Tag: datawarehousing

A New Era of Cloud Analytics

Posted on by Philip de Souza

A NEW ERA OF CLOUD ANALYTICS WITH SNOWFLAKE AS THE HADOOP ERA ENDS

Hadoop was regarded as a revolutionary technology that would change data management and completely replace data warehousing. Such a statement is partly accurate but not entirely true since it has not been the case ever since cloud solutions came into the picture. Hadoop mostly flourishes with projects that involve substantial data infrastructure, meaning it was more relevant around a decade ago when most data analysts Continue reading →

Snowflake vs Netezza

Posted on by Philip de Souza

Snowflake vs Netezza

Fifteen years ago, IBM introduced an appliance-based, on-prem analytics solution known as Netezza. It was purpose built, load ready, and met a lot of the needs of the day (back when on-prem was still largely the preferred choice for data warehousing solutions). One could say IBM really hit the ball out of the park, and Netezza has definitely enjoyed a good, solid run since then, but that was fifteen years ago, and times have Continue reading →

ETL vs ELT: Data Warehouses Evolved

Posted on by Philip de Souza

ETL vs ELT: Data Warehouses Evolved

For years now, the process of migrating data into a data warehouse, whether it be an ongoing, repeated analytics pipeline, a one-time move into a new platform, or both, has consisted of a series of three steps, namely: Continue reading →

Snowflake vs Hadoop

Posted on by Philip de Souza

Snowflake vs Hadoop

Lots of people are aware of Hadoop for its advantages, like ease of data loading and scaling, but more and more are becoming increasingly aware of its limitations, like Continue reading →

Snowflake vs Redshift

Posted on by Philip de Souza

Intro - Snowflake vs. Redshift

 

When it comes to cloud data warehousing solutions, Snowflake and Redshift are two of the most popular options. Both solutions offer fast and efficient data processing, but there are some key differences to consider that can help determine which one is the better fit for your organization.

In this article, we'll cover each one in-depth so you can decide what is the best option for you and your organization.

 

Snowflake:

 

Snowflake is known for its ease of use and scalability, making it an attractive option for organizations that need a solution that is simple to set up and manage. Its cloud-native architecture allows it to automatically scale up or down as needed, which helps ensure that performance remains consistent, regardless of data size. Additionally, Snowflake's user-friendly interface and support for ANSI SQL make it easy for teams to get started with the platform quickly.

 

 

Snowflake Benefits:

 

There are several benefits to using Snowflake Data Cloud, but the three most important ones are:

1. Ease of use and scalability

2. Cloud-native architecture that automatically scales up or down as needed

3. User-friendly interface and support for ANSI SQL

These features make Snowflake an attractive option for organizations that need a solution that is simple to set up and manage, with consistent performance, regardless of data size. Additionally, Snowflake's user-friendly interface and support for ANSI SQL make it easy for teams to get started with the platform quickly.

 

Redshift:

 

On the other hand, Redshift is a popular choice for organizations that are already using Amazon Web Services (AWS). Redshift integrates seamlessly with other AWS services, which can make it easier to manage your entire AWS infrastructure from a single console. Additionally, Redshift's pricing model can be more cost-effective than Snowflake's, depending on the size of your data warehousing needs.

 

Redshift Benefits:

 

There are several benefits to using Snowflake Data Cloud, but the three most important ones are:

1. Seamless integration with other AWS services.

2. Potentially more cost-effective pricing, depending on data warehousing needs.

3. High performance for complex queries and large datasets.

 

Conclusion:

 

It's important to carefully evaluate the needs and priorities of your organization when choosing between Snowflake and Redshift. Consider factors such as ease of use, scalability, compatibility with other services, pricing, and any specific requirements your organization may have. By testing both solutions and comparing their features, you can choose the data warehousing solution that best meets your needs.

Ultimately, the choice between Snowflake and Redshift will depend on the specific needs of your organization, but with careful consideration and evaluation of each solution, you can make an informed decision that will benefit your organization in the long run.

Snowflake vs Teradata

Posted on by Philip de Souza

Snowflake vs Teradata

To anyone with even a passing level of familiarity of this space, Teradata is quite rightly known as a powerhouse in the data warehousing and analytics arena. it's been the go-to technology for sectors ranging from various 3 letter intelligence agencies, to the most recognizable of medicine, science, auto, and telecom industry players combined.

We have been building data systems for years, including those incorporating Teradata (such as for a Data Warehousing project we executed for TicketMaster back in 2007), so this really is the most excited we've been in all that time, given all the new capabilities available now, as compared to then. Today we wanted to share with you some findings based on several industry-leading reports concerning cloud data warehousing and analytics in 2017/18, especially as it concerns Snowflake when compared to such an industry giant such as Teradata. Continue reading →