Five Microsoft SQL Server Data Warehouse Pain Points that Snowflake Can Solve
If your organization has big data needs – like compute power, space, timeliness, and shareability – you’ll start to find that Microsoft SQL Server struggles to keep up with your growing list of demands. Data-driven organizations have high expectations from their BI infrastructure. And that’s where Snowflake comes in.
Snowflake is a cloud-based data platform that allows users to instantly and automatically scale, use all their data and support all users, securely share data, control costs, access data from any cloud, and query all data with ANSI SQL. As a leader in cloud data warehousing, Snowflake can modernize your data management and get you past some of the common pain points you might be experiencing with SQL Server.
To help determine if your data needs have matured past MS SQL, we’ve identified five common data pains and how Snowflake solves them.
1. You Need Better Performance
At some point, you may realize you’re trying to push too much data and processing through the SQL Server engine. You may find your CPU capping out during large bulk processing, or even just during daily loads.
SQL Server tends to get bogged down in a swamp of resource contention when it tries to juggle all the competing tasks a business intelligence solution throws at it. Resource semaphores and deadlocks can start to get in the way of timely data processing.
How SnowFlake Computing is Different
Snowflake’s scalable and partitionable compute node setup means you could handle multiple, competing data processes without the same resource contention nightmare. You can provision separate compute nodes for all the different groups using the data warehouse. Then they can each independently query the same datasets without stepping on each other’s toes.
Additionally, sometimes you need to throw a lot of computing power at a specific data-processing need. But what about when it’s done? You don’t want all that compute power sitting around idle and costing you money in between jobs. Although server virtualization and Azure SQL Server scaling allow for you to scale up or down as needed, the process can be burdensome to manage, causes down time on the data warehouse, and takes too long.
Snowflake, on the other hand, was built from the ground up for this kind of dynamic provisioning of compute resources. You can manually control or automate exactly how much compute power each warehouse node gets and when, with usage per node being billed down to the second.
These native features with Snowflake mean that your data warehouse can expand its available compute resources up and out to meet the varying loads you put on it, regardless of how many simultaneous uses you have for the same dataset. You’ll buy yourself some breathing room and be able to process your data in a timely manner. All the while, you’ll keep simultaneous queries from getting in each other’s way. That’s a real win-win.
2. You Want to Start or Improve Your Data Lake
Every day more devices, processes, and business activities are generating massive amounts of valuable data. You might find yourself having to make the tough decision about what data gets to be brought into the data warehouse and what has to be ignored because of shrinking free space on your SQL Servers. This trade-off isn’t ideal when you are trying to build the hub for all the reporting and business intelligence activity in your organization.
You need to make sure your “single source of truth” has enough space to include all the answers to the questions the business users might ask.
Move to a Scalable database
Snowflake makes the perfect repository to dump all incoming data as fast as it arrives. It handles structured data as well as unstructured data like JSON and XML files with ease. Not to mention, it keeps all that data in relatively cheap cloud file storage, like Amazon S3 or Azure Blob Storage.
You can let your databases swell in size without worry for hitting a ceiling, and you won’t have to waste money on needlessly scaling up compute resources just for some more storage space.
The best part is that all that sea of data is easily queried and analyzed using ANSI SQL query language. You won’t have to jump through a ton of hoops or train your team on something like Scala or Hadoop. You’ll have all the data you want right at your fingertips, ready to use.
3. You’re Interested in Data Sharing
As a data-driven organization, you glean a lot of value from analyzing your data. But sometimes your customers or partners could benefit from access to the data as well. Or perhaps your partners or customers have data that YOU really want to combine with your own datasets.
Either way, Snowflake puts data sharing on the top of their supported features. Best yet, it can be done without the friction you’ll encounter when trying to do the same with SQL Server.
Easier Data Sharing with Snowflake
With Snowflake, you can effortlessly pull in datasets that other Snowflake accounts share with you, and you can also pick subsets of your own data to share with others. Snowflake also has a public data marketplace where you can pull down published datasets that can add insight to your in-house data.
Snowflake is constantly adding more features to make their sharing ecosystem more robust. Their rate of innovation in this direction means Snowflake is a good match for your organization if you intend to integrate your organization’s data with data from the outside. Likewise if you want to create seamless sharing between multiple Snowflake accounts belonging to the same organization.
4. You Spend Too Many Resources on Optimization
Query optimization is the bane of the data transformation process. There has to be an easier way to get your data flowing smoothly through your pipelines without having to frequently troubleshoot your SQL Server configurations. This can be a distracting nuisance for any team. But it’s an especially huge time drain for large organizations that have separated Database Admin, ETL Developer, Report Writing, and Server Admin teams.
Reduce Query Constraints
Snowflake removes the need for so much time to be spent on reworking the same ground. Their behind-the-scenes storage management essentially removes the need to ever lose sleep thinking about indexes or constraints again. Their aim is to enable your business intelligence team to spend less time pulling their hair out over query plans, and more time getting clean and informative data into the hands of business users.
So how does Snowflake work?
We’ve compiled six characteristics of Snowflake that business leaders should know.
5. You’re Looking for Easier Data Warehouse Oversight
In the business intelligence field, you’re in the business of providing answers to the organization’s questions. So why is it still so frustrating and difficult to get answers to your own questions—the ones you might ask yourself about your own data warehouse processes?
Who’s running what queries and when? What pipelines are eating up the most compute power? When was the last time a specific user logged in? How has the data warehouse size changed over time? How did the data in this table look a couple days ago before that latest set of changes? What was that one useful query you ran yesterday but forgot to save?
These answers should be easily accessible to you as you work on improving your organization’s data management. This is especially true when you really want to understand how the data is being used. With SQL Server, these answers require complicated tracing features, third-party software, or they just simply cannot be answered at all.
faster answers out-of-the-box
Snowflake makes it simple to get these answers. Their online portal allows you to monitor and analyze your data warehouse with an empowering set of out-of-the-box tools that feel like they were designed with a business intelligence manager in mind.
Is Snowflake the Right data platform for you?
Still not sure if Snowflake is right for you? Answer these questions:
- Do you have large data sets with terabytes of data?
- Does your database contain hundreds of millions of rows?
- Do you want to perform big data queries with complex calculations?
- Would you like to reduce your current infrastructure and administrative burden?
- Do you need elasticity in your solution and the ability to perform on-demand computing?
- Would you rather pay for what you use than be locked into a subscription tier?
If you answered yes to those questions, then it’s time to modernize your data warehouse platform.
Snowflake has been shown to achieve a high ROI over a short period of time.
See why in this helpful infographic.