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Enhance monitoring and debugging for AWS Glue jobs using new job observability metrics, Part 3: Visualization and trend analysis using Amazon QuickSight

AWS Big Data

In Part 2 of this series, we discussed how to enable AWS Glue job observability metrics and integrate them with Grafana for real-time monitoring. Grafana provides powerful customizable dashboards to view pipeline health. QuickSight makes it straightforward for business users to visualize data in interactive dashboards and reports.

Metrics 104
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End-to-end development lifecycle for data engineers to build a data integration pipeline using AWS Glue

AWS Big Data

Many AWS customers have integrated their data across multiple data sources using AWS Glue , a serverless data integration service, in order to make data-driven business decisions. Are there recommended approaches to provisioning components for data integration?

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Introducing Amazon MWAA support for the Airflow REST API and web server auto scaling

AWS Big Data

In this post, we’re excited to introduce two new features that address common customer challenges and unlock new possibilities for building robust, scalable, and flexible data orchestration solutions using Amazon MWAA. Args: region (str): AWS region where the MWAA environment is hosted. env_name (str): Name of the MWAA environment.

Testing 89
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Create an Apache Hudi-based near-real-time transactional data lake using AWS DMS, Amazon Kinesis, AWS Glue streaming ETL, and data visualization using Amazon QuickSight

AWS Big Data

Change data capture (CDC) is one of the most common design patterns to capture the changes made in the source database and reflect them to other data stores. a new version of AWS Glue that accelerates data integration workloads in AWS. On the QuickSight dashboard, choose your user name, then choose Manage QuickSight.

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Migrate an existing data lake to a transactional data lake using Apache Iceberg

AWS Big Data

Over the years, data lakes on Amazon Simple Storage Service (Amazon S3) have become the default repository for enterprise data and are a common choice for a large set of users who query data for a variety of analytics and machine leaning use cases. Analytics use cases on data lakes are always evolving. Choose ETL Jobs.

Data Lake 102
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10 Best Big Data Analytics Tools You Need To Know in 2023

FineReport

” This type of Analytics includes traditional query and reporting settings with scorecards and dashboards. The tool is designed to be intuitive, so even users with limited technical expertise can create reports and dashboards quickly and easily. Offers interactive and shared dashboards. Allows for batch processing.

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GraphDB and metaphactory Part II: An RDF Database and A Knowledge Graph Platform in Action

Ontotext

This might be sufficient for information retrieval purposes and simple fact-checking, but if you want to get deeper insights, you need to have normalized data that allows analytics or machine interaction with it. Semantic Data Integration With GraphDB. Now let’s have a look at how you can interact with this dataset.