Remove Dashboards Remove Data Architecture Remove Data Lake Remove Data Processing
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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. Open AWS Glue Studio. Choose ETL Jobs.

Data Lake 102
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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 113
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5 misconceptions about cloud data warehouses

IBM Big Data Hub

In today’s world, data warehouses are a critical component of any organization’s technology ecosystem. They provide the backbone for a range of use cases such as business intelligence (BI) reporting, dashboarding, and machine-learning (ML)-based predictive analytics, that enable faster decision making and insights.

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Eight Top DataOps Trends for 2022

DataKitchen

Data Gets Meshier. 2022 will bring further momentum behind modular enterprise architectures like data mesh. The data mesh addresses the problems characteristic of large, complex, monolithic data architectures by dividing the system into discrete domains managed by smaller, cross-functional teams.

Testing 245
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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

Building data lakes from continuously changing transactional data of databases and keeping data lakes up to date is a complex task and can be an operational challenge. You can then apply transformations and store data in Delta format for managing inserts, updates, and deletes.

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

AWS Big Data

The AWS modern data architecture shows a way to build a purpose-built, secure, and scalable data platform in the cloud. Learn from this to build querying capabilities across your data lake and the data warehouse. The following diagram shows a sample C360 dashboard built on Amazon QuickSight.

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How Amazon Finance Automation built a data mesh to support distributed data ownership and centralize governance

AWS Big Data

These inputs reinforced the need of a unified data strategy across the FinOps teams. We decided to build a scalable data management product that is based on the best practices of modern data architecture. Our source system and domain teams were mapped as data producers, and they would have ownership of the datasets.

Finance 83