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Data Warehouses

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?

Unleashing Business Potential: Exploring Oracle Fusion Analytics Warehouse

In the era of data advancement, current organizations are looking forward to innovative ways to utilize the potential of their data. Considering these scenarios, Oracle has developed a powerful tool, Oracle Fusion Analytics Warehouse, which has emerged as a game changer for many organizations looking to uncover their data’s capability in an easy and accessible manner.

10 Tips to Manage Redshift Costs Without Compromising Performance

Effective management of Redshift costs is closely tied to data storage optimization. Choosing the right data types and implementing data compression are pivotal in reducing storage footprints and costs. Redshift’s columnar storage format enhances query performance, which in turn can lead to significant savings. For a more comprehensive approach, integrating tools like Anodot can provide advanced analytics and real-time visibility to further streamline storage efficiency and optimize costs.

How to sync your Google Sheets data into your data warehouse in just a few minutes

I can’t count the number of times I’ve used the phrase: “There’s always a spreadsheet…” When I give a demo of the Keboola platform, I often start with Google Sheets as the data source of choice. The data source connector Google Sheets is one of the most popular components on our platform because, let’s face it, no matter the project, there’s always a spreadsheet involved.

Top 14 ETL Tools for 2024

Organizations of all sizes and industries now have access to ever-increasing amounts of data, far too vast for any human to comprehend. So far in 2023 so far, the world produced and consumed 328.77 million terabytes of data per day — an almost unimaginable number. However, all this information is useless without a way to efficiently process it, analyze it, and reveal the valuable data-driven insights hidden within the noise.

Announcing New Innovations for Data Warehouse, Data Lake, and Data Lakehouse in the Data Cloud

Over the years, the technology landscape for data management has given rise to various architecture patterns, each thoughtfully designed to cater to specific use cases and requirements. These patterns include both centralized storage patterns like data warehouse, data lake and data lakehouse, and distributed patterns such as data mesh. Each of these architectures has its own unique strengths and tradeoffs.

ETL and Data Warehousing Explained: ETL Tool Basics

Understanding ETL (extract, transform, and load) and data warehousing is essential for data engineering and analysis. As businesses generate large amounts of data from different sources, efficient data integration and storage solutions become crucial. This article breaks down ETL and data warehousing, providing insights into the tools, techniques, and best practices that drive modern data engineering.