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“Data Wrangling” = Data Rankled

Data wrangling is the latest clever way to describe the lengthy and cumbersome data preparation process required to make modern complex data (aka: post-relational, multi-structured, semi-structured…) fit legacy (read: relational) analytics tools.

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Wise Up NoSQL!

SQL was the glue that held together the relational database market for the past 40+ years. More importantly SQL allowed it to thrive as an ecosystem. Solutions like Business Intelligence (BI), Data Warehousing, and RDBMS management tools would not have been possible without the Lingua Franca.

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B2B SaaS Breakthrough: Scale Secure Dashboards On MongoDB For Your Clients

If you’re reading this, then you’re in the know. You know that MongoDB’s “flexibility” around data models kicked the doors down and ushered in a brave new world of software development. Development is now exponentially faster, easier and cheaper to build. And MongoDB has moved beyond cloud-based consumer apps and is now no doubt running in production environments in government, healthcare, industrial, energy — really all over the enterprise.

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Agile Analytics On Modern Data

It’s essentially analytics that is delivering to you all the reasons that you picked a NoSQL database in the first place. And that’s what’s missing today.

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What Is A Data Hub?

SlamData CEO, Jeff Carr: “Instead of a 6 or 12 month project to build a data warehouse, you can stand up a Data Hub in a couple of days using SlamData and MongoDB or another NoSQL database.”

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Why Modern BI is Failing

Legacy BI tools were not built to handle modern NoSQL data models like JSON or XML. Change starts with real innovation, not tweaking the status quo.

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News, Analysis and Blogs

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The Characteristics of NoSQL Analytics Systems

  • The Nature of NoSQL Data
    • APIs
    • NoSQL Databases
    • Big Data
    • A Generic Data Model for NoSQL
  • Approaches to NoSQL Analytics
    • Coding & ETL
    • Hadoop
    • Real-Time Analytics
    • Relational Model Virtualization
    • First-Class NoSQL Analytics
  • Characteristics of NoSQL Analytics Systems
    • Generic Data Model
    • Isomorphic Data Model
    • Multi-Dimensionality
    • Unified Schema/Data
    • Post-Relational
    • Polymorphic Queries
    • Dynamic Type Discovery & Conversion
    • Structural Patterns

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