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Oracle Database Appliance Competitors and Alternatives

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Anirban Bhattacharya
Practice Head, Data & Analytics at a computer software company with 10,001+ employees
Real User
Top 5Leaderboard
Exceptionally good technology that addresses data warehousing challenges and is built and designed in a good way

Pros and Cons

  • "The way it is built and designed is valuable. The way the shared model is built and the way it exploits the power of the cloud is very good. Certain features related to administration and management, akin to Oracle Flashback and all that, are very important for modern-day administration and management. It is also good in terms of managing and improving performance, indexing, and partitioning. It is sort of completely automated. Everything is essentially under the hood, and the engine takes care of it all. As a data warehouse on the cloud, Snowflake stands strong on its ground even though each of the cloud providers has its own data warehouse, such as Redshift for AWS or Synapse for Azure."
  • "There are three things that came to my notice. I am not very sure whether they have already done it. The first one is very specific to the virtual data warehouse. Snowflake might want to offer industry-specific models for the data warehouse. Snowflake is a very strong product with credit. For a typical retail industry, such as the pharma industry, if it can get into the functional space as well, it will be a big shot in their arm. The second thing is related to the migration from other data warehouses to Snowflake. They can make the migration a little bit more seamless and easy. It should be compatible, well-structured, and well-governed. Many enterprises have huge impetus and urgency to move to Snowflake from their existing data warehouse, so, naturally, this is an area that is critical. The third thing is related to the capability of dealing with relational and dimensional structures. It is not that friendly with relational structures. Snowflake is more friendly with the dimensional structure or the data masks, which is characteristic of a Kimball model. It is very difficult to be savvy and friendly with both structures because these structures are different and address different kinds of needs. One is manipulation-heavy, and the other one is read-heavy or analysis-heavy. One is for heavy or frequent changes and amendments, and the other one is for frequent reads. One is flat, and the other one is distributed. There are fundamental differences between these two structures. If I were to consider Snowflake as a silver bullet, it should be equally savvy on both ends, which I don't think is the case. Maybe the product has grown and scaled up from where it was."

What is our primary use case?

It is used in my company as well as in my client's company. We are a system integrator, so naturally, we need to have the centers of excellence and competencies in Snowflake.

What is most valuable?

The way it is built and designed is valuable. The way the shared model is built and the way it exploits the power of the cloud is very good. Certain features related to administration and management, akin to Oracle Flashback and all that, are very important for modern-day administration and management.

It is also good in terms of managing and improving performance, indexing, and partitioning. It is sort of completely automated. Everything is essentially under the hood, and the engine takes care of it all. As a data warehouse on the cloud, Snowflake stands strong on its ground even though each of the cloud providers has its own data warehouse, such as Redshift for AWS or Synapse for Azure.

What needs improvement?

There are three things that came to my notice. I am not very sure whether they have already done it. The first one is very specific to the virtual data warehouse. Snowflake might want to offer industry-specific models for the data warehouse. Snowflake is a very strong product with credit. For a typical retail industry, such as the pharma industry, if it can get into the functional space as well, it will be a big shot in their arm.

The second thing is related to the migration from other data warehouses to Snowflake. They can make the migration a little bit more seamless and easy. It should be compatible, well-structured, and well-governed. Many enterprises have huge impetus and urgency to move to Snowflake from their existing data warehouse, so, naturally, this is an area that is critical.

The third thing is related to the capability of dealing with relational and dimensional structures. It is not that friendly with relational structures. Snowflake is more friendly with the dimensional structure or the data masks, which is characteristic of a Kimball model. It is very difficult to be savvy and friendly with both structures because these structures are different and address different kinds of needs. One is manipulation-heavy, and the other one is read-heavy or analysis-heavy. One is for heavy or frequent changes and amendments, and the other one is for frequent reads. One is flat, and the other one is distributed. There are fundamental differences between these two structures. If I were to consider Snowflake as a silver bullet, it should be equally savvy on both ends, which I don't think is the case. Maybe the product has grown and scaled up from where it was.

For how long have I used the solution?

I have been using this solution for close to three years. I kept a tab on Snowflake and its progress since it came into the market.

Which solution did I use previously and why did I switch?

Personally, I have worked extensively with Oracle, SQL Server, and Teradata. SQL Server has the Fast Track Data Warehouse (FTDW) appliance. Oracle has both the database and the appliance. I haven't worked on Parallel Data Warehouse, which is a big one offered by Oracle. Teradata is an appliance in itself. There is also Metadata. I haven't worked on DB2. 

All of these had their own lacunae. Data warehouses had their own problems. There were failures, challenges, and difficulties in adoption, and all of these have been addressed by Snowflake a big way. It has tried to marry the best of both worlds in terms of turnaround time, scalability, adoption, and seamlessness.

I hail from a classical data warehouse background. Snowflake has been kind of a silver bullet. It is trying to meet the best of both worlds. I wish I could do much more on Snowflake, but I'm tied up with many other things, which is why I'm not able to concentrate that much, but it is an exceptionally good technology.

How was the initial setup?

Its initial setup is very simple, which is its plus point. It is not at all a problem. You only need to understand a bit of the cloud ecosystem. When Snowflake is on Azure or AWS, you need to understand

  • What exactly is happening?
  • How these two are handshaking with each other?
  • What part Snowflake is playing?
  • How Azure or AWS is complementing it?

If these things are clear, the rest shouldn't be a problem.

What other advice do I have?

This could be something that might be debated upon, but Snowflake has two parts to it. One is the data warehouse itself, and the other one is the cloud. It is important to know about the cloud in terms of:

  • How a cloud functions?
  • How a cloud orchestrates through its services, domains, invocation of services, and other things?
  • How a cloud is laid out?

For example, let's take AWS. If AWS is invoking Lambda or something else, how will S3 come into the picture? Is there a role of DynamoDB? If you're using DynamoDB, how would you use it in the Snowflake landscape? So, cloud nuances are involved when we speak of Snowflake, and there is no doubt about that, but a more important area on which Snowflake consultants need to focus on is the core data warehousing and BI principles. This is where I feel the genesis of Snowflake has happened. It is the data warehouse on the cloud, and it addresses the challenges that on-prem databases had in the past, such as scalability, turnaround times, reusability, adoption, and cost, but the genesis, principles, and tenets of data warehousing are still sacrosanct and hold good. Therefore, you need the knowledge or background of what a data warehouse is expected to be, be it any school of thought such as Inmon school, a Kimball school, or a mix. You should know:

  • Data warehouse as a discipline.
  • The reason why it was born.
  • The expectations out of it in the past.
  • The current expectations.
  • What being on the cloud would solve?

These things on the data warehouse side need to be crystal clear. The cloud part is important, but it is of lesser essence than the data warehouse part. That's what I see, personally, and I guess that's the way the Snowflake founders have built the product.

As a data warehouse, I would rate Snowflake an eight out of ten.

Disclosure: My company has a business relationship with this vendor other than being a customer: reseller
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MS
System Administrator at a university with 501-1,000 employees
Real User
Top 20
Less expensive than other high-end solutions this can be a powerhouse for small low-budget companies

Pros and Cons

  • "It is not a pricey product compared to other data warehouse solutions."
  • "It could be made more user-friendly for business users which would increase the user base."
  • "They need to incorporate a machine learning engine."

What is our primary use case?

In the past, our use case for the product was just to collect data from different data sources. Now, we are trying to build websites and the business intelligence layer above the SQL Data Warehouse.  

What is most valuable?

In our economic situation, the price is really the most important consideration. It is not a pricey product compared to other data warehouse solutions like Oracle and similar products. It is less expensive, but it still achieves our goals and fulfills all our needs. So I think it is a comfortable solution for us and we can afford the price.  

What needs improvement?

I think that building better data protection and a business intelligence layer over the SQL would be great and improve the product. If they could make it more like what Oracle in that way it would be good. We have to take the time and resources to build our own business intelligence when other products already incorporate such solutions.  

If they could make it more user-friendly for business users it would be a more desirable product. Add a business intelligence layer that is user friendly and the user-base can grow.  

I think they should also add a machine learning engine. This is one of the most important features of newer technologies that they currently do not have.  

For how long have I used the solution?

We have been using the solution for more than four years.  

What do I think about the stability of the solution?

Data Warehouse does not have any issues related to the product itself when it comes to stability. Maybe there is more likely to be another issue in the network or conflicts and things of that nature that can cause instability. But the product itself is stable.  

What do I think about the scalability of the solution?

I think that theoretically, it is both possible and not that hard to scale. But I am also sure that the cloud would be easier to work with for the sake of scalability if scalability is the goal.  

We have a dedicated team to work with the data warehouse which has between four to six members. The data warehouse is one of our main software solutions that we use on a daily basis. We need it for the tech that we use which focuses entirely on SQL Server.  

We are looking to scale the usage by building a business intelligence layer over the data warehouse. The tech team is searching for how to implement that with the Microsoft SQL Server technologies. I think we are going to achieve this and expand the setup. So it is scalable in various ways.  

How are customer service and technical support?

We have not had to ask for any external help from technical support. Usually, the internal team we have is skilled enough to solve all the problems that we may encounter.  

How was the initial setup?

I think the initial setup was easy. I think you have to count the time that it would take to deploy in days rather than weeks.

The dedicated team where I work built the setup and we deployed this product by ourselves within your company.  

What about the implementation team?

We did not need a consultant or an integrator to help with the installation. The internal team that we have works to maintain the product, but they have other responsibilities as well. Sometimes, there will be two or three persons taking the responsibility of maintaining and working with the product on other levels. During the deployment, the entire team did the deployment. But right now, at most, there is only a two or three-person team that works with it consistently.  

What's my experience with pricing, setup cost, and licensing?

We do not have any external support contracts. All the features that we use do not require any additional subscription or yearly fees. We just only use features that we pay for on a one-time basis.  

What other advice do I have?

For a small-sized company like ours, I really recommend this product. It can handle a big workload. I would say other products could not be better than Microsoft for small businesses with a lesser budget.  

On a scale from one to ten where one is the worst and ten is the best. I would rate Microsoft SQL Server Parallel Data Warehouse as a seven-out-of-ten.  

Which deployment model are you using for this solution?

On-premises
Disclosure: I am a real user, and this review is based on my own experience and opinions.
MukundMishra
Practice Lead (BI/ Data Science) at a tech services company with 11-50 employees
Real User
Top 20
Good for managing and replication of big data but needs a better user interface

Pros and Cons

  • "It's good for storing historical data and handling analytics on a huge amount of data."
  • "The solution could use a better user interface. It needs a more effective GUI in order to create a better user environment."

What is most valuable?

The solution is perfect for when you have big data. It's good for managing and replication.

It's good for storing historical data and handling analytics on a huge amount of data.

What needs improvement?

It could be because the solution is open source, and therefore not funded like bigger companies, but we find the solution runs slow.

The solution isn't as mature as SQL or Oracle and therefore lacks many features.

The solution could use a better user interface. It needs a more effective GUI in order to create a better user environment.

For how long have I used the solution?

I've been using the solution for seven years.

What do I think about the stability of the solution?

The solution is stable.

What other advice do I have?

I've used the solution under cloud, hybrid and on-premises deployment models.

I'd recommend the solution, but it depends on the company's requirements. If you don't have huge amounts of data, you probably don't need Hadoop. If you need a completely private environment, and you have lots of big data, consider Hadoop. You don't even need to invest in the infrastructure as you can just use a cloud deployment.

I'd rate the solution seven out of ten. I'd rate it higher if it had a better user interface.

Disclosure: I am a real user, and this review is based on my own experience and opinions.
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