SSIS Reviews

193 Ratings
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Score 8.2 out of 100

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zahit bogus profile photo
Score 9 out of 10
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In my department, we design and manage ETL processes. We use SQL Server Integration Services (SSIS) to design a data warehouse. To create a data warehouse, we can make the stage layer and DWH layers with the SSIS ETL tool. We create data marts using aggregate functions in the SSIS ETL tool. In addition, we use the SSIS ETL tool to move data between databases.
  • 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.
For enterprise business intelligence projects, the SQL Server Integration Services ETL tool is a logical choice to move your data to the data warehouse on a daily basis. The SSIS ETL tool has enough functions and capabilities to design the data warehouse. If you want to send your JSON data to the database, SSIS does not work here.
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Steven Gockley, MBA, MCSA profile photo
Score 9 out of 10
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We use SSIS in a number or different use cases. 1. Traditional ETL for our data warehouse. 2. Automate reporting. We have a number of reports that go out to thousands of customers daily. We have implemented SSIS to run these. 3. Application integration. With SSIS we are able to quickly integrate from our order entry and operations software into our ERP and Financial software.
  • 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.
If you are a SQL Server shop, it is almost not even a question. The tool slides in nicely and is fairly easy to get going and implement. I have used it for Data Warehousing, Report Automation, DBA Task Automation, System Integrations and even Web Testing. It is extremely flexible and anyone working heavily with SQL Server should really look at using it.
Read Steven Gockley, MBA, MCSA's full review
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November 24, 2019

Our use of SSIS

Score 9 out of 10
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We use SQL Server Integration Services to import data into and out of our main SQL server databases. This data comes from a variety of external sources and sometimes there is data format mismatch between source and destination. With an SSIS package, we can retain the mapping and data formats integrity from the source to the destination without having to go through the process every 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.
Well suited and designed to enable flexible data extract and transformation to and from an SQL server. It works very well for repetitive tasks and it is easy to manage (and change) packages once built. The use of templates makes the initial startup process simpler.

SQL Server Integration Services (SSIS) is not easy for new users due to the plethora of options available.
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Score 10 out of 10
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The IT department at our organization uses SQL Server Integration Services. We use SSIS to perform extract, transform, and load (ETL) data operations. Our primary use is to move data from a source system or database, restructure the data to optimize it for reporting, and store it in a database instance used for reporting.
  • 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.
SSIS is well-suited for scheduled data loads, such as scraping web pages for currency rates or storm-related delivery delays and writing the results to an application database or copying transactional data from a source system, optimizing it for reporting, and writing to a reporting server. SSIS is also great in helping to combine data from disparate sources to build a deeper data analysis platform.
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Score 10 out of 10
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We are currently using SSIS packages on two major projects:
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.
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Score 8 out of 10
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In my work in the health care field, we use SQL Server Integration Services as our default ETL tool of choice for pulling data into SQL Server from Teradata, Oracle, flat file and other SQL Server databases, and for creating data extracts to send to our clients, typically in the form of flat files.
  • 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.
SQL Server Integration Services is extremely well built for creating packages to run ETL operations in environments where the structure of the source and/or destination data never or rarely changes, however, it tends to be difficult to maintain packages in production environments where the structure of the data changes frequently.
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Score 10 out of 10
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Even though we are a smaller company, we use this huge army Swiss knife to accomplish a number of technical IT tasks. First and foremost we use it to integrate systems like MS CRM and SAP, constantly enhancing the data flow and making tasks easier to accomplish for the users. Secondly, I personally use it clean up data and maintaining my BI ETL scripts running out 12-year-old data warehouse for our analysis cube. The actual tool is currently only in use by the IT department by using Visual Studio (BIDS).
  • 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.
Any integration tasks in a Windows environment like AD or application integration, DB integration, etc. The drag and drop workflow and easy language, for example, derived data changes are so easy to make that it reminds of Excel functions. Full flexibility to draw up any workflow and easy troubleshooting using the data viewer.
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Score 8 out of 10
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We implement data solutions for our clients. We use SSIS frequently as an ETL and Data Integration Tool for clients that have the Microsoft enterprise licensing as it offers decent base-level capabilities for traditional database management systems, and we can justify the tool selection since the technology cost-effective under MS licensing.
  • 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 is very well suited if your project(s) involves ETL or data integration of traditional relational database systems.

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.
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Stu Teel profile photo
Score 8 out of 10
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We use SSIS to connect to API's and other DB stores/wh/marts, etc. In our projects, many different data sources and types exist, and we build BI from the disparate sources that exist within our larger clients. SSIS is used as tool to harvest those disparate sources and run jobs to populate our end DB which feeds our visualization tools.
  • 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.
It's well suited to play a role in bring a bunch of different sources and types together into 1 single/useable location. It is less appropriate for customers on a very tight cloud DB budget. If you wanted to run nightly jobs, those tally against your consumption of data usage and jobs/procedures can add up quickly in the cloud computing world.
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Score 6 out of 10
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SSIS is used within my organization to move data from one data source to another, performing data translations, transformations, lookups and calculations during the data movement. This process often includes very complex data transformation processes including the use of APIs, external references and various class libraries. SSIS is currently used in various areas across the entire organization to solve SQL server-based data transformation issues.
  • 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
Microsoft SQL Server Integration Services is suited for development by those who are NOT very experienced developers. End-users with some database experience may find the development environment easy to use allowing development of basic ETL. Experienced developers will likely feel restricted by the "Microsoft-only" interface. Additionally, many larger organizations that have made a significant investment in databases other than SQL Servers will be unable to use SSIS against those database servers.
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Greg Goss profile photo
Score 10 out of 10
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Like most businesses, we have various sources of data that management likes to be able to compare to each other. I use SSIS primarily to move data between our source systems and data marts and warehouses that our reporting software can be pointed at. I also use SSIS to deliver scheduled file exports to external customers or to import files into one of our critical systems for use. I even tend to use it for non-SQL related things such as file system and ftp tasks. If it needs to be extracted, transformed, or loaded somewhere, I use SSIS to do it.
  • 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.
If you need to move data around or direct the workflow of a process, SSIS can do it. It is a very capable piece of software that I use heavily every day. You do need to be careful because you can over-utilize it for simple things. If you just need to run a piece of SQL every hour to update some values, just use the Agent Scheduler, it's easier. But if you need to automate things in a repeatable and consistent manner, SSIS is a very good product.
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Jose Pla profile photo
Score 9 out of 10
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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
  • https://sqldusty.com/2011/11/06/data-cleansing-with-regular-expressions-in-ssis/
  • https://www.linkedin.com/pulse/using-regular-expression-file-filter-ssis-sean-werick/
  • 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.
Extracting, transforming and loading data from multiple sources with different formatting is not that easy. SSIS provides different ways to connect or import from html, json, comma separated, xml, or other databases, which makes it a very diverse tool.

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.
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Score 8 out of 10
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It is mainly used by technical analysts with the purpose of delivering data integration and reporting projects to customers. It addresses the need for complex multi-source data ingestion, data manipulation, and integration to a MSSQL database.
  • 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.
SSIS is suitable for MSSQL related projects/works due to how well it integrates and performs data manipulation. It is suitable for moderate data input ingestion rates but not suitable for projects where a high volume of data is required to be ingested and processed rapidly.
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Score 8 out of 10
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SSIS was being used as an ETL tool both by IT as well as business teams. It is now replaced by a competitor though. The tool was being used for basic extraction and loading purposes, with hardly any complex data transformation being done. Though there were use cases to exploit the tool, neither Microsoft nor in-house consultants really helped with the tool, with the result being it getting replaced.
  • 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
If the back end RDBMS is SQl Server and if you are migration from Oracle or DB2 to SQL Server, SSIS does the migration job very well. It's tightly integrated with SQL Server. However, issues like NULL handling etc persists. Also, if the integration platform has unsupported connectivity or drivers, then SSIS usage becomes challenging.
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Score 9 out of 10
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SQL Server Integration Services is used for the following purposes in my organization.
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
  • Lookups
  • Performance
  • Script components are always a problem
  • Cannot debug properly inside of Script component
SQL Server Integration Services is best when we use it with Microsoft SQL server database
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February 15, 2018

Great ETL tool

Score 8 out of 10
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We currently use SSIS for imports of purchase orders into our ERP - SYSPRO. These purchase orders come in various file formats: EDI, XML and excel spreadsheets. SSIS helps us aggregate these various files into a common import platform and apply business logic such as ship date calculations, SKU availability checks, customer hold checks etc.
  • 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.
1. Great for ETL (Extract-Transform-Load) data operations.
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.
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Eddie Brady profile photo
August 10, 2017

SSIS

Score 9 out of 10
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I've used SSIS to support individual departments within an organization. Typically I use SSIS to automate migrating and transforming data from one location to another. SSIS has a diverse range of source and destination formats that makes it easy to move data between different systems. There are many add on tools for other source / destinations that are not out of the box. For example, Dynamics CRM.
  • 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.
SSIS is well suited for any processes that can be automated to move data from a source to a destination. However, I don't think SSIS can work directly with Rest API's during it's processing. If that is required than it would be necessary to build your own custom SSIS component to enable this functionality. Extending SSIS to permit this is possible.
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David Milillo profile photo
March 29, 2017

SSIS Addict

Score 8 out of 10
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Integration Services is the primary extraction, transformation, and loading tool we use to populate our SQL Server and Azure SQL DB and DW for our data and our clients' data. We do a majority of our logic for preparing both reporting and application data within SSIS components, scripts, or within T-SQL Stored Procedures executed within SSIS Control flows. It is only used within my group but my group is the only group directly populating our reporting databases.
  • 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
  • SFTP
  • 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
Well suited:
  1. Full refresh loading files (Excel and Flat File) into SQL Server.
  2. Integrating .Net (VB/C#) scripting
Less suited:
  1. Incremental loading
  2. OLAP database loading
Not suited:
  1. Streaming, real-time/near real-time loading
  2. Big data loading
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Hung Nguyen profile photo
Score 8 out of 10
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I cannot say for the whole organization, but we use SSIS for just about all our automation processes. When managing a large data warehouse it is incredibly useful to automate the ETL process. We primarily use it for the data warehouse, but it's versatile enough to use for other automation tasks, reports, and notifications.
  • Clear GUI and ETL workflow. It's very easy to understand how the data is being managed. When pulling up a SSIS solution that someone else has created, it's very easy to see what's going on-- how the data is extracted, how it is transformed, and how it's being loaded.
  • Deploying scripts. Once a proper package store is configured, you just need to hit deploy and it handles the rest. It's also flexible enough that you can still use SSIS packages without using an SSIS DB for version control by calling them through the file system. Or if you're one of those people who love batch scripts, you can also execute the packages through command line.
  • SSIS Package Store. It's a great way to manage your versions and deployments. Bonus is that if you use a package store, it'll also give you error reports after the fact if a package fails for debugging. It'll tell you exactly what step failed and why.
  • I think it handles undefined/dynamic data sources poorly. Considering that we use it primarily to ETL data from other systems across the whole organization to bring into our BU's data warehouse, we sometimes have issues when the source has changed. If someone adds a column without letting us know, we'll need to modify the SSIS packages.
  • Sometimes the error codes are vague or cryptic. When debugging a SSIS package I have to google the code or error message and hope someone has a similar issue on stack overflow.
  • SSIS really only works if you're already using a lot of Microsoft Products like Microsoft SQL Server or SQL Server Reporting Services. As mentioned in the name of the application, "integration services", it's designed to integrate your products together so that you can get the most out of it.
As mentioned in the pros and cons, SQL Server Integration Services is great when you're running a Microsoft stack. We're loading data from all over into our data warehouses and moving them between other SQL instances all the time. I can whip up a package and deploy it in less than 5 minutes to get our data moving between SQL servers. It integrates really well and is flexible enough that you can supplement any lacking functionality using third party plugins or building your own tools. Although this has been solved in later iterations, SQL Server Data Tools (which is used to build SSIS packages), did not have the functionality to download files from an FTP server using SFTP. I built a C# app that I could run using SSIS.
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Eugene LaRoche profile photo
Score 7 out of 10
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SSIS is utilized as a systems and data integration tool, and for performing a variety of ETL tasks. It is utilized by the IT department to support business applications, particularly where two or more systems require data exchange. It is a mature product (stable and reliable) and comes as part of standard SQL Server implementations so its fairly simple to utilize.
  • SSIS Integrates very well with other Microsoft products including Excel and Access. Other ETL tools may have a difficult time integrating with Access, so we have observed SSIS to be superior in this regard.
  • SSIS has the capacity to do a fast bulk load (BCP) with transformations, within the bulk load itself. This feature is not available when utilizing the BCP utility outside of SSIS or from other ETL tools. To be clear, the transformation is occurring within the BCP component itself. Other ETL tools will have to utilize a non-BCP load (slower) or do the ETL after the load. This is a great feature I have not seen replicated in other tools.
  • SSIS integrates seamlessly with SQL Server RDBMS, including SQL Jobs and Stored Procedures.
  • SSIS has nice support, tools, and wizards for fixed length file processing.
  • SSIS IDE (SQL BIDS) is lacking, particularly when compared to Visual Studio for .NET development. It was carried over (at least in look and feel) from the legacy DTS tool. It could use a complete redesign from scratch. Considering how superior the VS .NET IDE is, the inferior SSIS BIDS IDE is unacceptable.
  • SSIS is very Microsoft centric. This is a strength when dealing with pure MS technologies, but becomes a weakness when dealing with disparate, distributed systems, including cloud computing. Other ETL tools for example easily integrate with everything from AWS to Google Drive to Sales Force.
  • SSIS deployment model is clunky and non-intuitive.
SSIS is best suited for use in a pure Microsoft environment, or where interfaces to external systems are file based. It is not ideal for integration into disparate systems that are not interfaced via flat file. SSIS is also ideal when utilizing the job scheduler built into SQL Server, as it is seamlessly integrated with SSIS. In other words, it's very easy to schedule an SSIS package to run automated using the SQL job scheduler. Running SSIS packages from other job schedulers is more problematic, unless that tool has built in SSIS support.
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Chris Morgan profile photo
Score 10 out of 10
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Our laboratory uses SSIS to transfer data between servers and databases, to data warehouses, reporting services and to and from file sources in a complex SQL Server environment that includes Merge Replication and SQL Server Reporting Services. Besides internal transfer, transformation, and manipulation of data, including archiving of data, we use SSIS to export laboratory results to external systems and import scheduling and sampling information from external systems.
  • SSIS can query, filter, and transfer data between databases on different servers without establishing explicit trust relationships between those servers.
  • SSIS can be used to refresh a reporting database from a transactional source database, transforming or flattening the data and tables as necessary to facilitate reporting. This can be done incrementally, or by emptying and refilling the reporting database from scratch.
  • SSIS is configured through graphical interfaces that make it relatively easy to see the flow of data including where problems occur.
  • SSIS has a number of tools that allow you to debug SSIS packages and track down problematic data or configurations.
  • SSIS allows you to program Script Tasks in C# and VB allowing extremely powerful functionality including looping and sending consolidated alerts.
  • SSIS allows you to control virtually every part of the SSIS package (connections, variables, etc.) using configuration files so you can have one package that can be used in several different places (such as dev, test, and production environments) only by editing the configuration file that the package uses when the job is scheduled.
  • One of the most frustrating things about SSIS, at least in its 2008R2 incarnation, is that of the annotations. Formatting annotations, getting them to wrap in the box, etc., is either extremely difficult or impossible without editing the XML of the SSIS package. You also CANNOT copy and paste annotations, so if you have several similar ones, you have to type every one of them from scratch. It makes you want to scream.
  • In the 2008R2 incarnation, setting up tasks to pivot data is very tedious and non-intuitive. I have heard that this improved in SSIS 2012, along with performance. We are trying to upgrade our SSIS and SQL server environment to 2012 or 2014 as soon as possible.
  • Sometimes datatype conversion doesn't work implicitly when it should and you have to do it either explicitly with a query or with a datatype conversion task.
Whenever you are moving data from one database to another, either on the same server or a different one, SSIS is a great tool. This is true for one-time transfers because it is so easy to set up and configure what you want to happen, and it is true for automated transfers that occur every day or every hour. If complex transformations of the data are necessary, including filtering, generation of new columns, merging datasets, whatever, SSIS is the best tool out there for ETL. If the task takes place within a database or between databases on the same server, and the task can be accomplished with a simple SQL script, it will often perform much faster than an SSIS package and require fewer server resources.
Read Chris Morgan's full review
Samir Patel, PMP profile photo
Score 8 out of 10
Vetted Review
Verified User
Review Source
SSIS is our main ETL tool since we are mostly a SQL Server shop. We use it to integrate data into all of our reporting environments, transforming and massaging data dynamically and connecting to many various sources along the way. It is also a quick way to move data out of other Microsoft environments such as Excel and Access Database.
  • Transform data
  • Issue Logging
  • Event Notification
  • Needs more connections to environments such as Hyperion
  • Not as robust as Informatica
  • No built in metadata management

When dealing with Microsoft environments, it is the tool that is easiest to use and implement. When it comes to connecting to Oracle Hyperion or pushing data to non Microsoft environments there can be challenges.

SSIS allows for quick implementations where the flow and environment is not too complex or large.

Read Samir Patel, PMP's full review
Waheed Abualrous, MCP, MCTS, MCE profile photo
Score 8 out of 10
Vetted Review
Verified User
Review Source
Ee create master data management and workflow software for oil and gas companies which involves getting and writing data to other systems. We use SSIS for all integration as it helps dealing with different systems.
  • Makes it very easy to move data form one system to another
  • So many out of the box transformations
  • The script task gives you so much flexibility as you can write C# code to do almost anything
  • Easy deployment and configuration after the 2012 release
  • Project deployment model to allow deployment of single packages
  • Allowing to apply one transformation on all columns, like changing all column types from nvarchar to varchar instead of doing it one by one
  • Mamed parameters from inline SQL statments using OLEDB command, currently they are called parameter 1, parameter 2...etc., which is confusing when you have many paramters
  • Pass authorization tokens when calling remote processes like a console application, currently being called as anonymous
if you don't have an Sql Server license or are deploying to any OS other than Windows you need to consider a different technology.
Read Waheed Abualrous, MCP, MCTS, MCE's full review
Tom Jaskula profile photo
Score 8 out of 10
Vetted Review
Verified User
Review Source
SSIS is used by the IT department for extracting, transforming, and loading data. The most common application for SSIS would be for cleansing and restructuring data while it is being loaded into a data warehouse.
  • Providing developers with a wide range of architectural options.
  • Providing the ability to connect to a wide array of data sources.
  • Proving many different deployment options.
  • While SSIS does provide a plethora of architectural options, all of these options can at times be overwhelming. Some competing products offer a more straight forward and streamlined approach.
  • SSIS does not currently provide any templates, although this is supposed to be addressed with the upcoming release of SQL Sever 2016
  • Connecting to Oracle databases is not easy, SSIS still requires the installation of other tools.
SSIS is a good fit when you have structured data. If you're looking to prepare unstructured data for doing text analytics, this is not the right tool.
Read Tom Jaskula's full review
Luca Campanelli profile photo
Score 9 out of 10
Vetted Review
Verified User
Review Source
In our department we use SQL Server Integration Services daily. The main use is to do data processing for several ETL processes. Thanks to the use of SQL Server Integration Services we can upload data to the data warehouse in different ways such as a bulk load or with incremental loads thanks to the use of SCD task data.
  • High data load speeds
  • Many data processing modes
  • Many data sources and destinations data possible
  • For some tools you need to integrate third-party packages
  • Depending on the version you must have the right visual studio version to develop and deploy
  • Not really intuitive
SQL Server Integration Services is optimal for any process of data extraction, data transformation and data loading, and can be used quickly for massive loads. It can be used for scheduled and incremental processes. It is not optimal for data structures that continuously change their structure because in this case it requires continuous changes and deploys.
Read Luca Campanelli's full review

Feature Scorecard Summary

Connect to traditional data sources (34)
9.5
Connecto to Big Data and NoSQL (26)
7.6
Simple transformations (34)
9.9
Complex transformations (33)
8.2
Data model creation (17)
7.4
Metadata management (21)
6.7
Business rules and workflow (27)
8.2
Collaboration (25)
6.3
Testing and debugging (32)
7.4
feature 1 (1)
7
Integration with data quality tools (24)
8.6
Integration with MDM tools (24)
7.5

About SSIS

Microsoft's SQL Server Integration Services (SSIS) is a data integration solution.
Categories:  Data Integration

SSIS Technical Details

Operating Systems: Unspecified
Mobile Application:No