We primarily use the solution for analytic purposes. It allows us to store data for years and then go back and look up information to learn things like how users function, react, follow trends, etc. It allows us to follow past and recent demands for products as well so that we might be able to find the reasoning behind the action or trend.
With this solution, I can better understand buying patterns and user habits. I can tell in real-time a customer's review and purchase based on that. I can tell that from the cloud data of Azure Data Warehouse.
The solution offers a variety of different features.
There are two layers, so data storage and computation are separated.
For the customer, there aren't any storage limitations, so you are able to explore and size of data including megabytes and terabytes. Once you have stored the data you can analyze the data that if you have in order to write some complex queries. After that, the solution makes it possible to visualize that data itself.
The computation makes it so that you can run scenarios without an impact on your storage location.
If you compare the product to other solutions, you'll notice it takes less time for less cost. All other vendors have different architecture so their pricing is a little bit different and they are charging pricing per second. Microsoft charges per unit, or DTU, Data Transfer Unit. It will charge based on how much data you are consuming and how much data you are doing transactions with. It is monthly not daily.
Microsoft provides both the platform and the data center, so you don't have to look for a cloud vendor. It saves you from having to deal with two vendors for the same task.
The solution is consistently improving a lot of things, including security features. There are eight or nine different security features there. You can even encrypt your data.
There are features coming int he next few quarters that will be helpful. Soon, Power BI will be directly integrated into Azure. We need to have some Spark tools also available so we can directly select customers and don't need to install everything.
There will be features added that relate to application development. There's hopefully going to be more flexibility with the XML. Right now, for example, Data Warehouse is not able to give XML files and your file put is not correct. The feature will hopefully allow us to read XML.
The performance needs to improve in future releases.
We're hoping that Microsoft will add integration with the Amazon AWS platform.
I've been using the solution for four years.
The solution is quite stable. We haven't had any issues with it in terms of experiencing bugs and glitches.
Microsoft has different sizing options that comes at different price points. It's easy to scale up or down. If you find, after a few years, your needs are growing, you can increase your BTU size the maximum may be about 13,000.
With this solution, we can actually automate scalability, which makes it very easy to scale. If you require more data, it will automatically increase your DP size and process the data. We have the flexibility we need so that, whenever a node gets filled, we never have to worry. Another node will pick up and process the data.
With Snowflake, in comparison, if your system is not running, then Snowflake will go into an auto suspend mode. You can sync the time, and, within the 10 minutes, if the system is not running, it will go to an automatic suspended mode. There is no charge in this mode. In that case, we have to manually find what we need to virtualize the function.
Azure has other mechanisms in place. We can write Azure functions and we can schedule the Azure functions in order to automate them. We can do similar kinds of functionality, however, we have some additional coding we require for Snowflake.
We've had no need for support. Everything is automated and pretty much taken care of. We need to configure everything and the Azure portal will get us to the dashboard. In that dashboard, you'll get all of your current information including information on how the system and the cluster is running.
The backend is very nice. There are a lot of additional features that help you manage the product. As users, you will get the visualization dashboard. It is very easy to see, which nodes are running and how much data is processed. We can see everything on the portal, and that makes everything very easy to handle.
I've had prior data warehouse experience with traditional systems like the Oracle, Superserver, or Teradata.
Snowflake is the only provider with no cloud platform. You have to buy a platform in order to adopt it. Microsoft Azure, AWS, and Google Cloud all have their own dedicated platform. They have their own dedicated data centers.
The initial setup is not difficult. If you have access to the subscription you can start using Azure Data Warehouse and being to create the services. There are security features also. You can give someone full access, and you can set access for others too. Developers will need access so that they can develop it out. It's a pretty straightforward process.
In comparison, I find that AWS is much more costly.
We're a Microsoft partner.
I have experience with the Microsoft Cloud Data Warehouse specifically within the Unix cloud environment.
We're using the latest version of the solution.
It's important for organizations considering the solution to consider their business requirements and expectations. They need to be clear about what type of cloud solution they are looking for. We help our clients do this and interview them to find out what their needs are so that the best platform can be chosen for them. It may be Azure. It may be Snowflake. It depends on the company's needs.
At the end of the day, the customer will always want the best possible pricing. They'll typically ask how they can save money but have high throughput or more input with less price. If that's the case, Microsoft may be the perfect solution.
I'd rate the solution eight out of ten.
We are a service provider and we currently have five clients with active IT implementations that use Amazon Redshift. We also use it ourselves.
My clients primarily use this product for data analytics. They are mostly working with big data and using the machine learning functionality.
I like the cost-benefit ratio, meaning that it is as easy to use as it is powerful and well-performing. There are only three parameters that you need to understand, which are the distribution key, the sort key, and the compression method or encoding method. Once you understand these, you can tune the performance.
I would like a better way to ingest data in realtime because there is a bit too much latency.
There are too many limitations with respect to concurrency. It is now possible to auto-scale it, although that is still slow.
It could offer smaller nodes with decoupling of storage and processing because for the moment, the only nodes available to work that way are huge, and for large companies.
My first implementation of Redshift was three and a half years ago, in 2017.
We have not had many issues with stability.
Scalability can be a problem if you don't write your database queries correctly. For example, if you write a cartesian product in Redshift then you may end up consuming all of the resources. However, it does have features like workload management to prevent this from happening.
Our clients are mid-sized to very large companies.
I have been in touch with Amazon technical support and they are very good. They are efficient and resolve problems quickly. They know what they're doing and they're very professional.
I have also used Snowflake and its methods for ingesting real-time data are faster. It also offers a bit more functionality and a bit more flexibility. It's a bit easier to maintain and faster to scale, but more expensive as well.
To me, the big drawback with Snowflake is that the data is not stored in your AWS or Azure subscription, or AWS account. They store the data in their own account that they manage for you, which might be a problem for some companies in terms of compliance and legal requirements.
Azure Synapse and Google BigQuery are also competing solutions.
The deployment is very straightforward and it usually takes a couple of minutes. This is one of the reasons I like it.
As long as a person understands the AWS landscape, they can deploy it on their own. Otherwise, without realizing it, they might for example deploy a Redshift cluster that is not properly secure. Similarly, it could cost a lot of money if they don't know what they're doing. You don't need a very in-depth technical expertise, but you do need to understand how AWS works.
I have a team that provides maintenance for our customers. It is spread between France and Belgium and I have 25 people who report to me, with another 20 who I work with indirectly.
The cost of Redshift ranges from a few hundred dollars a month to thousands of dollars a month, according to the resources that you're going to use, the number of nodes, and the type of nodes.
My customers have implementations that cost about $500 a month for a very small one. I also have a customer with a monthly invoice of about $25,000 USD.
With the most recent update, we should now be able to decouple storage from processes.
My advice for anybody who is implementing Redshift is to make sure that they are using it for what it is made to do. It's an analytical database, so it's not meant to process transactional data. It's the perfect tool if you use it for the right purpose.
Overall, it is a very stable and robust product. That said, there is still plenty of potential for improvement.
I would rate this solution a seven out of ten.
This is a cloud solution from Google that is completely cloud based. BigQuery is similar to Snowflake in the way it manages data analytics. It has a proprietary way of storing and accessing data in its own data store and is 100% managed without you needing to install anything. There is no need to arrange for any infrastructure to be able to use this solution. Go onto BigQuery.com, create an account and you will get a console on your webpage in that browser where you can create databases, pipelines and transformations.
There are many tools that you have to use with BigQuery that are different services also provided for by Google. They need to all be integrated into BigQuery to make the solution easier to use.
I have been using this solution for two years.
This is a stable solution. All native abilities of Google solutions are inbuilt in BigQuery. I would predict that Snowflake and BigQuery will occupy a much larger share of the cloud data analytics space than Azure Synapse in the future.
This is a very scalable solution. BigQuery's pricing is more suitable to big operations that plan to scale. For smaller businesses, this may be an expensive solution. Creating a BigQuery account is free, but as soon as you start using computations and data capabilities, they begin charging you.
There is no installation involved in using this solution. It is as simple as opening a Gmail account and creating your own warehouse. You can start creating a database schema and writing SQL. You don't have to install different tools or licenses.
Between two to seven people were needed for deployment. This included one or two admins, ETL developers and database staff who know SQL querying and database design.
One terabyte of data costs $20 to $22 per month for storage on BigQuery and $25 on Snowflake. Snowflake is costlier for one terabyte, but BigQuery charges based on how much data is inserted into the tables.
BigQuery charges you based on the amount of data that you handle and not the time in which you handle it. This is why the pricing models are different and it becomes a key consideration in the decision of which platform to use.
Snowflake and BigQuery are very similar in the way they operate. However, I would rate Snowflake higher than BigQuery. I would rate Azure Synapse third and AWS Redshift fourth. The way Snowflake operates and has integrated with other systems makes it a better alternative to BigQuery.
BigQuery takes a different approach to design and this is important to consider. BigQuery on its own is not enough and you need other tools also offered by Google to transform data.
The BigQuery ecosystem is a little more complex than the Snowflake ecosystem. Those who have traditionally worked on on-premise data warehouses, find Snowflake much easier to set up. Those who are trying to establish warehouses for the first time, find Google easier.
I would rate this solution a seven out of ten.
We are resellers and we provide products for our customers.
Our clients are using this solution in two ways; one is for a data warehouse, and the second is for analytics in the database.
The data warehouse has exceedingly high performance and has the ability to do large scale queries very effectively. It fits well in large enterprises.
All features are valuable. It's a combination of capabilities that's all in one place, which is incredibly powerful.
For me, it's performance, scalability, low cost, and it's integrated into enterprise and big data environments.
Some of our small to medium-sized customers would like to see containerization and flexibility from the deployment standpoint. They don't currently offer this as a platform as a service. Snowflake is offering this capability.
They're available in the cloud. They're available on every cloud, but they're not available as a managed platform as a service offering.
We have been dealing with Vertica for two years.
We use both version 9.3 and version 10. Version 10 is the latest one.
Vertica is a stable solution, it's rock solid. Production workloads being run on it are super steady. It's high availability, massively parallel processing. Nodes go down and you don't even notice.
This is a scalable solution. For example, if we look at Uber drivers, they are able to monitor the position and availability of the drivers and match that against the number of customers for every customer and every driver worldwide globally.
They do a geospatial analysis and calculate their search pricing. They are defined by geographical boundaries, they are defined by where the people are and where the drivers are. This is done for every city in the world for every driver. That gives you an idea of the scale they are able to do in this particular use case.
This gives you the idea of the scale, the performance, and the ability to do analytics in the database. They do this so much more cost-effectively than they would on any other platform.
Technical support is extremely good. They know the product and they're very good.
I don't have any complaints regarding technical support.
Vertica is known for its ease of administration. I would say that the initial setup is easier than most.
The price varies completely. Cost information is available publically where you can compare with other solutions.
From a cost perspective, the software is less than most of its competitors.
Customers save money by a smaller hardware footprint, fewer nodes, less storage, lower-cost storage, and no appliances. So it is typically a lot less money than an Oracle, Teradata, or Snowflake.
Overall, they are highly competitive when it comes to pricing.
The customers love them. They absolutely love them.
Before implementing this solution, make sure that it is on the list and that you evaluate it.
I would rate Vertica a ten out of ten.
We used to work on business intelligence projects, so we really enjoyed the service-like performance. It's been great.
The business intelligence is very good.
We have found that the analytics environment is excellent.
We've discovered that the solution is quite a complex product, which can make it difficult to sell. Snowflake, for example, is simpler and therefore an easy sell.
The customization can sometimes be difficult to achieve.
We've been working with the solution for a very long time. It's been more than 15 years personally and at least 15 years at this company, so it's been well over a decade at this point.
We don't deal with bugs or glitches and the product doesn't crash or freeze. The only real issues we come across are related to customization. It's quite stable. We haven't had problems.
We have about 100 people using the product currently.
Our clients are quite sizeable companies.
I've never used technical support and therefore can't really comment on their responsiveness or level of knowledge. I have an internal team that assists with troubleshooting as necessary.
As a consultancy, we work with a variety of products. This includes Oracle and Snowflake, for example.
We are an integrator of the solution. Our clients are sizeable companies, and there are several providers involved in different projects with Exadata. We're not the only administrators.
I personally haven't handled an installation. We're mostly focused on projects related to BI and analytics. Someone else handles the setup and we just use the product or have our clients use it. I don't have a sense of whether the implementation is difficult or complex or how long deployments take.
We are a consultancy. We are partners with Oracle, with Snowflake, and with other vendors, software vendors.
We work in different deployment models with our customers. We try to fit our customers' needs. It depends on the customer and the project in terms of which deployment model we'll recommend or use.
Overall, I would rate the solution a nine out of ten. It's very good. We've had a great experience using it over the years.
