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In Data Warehouse

Five Critical Reasons To Move Your Legacy Data Warehouse To The Cloud

4 Mins Read

Over 25 years ago, the average business was just becoming acquainted with how to effectively capture and store its data. Back then, data was used to help retail companies manage their inventories, enable commercial airlines to effectively maintain their flight schedules, and allow service-oriented businesses to capture and store their customer contact information. The pace was slower, and the information systems used to compute silo data queries were capable of delivering on time and on-premise.…

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In Data Warehouse

Organic Food Pioneer Santiveri Expects Autonomous Tech To Drive Sales And Redefine IT’s Role

4 Mins Read

The organic food and beverage market is expected to hit a mouth-watering $320 billion by 2025—more than four times the value of just a few years ago. For Santiveri, a Spanish company that’s been producing and selling organic products since 1885, that’s validation of its strategy. But it’s also an invitation for ever-rising competition, forcing Santiveri to defend its share against big brands eyeing this surging market. Santiveri sells a huge array of products you can…

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big data analytics tools
In Data Warehouse

Autonomous Capabilities Will Make Data Warehouses — And DBAs — More Valuable

2 Mins Read

As the saying goes, you can’t manage what you don’t measure. In a data-driven organization, the best tools for measuring the performance are business intelligence (BI) and analytics engines, which require data. And that explains why data warehouses continue to play such a crucial role in business. Data warehouses often provide the source of that data, by rolling up and summarizing key information from a variety of sources. Data warehouses, which are themselves relational databases,…

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In Data Warehouse

Should you consider adopting a cloud data warehouse?

6 Mins Read

Both the data lake and the data warehouse within the cloud have their benefits. While data lakes consist of unorganised lagoons without categories, they are great for data scientists to analyse different kinds of data at once. The two kinds of data storage also differ in the tools that can be accessed. “In general, for the likes of Redshift, Snowflake, Azure, SQL Data Warehouse, one of the most important things when you talk about a…

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