One of the biggest challenges that face the modern market is the simple problem of data. Because of the nature of how fast technology has evolved and the level of connection that permeates commerce, having challenges when it comes to intelligent data acquisition is not uncommon. 

Why is Data a Problem?

Data is collected constantly and can either be transformed into a useful tool that pushes forward business intelligence, or it can simply just take up space. Data itself, collected through the millions of interactions that happen each and every day, can be hard to utilize. This data can be disconnected, unrefined, and impossible to actually push into areas of business intelligence like analytics and marketing. However, that’s where you have certain SaaS companies that have developed solutions. 


 

When data accumulates in a company in a way that doesn’t push the company forward, this data is said to be in a data silo. This is a place where raw data that doesn’t really push the ball forward ends up living. There can be several reasons as to why this data lives here, from advancements in technology to poor communication, or simply a lack of interest in making it accessible to the rest of the company. 

 

Regardless, when this data is not being utilized, a company loses out on an invaluable tool that could help push their business intelligence forward. For data integration, there are typically two main contenders on the market – Snowlfak and Databricks. 

 

Snowflakes and Databricks both do similar things, however, they actually service clients in uniquely separate ways that make them valuable to different businesses. The choice for whether you should invest in Snowflake or Databricks doesn’t have to do with brand loyalty as much as overall need. 

 

Here is everything you need to know about Databricks vs. Snowlfake.

Data Warehouses

This is typically where a data warehouse can be utilized to help a company take a data silo and turn it into something profitable. The idea here is that you are taking data that is hard to use from a silo, making necessary changes to increase its worth, and transferring it to a data warehouse where it is available for use. 

 

The key takeaway about a data warehouse is that data that enters a warehouse is modified, or transformed, so as to be accessible. This means that data warehouses hold relevant data that can be used by a company to further their business intelligence and are a great solution to the problem of data silos. 

Snowflake, The Largest Data Warehouse Solution on the Market

One of the largest SaaS solutions for this problem currently is the software company Snowflake. Snowflake focus on taking a company’s data that is either hard to access or not able to benefit, from and transforming it into a useable source called a warehouse. 

 

Snowflake supports both ETL and ELT data transferring and has grown to become one of the lead SaaS providers of this service in the world. 

What Limitations do Data Warehouses Have?

Data warehouses are powerful tools that providers like Snowlfak can give to businesses that help them integrate and transform their data into useable analytics power. However, there is a limitation. Data warehouses are not specifically designed to take on large amounts of data, or Big data.

 

This means that it can be perfect for large portions of the market that won’t ever need to worry about data limits, however, it may not be able to service larger companies like Google and Yahoo. 

Solution for Big Data – Databricks

Big data, like Yahoo or Google, require what is known as data lakes and data lakehouses. These are similar to warehouses in that they both represent another option that doesn’t involve a data silo. However, the sheer magnitude of the amount of data is where these differentiate themselves from a warehouse. 

 

A data warehouse has data that is ready to be easily, and quickly accessible, while a data lake holds both refined, transformed data, as well as raw data. This mixture is still accessible, but it represents a greater challenge to a company when it comes to utilizing it. The mixture is typically a result of simple necessity due to the sheer volume of data, as opposed to a warehouse that has a manageable amount. 

Conclusion 

Databricks vs. Snowflake is more about what your company needs. If you have Big data that needs to be sorted, stored, and transformed, then Databricks may be the more accessible tool for such a task. Databricks actually utilizes what is known as data lakehouses that are a mixture of both a refined, transformed warehouse and a data lake that still holds raw data. 

Finding out which SaaS is right for your company comes down to understanding what your data integration needs actually are. Both services are similar, but they are both designed for tasks that are uniquely their own.

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