Snowflake Consulting Services

In nearly every industry, data has become an indispensable resource. Organizations that are very successful usually manage their data efficiently. That’s where the data warehouse plays a key role.

Snowflake data warehouse offers immense benefits over on-premises systems. Snowflake implementation gives companies an edge as it increases agility, security, reliability and flexibility to manage and leverage data like never before.

Before discussing how you will make a transition from the on-premises data warehouse to Snowflake, let’s look at what data types you can migrate to Snowflake and how to prepare your data for Snowflake migration.

Let’s look at what data types you can migrate to Snowflake You can migrate all data types, including structured, semi-structured and even unstructured data.

You can store your structured data at any size in Snowflake, according to your needs. Snowflake supports any valid, single-byte delimiters, including CSV and TSV formats. Snowflake also supports certain semi-structured data types, including JSON, Avro, ORC, XML, Parquet, etc. For unstructured data, you need to use Snowflake’s Snowpark. Snowpark enables the storage, processing and querying of unstructured data, such as medical images or call center recordings.

Here’s a three-step process to migrate data from the on-premises data warehouse to Snowflake. The following steps use a CSV example to illustrate the steps involved in simple data migration.

Select and split your data

Use a file splitter like GSplit or an ETL tool to split the large file into smaller chunks. Doing so allows you to take advantage of Snowflake’s parallel processing capabilities.

Migrate data to a Snowflake staging area

Use the SnowSQL command line client to stage the data. Then, a PUT command with the correct Snowflake syntax will begin staging your local files.

Verify your migration to the cloud

Once the process is complete, you’ll have successfully transferred your local CSV files to Snowflake’s internal stages. Using the command ls, you can list all the files in your directory.

Snowflake cloud services offer businesses many features and improvements over traditional on-premises solutions. Plus, migrating from on-premises to the cloud isn’t as daunting as you may have thought – especially with the right custom software developer.

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.