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Redshift vs. Snowflake: The Definitive Guide

What Is Snowflake?

At its core Snowflake is a data platform. It's not specifically based on any cloud service which means it can run any of the major cloud providers like Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP). As a SaaS (Software-as-a-Service) solution, it helps organizations consolidate data from different sources into a central repository for analytics purposes to help solve Business Intelligence use cases.

Once data is loaded into Snowflake, data scientists, engineers, and analysts can use business logic to transform and model that data in a way that makes sense for their company. With Snowflake users can easily query data using simple SQL. This information is then used to power reports and dashboards so business stakeholders can make key decisions based on relevant insights.

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Migrating to Snowflake, Redshift, or BigQuery? Avoid these Common Pitfalls

The Drive to Migrate Data to the Cloud

With data being valued more than oil in recent years, many organizations feel the pressure to become innovative and cost-effective when it comes to consolidating, storing, and using data. Although most enterprises are aware of big data opportunities, their existing infrastructure isn’t always capable of handling massive amounts of data.

By migrating to modern cloud data warehouses, organizations can benefit from improved scalability, better price elasticity, and enhanced security. But even with all these benefits, many businesses are still reluctant to make the move.

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How to Migrate Your Data From Redshift to Snowflake

For decades, data warehousing solutions have been the backbone of enterprise reporting and business intelligence. But, in recent years, cloud-based data warehouses like Amazon Redshift and Snowflake have become extremely popular. So, why would someone want to migrate from one cloud-based data warehouse to another?

The answer is simple: More scale and flexibility. With Snowflake, users can quickly scale out data and compute resources independently by automatically adding nodes. Using the VARIANT data type, Snowflake also supports storing richer data such as objects, arrays, and JSON data. Debugging Redshift is not always straightforward as well, as Redshift users know. Sometimes it goes beyond feature differences that could trigger a desire to migrate. Maybe your team just knows how to work with Snowflake better than Redshift, or perhaps your organization wants to standardize on one particular technology.

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Tutorial: How to Run SQL Queries With Presto on Amazon Redshift

Presto has evolved into a unified SQL engine on top of cloud data lakes for both interactive queries as well as batch workloads with multiple data sources. This tutorial will show how to run SQL queries with Presto (running on Kubernetes) on AWS Redshift.

Presto's Redshift connector allows querying the data stored in an external Amazon Redshift cluster. This can be used to join data between different systems like Redshift and Hive, or between two different Redshift clusters. 

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Your Ultimate Guide to Redshift ETL: Best Practices, Advanced Tips, and Resources

Introduction

Amazon Redshift makes it easier to uncover transformative insights from big data. Analytical queries that once took hours can now run in seconds. Redshift allows businesses to make data-driven decisions faster, which in turn unlocks greater growth and success.

For a CTO, full-stack engineer, or systems architect, the question isn’t so much what is possible with Amazon Redshift, but how? How do you ensure optimal, consistent runtimes on analytical queries and reports? And how do you do that without taxing precious engineering time and resources?

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MySQL to Redshift: 4 Ways to Replicate Your Data

MySQL is the most popular open source cloud database in the world, and for good reason. It’s powerful, flexible, and extremely reliable. Tens of thousands of companies use MySQL to power their web-based applications and services every day.

But when it comes to data analytics, it’s a different story. MySQL is quickly bogged down by even the smallest analytical queries, putting your entire application at risk of crashing. As one FlyData customer said to us, “I have nightmares about our MySQL production database going down.”

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Veeva Nitro and AWS SageMaker for Life Sciences Data Scientists

Introduction

There is a rise in industry-specific data analytics solutions because building up and maintaining custom data warehouses is difficult. It requires extensive development and operational efforts to define the appropriate industry-specific data model for the business intelligence tools, follow all the shape changes over time (new tables, new columns, new relationships) and design the ETL processes for a wide variety of data sources. It is just hard to build a solution on top of a generic data warehouse where you can get great platform capabilities but you still have to start with a CREATE DATABASE SQL command.

This is the reason why Veeva decided to build Nitro, the data science and analytics platform. It is designed to accelerate time-to-value by getting data quickly from Veeva Commercial Cloud (CRM, Vault, Align, Network)  and other common life sciences platforms (e.g. Salesforce Marketing Cloud) into Nitro using predefined intelligent connectors. 

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API-First Product Managers’ Popular API Tools and API Metrics

We interviewed the product managers at a number of the larger API-first companies that are based in San Francisco. The companies are all publicly traded, have TTM revenue of more than $100M and are in the fields of billing, security, communications and workflow automation.

The PMs were asked what were their favorite tools and what API metrics they cared most about. Where possible we identified tools and metrics that were common across all market segments, excluding the (many) edge cases that you’d expect when your customer base numbered in the 1,000s.