Microsoft Parallel Data Warehouse Reviews

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.

It's a very good solution because we can manage large volumes of data. We can store the data in a data lake for a very low cost. We can move the data in Parallel when we need more performance or decide to leave the data in the data lake. It's a very good product at the moment.

The product does not have all of the features that the native products have. If we want to do something advanced, we must use Data Factory and Databricks.

I have been working with the product for about three to four years. I am leading a team. I don't work in depth with the solution.

The solution is pretty stable. Sometimes we have problems, but Microsoft manages and solves them quickly. We had some problems with Python and ETL processes inside Synapse. However, Microsoft solved the problem in less than an hour.

The tool is scalable by design. Our customers are medium and enterprise-level businesses.

The support is pretty good. Support could be faster. The team must be more knowledgeable about Azure.

Positive

The initial setup is really easy. We don't need to do anything major. The deployment takes a few minutes.

The number of people required to deploy the tool depends on the number of environments we have. Usually, one architect is enough to deploy the product.

Maintaining the product is easy because Microsoft does it for us. We don't need to do anything. It is upgraded by default.

The pricing depends on the solution. If we decide to use a low-cost product, it is not expensive. The tool could be expensive if we need to manage a lot of data. The pricing depends on what we need to do. The tool might be expensive if we need high performance and want to manage a lot of data.

All of the components related to the pipeline are included in Synapse. We have some ETL tools based on Data Factory and other advanced functions based on Databricks. Not all of the functionality is included in Synapse. It makes no sense to have the product without all of the functionality.

It is tricky to manage the storage mode of the data because if we don't read the documentation and don't create a good distribution of the data, we will have problems in the performance. If we do it, we will have no problem. 

Overall, I rate the solution an eight out of ten.

We provide this solution as a service to customers. Sometimes, it is just used as a data warehouse. However, a couple of our customers use the data warehouse to collect information from everywhere, e.g., from their subsidiaries, such as electric or petrol companies.

We are also developing some dashboards using the data warehouse. Sometimes, you are using Microsoft BI, but most of the time, you are using Qlik. It depends on the customer needs at the end of the day. 

Most of our limitations until this moment have been on-premise. There is some sensitive information, like the billing system for an entire country's electricity. However, because of the current circumstances with COVID-19, we are discussing with them how to move it to the cloud. Mostly because of the regulation that any government data cannot be hosted on a cloud, we need to host it on a local cloud at the moment. We cannot use AWS or similar things as of now. 

While we do have local cloud vendors that we are working with, this is something in progress.

• Availability of resources everywhere
• The cost of resources
• The information knowledge is there.
• It is easy to use. You can do what you want with the tool.
• It integrates well with SQL Server.

Collecting the data through SSIS packages from different sources and putting them all in one data repository is the most powerful thing. While others have this feature, they don't have the simplicity or ease of use when getting a resource and knowing everything about it. You can collect data from Oracle Databases, SQL databases, or file systems where it is then saved in one location, building cubes in an easy for SSIS packages. 

The setup has room for improvement. Some customers want to go to non-Windows Servers. Have the entire SQL Server done there though is an obstacle for us, as the configuration of Microsoft BI with cubes is a bit complicated.

I would like the integration to be offered in a simpler way. I would like improvement in the integration between Microsoft SQL cubes and Parallel Data Warehouse with other members of the Microsoft family, such as Microsoft Power BI and SharePoint.

This tool needs a lot of memory for processing. As it does a lot of jobs, it may need this memory while other tools don't need that much. It would help if they enhanced the memory and hardware usage of the tool in order to support the performance of doing the queries

I would like the tool to support different operating systems.

I want a dashboard.

We have been using it for five to six years.

It is stable. We have migrated millions to billions of data points in Cubes from system to system, while running data cleansing conditions. Putting restraints on this can be time consuming which is why people may want to go to another server. When you inject equation and conditions, then it consumes a lot of time. Though, I have never compared this time consumption to other tools.

I have no concern about the scalability. We have never faced any problems regarding the scalability. If we wanted to extend this, it is extendable.

The online help is good enough along with our technical expertise. 

If everything is available, such as information knowledge sources and connections, I can have the solution deployed in five days.

I recommend getting assistance from an integrator or consultant with the integration.

Microsoft has an agreement with the government in our country, so our customers get their licensing costs from the Ministry. Whenever we work with any government, company, or government institute, which is mainly what we are doing, that license comes directly from the Ministry of Technology and Information. 

We actually do not know that much about the price of Microsoft tools. Even if I ask for the price, I'm asking it as a partner.

We have had a good experience working with this tool.

If you don't have a problem with memory and have good processors, then you won't have an issue. However, depending on your needs, you may want to go check out other tools.

I would rate the tool as an eight out of 10.

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.

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.