Remove 2023 Remove Dashboards Remove Data Lake Remove Data Warehouse
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Use Apache Iceberg in a data lake to support incremental data processing

AWS Big Data

Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback. and later supports the Apache Iceberg framework for data lakes. AWS Glue 3.0 The following diagram illustrates the solution architecture.

Data Lake 119
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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.

Data Lake 116
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Enhance monitoring and debugging for AWS Glue jobs using new job observability metrics

AWS Big Data

The new metrics provide aggregate and fine-grained insights into the health and operations of your job runs and the data being processed. In addition to providing insightful dashboards, the metrics provide classification of errors, which helps with root cause analysis of performance bottlenecks and error diagnosis.

Metrics 98
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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. QuickSight makes it straightforward for business users to visualize data in interactive dashboards and reports. Sample AWS CDK template This post provides a sample AWS CDK template for a dashboard using AWS Glue observability metrics.

Metrics 108
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Backcountry modernizes for the cloud era

CIO Business Intelligence

Backcountry also lacked many core services critical for an online retailer — no CMS, no analytics, no data platform, and no data lake. In recent years, e-commerce platforms have evolved into a combination of cloud, analytics, CX UIs, and data lakes dubbed customer data platforms (CDPs).

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Building a vision for real-time artificial intelligence

CIO Business Intelligence

Most current data architectures were designed for batch processing with analytics and machine learning models running on data warehouses and data lakes. Complexity from disparate data platforms will not support the speed and agility that data needs to work at to support real-time AI. It isn’t easy.

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Amazon Redshift announcements at AWS re:Invent 2023 to enable analytics on all your data

AWS Big Data

In 2013, Amazon Web Services revolutionized the data warehousing industry by launching Amazon Redshift , the first fully-managed, petabyte-scale, enterprise-grade cloud data warehouse. Amazon Redshift made it simple and cost-effective to efficiently analyze large volumes of data using existing business intelligence tools.