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Orchestrate an end-to-end ETL pipeline using Amazon S3, AWS Glue, and Amazon Redshift Serverless with Amazon MWAA

AWS Big Data

Amazon Managed Workflows for Apache Airflow (Amazon MWAA) is a managed orchestration service for Apache Airflow that you can use to set up and operate data pipelines in the cloud at scale. Apache Airflow is an open source tool used to programmatically author, schedule, and monitor sequences of processes and tasks, referred to as workflows.

Metadata 108
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Setting up and Getting Started with Cloudera’s New SQL AI Assistant

Cloudera

Please refer to the product documentation for more information about specific releases. Supported AI models and services The SQL AI Assistant is not bundled with a specific LLM; instead it supports various LLMs and hosting services. Log in to the Cloudera Data Warehouse service as DWAdmin. or higher on the public cloud.

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Power enterprise-grade Data Vaults with Amazon Redshift – Part 2

AWS Big Data

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

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Petabyte-scale log analytics with Amazon S3, Amazon OpenSearch Service, and Amazon OpenSearch Ingestion

AWS Big Data

At the same time, they need to optimize operational costs to unlock the value of this data for timely insights and do so with a consistent performance. With this massive data growth, data proliferation across your data stores, data warehouse, and data lakes can become equally challenging.

Data Lake 117
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Query your Apache Hive metastore with AWS Lake Formation permissions

AWS Big Data

Apache Hive is a SQL-based data warehouse system for processing highly distributed datasets on the Apache Hadoop platform. The Hive metastore is a repository of metadata about the SQL tables, such as database names, table names, schema, serialization and deserialization information, data location, and partition details of each table.

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

Reporting being part of an effective DQM, we will also go through some data quality metrics examples you can use to assess your efforts in the matter. But first, let’s define what data quality actually is. What is the definition of data quality? Why Do You Need Data Quality Management? 2 – Data profiling.

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Governing data in relational databases using Amazon DataZone

AWS Big Data

It also makes it easier for engineers, data scientists, product managers, analysts, and business users to access data throughout an organization to discover, use, and collaborate to derive data-driven insights. The producer also needs to manage and publish the data asset so it’s discoverable throughout the organization.