Remove Data Architecture Remove Data Processing Remove Metadata Remove Visualization
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The Top Three Entangled Trends in Data Architectures: Data Mesh, Data Fabric, and Hybrid Architectures

Cloudera

Each of these trends claim to be complete models for their data architectures to solve the “everything everywhere all at once” problem. Data teams are confused as to whether they should get on the bandwagon of just one of these trends or pick a combination. First, we describe how data mesh and data fabric could be related.

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

AWS Big Data

Cargotec captures terabytes of IoT telemetry data from their machinery operated by numerous customers across the globe. This data needs to be ingested into a data lake, transformed, and made available for analytics, machine learning (ML), and visualization. The target accounts read data from the source account S3 buckets.

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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

Profile aggregation – When you’ve uniquely identified a customer, you can build applications in Managed Service for Apache Flink to consolidate all their metadata, from name to interaction history. Then, you transform this data into a concise format. Data exploration Data exploration helps unearth inconsistencies, outliers, or errors.

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Build incremental data pipelines to load transactional data changes using AWS DMS, Delta 2.0, and Amazon EMR Serverless

AWS Big Data

The Delta tables created by the EMR Serverless application are exposed through the AWS Glue Data Catalog and can be queried through Amazon Athena. Solution overview The following diagram shows the overall architecture of the solution that we implement in this post. Monjumi Sarma is a Data Lab Solutions Architect at AWS.

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How smava makes loans transparent and affordable using Amazon Redshift Serverless

AWS Big Data

Overview of solution As a data-driven company, smava relies on the AWS Cloud to power their analytics use cases. smava ingests data from various external and internal data sources into a landing stage on the data lake based on Amazon Simple Storage Service (Amazon S3).

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What Is Embedded Analytics?

Jet Global

This is in contrast to traditional BI, which extracts insight from data outside of the app. We rely on increasingly mobile technology to comb through massive amounts of data and solve high-value problems. Plus, there is an expectation that tools be visually appealing to boot. Their dashboards were visually stunning.