Remove Data Lake Remove Data Warehouse Remove Unstructured Data
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Differentiating Between Data Lakes and Data Warehouses

Smart Data Collective

The market for data warehouses is booming. 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. Data Warehouse.

Data Lake 135
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Understanding the Differences Between Data Lakes and Data Warehouses

Smart Data Collective

Data lakes and data warehouses are probably the two most widely used structures for storing data. Data Warehouses and Data Lakes in a Nutshell. A data warehouse is used as a central storage space for large amounts of structured data coming from various sources.

Data Lake 140
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Setting up Data Lake on GCP using Cloud Storage and BigQuery

Analytics Vidhya

Introduction A data lake is a centralized and scalable repository storing structured and unstructured data. The need for a data lake arises from the growing volume, variety, and velocity of data companies need to manage and analyze.

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United Airlines sets its flight plan for gen AI success

CIO Business Intelligence

Birnbaum says Bedrocks support for foundational gen AI models from a variety of vendors gives United developers flexibility, while the airlines homegrown data hub gives them connected access to a vast amount of mostly unstructured data for AI development.

IT 131
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Run Apache XTable in AWS Lambda for background conversion of open table formats

AWS Big Data

Data architecture has evolved significantly to handle growing data volumes and diverse workloads. Initially, data warehouses were the go-to solution for structured data and analytical workloads but were limited by proprietary storage formats and their inability to handle unstructured data.

Metadata 105
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Amazon Web Services named a Leader in the 2024 Gartner Magic Quadrant for Data Integration Tools

AWS Big Data

Given the diverse data integration needs of customers, AWS offers a robust data integration system through multiple services including Amazon EMR , Amazon Athena , Amazon Managed Workflows for Apache Airflow (Amazon MWAA) , Amazon Managed Streaming for Apache Kafka (MSK) , Amazon Kinesis , and others.

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Complexity Drives Costs: A Look Inside BYOD and Azure Data Lakes

Jet Global

OLAP reporting has traditionally relied on a data warehouse. Again, this entails creating a copy of the transactional data in the ERP system, but it also involves some preprocessing of data into so-called “cubes” so that you can retrieve aggregate totals and present them much faster. Option 3: Azure Data Lakes.