Enterprise Strategy Group compared BigQuery to alternative cloud-based enterprise data warehouse solutions.

On top of their industry-leading cloud infrastructure, Amazon Web Services (AWS) offers more than 15 cloud-based analytics services to satisfy a diverse range of business and IT use cases. For AWS customers, understanding the features and benefits of all 15 AWS analytics services can be a daunting task - not to mention determining which analytics service(s) to deploy for a specific use case.

Making sense of the ever-growing mountains of data is a challenge that organizations of all sizes face today. This article explores the powerful synergy between data warehousing and business intelligence, demonstrating how they work hand-in-hand to transform raw data into actionable insights. We’ll also delve into the role of APIs in facilitating real-time data integration and automation. Here are the key things to know about data warehousing and business intelligence: Table of Contents.

52% of IT experts consider faster analytics essential to data warehouse success. However, scaling your data warehouse and optimizing performance becomes more difficult as data volume grows. Leveraging data warehouse best practices can help you design, build, and manage data warehouses more effectively.

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 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.

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.

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.