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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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Detect, mask, and redact PII data using AWS Glue before loading into Amazon OpenSearch Service

AWS Big Data

Ingestion: Data lake batch, micro-batch, and streaming Many organizations land their source data into their data lake in various ways, including batch, micro-batch, and streaming jobs. Amazon AppFlow can be used to transfer data from different SaaS applications to a data lake.

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Data governance in the age of generative AI

AWS Big Data

Data is your generative AI differentiator, and a successful generative AI implementation depends on a robust data strategy incorporating a comprehensive data governance approach. As part of the transformation, the objects need to be treated to ensure data privacy (for example, PII redaction).

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The rise of the data lakehouse: A new era of data value

CIO Business Intelligence

Previously, Walgreens was attempting to perform that task with its data lake but faced two significant obstacles: cost and time. Those challenges are well-known to many organizations as they have sought to obtain analytical knowledge from their vast amounts of data. Lakehouses redeem the failures of some data lakes.

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Data science vs data analytics: Unpacking the differences

IBM Big Data Hub

Overview: Data science vs data analytics Think of data science as the overarching umbrella that covers a wide range of tasks performed to find patterns in large datasets, structure data for use, train machine learning models and develop artificial intelligence (AI) applications.

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Build an Amazon Redshift data warehouse using an Amazon DynamoDB single-table design

AWS Big Data

A typical ask for this data may be to identify sales trends as well as sales growth on a yearly, monthly, or even daily basis. A key pillar of AWS’s modern data strategy is the use of purpose-built data stores for specific use cases to achieve performance, cost, and scale. This is achieved by partitioning the data.

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Five Strategies to Accelerate Data Product Development

Cloudera

A common pitfall in the development of data platforms is that they are built around the boundaries of point solutions and are constrained by the technological limitations (e.g., a technology choice such as Spark Streaming is overly focused on throughput at the expense of latency) or data formats (e.g., data warehousing).

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