Storing data and maintaining it has become very important for business success. Snowflake and Oracle are two different database management systems. In this blog post, we shall discuss how to migrate data from Oracle to Snowflake and a few differences between the two traditional database management systems.
What is Oracle?
Oracle Autonomous Data Warehouse is a cloud data distribution center that removes almost all of the intricacies associated with running a data warehouse, securing data, and developing data-driven applications. It focuses on automating data warehouse procurement, customizing, trying to secure, adjusting, scalability, repairing, syncing, and rebuilding. Unlike other comprehensively managed service cloud data platform solutions, which only tweak and keep updating their service, it includes elastic, automated scalability, configuration management, protection, and a wide range of built-in fully integrated database services that enable simpler queries across multiple network types, computerized data evaluation, simple data trying to load, and data visualizations.
What is Snowflake Technology?
Snowflake allows rapid, easier-to-use, and far more adaptable data storage, preparation, and data analysis remedies than traditional selections. Snowflake’s data platform is not based on any established database systems or “big data” software platforms like Hadoop. Snowflake allows rapid, easier-to-use, and far more adaptable data storage, preparation, and analytic remedies than traditional selections. Snowflake’s data platform is not based on any established database systems or “big data” software platforms like Hadoop.
Migrating from Oracle to Snowflake ?
The following are the steps for migrating data from Oracle to Snowflake :
Stage 1: Extract information from Oracle to CSV utilizing SQL*Plus
SQL*Plus is a SQL tool for queries that is introduced with each Oracle Database Server or Client establishment. It very well may be utilized to question and divert the consequence of a SQL inquiry to a CSV record
Stage 2: Data type transformation and organizing
While moving information from Oracle to Snowflake, information may change according to business needs. Aside from such use case-explicit changes, there are sure significant things to be noted for smooth information development.
Stage 3: Stage Files to S3
To stack information from Oracle to Snowflake, it must be transferred to a cloud organizing region first. On the off chance that you have your Snowflake occasion running on AWS, the information must be transferred to a S3 area that Snowflake approaches. This interaction is called arranging. The snowflake stage can be either inward or outer
Stage 4: Copy organized documents to Snowflake table
Up until this point – you have removed information from Oracle, transferred it to an S3 area, and made an outside Snowflake stage highlighting that area. The following stage is to duplicate information to the table. The order used to do this is COPY INTO. Note: To execute the COPY INTO order, register assets in Snowflake virtual stock items are required and your Snowflake credits will be used.
Snowflake vs Oracle
- Ease of Setup: There is no hardware to configure, and no system software or database software to implement. There are no spots to install or database improvements to plan.
- Management of Database: In Snowflake technology, there are no indicators to handle, no physical and logical partitioning required, no numbers to capture, and there are no errors of a query performance even in cases where it is not caught and maintained correctly.
- Staying Tuned: Snowflake is presently accessible on the Amazon AWS and Microsoft Azure platforms, and it was announced recently that it will be available on the Cloud Platform of Google.
- Balancing the Power: with the aid of isolating the workload feature, Snowflake has eliminated the resources related to heavy information rush. Rather than developing a solitary, comprehensive, and multi-core database server, it is easy set up an infinite number of entirely independent warehouses supported on the cloud.
- Simple Disk Upgrades: In Snowflake technology, the user never runs out of circle space at any point in the future. It upholds in a real sense limitless information volumes on Google Cloud Platform, AWS, and Microsoft Azure.
- Compressing Heavy Data: With no compelling reason to tender the permit cost of the OLTP choice or cautiously load information to expand information pressure utilizing embed attach on Oracle platform. In Snowflake technology, every one of your information is naturally packed utilizing columnar pressure, regularly to a variable of somewhere in the range of 3 and multiple times.
- Easy migration due to server flexibility: As the graph underneath represents, Snowflake is gradually versatile, with a basic arrangement of big sizes such that of a rug, and can be expanded from an additional a little to a server of 4X-Large size, within milliseconds. The outline shows the decrease in a slip by the season of a 256Gb table join as the waiter size is expanded.
- High Availability Deployment: The user has no burden to build an expensive data center hub with persistent storage and fail-over for increased reliability. Snowflake writes data transparently way to three Network Segments in a region and can instantaneously resist the loss of any two of them.
- Timely Backups: Snowflake allows for a time machine of up to 90 days, as well as the capacity to un-drop a table, schema, or if there is a need sometimes the whole database within seconds.
- Top Knotch Security of Data: Snowflake incorporates start to finish encryption with programmed key pivot, Multi-Factor Authentication, and surprisingly the choice of committed cloud equipment with the choice of going for a cloud-hosted Private Snowflake.
Snowflake vs Oracle is a much-debated topic amongst cloud computing professionals. Both the technologies have their own pros and cons. The suitability of either of the technologies depends on the needs of the user.
Author Bio :
I am Anusha Vunnam, Working as a content writer in HKR Trainings. I Have good experience in handling technical content writing and aspire to learn new things to grow professionally. I am expert in delivering content on the market demanding technologies like Artificial Intelligence, Business Intelligence etc.