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How to Manage Risk with Modern Data Architectures

Cloudera

To improve the way they model and manage risk, institutions must modernize their data management and data governance practices. Implementing a modern data architecture makes it possible for financial institutions to break down legacy data silos, simplifying data management, governance, and integration — and driving down costs.

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The Future of Data Lineage and the Role of Metadata

Alation

But what data things are interconnected? Most of the time we think about data fields & files, columns & tables, reports & dashboards. The complex challenge here is to have the lineage be intelligently updated as the data landscape and processing dynamically bubbles and changes daily across an enterprise.

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BMW Cloud Efficiency Analytics powered by Amazon QuickSight and Amazon Athena

AWS Big Data

The CLEA dashboards were built on the foundation of the Well-Architected Lab. For more information on this foundation, refer to A Detailed Overview of the Cost Intelligence Dashboard. Data providers and consumers are the two fundamental users of a CDH dataset. These ingested datasets are used as a source in CLEA dashboards.

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Extracting key insights from Amazon S3 access logs with AWS Glue for Ray

AWS Big Data

We will partition and format the server access logs with Amazon Web Services (AWS) Glue , a serverless data integration service, to generate a catalog for access logs and create dashboards for insights. These logs can track activity, such as data access patterns, lifecycle and management activity, and security events.

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How Huron built an Amazon QuickSight Asset Catalogue with AWS CDK Based Deployment Pipeline

AWS Big Data

Having an accurate and up-to-date inventory of all technical assets helps an organization ensure it can keep track of all its resources with metadata information such as their assigned oners, last updated date, used by whom, how frequently and more. This is a guest blog post co-written with Corey Johnson from Huron.

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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.

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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.

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