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Data Warehouses

ThoughtSpot supports Amazon Redshift Serverless

As companies go all in on the cloud to dominate the decade of data, agility, flexibility, and ease of use are critical to success. That’s why we’re so excited to announce ThoughtSpot’s support for Amazon Redshift Serverless which allows customers to leverage the Modern Analytics Cloud to run and scale analytics on Amazon Redshift without having to provision and manage any data warehouse infrastructure.

Snowflake vs. Oracle: Which Data Warehouse is Better?

Snowflake and Oracle Autonomous Data Warehouse are two cloud data warehouses that provide you with a single source of truth (SSOT) for all the data that exists in your organization. You can use either of these warehouses to run data through business intelligence (BI) tools and automate insights for decision-making. But which one should you add to your tech stack? In this guide, learn the differences between Snowflake vs. Oracle and how you can transfer data to the warehouse of your choice.

Support Multiple Data Modeling Approaches with Snowflake

Since I joined Snowflake, I have been asked multiple times what data warehouse modeling approach Snowflake best supports. Well, the cool thing is that Snowflake supports multiple data modeling approaches equally. Turns out we have a few customers who have existing data warehouses built using a particular approach known as the Data Vault modeling approach, and they have decided to move into Snowflake. So the conversation often goes like this.

Data Lake vs Data Warehouse: 7 Critical Differences

Here are seven key differences between data lakes vs data warehouses: A lot of terms get thrown around in the big data space that every business should understand. Many of these terms are easily confused with each other. This is the case with data lakes vs data warehouses. What are some of the most important differences between them, and how can your business use them most effectively for data analytics and data management? Read on to learn the differences between data lakes and data warehouses.

Manage Your Amazon Redshift Data With

Amazon Redshift is one of the world’s most popular cloud data warehousing solutions. Along with other Amazon Web Services offerings, such as Amazon S3 and Amazon EMR (Elastic MapReduce), Redshift can help enhance your data workflows and manage your enterprise data. With the right expertise, Redshift can help you integrate all of your data sources, from operational databases and data lakes to third-party files and websites.

Memory Optimizations for Analytic Queries in Cloudera Data Warehouse

Apache Impala is used today by over 1,000 customers to power their analytics in on premise as well as cloud-based deployments. Large user communities of analysts and developers benefit from Impala’s fast query execution, helping them get their work done more effectively. For these users performance and concurrency are always top of mind.

How To Extract Data From AWS Redshift Through SQL With Ease

SQL is one of the most widely adopted domain languages (i.e., used by over 65 percent of data scientists and analysts), which can help you access and interpret valuable data from AWS Redshift. As a modern-day decision-maker, AWS Redshift and SQL are vital components that drive your SDK. Through PostgreSQL, you can make data-based decisions with Amazon Redshift while minimizing the overall cost of your operations.

What Is the Difference Between AWS Redshift and RDS?

AWS Redshift and RDS are two different database products that AWS offers. If you're not sure which one is right for you, there are a few essential questions to answer before making your decision. This article will explore the differences between these two products and help determine which one would be best for your needs. We'll also take a look at how much it costs to use each product so that you can compare them side-by-side and see what's most affordable for your business.