Remove 2015 Remove Data Analytics Remove Data Lake Remove Testing
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How SumUp made digital analytics more accessible using AWS Glue

AWS Big Data

Unless, of course, the rest of their data also resides in the Google Cloud. In this post we showcase how we used AWS Glue to move siloed digital analytics data, with inconsistent arrival times, to AWS S3 (our Data Lake) and our central data warehouse (DWH), Snowflake.

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Speed up queries with the cost-based optimizer in Amazon Athena

AWS Big Data

Athena provides a simplified, flexible way to analyze petabytes of data where it lives. You can analyze data or build applications from an Amazon Simple Storage Service (Amazon S3) data lake and 30 data sources, including on-premises data sources or other cloud systems using SQL or Python.

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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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The Very Group adopts a data catalog to better organize and leverage its online retail capabilities

CIO Business Intelligence

It launched its first online-only brand, Very, in 2009 and finally abandoned its printed catalogs to go all-in online in 2015. He found a rich collection of data assets, including information on over 2.2 Establishing a clear and unified approach to data. The whole company rebranded as Very in 2020, the year Pimblett joined.

IT 82
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How The Cloud Made ‘Data-Driven Culture’ Possible | Part 1

BizAcuity

2015: Google announces Google Kubernetes Engine for the cloud. Google launches BigQuery, its own data warehousing tool and Microsoft introduces Azure SQL Data Warehouse and Azure Data Lake Store. AWS rolls out SageMaker, designed to build, train, test and deploy machine learning (ML) models. To be continued.