Remove Data Lake Remove Data Processing Remove Data Strategy Remove Modeling
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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

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

AWS Big Data

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data.

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Themes and Conferences per Pacoid, Episode 8

Domino Data Lab

Instead, we must build robust ML models which take into account inherent limitations in our data and embrace the responsibility for the outcomes. How did the challenges and opportunities related to security, data management, and system architecture get braided together throughout the past ~6 decades of IT?

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Real-time streaming data top picks you cannot miss at AWS re:Invent 2023

AWS Big Data

High-quality data is not just about accuracy; it’s also about timeliness. To derive meaningful insights and ensure the optimal performance of machine learning (ML) and generative AI models, data needs to be ingested and processed in real time. With real-time streaming data, organizations can reimagine what’s possible.

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Empowering data-driven excellence: How the Bluestone Data Platform embraced data mesh for success

AWS Big Data

Each data producer within the organization has its own data lake in Apache Hudi format, ensuring data sovereignty and autonomy. This enables data-driven decision-making across the organization.

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

AWS Big Data

Consumers prioritized data discoverability, fast data access, low latency, and high accuracy of 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.

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