Remove Data Lake Remove Data Transformation Remove Risk
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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

One key component that plays a central role in modern data architectures is the data lake, which allows organizations to store and analyze large amounts of data in a cost-effective manner and run advanced analytics and machine learning (ML) at scale. Why did Orca build a data lake?

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Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

Fragmented systems, inconsistent definitions, legacy infrastructure and manual workarounds introduce critical risks. These issues dont just hinder next-gen analytics and AI; they erode trust, delay transformation and diminish business value. Data quality is no longer a back-office concern. Embed end-to-end lineage tracking.

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How ANZ Institutional Division built a federated data platform to enable their domain teams to build data products to support business outcomes

AWS Big Data

Globally, financial institutions have been experiencing similar issues, prompting a widespread reassessment of traditional data management approaches. With this approach, each node in ANZ maintains its divisional alignment and adherence to data risk and governance standards and policies to manage local data products and data assets.

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The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure

DataKitchen

Here are a few examples that we have seen of how this can be done: Batch ETL with Azure Data Factory and Azure Databricks: In this pattern, Azure Data Factory is used to orchestrate and schedule batch ETL processes. Azure Blob Storage serves as the data lake to store raw data. Azure Machine Learning).

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

IBM Big Data Hub

As more businesses use AI systems and the technology continues to mature and change, improper use could expose a company to significant financial, operational, regulatory and reputational risks. It includes processes that trace and document the origin of data, models and associated metadata and pipelines for audits.

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Lay the groundwork now for advanced analytics and AI

CIO Business Intelligence

But reaching all these goals, as well as using enterprise data for generative AI to streamline the business and develop new services, requires a proper foundation. Each of the acquired companies had multiple data sets with different primary keys, says Hepworth. “We

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Turning the page

Cloudera

In summary, the next chapter for Cloudera will allow us to concentrate our efforts on strategic business opportunities and take thoughtful risks that help accelerate growth. Datacoral powers fast and easy data transformations for any type of data via a robust multi-tenant SaaS architecture that runs in AWS.