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Accelerate analytics on Amazon OpenSearch Service with AWS Glue through its native connector

AWS Big Data

As the volume and complexity of analytics workloads continue to grow, customers are looking for more efficient and cost-effective ways to ingest and analyse data. Attach the AWS managed policy GlueServiceRole. Attach the following policy to the role. About the authors Basheer Sheriff is a Senior Solutions Architect at AWS.

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Handle UPSERT data operations using open-source Delta Lake and AWS Glue

AWS Big Data

Many customers need an ACID transaction (atomic, consistent, isolated, durable) data lake that can log change data capture (CDC) from operational data sources. There is also demand for merging real-time data into batch data. Delta Lake framework provides these two capabilities. Choose Create policy.

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How SumUp made digital analytics more accessible using AWS Glue

AWS Big Data

Founded in 2012, SumUp is the financial partner for more than 4 million small merchants in over 35 markets worldwide, helping them start, run and grow their business. Unless, of course, the rest of their data also resides in the Google Cloud. The Data Science teams also use this data for churn prediction and CLTV modeling.

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Run Spark SQL on Amazon Athena Spark

AWS Big Data

Modern applications store massive amounts of data on Amazon Simple Storage Service (Amazon S3) data lakes, providing cost-effective and highly durable storage, and allowing you to run analytics and machine learning (ML) from your data lake to generate insights on your data.

Data Lake 101
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Design a data mesh on AWS that reflects the envisioned organization

AWS Big Data

Users can also raise requests to producers to improve the way the data is presented or to enrich the data with new data points for generating a higher business value. At the same time, each team can also map other catalogs to their own account and use their own data, which they produce along with the data from other accounts.

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How The Cloud Made ‘Data-Driven Culture’ Possible | Part 1

BizAcuity

Amazon strategically went with the pricing model of ‘on-demand’, allowing developers to pay only as-per their computational needs. 2012: Amazon Redshift, the first of its kind cloud-based data warehouse service comes into existence. AWS rolls out SageMaker, designed to build, train, test and deploy machine learning (ML) models.

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Why We Started the Data Intelligence Project

Alation

In the 2010s, the growing scope of the data landscape gave rise to a new profession: the data scientist. This new role, combined with the creation of data lakes and the increasing use of cloud services, created new employment opportunities in data analytics, data architecture, and data management.