Effective data management is the cornerstone of holistic business development and overall growth in the competitive market. To thrive and yield effective business outcomes, data analysts need to store, manage and utilize data and improve their decision-making process. Conventional relational databases are not efficient enough to handle large data volumes. Snowflake, explicitly designed for the cloud, is a widely used data warehouse solution for SMEs and large enterprises, and that is why most enterprise solutions opt to have Snowflake implementation.
The unique architecture of Snowflake, built on top of AWS (Amazon Web Services), makes it a fast and flexible data management solution. Snowflake gathers all the heterogeneous data from different sources and loads it into a single data warehouse. For efficient data transfer with a standard format, enterprises must plan and execute a Snowflake implementation strategy. This guide will cover the best Snowflake implementation practices for migrating data into the single-stop virtual data warehouse.
Evaluate Virtual Data Warehouse Options
According to SLINTEL, a 6sense company, over 10788 enterprises have shifted to Snowflake data warehousing solutions in 2022, with around 7307 customers from the United States alone. Enterprises should start the Snowflake migration strategy by evaluating the virtual warehouse options in modern database solutions. These are a cluster of computing resources that plays a vital role in executing SQL SELECT statements, performing DML operations, and other essential tasks.
Experts must evaluate different warehouse sizes before picking the one that fits the business’s data needs and workload requirements. It is crucial to determine the warehouse size based on Snowflake’s “auto-resume” and “auto-suspend” features to save costs, as large warehouses require high expenses, leading to the stretching of the budget.
Ascertain The Right Warehousing Size
Another critical consideration for the success of the Snowflake migration strategy is selecting the right warehousing size, and enterprises must focus on aligning the virtual data warehouse size with the workload of the business for successful data migration. Some of the other crucial points to consider during warehouse sizing include the following:
- Recognizing the workload patterns and gradually increasing the size of the warehouse to avoid overloading issues
- Identifying the correct cluster size and improving the individual query performance by scaling the warehousing size up and out
- Automating suspend/resume by suspending idle time settings and offering end-user workloads
- Aligning the warehouse size with the workload by keeping the business team in a loop
- Keeping in mind the credit requirement per hour Snowflake before finalizing the warehouse size
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Understanding Credit Charges on Snowflake
Snowflake charges according to the pay-per-second billing that includes the number of clusters, servers per cluster, and time duration for each cluster run. A complete understanding of credit charges will allow enterprises to scale up and down, save costs, and get the maximum profits from the virtual data warehouse. Optimizing resources, computing, and storing charges is another essential factor that will make the Snowflake implementation hassle-free. Here are a few best practices for cost optimization during Snowflake database implementation.
- Enable proactive notifications and setting on account level if team-by-team quotas are not required to manage resources
- Compute the viewing usage and resource monitors to cut down costs
- Focus on crucial areas such as dimensional tables, materialized relationships, persistent staging areas, and business rules while storing.
Have A Robust Network Security Policy
For a successful data migration to the Snowflake database, enterprises need to have a tight network security policy for network, object, account, and data.
Some of the best ways to secure data migration include restricting access to specified IP/IP ranges, schema, tables, views, and databases, enabling multi-factor authentication to mandate user access, and following an end-to-end data encryption policy.
Streamline The Data Loading Process
Breaking the overall migration process into predefined steps can ease data transformation to the Snowflake database warehouse.
- Focus on the ingestion pattern and transfer the bulk data into tables
- Prioritize the file size by splitting the larger files into smaller ones and aggregating the smaller files to load faster
- Prepare a delimited text file with data in ASCII format and UTF-8 as the default character set.
- Enhance the query performance by implementing the clustering key for large tables and using separate warehouses for load tasks and queries
Make Seamless & Successful Snowflake Implementation
Organizations must adhere to the best practices and guidelines to ensure that Snowflake’s implementation strategy is executed well and without errors. Following the above rules will help enterprises propel toward a successful implementation that allows them to achieve expected goals and outcomes.
Inferenz can help you switch to Snowflake, the fully managed cloud data warehouse, to store data, run analytics, and make better business decisions. Feel free to contact us to learn more about our Snowflake implementation strategy.