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.

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.

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?

Digital Transformation has ushered in a new era in business where every company is becoming a technology company (and with good reason). According to IDC, digitization has created a global market opportunity worth a staggering $20 trillion (over 20% of global GDP).

BigQuery is a powerful tool for data analytics and business intelligence, but it isn't very easy to get the most out of it without some help from ETL (extract, transform, load). In this blog post, we'll go over some of the best ETL solutions for BigQuery to help you make sense of your company's data.

When it comes to providing the latest and greatest ETL and ELT tools, the platforms Stitch, Fivetran, and Xplenty are all top contenders. That being said, each platform also has its own set of pros and cons. Ultimately, the best ETL/ELT platform for your company will largely depend upon the needs of your organization. So, which platform will reign supreme for your company in the Stitch vs Fivetran vs Xplenty matchup?

As a company’s data assets grow, the need for cloud computing increases in tandem. For keeping pace with this growth, Snowflake stands above the rest. What makes Snowflake so special? This cloud-agnostic platform takes the best of traditional database technology and combines it with modern cloud computing to drive the agility and innovation companies need to remain competitive. It features on-the-fly scaling, flexible clustering options, and the capability to hold several petabytes of information.

Heroku leverages the open-source technology of PostgreSQL to deliver a powerful and reliable database-as-a-service solution. Heroku Postgres acts as both a source and destination for data integration. With native support for a variety of programming languages and features that ensure security and compliance, it is a top resource for many companies. What do you need to know about Heroku, and what tools should you use to perform ETL (extract, transform and load) and ELT (extract, load and transform)?