Microsoft Parallel Data Warehouse Reviews

The primary use cases for this solution are for recording and transactions.

The most valuable feature for me is querying.

I would like to see better visualization features.

A stateless update functionality for the forms may help. Without this, you have to perform updates manually using the drop-down menu.

The user interface should be more user-friendly.

I have been using the SQL Data Warehouse for fifteen years.

The stability of this solution is based on the set up from the outsourcing team. There are a lot of things to consider, including the connection.

It is constantly being used with transactions occurring every minute.

It is quite easy to scale this solution. The whole company of approximately 2,500 people uses it.

Prior to using this solution, we used Oracle. We switched because my daily requirements are on Microsoft SQL. 

I find this setup of this solution to be simple, but that is based on fifteen years of working with it.

We use this solution for keeping track of sales, goods, times of shipping, and other information. It is used for our KPIs.

This solution helps the higher levels of the organization because they have better visibility of the whole company. This helps with decision making in terms of what should be improved or implemented.

The most valuable features are the performance and usability.

Microsoft Parallel Data Warehouse is simple to use, and the user interface is intuitive.

More tools to help designers should be included.

I have been using this solution for about two years.

This is a stable solution. Once your data is stable, the data warehouse is stable too. If the structure of the data changes then you can't change the warehouse to add or delete fields or columns.

Scalability depends on how you design the warehouse. It can be scalable, but it depends on how much data you have to put into it.

We have about six hundred users.

Unfortunately, we are in a country that has a limitation that means we cannot contact Microsoft directly. Most of the time we use Google and I can help myself to solve problems.

Prior to Microsoft Parallel Data Warehouse, we used a standard relational SQL database. We switched because of the performance and because the data and KPI changed. It would be difficult to use a relational database. We switched to a data warehouse solution because it was acceptable.

The initial installation and setup were easy and we did not have any issues. The deployment was completed within hours.

We deployed using our in-house people.

My advice to anybody who is implementing this solution is to design the databases as well as they can because it is difficult to make changes in the future. It is also important to have a time field in your data in case you want to use it in the future as a reference.

This is a good solution but all software can be improved and made better.

I would rate this solution an eight out of ten.

I work for a Microsoft partner. We're one of the Gold partners, so we implement on their databases. We are also Dynamics 365 specialists, and I'm a Business Intelligence consultant, so I do SQL, Power BI, Azure SQL, SQL Data Warehouses, and a few others.

What I like the most about this solution is the fact that you can basically add capacity behind your data modeling, and you speed up the process before it goes into the Cube holding section. That's a great integration layer. You can basically collect all your data, and then that becomes a staging database for other models, where you can then either report directly with Power BI, or Excel, or other applications, and in more specifically, data Cubes with Microsoft Analysis Services.

Something that needs to improve, is the integration layer itself connecting to other non-Microsoft layers. But I don't know if that can be improved, due to the complexity of the data that they're connecting to. But I think they can maybe look at a way to do incremental updates, as it is slightly different.

The program is very stable - even during power outages it only takes a few minutes to be up and running again.

I think Microsoft is the largest scalable company in terms of data warehousing, elastic pools, elastic servers. Even with Power BI conducting parallel data warehouses. Those can scale up pretty large, and if you really want to, you can move into the data lakes.

I am very satisfied with the customer service/technical support. We work for a Microsoft company, so we've got a direct line to Microsoft, and because I am also an ambassador for one of the other larger event companies, I have great connectivity - I speak to some of the black belts at Microsoft!

The initial setup was quite complex. The time deployment takes will depend on all the components they specify. We've had deployments that took a couple of weeks, and we've had deployments that's been spread out over multiple years, because we cover 60 countries, in six continents.

I think the program is well-priced compared to the other offerings that are out in the market.

On a scale from one to 10, I rate this solution a nine. In the future I would love to see a slightly better automation engine, just for the data integration layer, to make it slightly easier for end-users or junior developers to get involved in incremental updating.

The biggest part that we battle with in terms of costing, and explaining to people why it takes so long to develop some of those things, is just to get the data into the actual data warehouse and automating that. It's purely an integration layer to actually get the data into the data warehouses.

People need to do their research very well to understand the terminology and the technology when they speak to people that are technically inclined, because there's a lot of miscommunication in terms of what they expect from the program and what's delivered at the end of the day.

The biggest lessons I've learned through the years are that Microsoft is probably the largest research company there is. So people should stick to people that know what they're doing, and Microsoft definitely has some very, very capable people designing these products.

And that's probably why I've stayed with Microsoft so long. I've actually tried out a few other suppliers, but I always go back to 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.

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.

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.

The bulk data-load feature with fast response is one of the most intriguing features. In addition, clustered column store index boosts OLAP query performance significantly.

For large scale big data analytics, we have been using this product for two years. It has inherent Hadoop integration that can refer HDFS by means of external tables. Thus, large scale historic data retention for business function improvement is quite easy, thus boosting customer confidence.

It supports partitioning to improve query performance. However, this has a bottleneck if there are many partitions in the baseline table and the underlying query performance degrades significantly.

I've used it for two years.

We have not faced any major issues with this product.

Unlike other MPPs, such as Netezza, this requires excellent expertise in SQL to reap the benefits of using it.

7/10

7/10

I have used Netezza as an MPP solution for around eight years, and from my personal experience, I believe Netezza has an edge over PDW due to user friendliness.

The initial setup was straightforward as it is an appliance. The Microsoft support team work closely with us. We always do it in collaboration with the vendor team and thus ensure we get the best out of our investment. The project team is involved during the initial setup process, and thus optimal installation is ensured.

We always do it in collaboration with the vendor team, and thus ensure we get the best out of our investment.

We are happy with the ROI. There is significant scope for improvement in this area.

Pricing and licensing is competitive.

Any potential customer should explore PDW along with other MPP solutions before making a final decision on defining their OLAP analytics. We evaluated Teradata, as we had more expertise in SQL server, we were intrigued by PDW, and finally it emerged as the winner.

This is a great product for big data analytics as it can challenge other MPPs quite well.