When you have a system in place that has served your data retrieval and storage needs for long, why would you want to switch to another comparatively new solution?

Microsoft SQL Server is one that has for long met most SME requirements and workloads. It supports most applications across the web or on a local network system on a single machine and blends seamlessly with the full Microsoft ecosystem. Then, why do many organizations today want to replicate data SQL Server to Snowflake?

Snowflake has an advantage if you have big data needs but not much for relatively small datasets or low currency/load. This cloud-based data warehousing solution has almost unlimited storage capacity, a friendly user-interface, and very stringent data control and security measures. All these attributes are expected from a data warehouse but not available fully in Microsoft SQL Server.

There are other benefits to Snowflake. It offers separate computing and storage facilities. Users can scale up or down in either of them, paying only for the resources used. Further, multiple users can work simultaneously on intricate queries and workloads without experiencing any lag or drop in performance.

Basically, Microsoft’s SQL Server is a database server with primary functions being to store and retrieve data. It is a combination of the Relational Database Management System (RDBMS) and the Structured Query Language (SQL). Many specialized versions released recently though cater to a wide range of workloads and demands.  

Snowflake, on the other hand, is cloud-based and offered as a Software-as-a-Service product. It runs on AWS, the most popular and commonly used cloud provider. As in other databases, it is possible to query any structured data in Snowflake tables through the standard SQL data types such as NUMBER, BOOLEAN, VARCHAR, TIMESTAMPS, and more. These features are added incentives to replicate data SQL Server to Snowflake.

Tools to replicate data SQL Server to Snowflake

The process of replicating data to Snowflake need not be a long-drawn-out and tedious process if you use the most optimized and effective available tools. Given here are some features of the tools that should be checked before you opt for one.

·        Handling large volumes of data – Make sure that the tool you choose is able to handle massive volumes of data effectively and without any performance degradation.

·        Completely automated – The tool should automatically merge, transform, and reconcile data through a simple point-and-click interface regardless of the quantum of data being replicated.

·        Be able to use SQL Server CDC – By using SQL CDC (Changed Data Capture) the tool should utilize database transaction logs at source and copy modified and changed data only to the Snowflake database. Complete data refreshes every time a change is made is thus not necessary.      

Microsoft SQL Server is a relational database with several inherent advantages. The most important is that the complexity of installing the software is low as the setup wizard automatically detects and downloads all updates. This considerably reduces maintenance costs. Further, it is highly secured, using Policy-Based Management to detect non-compliant security policies.  

Why then would you want to replicate data SQL Server to Snowflake?

Snowflake is a cloud-based data warehousing solution that can hold both structured and unstructured data automatically. It manages how data is stored and organized and provides file size, structure, compression, metadata, and statistics. Storage is independent of computing and users can scale up and down in data usage, paying only for the quantum of resources used. It also enables multiple users to work simultaneously on multiple workloads without lag in performance.

What then should be the features of the tools that optimize the process to replicate data SQL Server to Snowflake?

Check for the following –

Fully automated – Select a tool that automates mergers, transforms, and reconciles data through a point and click interface. Regardless of the quantum of data to be replicated it speeds up the process. The traditional SQL Server data tools, on the other hand, require setting up of connectors and pipelines before replicating data to Snowflake. Additional coding too is often required.  

 Able to handle massive data volumes – The best of tools will be able to replicate huge volumes of Microsoft SQL data to Snowflake without any contention or drop in performance and speeds. Most tools are not up to this task, taking a long time to replicate data, thereby adversely affecting organizations’ productivity.

User-friendly – With a superior tool, there is no need for constant intervention by Database Administrators during replication. This leads to low Total Cost of Ownership (TCO) as DBAs do not have to spend hours to replicate data SQL Server to Snowflake. Managing dependencies and configuring backups are all automated in the best tools. The process becomes easier and less tedious.

Use CDC for replication – A tool where you have to opt for a full data refresh when you want to update changed source data at the destination is not ideal for you as it will affect your productivity and performance. Instead, the best is one that uses SQL Server CDC (Changed Data Capture) to Snowflake. The tool will utilize database transaction logs to query SQL Server data at the source. The modified and changed data will only be copied to the Snowflake database.  

Continuous data reconciliation – The chosen tool should be able to continually reconcile data in the Snowflake data warehouse. To replicate data SQL Server to Snowflake the option to validate data in the snowflake database continuously or at a frequency of choice should be available.

Why are organizations opting to transfer data from Microsoft SQL Server to Snowflake? What are the steps required to go through the process?

Microsoft SQL Server supports applications on a local area network or across the web on a single machine and blends easily into the Microsoft ecosystem. Snowflake, on the other hand, is a recently introduced cloud-based data warehousing solution that resolves many issues that were hitherto hurdles in traditional systems.

There are multiple benefits of Snowflake, reasons why organizations now prefer to load data from SQL server to Snowflake.

•Snowflake architecture supports a wide range of cloud vendors and users can use the same set of tools to work with and analyze data from different vendors.

•Storage and computing facilities are separated and users can scale up or down according to their requirements, making payments only for resources used.

•Snowflake automatically clusters data and no indexes are to be defined. But when users work with large volumes of data Snowflake’s clustering keys are utilized to co-locate table data.

•The same set of data can be accessed by multiple workgroups for multiple workloads simultaneously without any dip in performance or speed.

•Both structured and unstructured data can be loaded natively to Snowflake. This data warehousing solution supports JSON, Avro, XML, AND Parquet data.

It is now obvious why businesses would want to work with this current generation cloud-based data warehousing solution.

Follow these steps to load data from SQL server to Snowflake.

•The first is mining data from Microsoft SQL Server through queries for extraction. Select statements are used to sort, filter, and limit the data being retrieved. Microsoft SQL Server Management Studio may be used to export bulk data or entire databases.  

•The extracted data has to be processed and prepared for loading. It should be ensured that the data structure in Microsoft SQL Server matches the data types supported by Snowflake. However, this is not necessary when loading JSON or XML data into Snowflake.

•Data files have to be loaded now into a temporary location before they can be transferred to Snowflake. This process is called Staging. There are two components here. The first is the Internal Stage which is created with respective SQL statements and offers a great degree of flexibility while loading data. The second is the External Stage. Amazon S3 and Microsoft Azure are the locations presently supported by Snowflake.