Remove Data Analytics Remove Data Integration Remove Data Lake Remove Metadata
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Modernize your ETL platform with AWS Glue Studio: A case study from BMS

AWS Big Data

In addition to using native managed AWS services that BMS didn’t need to worry about upgrading, BMS was looking to offer an ETL service to non-technical business users that could visually compose data transformation workflows and seamlessly run them on the AWS Glue Apache Spark-based serverless data integration engine.

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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. As part of the transformation, the objects need to be treated to ensure data privacy (for example, PII redaction).

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Data architecture strategy for data quality

IBM Big Data Hub

Next generation of big data platforms and long running batch jobs operated by a central team of data engineers have often led to data lake swamps. Monitor and identify data quality issues closer to the source to mitigate the potential impact on downstream processes or workloads.

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Five benefits of a data catalog

IBM Big Data Hub

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. Technical metadata to describe schemas, indexes and other database objects.

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How Cargotec uses metadata replication to enable cross-account data sharing

AWS Big Data

This data needs to be ingested into a data lake, transformed, and made available for analytics, machine learning (ML), and visualization. For this, Cargotec built an Amazon Simple Storage Service (Amazon S3) data lake and cataloged the data assets in AWS Glue Data Catalog.

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Week in the Life of an Analyst at Gartner US IT Symposium (virtual) 2021

Andrew White

Lakehouse (data warehouse and data lake working together) 8. Data Literacy, training, coordination, collaboration 8. Data Management Infrastructure/Data Fabric 5. Data Integration tactics 4. Metadata Strategy 3. Figure 3: The Data and Analytics (infrastructure) Continuum.

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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. In this post, we describe Orca’s journey building a transactional data lake using Amazon Simple Storage Service (Amazon S3), Apache Iceberg, and AWS Analytics.