1. To populate our data warehouse. Running SSIS packages from the server on a …
Simple transformations (40)
Connect to traditional data sources (40)
Complex transformations (39)
Testing and debugging (38)
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- FTP Downloads.
- Run 3rd party softwares.
- Intuitive UI.
- Need more connectors/sources/destination components.
- Ability to support not just Microsoft services but others too.
- More updates & new features.
- If the job fails, we are alerted
- It has a easy to use GUI to build projects
- Does well with gathering data from different types of data sets
- Converting flat files of data requires a data transformation that is tedious
- Extraction and convert data from various sources
- Pipelining in the data extraction process
- Unions between different data sources akin one meta datasource
- Some labels in Visual Studio snap-in for MS SQL Server are collapsed on non-English (German & Russian tested) locales.
Less appropriate: big key-value data storages processed slowly, and hard to make data mining through uniting non-RDBMS and RDBMS data sources naive way. The data from non-SQL databases should be prepared accordingly to be represented in a table-like way if possible.
- SQL Server Integration Services design of dataflows allow us collect and merge data from a large variety of sources.
- The structure of packages in a solution allow us to separated distinct functions yet share a common collection of parameters to ensure consistency.
- The availability of environments to provide differing parameters is also a powerful option to allow us to design reusable solutions.
- The deployment process could use the improvement of deploying the Parameters into an environment of choice. Having to do this manually is time consuming and error prone.
- Propagation of values from within a package to a parent package is lacking. This is helpful when using a parent package to execute and report on a collection of smaller related packages.
- Import data into tables
- A lot of documentation/support on the web
- Scheduling packages
- Very easy to use
- Error messages are could be more clear
- Process flow.
- Connection to a wide array of sources.
- Built-in upsert component.
- Better operability with source control systems.
- Data migration is pretty fast.
- SQL Server is highly compatible with the database.
- We receive errors in moving JSON data to the database. It does not work efficiently in JSON-related data.
- ODBC connections give a connection error after a certain period of time.
- Easy to manage projects and packages.
- Ideal for repetitive tasks.
- Can handle complex tasks consisting of multiple, diverse packages.
- Include SSIS in the default installation of SQL.
- The flexibility and different packages and options can make it confusing for first-time users.
- Recommendations in the selections could make it easier to build a solution.
SQL Server Integration Services (SSIS) is not easy for new users due to the plethora of options available.
- Handles multi-step, complex data moves.
- Pulls from a variety of data sources.
- Add-ons are readily available to extend their usefulness.
- Integration with SQL Server and data tools.
- The package publishing feature has gotten better over the years, but it could still be simplified.
- Incorporating features from add-ons into the standard application would be helpful (mostly in relation to data sources).
- Easier configurations for multiple publishing targets (dev/test/prod) with associated data connections.
- It sits inside of Visual Studio and SSMS so you have a consistent look and feel across toolsets
- Extensions. We use Pragmatic work transforms that seamlessly fit into SSIS to make certain tasks easier
- Performance. SSIS is not the fastest tool out there but it is more than enough for our needs and since it is bundled with SQL Server you get great value.
- Funny thing, working with Excel requires hacks and work arounds. Really wish Microsoft would fix this.
- More transformations to reduce the need of 3rd party tools. Tasks like SFTP would be nice.
1. To populate our data warehouse. Running SSIS packages from the server on a 10-minute schedule, we extract, transform and load the data into the warehouse to support all internal reporting and provide data as a service to our global partners.
2. Bulk data import to our CRM system. Building packages to run on-demand to bulk import structured data to our CRM instance.
In the past, we have used SSIS packages to complete a one-time migration from a legacy CRM system to the current CRM.
- Ease of use - can be used with no prior experience in a relatively short amount of time.
- Flexibility - provides multiple means of accomplishing tasks to be able to support virtually any scenario.
- Performance - performs well with default configurations but allows the user to choose a multitude of options that can enhance performance.
- Resilient - supports the configuration of error handling to prevent and identify breakages.
- Complete suite of configurable tools.
- Connection managers for online data sources can be tricky to configure.
- Performance tuning is an art form and trialing different data flow task options can be cumbersome. SSIS can do a better job of providing performance data including historical for monitoring.
- Mapping destination using OLE DB command is difficult as destination columns are unnamed.
- Excel or flat file connections are limited by version and type.
- SSIS is particularly well suited for jobs that need to be consistent, repeatable, and error managed.
- Ongoing extract, transform, load [ETL] jobs that are scheduled or manual.
- One-time ETL with complex datasets.
- Migrations of large datasets.
SSIS is not well suited for small or simple datasets that can be copied or exported safely to flat files for import. It is possible to do this but would generally take longer to build in SSIS unless there was a good reason to .remove manual handling of the data in transport or the action needed to be testable/repeatable.
- Almost no programming is needed, like drawing simple flow diagrams.
- If you want to be more advanced you can add some VB or C# programming if needed.
- Microsoft tool using all the great connectors, using any data source
- Easy to add a third part like Cozyroc
- File handling
- Integration with office tools could always be improved, MS, as usual, provides these 80% solutions to make room for third vendors.
- Error message or event handling, better messages and link to processes.
- SSIS works very well pulling well-defined data into SQL Server from a wide variety of data sources.
- It comes free with the SQL Server so it is hard not to consider using it providing you have a team who is trained and experienced using SSIS.
- When SSIS doesn't have exactly what you need you can use C# or VBA to extend its functionality.
- SSIS has been a bit neglected by Microsoft and new features are slow in coming.
- When importing data from flat files and Excel workbooks, changes in the data structure will cause the extracts to fail. Workarounds do exist but are not easily implemented. If your source data structure does not change or rarely changes, this negative is relatively insignificant.
- While add-on third-party SSIS tools exist, there are only a small number of vendors actively supporting SSIS and license fees for production server use can be significant especially in highly-scaled environments.
- Very good for traditional RDBMS ETL and integration.
- Good developer community support online.
- Good at ingesting structured flat files (CSV, TXT, Excel).
- The tool struggles out of the box handling emerging datasets such as JSON feeds.
- Unstructured datasets can be challenging to work with.
- Some out of the box can be very resource heavy, and the UI is not very straight forward. Luckily there's a large community of SSIS users that can provide guidance.
SSIS may not be the best tool if you are using it for ETL and data integrations for JSON and XML feeds. The native tree parser is not very good.
- It handles SQL Server databases flawlessly
- It provides a robust developer interface
- It allows a developer to encapsulate complex scripts directly within an SSIS project or reuse scripts across projects
- It interfaces quite well with a large number of available libraries
- SSIS memory usage can be quite high particularly when SSI and SQL server are on the same machine
- SSIS is not available on any environment other than Microsoft Windows
- SSIS does not function with any database engine back-end other than Microsoft SQL Server
- Source system connectivity (API's, SQL DB's, Olap Cubes, etc)
- UI which allows less technical people able to quickly and easily complete the tasks.
- Debug and quickly/easily troubleshoot logic and errors while running jobs or procedures.
- Version control sometimes seems to be an issue when many different sources are coming into play.
1. Extract data from the source database and transform it and then load it into a destination database
2. Use this for Balancing between 2 or more different databases
3. Extract the data from relational databases and load into data ware house.
- Transform the data
- Script components are always a problem
- Cannot debug properly inside of Script component
- Visual programming makes configuration easy and accessible
- The ability to code also allows users to implement complex logic for data manipulation and etc.
- Easy integration with MSSQL Database instances.
- Component properties are not very well defined, which makes the learning curve harder
- As control flow and data flow often looking similar visually at first glace, it takes awhile to differentiate which one you are working on as users need to look at the tiny symbol and text on the tab to do so. A more straightforward color-coded or larger visual cue to differentiate between the two would make this easier.
- Source systems connectivity (RDBMS, Flat Files etc)
- Embedding SQL and other code in case of complex business logic and data transformations
- Multitude of data transformation options
- Ease of use, easy to learn
- Skills availability in the market
- version control/configuration management
- Programmatic issues like NULL handling (it's RDBMS counterpart SQL Server database uses NULL differently)
- The source connectivity options should be enhanced
- There are many good workflow tools and ways to control the order in which things happen. In a short amount of time, you can quickly create a package that will move data from point A to point B and have it scheduled to run 4 times a day. Or if you need error handling or other business logic, you can spend more time and completely automate repetitive tasks. Robust? Check!
- SSIS can consume multiple sources of data. From flat files, to Excel, to Oracle, or DB2...I've been able to access multiple data types and move them in and out of SQL databases with SSIS. We had one linux system that ran a Basis database system and there was a need to have something done, but no one could figure out how to make it work. I was able to use SSIS to import files and execute code on a server that had nothing to do with SQL server. So flexible? Check!
- We already use SQL server almost exclusively for our enterprise database needs. The fact that we already have access to this tool at no additional cost to the business is a bonus. The fact that it is powerful, even better. Value? Check!
- I know in my "pros" comments, I said it was nice because we already had access to SSIS by virtue of being able to install it on existing SQL servers with no additional license cost. But, if you rely heavily on SSIS, you will want to have it on its own server rather than letting it share resources with a very active SQL server. That means additional licenses. It can consume a lot of resources, depending on the amount of data you're pushing through SSIS at any given time.
- Current versions of SSIS do a much better job of managing deployment of packages into production. It used to be an all-or-nothing proposition so if you had to make a small change to a project that had many packages in it, you'd have to redeploy the entire project which means lots of extra testing. The introduction of package level deployment was welcome.
- SQL server and SSIS play very well together when they have enough resources. If you're using virtual servers and can add CPU/RAM/Space easily, then by all means, put them together and manage the resources so they stay out of each other's way. If you don't have the capability to do that, then you'd be better off having SSIS on a separate server. When everything is working well, it is amazing. But if you make SSIS and SQL fight over resources, it's not pretty (SQL wins that fight by the way in case you were wondering!)
- If I'm being honest, I haven't had to point SSIS to a huge variety of source systems. It could be that SSIS doesn't play well with certain DBMS' (I've heard Sybase compatibility complaints before) and you'll need to do some research and testing before actually using it in production.
In the beginning, we had hundreds of Stored Procedures, instead of SSIS packages. The Stored Procedures were poorly made by some users, only thinking on the resulting query and not the execution performance, plus the people doing data mining created tables for a report and then they didn't eliminate such tables that only had one use, also some of those tables kept growing without being needed any longer.
The implementation and onboarding of SSIS was made with the intention to correct some of these T-SQL coding issues. It is easier to understand a diagram than sheets of T-SQL code with good documentation. Besides the performance for bulk inserts was better with SSIS than normal inserts in stored procedures. We were able to divide and define a bit better the roles, between SQL developers, Data miners, and BI engineers.
- Logging, this is essential when you do ETL. With SSIS you can run the package and see step by step the progress, how many tuples complied with the filters, like how many went left and how many were correct, or excluded.
- Using regular expressions with C# direct code by adding Script Components it's easier with SSIS
- Performance, it is difficult to demand good SQL code to every member of the BI team not everyone is specialized in T-SQL.
- SSIS standardizes a bit more the code and allows users not completely familiar with SQL or even C# to achieve what they needed, the package still needs to go through a code review but it is quite easier to understand.
- Be careful when you edit a package, if the version is above the SSMS you are using then it will not be compatible. You have to compile or edit the SSIS package in the same version of SSMS you are using.
- To explain it a bit better if you have SQL 2014 in your laptop, pull a package for the DB server which is running SQL 2012, after you edit the package it will not be allowed in the SQL server.
- Python, Perl scripts are still a high competition for SSIS, mostly because they are very easy to manipulate, if you need a change you can do it directly with notepad.
- Plus Python now has an add-on called Pandas which is great for manipulating data.
The only main competition I have noticed is the combo of Python, Pandas, and Jupyter; but for that other solution, you will need an experienced team in scripting. So at the end is choose what your team feels more comfortable.
- Great for parsing data from various file formats into SQL server. As an example, we use it to extract data from XML, EDI and other flat files.
- Great for applying custom business logic in the ETL process. These business logic could be built into functions, stored procedures and applied through the SSIS packages.
- I like it's exception handling capabilities and how it's able to show the module that threw up the exception by highlighting it in red.
- Works very well with Visual Studio and as a matter of fact, you can build all your SSIS packages right from SQL without even opening up SQL server or BIDS.
- Not sure if it has JSON support but if it does, that would be awesome! Basically, the ability to consume data from a JSON data set.
- In as much as Microsoft built it for the SQL database, it would be awesome if we could leverage SSIS for data ETL into other databases like MySQL and Oracle etc.
- Add more color themes! The default color theme is old school and really sucks if you ask me.
2. Amazing if your primary database environment is SQL server.
3. Works great with Visual Studio and Microsoft even has it now on the Azure platform.
4. Works great with various file formats - XML, EDI, spreadsheets, flat files etc.
5. Works great in scenarios where it is necessary to apply business logic through stored procedures etc.
- SSIS allows you to run many processes in parallel. Thus, you can run multiple data flows simultaneously to increase the throughput of the migration process.
- SSIS provides many tools for transforming data during the migration process.
- The one issue that I have with SSIS is that sometimes the business logic gets baked into the SSIS package. This can make it harder to debug. In some cases this makes sense if the source and destination is not a database. However, when using a database as a source I prefer to manipulate and transform the data via sql and then simply expose the dataset to SSIS after the data has been prepared. I find it easier to write and debug sql directly rather than working in SSIS. However, in cases when a database is not involved then putting the business logic in SSIS makes sense.
- Presents the flow of data processing very well, making it easy to learn/understand SSIS packages.
- T-SQL and C# friendly.
- Comprehensive configuration, logging, and error handling.
- Some components are not working very well, including sorting, SCD, etc.
- Different components could have different syntax or data type definition.
- Not enough scripting learning materials.
- Native data connections to SQL Server and Azure SQL DB and DW
- Flat file processing
- .NET C#/VB scripting
- Ease of use in designing and implementing control flows within conditional processing and looping
- Integration with Access/Excel should be more seamless and less problematic
- CASS certified address standardization
- Higher performing Slowly Changing Dimension functionality
- Incremental loading (deletion, upsert, etc.)
- PowerBI integration. I really really really want to be able to refresh reports via IS packages
- More Azure administration tasks
- Office365 and Sharepoint integration
- Full refresh loading files (Excel and Flat File) into SQL Server.
- Integrating .Net (VB/C#) scripting
- Incremental loading
- OLAP database loading
- Streaming, real-time/near real-time loading
- Big data loading