One of the biggest pain-points for analysts when working with large volumes of data is trying to collect, manipulate, and structure data. Having a one-stop-shop such as a cloud data warehouse has drastically helped analysts free their time to focus on more important tasks and has empowered organizations to have a unified view of their data. 
A data warehouse is a system used for reporting and data analysis, and is considered a core component of business intelligence. Data warehouses are central repositories of integrated data (both current and historical) from one or more disparate sources. 
The development of integrated ecosystems across multiple digital environments has connected business intelligence tools and other analytics software to accelerate insights. Transitioning computing, processing, and storage power into the cloud became the next step in the evolution of analytics software. 
Many different warehouses tried to provide comprehensive data services, but they usually fell short. Here’s where Snowflake thrived. They created a system that solves many problems you see today with data warehousing. This is why Snowflake is still one of the most used data warehouses in the world. 
Below, we’ve compiled a list of general tips for getting started with Snowflake. 

Tips for Getting Started With Snowflake

  • Make use of everything Snowflake offers

Snowflake is not just a data warehouse, it can also serve other purposes such as a data lake, data mart, ODS, and database. To build a complete enterprise environment, you can leverage all of these to accomplish your goals. Snowflake will give you seamless integration between all of these platforms.

  • Prioritize ELT over ETL

Moving corporate data to a single platform should be the first priority for any IT department. However, doing so requires a lot of resources and time in a typical ETL environment and would not give overtime flexibility. ELT gives flexibility and ease of storing new, unstructured data. With ELT, you can save any type of information, even if you don’t have the time or ability to transform and structure it first.

  • Have a data model 

Sometimes a business doesn’t know their own data needs or landscape. They often use different words for the same data sets or the same words for different data sets. Modeling the information for a business can be an eye-opener for all parties.

  • Build data-flow diagrams 

Data flow diagrams can determine the best steps for moving forward and prioritize which actions to take according to business data sources and seeing how data moves in different systems.

  • Build a source agnostic integration layer

The sole purpose of integration layers is to pull together information from multiple sources. This is generally done to allow better business reporting, unless the company has a custom application developed with a business-aligned data model on the back end. Choosing a third-party source to align with defeats that purpose. Integration MUST align with the business model.

  • Use a data warehouse architecture standard

Standard data warehouse architectures (3NF, star schema, Data Vault, BEAM) will enable efficiency within a data warehouse development approach. Picking a standard and sticking with it regardless of the actual approach chosen allows new staff to join the team and ramp-up faster.

  • Consider adopting an agile data warehouse methodology

Businesses have a wide variety of data sources that have to be integrated in the central repository. Due to the nature of adopting new technologies and rapid changes in the existing technologies, data warehouse projects can no longer be large, monolithic, multi-quarter or yearly efforts. Currently, data warehouse projects can be broken down into smaller, faster, and deliverable pieces that return value more quickly. This allows you to prioritize the warehouse when business needs change.

  • Harness the power of Snowflake

Snowflake’s ability to instantly scale up and out can provide faster results when harnessing its power. When creating new data pipelines, try to move some of the workload to Snowflake.

  • Build an audit balance and control (ABaC) framework

An ABaC framework helps control data lineage and quality but also allows for database performance optimization vs. computing cost (credits).

  • Validate data as a source

Data must be validated at the source using scripts or different software to bring quality data into the central repository.

  • Adopt a data warehouse automation tool

Automation allows you to be able to leverage your IT resources fully, iterate faster through projects, and enforce coding standards (i.e., Wherescape, AnalytixDS, Ajilius, homespun, etc.) for easier support and ramp-up.

  • Get your staff trained by utilizing experts

Snowflake is the first data warehouse built from the ground up for the cloud. Even if it’s fully ANSI SQL compliant, it comes with a wide variety of new technologies and concepts that don’t exist in other tools. To leverage all of its capabilities your staff should be trained by specialists.
To architect a viable solution for Snowflake and integrate it with your existing cloud and on-premise tool, you’ll most likely need experts in the field to architect/execute your desired solution. Here at Cognetik, we have Snowflake certified staff that work with multiple Fortune 1000 companies to implement Snowflake analytics solutions.

Why You Should Use Snowflake in 2020

One of the key benefits of using Snowflake is its ability to scale your storage independently from the actual processing of that data. For example, if different groups in an organization have different data needs, such as dumping a lot of data or querying it, Snowflake easily allows businesses to handle that split.  
Snowflake is also price-efficient. Their cloud-based business model does not charge until you start using it for storage and querying. You can simply create an account and start exploring the capabilities they have to offer. 
Another benefit of using a cloud-based platform is the ability to scale according to your needs. This is also extremely cost-efficient. You only pay if you need extra storage or processing, and don’t have to buy extra equipment. 
If you have any questions on how to implement Snowflake, contact us today.

About the author

Andrei Panait

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