We are also working with Teradata.

The main difference is that Snowflake is on a cloud server and Teradata is still a legacy system. Teradata was one of our source systems. We were loading it into Snowflake. With Teradata, when you create primary care, something constrains the way you want to do it so that you do not put an additional effort into your ETL. That changes when it comes to Snowflake. If you're clustering something on a case, obviously you end up paying the extra cost. That is one basic and main difference that we saw.

That said, you can process a huge amount of data in Snowflake. This becomes a challenge in Teradata. When we were reading the data, we had to load that data into Snowflake. When we were trying to read that data, we had to obviously divide it into chunks and then load it into Snowflake. Loading into Snowflake was like a cakewalk. Everything is moving into the cloud now. Legacy systems like Teradata just can't handle the amount of data required. 

I've worked on many of the legacy systems like Oracle, DB2, et cetera. Migration from one environment to a higher environment was a huge thing. The DVS used to take two or three days sometimes depending on how many tables or what the objects are and what they had to create. Now, with the cloning feature that we have, the idle time for the developers, or even the people who are looking at the data, the analysts, and everyone, is reduced to none. We just clone it, we load the data and the data is available without any hindrances.

I did not previously use a different solution at my current company. 

We didn't really have a traditional data warehouse application. We were just using Microsoft SQL Server, but we didn't actually have a traditional MPP-based data warehouse solution. We were still a very growing organization. As we continue to grow our business and increase in size, we have to get better tools that are meant to actually do what we're trying to do with other tools.

We are currently moving everything from Oracle to Snowflake. 

We've used a combination of other tools in the past. We've used Microsoft's Stack, for example. We were using a SQL database in the past. 

We previously worked in AWS.

We weren't satisfied with our data warehouse, AWS Redshift, Oracle, and some on-prem elements such as a SQL Server. We wanted a cloud data warehouse that didn't require a lot of manual intervention and maintenance, DBAs and so on. We wanted a solution that could scale automatically and pay-as-you-go to cut down on wasteful infrastructure. Therefore, Snowflake made a lot of sense, plus compared to Redshift at the time, the separation of storage and computing was huge. That was an essential differentiator for us.

We previously used ThoughtSpot, specifically their Falcon engine, their appliance version, and it did everything on its own. We brought in Snowflake later when ThoughtSpot introduced their product called Embrace. We were among the earliest adopters to switch, and six to eight months after, we integrated with Snowflake.

In the past, we used Google Cloud SQL. However, Snowflake offered cost optimization among the many other useful features. They also introduced app building on top of the data hosted. 

I come from an Azure background as well, so Microsoft also comes with Azure Synapse, where it's a similar functionality as Snowflake, where it's warehousing on the cloud. Azure Synapse is also good. I'm unaware of AWS or GCP, and I heard that Google Cloud Platform also has Big Query and big data capabilities, which are tough competitors for Snowflake and other cloud warehousing tools.

We previously used Amazon Redshift.

This is a net-new solution. So, it's brand new. We chose Snowflake for a variety of reasons, but mainly, we chose it for its scalability and data sharing capabilities.

I personally don't see any of the competing cloud platforms coming close right now to what Snowflake offers. An argument could be made with GCP and Datadog are getting closer. Also, a new AWS Redshift is on the horizon, like a whole new AWS Redshift 2.0. But right now, I've not seen anything that comes close. Snowflake, to my understanding, is the only platform that fully separates your storage and computing, essentially. And it's the only platform I've seen with things like time travel. It's got a whole bunch of great features that I don't know if other tools also have, but it supports semi-structured data. It supports automated tasks, alerts, and reporting. And the data sharing is a massive one. GCP now also has its own data-sharing potential, where you can share data with other GCP accounts. I've not used it myself, but to my knowledge, whilst they have the sharing, they don't have anything that even comes close to the Snowflake data marketplace that allows customers to sell or share their data outside the wider world. And it doesn't have anything that comes close to the kind of private equipment where customers might share their own data internally or to their own. And I think there was one more thing. 

Snowflake also have some really good support for Python, Scalar, and Java through what they call Snowpark, which was launched last year. But more recently, this year, it was announced they're really pushing forward with their StreamLINK integration. It will allow customers to host applications on Snowflake and share those applications with other users in a very similar kind of marketplace environment they use for data sharing. I don't think there's anything that any of the other competitors have right now.

Prior to Snowflake, it was a completely Greenfield requirement.

I have used many on-premise solutions in the past and also Azure Synapse Analytics.

Used it at a previous company.

We had SAP BW and SAP HANA as our main data platforms. We are slowly decommissioning SAP BW and SAP HANA and completely migrating to Snowflake. We wanted to have a single repository for all the data. The cost was also a factor.

We use Snowflake in conjunction with Matillion, which is another AWS-based ETL tool. It is being used as a bridge between our on-premises data and Snowflake. 

We previously used IBM Netezza. We switched to Snowflake in 2020 because it provided us control over its scalability and costing model.

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.

I also use Databricks for proof of concept and training. I have used Databricks much longer than Snowflake. Both have their own pros and cons.

In terms of ease of use, UI, and setup time, Snowflake is good. I would rate Snowflake a nine out of 10 from these aspects. In terms of workload, Databricks has higher points. The underlying infrastructure is faster for Databricks, which is not the case with Snowflake. Snowflake is a cloud database. So, in terms of processing power, Databricks has an advantage over Snowflake. Databricks is more suitable for larger workloads, whereas, for a regular or typical data warehouse that you want to run on the cloud, Snowflake is more suitable. 

Before Snowflake, we used Azure.

Our clients previously used the RTS based MySQL and migrated to Snowflake from there. The primary reasons they moved was because of scalability and performance. Other than that, Snowflake reduces costs quite significantly. I also have experience with BigQuery which is particularly used for Google Cloud although these days they have a multicloud enrollment. Snowflake is vendor independent so you don't have to stick everything in Google Cloud. In terms of performance, Snowflake is faster than BigQuery. 

About three years ago, Databricks was sort of the hot thing among our clients, and everyone was using it for low-code analytics. We had to deliver data in a format that was specific to Databricks. Databricks had this massive growth, use, and adoption. They have a very good footprint now, but we see those same clients shifting their data to Snowflake, and pretty much nobody asks for Databricks anymore.

I think there's this big war sort of brewing between Databricks and snowflake. Snowflake is going to come out with the analytics capability that Databricks has. They're working furiously to get it released. I don't know what it's going to look like, but they're going head-to-head with Databricks. I think Snowflake is going to crush them.

I have some experience with Teradata.

We've used a few other solutions including MySQL and a few other notable databases.

Right now, we are looking for some other options as well.

We have worked a lot with Tableau previously.

I've used a lot of different data warehousing solutions at different companies.

I have experience with Informatica PowerCenter and Oracle. PowerCenter uses ETL techniques instead of ELT. Oracle does not automatically perform micro-partitioning. Instead, you have to partition manually and it is a static partition.

Prior to Snowflake, I was using an on-premises data warehouse. Snowflake is the first experience I have had with a cloud-based data warehouse. It is an awesome tool.

We use several products together for our framework. We have our data warehouse which is in Snowflake, we use Domo for standard reporting and we use R for data science analysis.

Before we had Snowflake we had a different solution. We switched to Snowflake because we felt the need to modernize our data warehouse architecture. We were also thinking about having other solutions in the cloud to reduce administration costs. With no effort on our part, we could have a stronger system compared to the effort and cost of doing a similar thing on-premises. This was the biggest advantage of Snowflake. We really do not need to have those administrative efforts anymore. Now we don't take care about when we run out of storage or that we need to buy better CPUs because if we need more computing power, we don't worry about it, we just use it and it is there.

In the past, we've used an SQL server. On the cloud side of it, we do have some experience. Snowflake, however, is the new data warehouse solution that we are looking into it.

The amount of data which we are currently handling, cannot be handled yet in any other data warehouse, but we do have experience with Azure Data Warehouse. We don't have to manage anything with Snowflake, but with Azure we do. Snowflake is much faster, and I believe it is cheaper as well. They have developed it on top of their own system and integrated the concept.

We were using SQL Server previously and we switched because of the increased performance, multi-clustered shared environment, scalability, and we wanted to use a cloud-based solution.

I also use Azure. Snowflake has more advantages than Azure.

We previously used HPE and Microsoft Insight. We switched to Snowflake for the availability, security, and loading times.

With Amazon Redshift, we gained a lot in terms of timing as well as project completion. Even through we are happy with Redshift, in terms of pricing and availability of nodes, I am more comfortable with Snowflake.

We're system integrators, so we use different solutions including SQL Server Data Warehouse and Greenplum as well as Snowflake at the moment.

I've used many data warehouse solutions, including Hadoop, and Oracle SQL Server Data warehouses. I switched from those to Snowflake because Snowflake is on the cloud and gives you separate computing and storage scalability, which Hadoop is unable to offer.

We also use Matillion. I find Snowflake a bit better.

We use Snowflake alongside other data warehousing tools. Ultimately it depends on what the client wants to implement and their existing architecture as to what would be the best choice.

I used Cloudera One about two years ago.

I have used BigQuery on Google Cloud Platform and Spark on an internal cloud.

Snowflake is not the only solution we are working with but it is the only solution that we are heavily focusing on and investing our efforts into the knowledge and the training.