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Proposals for model vulnerability and security

O'Reilly on Data

And at many companies, many different employees, consultants, and contractors have just that—and with little oversight. Residual analysis: Look for strange, prominent patterns in the residuals of your model predictions, especially for employees, consultants, or contractors.

Modeling 278
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Implement disaster recovery with Amazon Redshift

AWS Big Data

With built-in features such as automated snapshots and cross-Region replication, you can enhance your disaster resilience with Amazon Redshift. To develop your disaster recovery plan, you should complete the following tasks: Define your recovery objectives for downtime and data loss (RTO and RPO) for data and metadata.

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Five actionable steps to GDPR compliance (Right to be forgotten) with Amazon Redshift

AWS Big Data

Tagging Consider tagging your Amazon Redshift resources to quickly identify which clusters and snapshots contain the PII data, the owners, the data retention policy, and so on. Tags provide metadata about resources at a glance. Redshift resources, such as namespaces, workgroups, snapshots, and clusters can be tagged.

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Choosing an open table format for your transactional data lake on AWS

AWS Big Data

Iceberg doesn’t optimize file sizes or run automatic table services (for example, compaction or clustering) when writing, so streaming ingestion will create many small data and metadata files. Offers different query types , allowing to prioritize data freshness (Snapshot Query) or read performance (Read Optimized Query).

Data Lake 129
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AI at Scale isn’t Magic, it’s Data – Hybrid Data

Cloudera

I recommend you read the entire piece, but to me the key takeaway – AI at scale isn’t magic, it’s data – is reminiscent of the 1992 presidential election, when political consultant James Carville succinctly summarized the key to winning – “it’s the economy”. Sometimes the most important issue is hiding in plain view.

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How Amazon GTTS runs large-scale ETL jobs on AWS using Amazon MWAA

AWS Big Data

At a high level, the core of Langley’s architecture is based on a set of Amazon Simple Queue Service (Amazon SQS) queues and AWS Lambda functions, and a dedicated RDS database to store ETL job data and metadata. Amazon MWAA offers one-click updates of the infrastructure for minor versions, like moving from Airflow version x.4.z

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Simplify operational data processing in data lakes using AWS Glue and Apache Hudi

AWS Big Data

The File Manager Lambda function consumes those messages, parses the metadata, and inserts the metadata to the DynamoDB table odpf_file_tracker. Current snapshot – This table in the data lake stores latest versioned records (upserts) with the ability to use Hudi time travel for historical updates.

Data Lake 109