Data warehouses help you run logical queries, build accurate forecasting models, and identify impactful trends throughout your organization. But, what goes into designing a data warehouse? Whether you choose to utilize a pre-built vendor solution or you're starting from scratch — you'll need some level of warehouse design to successfully adopt a new data warehouse.

Snowflake is a robust data warehouse that has changed the data science game for many organizations. Snowflake lets you analyze your data using the most sophisticated query engine available today with its cloud-native architecture. But using Snowflake is not always as simple as using other products on the market. Below are nine expert tips to help you master the Snowflake platform.

Data teams across companies have continuous challenges of consolidating data, processing it and making it useful. They deal with challenges such as a mixture of multiple ETL jobs, long ETL windows capacity-bound on-premise data warehouses and ever-increasing demands from users. They also need to make sure that the downstream requirements of ML, reporting and analytics are met with the data processing.

Amazon S3 (Simple Storage Service) has been around since 2006. Most use this scalable, cloud-based service for archiving and backing up data. Within 10 years of its birth, S3 stored over 2 trillion objects, each up to 5 terabytes in size. Enterprises value their data as something worth preserving. But much of this data lies inert, in “cold” data lakes, unavailable for analysis. Also called “dark data”, it can hold key insights for enterprises.

Redshift’s JOIN clause is perhaps the second most important clause after SELECT clause, and it is used even more ubiquitously, considering how interconnected a typical application database’s tables are. Due to that connectivity between datasets, data developers require many joins to collect and process all the data points involved in most use cases. Unfortunately, as the number of tables you’re joining in grows, so does the sloth of your query.

PostgreSQL is the preferred platform of millions of developers around the world. The open-source tool is one of the most powerful databases on the planet, with the ability to handle sophisticated analytical workloads and high levels of concurrency. That makes PostgreSQL (also called Postgres) a popular DB for scientific research and AI/ML projects. It’s also a popular production database for data-driven companies in every industry. But no database is perfect.

In this guide, we’ll go over the Redshift COPY command, how it can be used to import data into your Redshift database, its syntax, and a few troubles you may run into.

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?

While Redshift is arguably the best data warehouse on the market, it can come with a hefty price tag. We’ve created this Redshift pricing guide to help you evaluate Redshift cheaply, create a budget for full implementation, and optimize your Redshift set up so that you’re getting the most bang for your data buck. Ready to get started? Think of this blog post as a “choose your own adventure” guide.

When choosing any SaaS application, you must start with a clear understanding of your business requirements. Then ask yourself the following questions: Develop a framework for data processing requirements, and you'll find a data warehouse solution that provides the right amount of power, functionality, and high performance for data analytics. Keep the answers to these questions in mind when reading through this article.