Data acquisition
Modernising data warehousing
Cloud data warehousing brings all the benefits of the cloud to analytical data infrastructures: agility, cost effectiveness, scalability and performance. In this article, Simon Spring, account director EMEA at WhereScape, explores the agility and simplicity of the cloud, and how organisations can maximise the return on their cloud investment
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n the past, implementing a data warehouse meant flipping a coin between data quality and business agility. When
they were first created, on-premises data warehouses were hugely expensive – building them took years and cost millions, meaning only the most affluent companies could afford the investment. Organisations were also discouraged by implementation. IT teams had to estimate how much storage and compute power would be necessary – if they bought too little hardware, for example, space would run out. If they bought too much, enormous sums could be wasted on unused resources, such as memory. This has become a familiar issue to anyone with experience of data warehouse development. Yet, the severity of the challenge is only set to increase. A report by IDC predicts that by 2025 data creation will grow to an enormous 163 zettabytes (ZB), ten times the amount of data produced in 2017. Consequently, data warehouse development is under pressure to modernise. This process can include a range of objectives, from realigning with current business goals and provisioning data for existing and future business cases, to leveraging new platforms
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and data-driven tools. In addition, modernisation strategies look to adopt new data management best practices and to adjust data warehouse teams and skills. Others focus more closely on the need to remediate the limitations of scale, speed, functionality and agility.
THE ROLE OF AUTOMATION Automation is becoming central to this process, because it provides the modern tooling required for data warehouse design, development and administration. With an ‘automation first’ philosophy guiding data warehouse development, developers can fix outmoded development methodology and
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practices. In doing so, it becomes possible to address the shortcomings of traditional approaches where productivity, flexibility, reuse and adherence to standards are limited. But this is not just about how much data can be stored and processed or how quickly meaning can be derived from it. Adding data warehouse automation software to the mix can deliver even more efficiencies and value in a much faster timeframe than hand-coding or using native tools without automation – in fact, it simplifies development to minimise both effort and risk in data integration and infrastructure projects. This allows companies to focus their effort and resources on providing analytic value to the businesses.
Once organisations have implemented
a data warehouse effectively, it can have a transformational effect on their ability to manage the influx of big data, automate manual processes and maximise the return on cloud investment.
September 2020 Instrumentation Monthly ’’
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