Prior to joining MaXware, I spent a significant amount of time working with Microsoft Identity Integration Server (MIIS). Since joining, I've had a lot of questions from old friends about how the new software stacks up (and vice-versa from new friends). So, I put together a summary of what I've found thus far. I'll discuss MIIS and MaXware Data Synchronization Engine (DSE). Thanks to its very descriptive product name, you can probably guess what DSE does. MaXware also offers the MaXware Identity Center (MIC) for advanced user lifecycle management (workflow, etc.). But for now, I'll stick to data synchronization.
I think the most obvious difference between DSE and MIIS is infrastructure. MIIS requires Windows Server 2003 and SQL Server. For practical purposes, it also requires Visual Studio. DSE, on the other hand, can run on any platform supporting Java Runtime 1.4 or later. DSE also doesn't require a central database, but can leverage Oracle, SQL Server, MySQL, Access or any other ODBC, JDBC or OLEDB compliant database. With DSE, you can build very simple jobs that read/write directly to/from one data source directly to another without setting up a central database. If you want to perform data joins, you can setup a collect database as part of the solution (similar to an MIIS metaverse). So, the DSE footprint is very small. And DSE runs very efficiently using Access as a central store even with a few hundred thousand entries. If performance is critical, support for the bigger databases is available.
Another advantage of DSE is that it doesn't rely on deltas to be managed at the connected data store. So, for example, you don't need to enable the Sun directory retro changelog in order to only process changed entries. DSE handles this by its ability to store its own delta table in the central database with a hash of each record and the ability to compare the hash before performing an action on the record. If you'd prefer to rely on the changelog, that's OK too. You may be able to improve performance that way. However, you might lose some level of audit capability since DSE won't know what attributes have changed.
Another difference is in the storage of server configuration. MIIS stores its server configuration in the SQL Server database. DSE stores its configuration in a single XML file. MIIS, though, does a pretty good job of providing XML-based server configuration export and import. It seems a little more complicated than DSE, but not by much.
MIIS uses a single metaverse for each MIIS instance. So, a set of Management Agents writes to and from the metaverse to perform actions on the user objects. This is fine until you want to manage multiple sets of users. If, for example, you want to manage test data and production data, it becomes difficult to manage due to join rules. It's easy to join incorrect objects by running an MA against a test directory instance with different data. With DSE, jobs are organized in groups. Each group is a set of Passes (a pass is similar to a management agent and a corresponding run profile). And you would setup each group to have its own collect database (or metaverse). This allows you to do more with a single instance of DSE and provides extensive flexibility.
DSE passes are one-way. MIIS management agents are bi-directional. So, if you want to read and write to a given directory, you would create a FROM pass, probably run a few other from passes and perform some actions on the data, then you would run a TO pass back to the directory. In the MIIS world, it's common to run a single MA multiple times to capture data changes that happened after the initial import run. While I prefer the way DSE handles this, I don't see this as a big advantage for either solution.
Both solutions provide logging, but DSE has the advantage of local delta tables for audit and reporting. Also, the separation of TO and FROM passes also make it easy to include a pass that writes to an audit table before losing the old and/or new attribute values. While XML data mining is possible with MIIS, I would much rather work with DSE if advanced audit reporting is a requirement -- especially at the attribute level. I'm not really sure if attribute-level auditing is possible with MIIS without some heavy scripting being built into each attribute flow rule.
DSE FROM and TO passes have a SOURCE tab and a DESTINATION tab. When importing data from a SQL database, the SOURCE tab allows you create a custom query that is as advanced as you would like. In addition, if you are creating a TO pass to any type of data source, you can write an advanced query against the collect database to select whatever subset of users you can imagine. MIIS is limited to import only from a single table (or view). And management agent operations are run against the entire connector space set for that agent (or the deltas). MIIS lacks the ability to create custom queries that define a subset of users to which a set of actions would be applied.
MIIS is obviously an identity data tool. You can modify the metaverse schema to accommodate whatever object types you need, but it's designed to manage identity data. DSE is designed to handle any kind of data. There are no pre-configured data structures. DSE does schema discovery on any kind of data source and can easily be used to synchronize any type of data. For example, if you want to synchronize customer order data from one type of database to another, DSE can handle it as easy as managing the customer identity data.
I think they two products are similar in price, but DSE allows the customer to leverage existing server and database infrastructure rather than requiring new licenses for Windows Server and SQL Server.
I realize that most of these points favor DSE over MIIS but that wasn't my predetermined intention. These are what came to mind when thinking about the differences between the two products. If you are a C# developer who lives in Visual Studio and already owns an open SQL Server license, you may prefer MIIS. Outside of that scenario, I think there are a number of compelling reasons to consider DSE. I'd be interested, though, in hearing opposing viewpoints -- there may be a perspective that I haven't considered. My hope is that this summary paints a picture of the basic differences between the two products and allows the reader to think about how these differences may or may not affect a given environment.