements can actually perform worse in some cases, such as many different small queries that are all only executed once. Because of this the odbc package now also supports direct queries by specifying immediate = TRUE.

This will use a prepared statement:

Is pyodbc becoming deprecated?

No. For at least the last couple of years pandas' documentation has clearly stated that it wants either a SQLAlchemy Connectable (i.e., an Engine or Connection object) or a SQLite DBAPI connection. (The switch-over to SQLAlchemy was almost universal, but they continued supporting SQLite connections for backwards compatibility.) People have been passing other DBAPI connections (like pyodbc Connection objects) for read operations and pandas hasn't complained … until now.

Is there a better way to achieve similar results without warning?

Yes. You can take your existing ODBC connection string and use it to create a SQLAlchemy Engine object as described here: