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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.
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Steps for Migrating Data from Oracle to Snowflake Oracle has many advantages – it can be ported to more than 100 hardware platforms and 20 networking protocols. Writing an Oracle application is quite safe from changes in direction in operating systems and hardware. Further, Oracle has the largest RDMS market share in VMS, UNIX, and OS/2 Server fields.
Given these multiple advantages, why would organizations want to migrate data Oracle to Snowflake? Snowflake is a cloud-based data warehousing solution. It has addressed many issues that were hitherto inherent in traditional data warehouses leading to organizations preferring this platform. One of the many benefits is that it has separate computing and storage facilities and users can scale up and down in their utilization, paying only for the quantum of resources used. Also, there is no drop in performance and lag, even when multiple users simultaneously work with complex queries and multiple workloads.
Another advantage of Snowflake is that its architecture supports a wide range of cloud vendors. Users can, therefore, use the same tools to work with various cloud vendors without having to develop new skill sets.
Here is the process to migrate data Oracle to Snowflake. Extracting data from Oracle Converting and formatting the data After the data is extracted from the Oracle database it has to be converted and formatted to match the requirements of specific organizations. It has to be ensured that the data character at the source matches those supported by Snowflake. This is not very complex as Snowflake supports almost all types of primitive and advanced data as well as nested data structures The first step is extracting data from Oracle to CSV file through a SQL Plus query tool in the Oracle Database Server. It can query and redirect the data to a CSV file with a “Spool” command which will continue writing till such time it is switched off. When incremental data has to be mined only, that is the changed records after the last pull, the tool has to be run in appropriate conditions.
Uploading data to a cloud staging area Copying data to table Finally, to migrate data Oracle to Snowflake after staging, the “COPY INTO” command is used to transfer data to Snowflake. For this, the computing resources in Snowflake virtual warehouse are required and Snowflake credits are utilized. By following these steps, the process of migrating Oracle data to Snowflake can be carried out. The next step to migrate data Oracle to Snowflake is to upload data to a cloud staging area before it can be loaded to Snowflake. Two staging areas can be utilized, internal staging and external staging. In the first instance, users and tables will be allotted to an internal stage and a name assigned to it. File format and date format will be applied. The whole process is automated. For external staging, Snowflake currently supports Microsoft Azure and Amazon S3 only.