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Databricks’ new data lakehouse aims at media, entertainment sector

CIO Business Intelligence

The other 10% represents the effort of initial deployment, data-loading, configuration and the setup of administrative tasks and analysis that is specific to the customer, the Henschen said. Features focus on media and entertainment firms. Partner solutions to boost functionality, adoption.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

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Create a modern data platform using the Data Build Tool (dbt) in the AWS Cloud

AWS Big Data

The aim was to bolster their analytical capabilities and improve data accessibility while ensuring a quick time to market and high data quality, all with low total cost of ownership (TCO) and no need for additional tools or licenses. This process has been scheduled to run daily, ensuring a consistent batch of fresh data for analysis.

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Exploring real-time streaming for generative AI Applications

AWS Big Data

Both engines provide native ingestion support from Kinesis Data Streams and Amazon MSK via a separate streaming pipeline to a data lake or data warehouse for analysis. For more details, refer to Create a low-latency source-to-data lake pipeline using Amazon MSK Connect, Apache Flink, and Apache Hudi.

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The hidden history of Db2

IBM Big Data Hub

In today’s world of complex data architectures and emerging technologies, databases can sometimes be undervalued and unrecognized. Nedbank builds a scalable data warehouse architecture . Endless data but your queries aren’t fast enough. Vektis improves healthcare quality through data .

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Get maximum value out of your cloud data warehouse with Amazon Redshift

AWS Big Data

In this post, we look at three key challenges that customers face with growing data and how a modern data warehouse and analytics system like Amazon Redshift can meet these challenges across industries and segments. This performance innovation allows Nasdaq to have a multi-use data lake between teams.

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Implement slowly changing dimensions in a data lake using AWS Glue and Delta

AWS Big Data

In a data warehouse, a dimension is a structure that categorizes facts and measures in order to enable users to answer business questions. As organizations across the globe are modernizing their data platforms with data lakes on Amazon Simple Storage Service (Amazon S3), handling SCDs in data lakes can be challenging.