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Orca Security’s journey to a petabyte-scale data lake with Apache Iceberg and AWS Analytics

AWS Big Data

With data becoming the driving force behind many industries today, having a modern data architecture is pivotal for organizations to be successful. In this post, we describe Orca’s journey building a transactional data lake using Amazon Simple Storage Service (Amazon S3), Apache Iceberg, and AWS Analytics.

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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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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. AWS services like AWS Lake Formation in conjunction with Atlan help govern data access and policies.

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Governing data in relational databases using Amazon DataZone

AWS Big Data

Amazon DataZone allows you to simply and securely govern end-to-end data assets stored in your Amazon Redshift data warehouses or data lakes cataloged with the AWS Glue data catalog. Note that a managed data asset is an asset for which Amazon DataZone can manage permissions.

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CIOs weigh where to place AI bets — and how to de-risk them

CIO Business Intelligence

The CIO has strategies in place to address all three. Though a multicloud environment, the agency has most of its cloud implementations hosted on Microsoft Azure, with some on AWS and some on ServiceNow’s 311 citizen information platform. AI tools rely on the data in use in these solutions.

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Modern Data Architecture for Telecommunications

Cloudera

Previously, there were three types of data structures in telco: . Entity data sets — i.e. marketing data lakes . The result has been an extraordinary volume of data redundancy across the business, leading to disaggregated data strategy, unknown compliance exposures, and inconsistencies in data-based processes. .

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Build efficient ETL pipelines with AWS Step Functions distributed map and redrive feature

AWS Big Data

Solution overview One of the common functionalities involved in data pipelines is extracting data from multiple data sources and exporting it to a data lake or synchronizing the data to another database. There are multiple tables related to customers and order data in the RDS database.

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