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10 Examples of How Big Data in Logistics Can Transform The Supply Chain

Data Pine

To work effectively, big data requires a large amount of high-quality information sources. Where is all of that data going to come from? Financial efficiency: One of the key benefits of big data in supply chain and logistics management is the reduction of unnecessary costs.

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 Top 5 Data Preparation Tools In 2023

Astera

Manual export and import steps in a system can add complexity to your data pipeline. When evaluating data preparation tools, look for solutions that easily connect data visualization and BI reporting applications to guide your decision-making processes, e.g., PowerBI, Tableau, etc.

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Information Marts: Enabling Agile, Scalable, and Accurate BI

Astera

Their data architecture should be able to handle growing data volumes and user demands, deliver insights swiftly and iteratively. Traditional data warehouses with predefined data models and schemas are rigid, making it difficult to adapt to evolving data requirements.

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Top API Design Tools: Streamlining Development for Success

Astera

With built-in connectivity to a wide array of data sources, it is a versatile solution for various use cases. You can easily design, test, develop, manage, publish, and monitor your APIs and integrations using the no-code, intuitive user interface that comes equipped with advanced data integration capabilities.

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MySQL vs. SQL Server: What’s the Difference? 

Astera

Scalability : MySQL is known for its scalability and can handle large amounts of data efficiently. SQL Server also offers scalability, but it is better suited for larger enterprises with more complex data requirements. Also, you can transfer data between them seamlessly using this powerful integration platform.

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What’s the Difference Between Business Intelligence and Business Analytics?

Sisense

BI lets you apply chosen metrics to potentially huge, unstructured datasets, and covers querying, data mining , online analytical processing ( OLAP ), and reporting as well as business performance monitoring, predictive and prescriptive analytics.

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What is Data Pipeline? A Detailed Explanation

Smart Data Collective

The final point to which the data has to be eventually transferred is a destination. The destination is decided by the use case of the data pipeline. It can be used to run analytical tools and power data visualization as well. Otherwise, it can also be moved to a storage centre like a data warehouse or lake.