Microsoft Parallel Data Warehouse Reviews

I am using the Microsoft Parallel Data Warehouse for working with information services and datasets, creating reports. It is used primarily for our historical data, but we do some in real-time also. We can receive data weekly, nightly, or even hourly.

The most valuable feature of this solution is performance. It reduces the time it takes to process data.

The initial setup of this solution is complex and can be improved.

Sometimes you need to re-run the reports because they don't work.

The reporting for certain types of data needs to be improved.

I have been working with these kinds of services for the past five to ten years.

The stability depends on your data and whether it is null. If the data is set up properly then it will be more stable.

It is definitely scalable. We have more than 220 users in multiple cities. Most of the people, perhaps 70%, are information technology users. The remaining 30% are business analysts who are concerned only with reports.

We do plan on increasing our usage. We recently converted all of our Tableau data, and now we are working on permissions and security.

The technical support from Microsoft is good.

The initial setup is definitely complex.

We are not always aware of where the data comes from because it is distributed. Sometimes the data is fine and sometimes it needs to be set up. Once it is configured then it runs fine in parallel.

The length of time for deployment depends on the size of the data and whether we were also implementing a data warehouse or a data mart. When I was implementing this solution and copying data, it took between six and eight months. That is end-to-end with everything finished.

If it is a small data mart then it may only take a month.

Our in-house team, including our DBA, handled the deployment.

My advice for anybody who is implementing Microsoft Parallel Data Warehouse is to be aware of how big the data is. You have to be sure of how to secure and take care of the data. You have to think about scalability.

I would rate this solution a seven out of ten.

This is a solution that we implement for our clients. We provide end-to-end development, maintenance, enhancement, testing, and support. Everything is done by us.

We are able to monitor daily jobs, so if there is anything that needs to be done then we can do it. If there are discussions with our clients about changes that need to be made then it is us who implements them.

The most valuable feature is that we can deploy directly to the production environment, rather than having to go through a development environment first.

If the database is large with a lot of columns then it is difficult to clean the data.

I have been using the Microsoft Parallel Data Warehouse for two years.

The Microsoft Parallel Data Warehouse is a stable solution.

We have not had problems with scalability.

The initial setup is not difficult. It is easy and we are fine with that.

We deploy this solution for our clients.

I am not comfortable with the Microsoft Parallel Data Warehouse at the moment.

I would rate this solution a six out of ten.

We are using PDW as an EDW solution.

It helped, initially, as a replacement for our DW DB, but later on faced issues due to concurrency, which forced the customer to use the DB as a secondary solution.

Nothing specific, comparable to other solutions.

Concurrent queries are limited to 32, making it more of a data storage mechanism instead of an active DWH solution.

They need to improve the metadata being captured to a greater duration.

One to three years.

MPP processing gives us the ability to distribute large data sets across nodes.

We delivered a data warehouse for Contactless and Oyster at TFL.

Improve the speed of processing replicated tables.

We have been using this solution for years.

There were stability issues when the product was in beta.

There were scalability issues in that there is a limit to 32 concurrent queries.

Technical support is good.

We switched from the standard SQL server 2014. We use PDW to improve overall ETL and report performance.

The initial setup was complex. It was fairly challenging to migrate from SQL server to PDW.

Make sure your data volumes are very large: At least 60 million rows per table.

Microsoft SQL Server Parallel Data Warehouse provides significant performance boost from 10-100 X times faster for data loading operation.It minimized the cost of design with lower infrastructure requirements.It is designed to provide optimized performance on latest industry hardware.

It requires significant infrastructure expertise to implement. Possibility of over specified storage or under specified CPU (need proper planning before starting implementation)It requires significant SQL Server expertise. Microsoft SQL Server Parallel Data Warehouse is purposely built to provide high performance data warehousing. By adopting industry standard hardware it avoids vendor lock-in issue. It is best suited for mid-level to large scale organization.

Both hardware and software support is provided by Microsoft.

Analysing large volumes of data collected from auto ticket barriers at railway stations.

It has allowed fast daily loads and analysis of millions of rows of data, which eventually moved to near real-time.

It handles high volumes of data very well.

It needs more compatibility with common BI tools. 

It does not work well with normal ETL tools. Some functions do not work.

One to three years.

• SQL Data Warehouse provides our organization with a way to access all corporate data in a uniform way. • SQL Data Warehouse allows us to manage our database using network or hierarchical database systems rather than solely relying on relational DBMSs. • It supports the bottom-up process of data warehouse creation where several data marts can be joined to form one big warehouse. I found this very handy especially where a few teams are involved.

• Data cleaning involves the use of special software to help the cleaning team uniformly match attributes from the target source data to its destination. • It is expensive. SQL Data Warehouse is not supported by all versions of SQL Server such as the Express version and therefore becomes unavailable to many limiting its acquisition and use. • Uses a different approach to databases than that used in relational DBMS hence posing a challenge to novices.

• It comes as a special tool of cleaning data through the data transformation process where data is extracted from many sources before it is assembled and consolidated to form a single database. This is the main factor that led my company to choose SQL Data Warehouse as the special tool to consolidate relational databases. Close examination was done on SAP Data Warehouse and IBM Data Warehouse but SQL Data Warehouse provided a more extensive way of integrating data. I personally value the role that SQL Data Warehouse has contributed in achieving the goals of business intelligence. Users will to generate timely reports from huge databases as well as database cleaning should embrace this provision.

We primarily use the solution to build up our server analytics.

The data transmissions between the data models is the most valuable feature.

The database is enormous, so much so that we haven't exploited it completely.

I don't have any recommendations for new features at this time.

I've been using the solution for about one year.

The stability of the solution is good. We haven't had any issues with it.

The solution is very scalable.

My understanding is that the team's experience with technical support has been good.

We didn't previously use a different solution. Our customers chose this solution.

The initial setup was straightforward. We had a knowledgeable team working on the implementation. Deployment took about six to eight weeks.

We use the on-premises deployment model.

I'd rate the solution eight out of ten.