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

AWS Big Data

A data lake is a centralized repository that you can use to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data and then run different types of analytics for better business insights.

Data Lake 102
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Data Lakes: What Are They and Who Needs Them?

Jet Global

To address the flood of data and the needs of enterprise businesses to store, sort, and analyze that data, a new storage solution has evolved: the data lake. What’s in a Data Lake? Data warehouses do a great job of standardizing data from disparate sources for analysis. Taking a Dip.

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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

Grafana provides powerful customizable dashboards to view pipeline health. However, to analyze trends over time, aggregate from different dimensions, and share insights across the organization, a purpose-built business intelligence (BI) tool like Amazon QuickSight may be more effective for your business.

Metrics 105
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How HR&A uses Amazon Redshift spatial analytics on Amazon Redshift Serverless to measure digital equity in states across the US

AWS Big Data

HR&A has used Amazon Redshift Serverless and CARTO to process survey findings more efficiently and create custom interactive dashboards to facilitate understanding of the results. A combination of Amazon Redshift Spectrum and COPY commands are used to ingest the survey data stored as CSV files.

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How gaming companies can use Amazon Redshift Serverless to build scalable analytical applications faster and easier

AWS Big Data

A data hub contains data at multiple levels of granularity and is often not integrated. It differs from a data lake by offering data that is pre-validated and standardized, allowing for simpler consumption by users. Data hubs and data lakes can coexist in an organization, complementing each other.

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How to use foundation models and trusted governance to manage AI workflow risk

IBM Big Data Hub

It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits. How to scale AL and ML with built-in governance A fit-for-purpose data store built on an open lakehouse architecture allows you to scale AI and ML while providing built-in governance tools.

Risk 70
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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. Meaningful business context. Improved trust and confidence in data.