1 / 5

Steps to Migrate Data to Snowflake from Microsoft SQL Server

Ready-to-use data delivered to Amazon S3, Amazon Redshift, and Snowflake at lightning speeds with BryteFlow data management tool. This automated tool is completely self-service, low on maintenance and requires no coding. It can integrate data from any API and legacy databases like SAP, Oracle, SQL Server, and MSQL.

michalSmitth84
Télécharger la présentation

Steps to Migrate Data to Snowflake from Microsoft SQL Server

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Steps to Migrate Data to Snowflake from Microsoft SQL Server Before going into migrating data to Snowflake from Microsoft SQL Server, a look at the two is necessary.  Microsoft SQL Server is a platform where data is stored and retrieved. It is a combination of a Relational Database Management System (RDBMS) and Structured Query Language (SQL). The lower version caters to freeware while the data center version offers high applications support on a local area network or across the web on a single machine. The SQL Server integrates smoothly into the full Microsoft Ecosystem.

  2. Even with these advantages, why do organizations want to migrate data SQL Server to Snowflake? It is because of the many benefits that this cloud-based data warehousing solution has to offer.

  3. Snowflake is a Data Warehouse-as-a-Service (DWaaS) and operates on AWS, one of the most used cloud providers. Users can load both structured and unstructured data including JSON, AVRO, XML, and PARQUET data. The platform provides different computing and storage facilities with the option to scale up and down and pay for only the quantum of resources used. Further, multiple workgroups can work on multiple workloads on Snowflake without any drop in speed or performance. Finally, Snowflake supports a host of SQL data structures such as NUMBER, BOOLEAN, VARCHAR, TIMESTAMPS, and more. 

  4. There are several steps to migrate data SQL Server to Snowflake. Mining Data from SQL Server – Extracting data from SQL Server is the first step in the process. Generally, it is done through queries for extraction. Data is retrieved by using Select statements to sort, filter, and limit the data while carrying out this activity. The Microsoft SQL Server Management Studio tool is used to mine bulk data and entire databases in CSV, text, or SQL queries format.  Processing the extracted data – The data after extraction from SQL Server has to be processed before migration to Snowflake can take place. It has to be verified first that the data type extracted matches the data structures supported by Snowflake. For XML data and JSON, a schema is not required before loading. 

  5. Loading processed data to a temporary location – The processed data cannot be directly loaded to Snowflake. It has to be first kept in a temporary location. There are two components here, internal and external stages. An internal stage is created with SQL Statements with file options and names allotted to the stage. External stages currently supported by Snowflake are Microsoft Azure and Amazon S3 and data is uploaded here through their respective cloud interfaces.  Migrate data SQL Server to Snowflake – The data in either of the stages is finally migrated to Snowflake. The Snowflake wizard is used to load small databases while the Data Loading Overview is used for bulk data and large databases.  Before starting the process to migrate data SQL Server to Snowflake create a virtual data warehouse to facilitate this activity. 

More Related