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Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

While there is a lot of discussion about the merits of data warehouses, not enough discussion centers around data lakes. We talked about enterprise data warehouses in the past, so let’s contrast them with data lakes. Both data warehouses and data lakes are used when storing big data.

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The Key Components of a Successful Data Lake Strategy

Data Virtualization

Reading Time: 6 minutes Data lake, by combining the flexibility of object storage with the scalability and agility of cloud platforms, are becoming an increasingly popular choice as an enterprise data repository. Whether you are on Amazon Web Services (AWS) and leverage AWS S3.

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The Key Components of a Successful Data Lake Strategy

Data Virtualization

Reading Time: 6 minutes Data lake, by combining the flexibility of object storage with the scalability and agility of cloud platforms, are becoming an increasingly popular choice as an enterprise data repository. Whether you are on Amazon Web Services (AWS) and leverage AWS S3.

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Is Data Virtualization the Secret Behind Operationalizing Data Lakes?

Data Virtualization

Reading Time: 4 minutes The amount of expanding volume and variety of data originating from various sources are a massive challenge for businesses. In attempts to overcome their big data challenges, organizations are exploring data lakes as repositories where huge volumes and varieties of.

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The Data Lakehouse: Blending Data Warehouses and Data Lakes

Data Virtualization

Reading Time: 3 minutes First we had data warehouses, then came data lakes, and now the new kid on the block is the data lakehouse. But what is a data lakehouse and why should we develop one? In a way, the name describes what.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Though you may encounter the terms “data science” and “data analytics” being used interchangeably in conversations or online, they refer to two distinctly different concepts. Meanwhile, data analytics is the act of examining datasets to extract value and find answers to specific questions.

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Modern Data Architecture: Data Warehousing, Data Lakes, and Data Mesh Explained

Data Virtualization

For this reason, organizations must periodically revisit their data architectures, to ensure that they are aligned with current business goals.