The Snowflake Cloud Data Platform is the eponymous data warehouse with, from the company in San Mateo, a cloud and SQL based DW that aims to allow users to unify, integrate, analyze, and share previously siloed data in secure, governed, and compliant ways. With it, users can securely access the Data Cloud to share live data with customers and business partners, and connect with other organizations doing business as data consumers, data providers, and data service providers.
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SSIS
Score 8.0 out of 10
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Microsoft's SQL Server Integration Services (SSIS) is a data integration solution.
We use these tools for applications they are better suited for vs a Snowflake. For e.g. MS Fabric has powerful agentic AI capabilities; Redshift is our go to choice for the TMT vertical within the organization and Databricks is the default choice for AI/ML applications.
Snowflake provides various features, such as integration with Python using Snowpark. The reporting feature that caters to your small reporting needs is Snowsight. The Snowflake data marketplace is where you can get multiple data for free and even some of the data which you can …
These are comparable products that can make sense depending on the specific needs of your organization. All are certainly serviceable and have varying pros and cons. Snowflake seems to provide the greatest degree of flexibility and easy scalability as new data gets brought into …
We needed scalability and a new way of organizing our data; Snowflake allowed us to have a clearer view of our data warehouses and schemas. Snowflake is also way superior in terms of speed and quick insights from the raw data you query, which is very valuable to us.
Snowflake has an attractive pricing model with auto-suspend and auto-resume and pay per use. AWS Redshift requires higher administrative efforts to maintain and scale the platform whereas with Snowflake those admin tasks are not needed or automatically taken care of.
We had a MS SQL server with over 2 TB of ram & 51 processors that we were using, that could no longer handle our workload. Snowflake can handle 3 times that workload with ease and efficiency.
Snowflake is much faster and easier to write queries and pull data. But the visualization part of Snowflake is not as good as them. Also, Snowflake only supports SQL queries but not python or other languages. So basically Snowflake is the expert in its field but not suitable …
We particularly liked Snowflake's security model as well as its unique storage (whereby everything is essentially a pointer to immutable micro-partitions, which is the key behind its zero-copy cloning, its secure sharing, its time travel, etc.). and also how it separates …
While Snowflake is more open to cloud eco system, SAP integrated well with SAP eco system products like SAP ECC or SAP S/4. So for people who have invested heavily in SAP eco system including SAP ECC or S/4, it makes sense to go with SAP DWC which is also evolving very rapidly. …
In my opinion, the other tools have similar and some different features; however, when I ran proof of technologies between Synapse and Snowflake. Snowflake did things better or just had functionality that the other tools did not. One that stuck out at the time was scale up …
Each of the other solutions were cloud vendor specific, Snowflake can ride on either Amazon Web Services, Microsoft Azure, or Google Cloud. The fact that they are ANSI-sql compliant and have an effective means of offloading data makes them portable and easy to sell to teams …
Azure and Snowflake compared very similarly, but Snowflake provided more options to integrate and connect with tools/companies that were not partners. It seemed to be a more flexible environment. The barrier for entry on Oracle and Google we just too complicated. In particular, …
I have had the experience of using one more database management system at my previous workplace. What Snowflake provides is better user-friendly consoles, suggestions while writing a query, ease of access to connect to various BI platforms to analyze, [and a] more robust system …
Snowflake has won the match because it is giving an excellent performance with its efficient features and reliable results. This is a totally secure program for our precious and important data.
Our initial data warehousing solution was Treasure Data. We had issues with the costly pricing model, which would be exhorbitant if we want to hold our data in memory and query using Presto. As a result, some heavy lifting was done in Hive (managed by Treasure Data); …
In my experience running the data management practice at InterWorks, we believe that cloud data warehouse products will eventually serve the majority of data warehousing use cases and power data analytics at most companies. Of this cohort, we believe that Snowflake is the best …
Redshift compute and storage can be scaled up/down together (though they added some features recently, they don't quite add up). I haven't tried Avalanche or Firebolt but would love to in the near future, due to their pedigree or revolutionary billing methods.
- Cost was the main aspect on the decision. - Performance was in par or better compared to other tools in the market. - Snowflake in my opinion stacks better than other tools I have used in the past.
Accommodates future data types such as JSON and XML. Scalability is another advantage. Pay per use is beneficial for organizations like yours. Direct connectors with AWS help us to go with it. No limit on user creation and clone data not eating up extra disk space are a few …
Since we switch from amazon redshift to Snowflake, we found Snowflake is much better than redshift in many ways, including the data integrate and data pull. However, comparing directly pull data from amazon s3, Snowflake is quite slow in terms of data pull speed and the more …
Compared to Amazon Redshift, Snowflake is slightly easier and faster to achieve ROI but based on the user's perspective, the two tools have very little difference since both are leveraging SQL to pull data from AWS S3. Snowflake is also working with Microsoft Azure but it is …
Our issue with Redshift was that it was very expensive. On top of that, queries were still slow and if we used more of Redshift's memory, then it would have cost even more. Snowflake is not cheap, but less costly for us. Plus, the performance was much better. Also, we got to …
Both are very similar. Azure is cloud based. It is easier for the organization who uses cloud based application. The SQL Server Integration Services is cost effective. Azure was more on the expensive side for our organization. Azure was a little complex, it needed special …
I think SQL Server Integration Services is better suited for on-premises data movement and ADF is more suited for the cloud. Though ADF has more connectors, SQL Server Integration Services is more robust and has better functionality just because it has been around much longer
Fivetran, Stitch, and Etleap are all 1000x more modern than SSIS and 100x less aggravating. While those tools are mainly used to sync data rather than transform it, the ELT model works much better than the ETL model in most situations.
We just selected SSIS because we use SQL Server Management System (SSMS) to manage our database. As SSIS is a component of the Microsoft SQL Server there are no problems with integration and everything works perfectly. In addition, we don't have to learn how to use another …
Low-cost relative to other products - in fact, zero cost if one is considering the license cost as being for the database engine with Integration Services added on. It has a comparable range of functionality and performance and as such it's a 'no-brainer' to use SSIS over …
SnapLogic and Azure Data Factory are better than SQL Server Integration Services mostly because they are Integration Platform as a Service (IPAAS) services, whereas SQL Server Integration Services is an on-premise. So the basic differences such as, need a VPN to connect to the …
SSIS is similar to Alteryx and Informatica PowerCenter in a way because these are all drag-and-drop ETL tools with similar functionality. Alteryx is a step ahead because it has some advanced ETL functionalities including statistical calculations etc. and a better ability to set …
Alteryx Designer is easier to use for machine learning models. The functionality of drag and drop is the most valuable. It is a very user-friendly tool that can be understood easily. My teams also work with other solutions, such as Integration Services, and these solutions are …
I had nothing to do with the choice or install. I assume it was made because it's easy to integrate with our SQL Server environment and free. I'm not sure of any other enterprise level solution that would solve this problem, but I would likely have approached it with …
SAP Business Objects was a primary concurrent software against the MS SSIS but it has a more steep learning curve and requires additional investment into the SAP-related software infrastructure. With SSIS one can start easily with simple data extraction / DTL tools of Express …
I personally prefer SSIS. There are items that each do better than the others, but the ease of use of SSIS, along with its extensibility to 3rd party, ability to write any code required in the tool, and uses the same IDE for the MS BI suite (more of an issue if you're not a …
I used the Pentaho Data Integration (PDI) ETL tool. The PDI ETL tool does not have a public user collection like the SQL Server Integration Services(SSIS) ETL tool. Therefore, you may not be able to find instant solutions for your problems. But it has advantages over the SSIS …
SQL Server is already in our wheelhouse so it only made sense to utilize the tools we already had available to us--SSIS, SSAS, & SSRS. Other non-technical users seem to be more comfortable using alternatives to SSIS. However, these alternatives are not as good as SSIS at …
These are all great products and, honestly, can move data faster. They include more enterprise features and have some great qualities about each. However, they all cost a lot depending on the implementation you need. With SQL Server Integration Services, you do not have any …
When looking to evaluate different options, we looked first to the experience and software we had in-house that would accomplish the job. When assessing alternatives outside we were looking for the tool that would offer the most flexibility.
It’s basically a free tool and it has more features than anyone would ever need. If you look online for answers for SISS packages you will find a world of information that can cover almost any situation for your business. This tool can be used in any business and it provides …
SSIS is a very basic, developer-oriented ETL tool and while it lacks many of the nice UX features of its competitors it is a powerful tool that comes as a part of SQL Server and, in the hands of experienced developers with domain knowledge, can meet most organizations' ETL …
SSIS and Denodo differ in their approaches to ETL and Data integrations. SSIS is more affordable from a cost and licensing perspective (if you have Microsoft licensing), but Denodo is no slouch. If you go with Denodo, you are not creating data, there are pros and cons to …
SQL Server Integration Services does a good job for our SQL Server environments and was selected for that reason. For a SQL Server-only implementations, I would recommend SQL Server Integration Services. When we compared SSIS to other ETL providers against SQL Server, SSIS was …
If you need a quick query, snowflake is the way to go. It's super simple and scalable; we were struggling before with Azure, and with Snowflake, everything runs smoothly, and we have more control over our schemas and warehouses. Snowflake, in my opinion, is the next step when you want to scale your business and manage data. If your company is still small, there may be cheaper options.
Ideal for daily standard ETL use cases whether the data is sourced from / transferred to the native connectors (like SQL Server) or FTP. Best if the company uses MS suite of tools. There are better options in the market for chaining tasks where you want a custom flow of executions depending on the outcome of each process or if you want advanced functionality like API connections, etc.
Snowflake scales appropriately allowing you to manage expense for peak and off peak times for pulling and data retrieval and data centric processing jobs
Snowflake offers a marketplace solution that allows you to sell and subscribe to different data sources
Snowflake manages concurrency better in our trials than other premium competitors
Snowflake has little to no setup and ramp up time
Snowflake offers online training for various employee types
Do not force customers to renew for same or higher amount to avoid loosing unused credits. Already paid credits should not expire (at least within a reasonable time frame), independent of renewal deal size.
SnowFlake is very cost effective and we also like the fact we can stop, start and spin up additional processing engines as we need to. We also like the fact that it's easy to connect our SQL IDEs to Snowflake and write our queries in the environment that we are used to
SSIS is responsible for running core business processed managing core business data. It can be managed, improved and expanded using minimal internal resources. It is also able to support all of our current data infrastructure. Replacing SSIS would be time consuming and costly with no apparent ROI.
The interface is similar to other SQL query systems I've used and is fairly easy to use. My only complaint is the syntax issues. Another thing is that the error messages are not always the easiest thing to understand, especially when you incorporate temp tables. Some of that is to be expected with any new database.
It is easy to learn, works great on many features, but needs improvement on ETL troubleshooting and performance monitoring functionality. Great tool on Microsoft stack. it is great with simple, structured datasets. Once logic gets fancy like nested conditionals complex joins, reusable transformations, versioned logic …SQL Server Integration Services (SSIS) packages become hard to read and harder to maintain. Source control is painful. Errors can be cryptic Logging takes effort to set up well Debugging in production is limited.
Raw performance is great. At times, depending on the machine you are using for development, the IDE can have issues. Deploying projects is very easy and the tool set they give you to monitor jobs out of the box is decent. If you do very much with it you will have to write into your projects performance tracking though.
We have had terrific experiences with Snowflake support. They have drilled into queries and given us tremendous detail and helpful answers. In one case they even figured out how a particular product was interacting with Snowflake, via its queries, and gave us detail to go back to that product's vendor because the Snowflake support team identified a fault in its operation. We got it solved without lots of back-and-forth or finger-pointing because the Snowflake team gave such detailed information.
The support, when necessary, is excellent. But beyond that, it is very rarely necessary because the user community is so large, vibrant and knowledgable, a simple Google query or forum question can answer almost everything you want to know. You can also get prewritten script tasks with a variety of functionality that saves a lot of time.
The implementation may be different in each case, it is important to properly analyze all the existing infrastructure to understand the kind of work needed, the type of software used and the compatibility between these, the features that you want to exploit, to understand what is possible and which ones require integration with third-party tools
Snowflake provides various features, such as integration with Python using Snowpark. The reporting feature that caters to your small reporting needs is Snowsight. The Snowflake data marketplace is where you can get multiple data for free and even some of the data which you can buy according to your needs. And the integration options with various tools like Sigma are add-ons.
Both are very similar. Azure is cloud based. It is easier for the organization who uses cloud based application. The SQL Server Integration Services is cost effective. Azure was more on the expensive side for our organization. Azure was a little complex, it needed special training to use it. Azure was not accurate with complex data.
Without this, we would have to manually update a spreadsheet of our SQL Server inventory
We would also have poor alerting; if an instance was down we wouldn't know until it was reported by a user
We only have one other person who uses SQL Server Integration Services , he's the expert. It would fall to me without him and I would not enjoy being responsible for it.