We consider multiple factors when implementing Snowflake. The following are some points to consider based on that:
From deciding the data types the client needs to migrate to Snowflake, and the compute layers they will require to meet their business needs, to cloud services that provide reporting and analytics – our Snowflake developers help plan an end-to-end Snowflake implementation.
The Snowflake implementation journey is complex and involves many things to consider. For example, data loading is not merely loading data into cloud data centers. Here also, whether the client needs bulk loading, runs complex queries, or requires parallel data loading through multiple threads affects data loading speed and quality.
Similarly, data transformation is not limited to using ETL tools. In addition to ETL tools, run the search queries and consider using Snowflake scripts as well. This helps in solving dynamic queries with cursor variables in SQL. By combining these features with functions like Java UDFs and external functions, one can create high-end data transformation logic in Snowflake.
Further, during data transformation, based on managed services like Snowpipe, Streams, Tasks and the frequency of real-time data like streaming or batch, Snowflake costs vary. Moreover, we help clients’ data teams to test and debug the ingested data by breaking down data pipelines into smaller steps, which write results into transient tables.
Additionally, we help clients in selecting the right data warehouse size depending on the diverse query types and clients’ requirements. Our team ensures data security both in transit and at rest during Snowflake implementation. It is not enough to decide the data journey. It is equally imperative to manage and validate the data that flows into pipelines to gain trustworthy, valuable and actionable insights. Hence, we emphasize maintaining quality pipeline data for our clients as well. This optimizes cost and improves query performance by streamlining data loading.
To help clients maximize Snowflake and optimize their data, we take a detailed look at each consideration.