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Explore visualizations with AWS Glue interactive sessions

AWS Big Data

AWS Glue interactive sessions offer a powerful way to iteratively explore datasets and fine-tune transformations using Jupyter-compatible notebooks. This post is part of a series exploring the features of AWS Glue interactive sessions. To get started today, refer to Developing AWS Glue jobs with Notebooks and Interactive sessions.

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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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GraphDB in Action: Putting the Most Reliable RDF Database to Work for Better Human-machine Interaction

Ontotext

In today’s world, we increasingly interact with the environment around us through data. For all these data operations to flow smoothly, data needs to be interoperable, of good quality and easy to integrate. These 30 layers can be split into two kinds: a location-reference layer and a topic layer.

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Entity resolution and fuzzy matches in AWS Glue using the Zingg open source library

AWS Big Data

In today’s data-driven world, organizations often deal with data from multiple sources, leading to challenges in data integration and governance. This process is crucial for maintaining data integrity and avoiding duplication that could skew analytics and insights. csv" , header=True).createOrReplaceTempView("labeled")

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New Amazon CloudWatch log class to cost-effectively scale your AWS Glue workloads

AWS Big Data

AWS Glue is a serverless data integration service that makes it easier to discover, prepare, and combine data for analytics, machine learning (ML), and application development. For example, AWS Glue Auto Scaling and AWS Glue Flex can help you reduce the compute cost associated with processing your data.

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Use Amazon Athena to query data stored in Google Cloud Platform

AWS Big Data

Athena provides the connectivity and query interface and can easily be plugged into other AWS services for downstream use cases such as interactive analysis and visualizations. We use the following AWS services in this solution: Amazon Athena – A serverless interactive analytics service. To create the bucket, refer to Create buckets.

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Data governance in the age of generative AI

AWS Big Data

However, enterprise data generated from siloed sources combined with the lack of a data integration strategy creates challenges for provisioning the data for generative AI applications. For detailed implementation guidance, refer to Unstructured data management and governance using AWS AI/ML and analytics services.