Remove Data Lake Remove Data Warehouse Remove Manufacturing Remove Metadata
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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

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Accelerate HiveQL with Oozie to Spark SQL migration on Amazon EMR

AWS Big Data

Many customers run big data workloads such as extract, transform, and load (ETL) on Apache Hive to create a data warehouse on Hadoop. We split the solution into two primary components: generating Spark job metadata and running the SQL on Amazon EMR. The script generates a metadata JSON file for each step.

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Exploring real-time streaming for generative AI Applications

AWS Big Data

You can find similar use cases in other industries such as retail, car manufacturing, energy, and the financial industry. In this post, we discuss why data streaming is a crucial component of generative AI applications due to its real-time nature.

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Extreme data center pressure? Burst to the cloud with CDP!

Cloudera

Your sunk costs are minimal and if a workload or project you are supporting becomes irrelevant, you can quickly spin down your cloud data warehouses and not be “stuck” with unused infrastructure. Cloud deployments for suitable workloads gives you the agility to keep pace with rapidly changing business and data needs.

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Announcing the 2021 Data Impact Awards

Cloudera

Use cases could include but are not limited to: workload analysis and replication, migrating or bursting to cloud, data warehouse optimization, and more. Should you find yourself looking for inspiration for your entry, we encourage you to have a look at the incredible work of last year’s data superheroes.

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Week in the Life of an Analyst at Gartner US IT Symposium (virtual) 2021

Andrew White

Manufacturer (process or discrete) 8. Lakehouse (data warehouse and data lake working together) 8. Data Literacy, training, coordination, collaboration 8. Data Management Infrastructure/Data Fabric 5. Data Integration tactics 4. Metadata Strategy 3. Financial Services 4.

IT 52
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A Few 2016 Technology Predictions

In(tegrate) the Clouds

Aside from the Internet of Things, which of the following software areas will experience the most change in 2016 – big data solutions, analytics, security, customer success/experience, sales & marketing approach or something else? 2016 will be the year of the data lake. Is Netflix considered a software company these days?