Managing large volumes of data is a critical component of modern businesses. All crucial and cutting-edge decisions to increase operating efficiencies are taken after analyzing and processing massive volumes of data from internal and external sources. Hence, managing large databasesis the primary task of database administrators who have to ensure that data is processed quickly and effectively. One of the most common methods today to do so is to migrate databases from traditional systems to the latest cloud-based ones.
One such likely scenario is migrating database Microsoft SQL Server to Snowflake.
Microsoft SQL Server
Microsoft SQL Server integrates completely into the Microsoft ecosystem and supports applications on a single machine or a local area network across the web. It is a Relational Database Management System (RDMS) and is compatible with the Microsoft .NET framework out of the box. A wide range of analytics and business operations common in corporate ecosystems are supported by SQL Server. It is also one of the leading database technologies together with IBM’s DB2 and Oracle database. The database is based on SQL, a programming language commonly used by DBAs.
Snowflake is a cloud-based data warehousing solution and, in a short period, resolved many issues that are present in the prevailing database solutions. It is because of the many benefits of this data warehouse that organizations are now opting to migrate databases from Microsoft SQL Server to Snowflake.
Here are some of them.
- Support to multiple cloud vendors –The Snowflake architecture is compatible with a range of cloud vendors and hence users can use the same tools to work on any of them.
- Separate storage facilities – The computing and storage facilities are separate in Snowflake and users can scale up or down seamlessly in any of them by paying only for the resources used. Additionally, since Snowflake operates in the cloud, it provides businesses with unlimited storage space.
- Multiple workload access – An important reason for migrating databases from SQL Server to Snowflake is the high-performing abilities of the data warehouse. Multiple users can simultaneously carry out intricate queries on various workloads without facing any drop in speed and performance.
- Migrating different data types – Unstructured, semi-structured, and structured data can be loaded to Snowflake with in-built support provided to JSON, Avro, XML, and Parquet data.
- Automated support – Snowflake automatically clusters data without needing indexes to be defined. Various tasks like computing to the encoding of columns are automated too. For very large tables though, users have to manually use the clustering keys of Snowflake to co-locate table data.
These are some of the many benefits offered by the Snowflake data warehousing solution.
Migrating database from Microsoft SQL Server to Snowflake
This process is mostly automated and can be completed in a few clicks without the intervention of the DBAs. There are four steps to be followed here.
- Mining data from the SQL Server – The first step is mining data from the SQL Server through queries for extraction with select statements being used to sort, filter, and limit the dataduring the retrieval process. For exporting large databases in text, CSV, or SQL queries formats, the Microsoft SQL Server Management tool is used.
- Formatting the mined data –The raw data extracted from SQL Server cannot be migrated directly until it is processed and formatted to match the data types supported by the Snowflake architecture. For JSON or XML data there is no need to specify a schema before loading.
- Uploading to a temporary location – The formatted MySQL data also cannot be migrated to a Snowflake table but has to be first kept in a staging area. There are two staging areas to consider.
The first is an Internal staging area that has to be created by the user with SQL statements. It offers a great deal of flexibility while migrating a database from SQL Server to Snowflake as file formats and other options to named stages can be created as per specific requirements.
The second is the External stage where data is uploaded using the respective cloud interface. Presently, the external staging areas supported by Snowflake are Microsoft Azure and Amazon S3.
- Migrating data to Snowflake – This is the final stage where a database from the staging area is migrated to Snowflake. The data loading wizard of Snowflake is applied for migrating small databases. For large databases and bulk data, however, the Data Loading Overview, a unit of Snowflake has to be used. In this case, the PUT command is used to stage files and the COPY INTO command to load processed data into an intended table.
After the data is migrated from SQL Server to Snowflake, a provision should be compulsorily made to load fresh and incremental data as it is not possible to load the whole database every time a change is made to the source data. For this, a script has to be created that will track new data at the source and use an auto-field to continually update the target database.
This process to migrate a database from SQL Server to Snowflake is easy for DBAs with the right skill sets.