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Data Lakes Meet Data Warehouses

David Menninger's Analyst Perspectives

In this analyst perspective, Dave Menninger takes a look at data lakes. He explains the term “data lake,” describes common use cases and shares his views on some of the latest market trends.

Data Lake 287
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Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

The market for data warehouses is booming. One study forecasts that the market will be worth $23.8 While there is a lot of discussion about the merits of data warehouses, not enough discussion centers around data lakes. Both data warehouses and data lakes are used when storing big data.

Data Lake 135
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Oracle Wants to Be the Database for AI

David Menninger's Analyst Perspectives

Originally focused solely on the relational database market, the software provider operated as Relational Systems, Inc. While much of the event was under non-disclosure as product plans and launch schedules are finalized, it still served as a useful recap of the broad portfolio of data platform capabilities that Oracle has to offer.

Data Lake 130
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Accelerate analytics and AI innovation with the next generation of Amazon SageMaker

AWS Big Data

Unified access to your data is provided by Amazon SageMaker Lakehouse , a unified, open, and secure data lakehouse built on Apache Iceberg open standards. The data engineer asks Amazon Q Developer to identify datasets that contain lead data and uses zero-ETL integrations to bring the data into SageMaker Lakehouse.

Analytics 110
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Capital One Offers Cost Controls for Cloud Data Warehouses

David Menninger's Analyst Perspectives

The adoption of cloud environments for analytic workloads has been a key feature of the data platforms sector in recent years. For two-thirds (66%) of participants in ISG’s Data Lake Dynamic Insights Research, the primary data platform used for analytics is cloud based.

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The next generation of Amazon SageMaker: The center for all your data, analytics, and AI

AWS Big Data

With improved access and collaboration, you’ll be able to create and securely share analytics and AI artifacts and bring data and AI products to market faster. This innovation drives an important change: you’ll no longer have to copy or move data between data lake and data warehouses.

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Implementing a Pharma Data Mesh using DataOps

DataKitchen

During the launch phase, the focus is on marketing to patients through consumer channels. As generic alternatives become available, the market enters the maturity phase where cost efficiency and margins become most important. There are different teams within the pharmaceutical company that focus on the respective target markets.