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Zendesk - The Impact of COVID-19 on CX

Corinium

Our Benchmark Snapshot summarizes how recent events have affected customer experience in the recent months. Most teams responding to customers are now in a work from home environment, putting additional strain on their ability to respond to customers effectively. For many of us, that means learning and adjusting as we go.

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Analyze Elastic IP usage history using Amazon Athena and AWS CloudTrail

AWS Big Data

To gather EIP usage reporting, this solution compares snapshots of the current EIPs, focusing on their most recent attachment within a customizable 3-month period. AWS CloudTrail Lake supports the collection of events from multiple AWS regions and AWS accounts. It then determines the frequency of EIP attachments to resources.

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Apache Iceberg optimization: Solving the small files problem in Amazon EMR

AWS Big Data

This Iceberg event-based table management feature lets you monitor table activities during writes to make better decisions about how to manage each table differently based on events. To use the feature, you can use the iceberg-aws-event-based-table-management source code and provide the built JAR in the engine’s class-path.

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A Look Ahead at the Gartner Data & Analytics Summit

Cloudera

As we enter into a new month, the Cloudera team is getting ready to head off to the Gartner Data & Analytics Summit in Orlando, Florida for one of the most important events of the year for Chief Data Analytics Officers (CDAOs) and the field of data and analytics.

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Use Apache Iceberg in your data lake with Amazon S3, AWS Glue, and Snowflake

AWS Big Data

Snowflake integrates with AWS Glue Data Catalog to retrieve the snapshot location. In the event of a query, Snowflake uses the snapshot location from AWS Glue Data Catalog to read Iceberg table data in Amazon S3. Snowflake can query across Iceberg and Snowflake table formats.

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Use Amazon Athena with Spark SQL for your open-source transactional table formats

AWS Big Data

As data lakes have grown in size and matured in usage, a significant amount of effort can be spent keeping the data consistent with business events. It will never remove files that are still required by a non-expired snapshot.

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Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1

AWS Big Data

In this post, we will review the common architectural patterns of two use cases: Time Series Data Analysis and Event Driven Microservices. The streaming records are read in the order they are produced, allowing for real-time analytics, building event-driven applications or streaming ETL (extract, transform, and load).

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