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Evolving data integration strategies target new analytics needs

Most companies don't have a shortage of data, but it's often stored in siloed systems or inconsistent formats -- problems that data integration programs need to address.

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What's needed to integrate customer data for analytics

Customer data integration is a minefield for IT teams to navigate. But incorporating a set of core technical functions into an integration architecture can ease the process.

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Integration steps to maintain data consistency

Data integration processes that aren't managed properly can create inconsistent data in BI and analytics applications. Here are some steps to avoid that problem.

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Cloud payments path set by new data lake, user feedback

To create a cloud payment processing system, Global Payments first had to deploy a data lake in the Google Cloud. Getting quick user feedback was another early step.

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  • Big data management

    Hortonworks looks to grow its big data cloud footprint

    Hortonworks now supports Google Cloud Storage and has also broadened cloud deals with Microsoft and IBM, aiming to increase cloud uses of its big data platform.

  • Hadoop framework

    Stream processing meets GDPR

    StreamSets software for inspecting big data brings governance to data in motion. Such capabilities may find more use as the European Union's GDPR deadline looms.

  • Database management system (DBMS) software and technology

    MongoDB 4.0 expands ACID support for documents

    MongoDB is taking a deeper step into SQL-style processing waters with a 4.0 update that brings increased support for ACID-compliant transactions to its NoSQL database.

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