The Rise of Modern ETL

The Rise of Modern ETL

The ETL market has been stagnant and has seen little innovation in the last twenty years. ETL is usually the domain of data engineers and involves moving data from one or more sources, transforming the data, and then loading it into a new target system or data warehouse to service operational BI and reporting.
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A data-centric approach to data security

A data-centric approach to data security

According an IDC research, the world is predicted to create 175 zettabytes of data in 2025, up from 33 zettabytes in 2018. The continuous growth in data volume, variety and use also includes sensitive data across a complex application landscape, both on-premises and in the cloud.
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From Continuous to Autonomous Testing with AI

From Continuous to Autonomous Testing with AI

Artificial Intelligence augments testing to be zero-touch and autonomous

While DevOps and “continuous everywhere” transformation requires the ability to assess risks with every “sprint”, with speed and quality, testing on the other side has become the bottleneck to taking full advantage of more Agile methods to software development.

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Modernising your ETL for Temenos Transact real-time progressive migration projects

Modernising your ETL for Temenos Transact real-time progressive migration projects

ETL usually involves moving data from one or more sources, making some changes, and then loading it into a new single destination. Traditional ETL platforms have been handling data following this ‘extract, transform and load’ model. That’s pretty straightforward. However, with the explosion of data and cloud adoption, they have been trying to expand their support to new data sources and data types and the cloud or hybrid environments prevalent today.
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