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)?

Redshift has had the highest market share of any RDBMS (Relational Database Management System) solution for a number of years. Companies around the world use it to store and mine deeper insights from data. What is Redshift exactly, and what are the benefits that have given it the top ranking for companies around the world?

Imagine synchronizing every piece of customer data from across your organization into a single integrated platform. You could generate a panoramic view of consumer activities and develop an understanding of what your customers really want. Customer 360 helps you do all of that. It's the practice of integrating customer data from multiple sources so you can deliver better experiences for every person who engages with your organization. Below, learn about customer 360, how it works, and its benefits.

Choosing the right ETL solution for your business is essential, but how do your compare each option? Learn how to compare each potential ETL provider and discover what attributes and features stand out from one another and how that can benefit certain business endeavors.

It's a week after Black Friday, and the results are in! While online spending didn't break a new record this year, it still totaled a massive $8.9 billion, making Black Friday one of the biggest sales days ever for digital merchants. Online sales were even healthier on Cyber Monday, totaling $10.7 billion. But how did paid marketing contribute to all those holiday shopping season sales? Some e-commerce retailers struggle to measure the effectiveness of their paid campaigns.

ETL (extract, transform, load) is the backbone of modern data integration, efficiently migrating massive quantities of information into a data warehouse like Snowflake. But with so many Snowflake ETL tools on the market these days, how can you choose the best for migrating your data? Below we’ll discuss our favorite Snowflake ETL tools, including their pros, cons, and user reviews so that you can make the choice that’s right for your situation.

As the amount of big data generated every year grows exponentially, successful enterprises have ditched on-premise solutions for the cloud. Nowhere is this more apparent than in the world of big data analytics, where virtual environments offer more scope and scalability than in-house architecture. Data-driven companies crave cloud databases because of the copious amount of data they collect, process, share and analyze every single day.