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

AWS Big Data

Data engineers use Apache Iceberg because it’s fast, efficient, and reliable at any scale and keeps records of how datasets change over time. Apache Iceberg offers integrations with popular data processing frameworks such as Apache Spark, Apache Flink, Apache Hive, Presto, and more.

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How Amazon Devices scaled and optimized real-time demand and supply forecasts using serverless analytics

AWS Big Data

Architecturally, we chose a serverless model, and the data lake architecture action line refers to all the architectural gaps and challenging features we determined were part of the improvements. We also used AWS Lambda for data processing. To query data from REST APIs and other data sources, we used PySpark and JDBC modules.

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AWS Glue streaming application to process Amazon MSK data using AWS Glue Schema Registry

AWS Big Data

Acting as a bridge between producer and consumer apps, it enforces the schema, reduces the data footprint in transit, and safeguards against malformed data. AWS Glue is an ideal solution for running stream consumer applications, discovering, extracting, transforming, loading, and integrating data from multiple sources.

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Proposals for model vulnerability and security

O'Reilly on Data

Data poisoning attacks. Data poisoning refers to someone systematically changing your training data to manipulate your model’s predictions. Data poisoning attacks have also been called “causative” attacks.) To poison data, an attacker must have access to some or all of your training data.

Modeling 222
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5 Reasons to Use Apache Iceberg on Cloudera Data Platform (CDP)

Cloudera

Figure 1: Apache Iceberg fits the next generation data architecture by abstracting storage layer from analytics layer while introducing net new capabilities like time-travel and partition evolution. #1: Apache Iceberg enables seamless integration between different streaming and processing engines while maintaining data integrity between them.

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Build and manage your modern data stack using dbt and AWS Glue through dbt-glue, the new “trusted” dbt adapter

AWS Big Data

The dbt-glue adapter democratized access for dbt users to data lakes, and enabled many users to effortlessly run their transformation workloads on the cloud with the serverless data integration capability of AWS Glue. Data engineers define dbt models for their data representations. and Viewpoint.

Data Lake 108
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“You Complete Me,” said Data Lineage to DataOps Observability.

DataKitchen

It is important to have additional tools and processes in place to understand the impact of data errors and to minimize their effect on the data pipeline and downstream systems. These operations can include data movement, validation, cleaning, transformation, aggregation, analysis, and more.

Testing 130