Strategy for data migration to cloud
Scheduled Oct 5, 2021
Moving from on-premise to cloud can be complex. This guide walks you through what you need to know, and the pitfalls to watch out for.
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When managing large-scale data migrations, often with multiple legacy systems, the traditional approach means the repetitive effort grows with the project scope.
But with a repeatable framework approach that automates much of the simple work, you get faster results and significant cost savings.
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If you're struggling to meet stringent regulations such as Basel II or MiFID, automating the translation of these models can help you obtain a single, standardized version of the truth and provide a trusted link between documentation and production systems.
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Looking for data integration software but not sure where to start?
Start with our Buyers' Guide.
This guide walks you through everything you should be considering when choosing the right software for your business - from how to identify your organization's needs and capabilities to planning for the future and tips to get the most out of a demo.
Read now and discover what you need to be asking potential vendors.
Bad data is unavoidable. But you can (and should) design your data processes to manage that bad data effectively.
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Is a data warehouse or a data lake the best option for your business?
Or is your best choice for enterprise data architecture something different?
This white paper looks at what each option involves, and the advantages and disadvantages of data warehouses, data lakes, data vaults, data hubs, data marts and more.
Does your business handle data with an Excel file full of formulas and macros, being exchanged over email between people who don't really understand how it works?
If this approach is becoming unsustainable and error-prone, read this white paper to learn a better way.
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If you're facing the prospect of a data migration, this e-book will help you understand how to prepare and plan for a successful project.
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Data models provide a graphical, intuitive way for both business and technical stakeholders to visualize complex data structures and relationships.
But data models have historically required development effort to turn them into actionable code.
The CloverDX approach can help automate this process.
Bringing data models and data integration together can improve collaboration between business and IT, speed up time to production and turn data models into ETL jobs at the click of a button.
Download the Guide to Data Migration Projects and discover the 13 stages involved in a migration, and the best practices to make each one a success.
The guide will show you:
Getting high-quality test data can be a real challenge when it comes to testing.
Synthetic data can never represent the same use case coverage and quality as real production data. But using production data can risk violating security or privacy policies.
This white paper outlines how you can anonymize production data effectively to get the best possible data for testing.
10 Things You Need To Know About AnaCredit
AnaCredit (The Analytical Credit and Credit Risk Dataset), is a project from the European Central Bank (ECB) to create a shared database containing information on bank loans to companies. Credit institutions across the Eurozone will be required to report specific, standardized data on loans and other credits.
This e-book answers 10 key questions about AnaCredit and explains how to prepare for its data demands.