COVID-19 has propelled the demand for cloud and cloud computing across the globe. Businesses today are accelerating using the cloud to secure their data, ensuring its integrity and relevance. Nevertheless, the market is flooded with many organizations’ options for secure and robust cloud infrastructure. However, today’s competitiveness requires seamless data management and integration. Snowflake is one such platform that is explicitly built to keep data intact with the operations of the companies. In the following post, we will walk you through Snowflake’s benefits over various cloud platforms and explain what you should do and should not do while migrating your data from other clouds to Snowflake. Data migration is a process of transferring data stored from a source system to a destination without affecting operations. It involves 3 basic steps: Extract, Transform, and Load data. The process of data extraction requires data identification, where the information is categorized based on location, format, and sensitivity. Once the relevance of information is deduced, a data migration plan is formed to perform data profiling, data cleansing, data validation, and the ongoing data quality assurance process in the target system. The tools and resource access is granted to the staff once the project is confirmed. Finally, the data is migrated to the new system, ensuring the business’s confidential data safe. Organizations worldwide are striving to ensure their data is safe and seamlessly used for better business growth. As a result, organizations are explicitly migrating their data from their existing cloud infrastructure to a new cloud infrastructure. However, data migration involves various challenges that are mandatory to overcome to ensure its significance is maintained. Let’s have a look at some of the difficulties that bottleneck data migration: 1. One Platform, One Copy, Many Workloads: Snowflake can share multiple workloads for a business on a single platform using a single copy of data. One copy, one platform, and many workloads strategy reduces the data redundancy, ensuring the data’s relevance is maintained. The process supports data engineers, data scientists, and data operators to reduce time consumption in data analysis, monitoring, and processing. 2. Secure and Governed Access to Data: Data infringement or data leakage is entirely stopped by Snowflake’s authorized access to the data. The data is visible to the user as per its authorization and can perform only a particular set of operations on data. As a result of this governed access, the security of the data can be amplified. With Snowflake’s authorized access to data, businesses can leverage Role-Based Access Control, Comprehensive Data Protection, Dynamic Data Masking, and External Tokenization. 3. Ability to Independently Scale, Compute and Storage: Snowflake offers unlimited performance and state-of-the-art scalability to the user. Businesses can leverage a pay-per-server-per-second strategy to reduce operational costs. Snowflake also provides scalability with its multi-cluster warehouse, which significantly reduces the run-time and improves its operationality. Snowflake uniquely isolates storage from computing, enabling each to be fully elastic and scale independently. The benefits are quite attractive for businesses looking for a better cloud platform. However, companies must keep certain things in mind while migrating their data from various cloud platforms to Snowflake. So what are the dos and don’ts for data migration in Snowflake? Let’s have a look: 1. Use Snowflake Stages for Initial Full Loads: Snowflake offers several stages, such as ADLS Gen2, Blob, S3, and GCS. These stages make it easier for businesses to load data into the platform. Secondly, the steps reduce the redundancy of the data, such as duplicate information and misspellings. As a result, it keeps the integrity of the data intact. 2. Use Snowpipe for Incremental Load: When migrating data to Snowflake, there are chances that the data’s nature is incremental, i.e., the information has multiple branches that have more data in them. In such cases, it is recommended to use Snowflake’s Snowpipe. It shall keep the data categorized and ensure that the data from a particular branch can be accessed when called. 3. Build Integrations with Catalogs: While integrating the data to Snowflake, organizations are advised to use the catalogs such as Glue or Azure Data Catalog or Alation or Collibra, or Talend. 4. Use File-Based Transfers Instead of Row Level: While transferring the data to Snowflake, it recommended to use file-based transfer rather than using row level. Using file-based transfer can save time and resources for the businesses adding exceptional value to the business’ operations. 5. Build Robust Validations to Ensure Data is Copied Properly: Having a full-proof validation strategy is a must while transferring the data to Snowflake from other systems. It is essential to have a robust validation to ensure that data copied from a system to Snowflake is relevant and significant. 6. Migrate in Stages: Lift and Shift first, followed by optimization to minimize business impact. The migration in stages allows the stakeholders to have continuous monitoring of the data, ensuring the data’s safety and integrity. Since we now know what to do while migrating the data to Snowflake, let’s look at what not to do while migrating to Snowflake. 1. Avoid Using JDBC to Copy Large Volumes of Data: While migrating to Snowflake, avoid using JDBC to copy large volumes of data; it will reduce the speed of migration and affect the data’s integrity. 2. Avoid Using Snowpipe for Initial/Full Loads: We mentioned earlier that while migrating to Snowflake, we use Snowpipe for incremental data. However, it is not recommended to use the same Snowpipe for an initial or full load. The reason behind avoiding Snowpipe is that it will acquire the resources and hamper the migration due to its slower speed and redundancy. 3. Author Pick: Snowflake offers seamless and robust data migration and operation to businesses. It helps organizations to keep the relevance and significance of the data intact while ensuring the data’s security. Although data migration to Snowflake is a better option in today’s competitive world, one must keep certain things in mind to leverage the full potential of the platform. What is Data Migration?
What are the Challenges in Migrating the Data?
Webinar: Reduce Cost with Hadoop to Snowflake Migration
What Makes Snowflake Better?
Do’s
Dont’s
