DBTA Best Practices: Moving to a Modern Data Architecture

Underpinning the movement to compete on analytics, a major shift is taking place on the architectural level where data is captured, stored, and processed. This transformation is being driven by the need for more agile data management practices in the face of increasing volumes and varieties of data and the growing challenge of delivering that data where and when it is needed.

A growing number of organizations are modifying their data infrastructures with new technologies, from Hadoop and Spark, to MPP data warehouses, NOSQL databases, in-memory platforms, and a flourishing market of cloud-based solutions. To accommodate the different types of data sources, workloads, applications, and users that big data presents us with, a variety of systems are needed. And to avoid data silos, these technologies need to be integrated under a common architecture.

Download this special report to get a deeper understanding of the key technologies and best practices shaping the modern data architecture.

Table of contents include:

  • Nine Ways to Embrace Modern Data Architecture by Joe McKendrick
  • Data Management in the Big Mobile and Internet of Things Era by IBM
  • Converging Analysis and Operations by MarkLogic
  • The Data Warehouse is Dead, Long Live Data Management by Oracle
  • Creating a Business-Driven Data Architecture by Embarcadero
  • Got Hadoop? Here’s How to Streamline Its Integration Within Your Modern Data Architecture by Attunity

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