On Forbes.com CTO John De Goes: Hadoop Isn’t A Requirement For Big Data Projects
SlamData CTO John De Goes on Forbes: Have Your Cake And Eat It: Big Data Without Hadoop.
From the article:
Today’s NoSQL databases share many characteristics with Hadoop, but in some cases, they are easier to manage and develop for. So if you’re about to embark on a big data project, it makes sense to investigate at lease the leading NoSQL contenders. Because for data hubs, IoT and real-time analytics uses cases, one of these may be a wiser choice than a Hadoop-built solution.
But there are a few use cases where NoSQL databases are stronger. Among them are:
Data Hubs: If you need to consolidate information from a variety of sources of data (particularly online data), NoSQL databases are a perfect fit. Common examples of this include creating a single view of customer or patient data, or linking together data from lots of different departments inside a larger organization. The insurance company MetLife uses a NoSQL database in this fashion, and I have worked with several others companies doing the same.
IoT: The Internet of Things is rife with devices and sensors that create large amounts of data whose structure can change and whose primary purpose is rollup for monitoring, alerting and large-scale analytics. NoSQL databases excel at this and can handle incredible volumes of data with ease. Engineering firm Bosch is one company using NoSQL database technology for IoT.
Real-Time Analytics: Most NoSQL databases have the ability to perform real-time aggregation on streams of data (for example, clickstream or log data), which can make them an excellent choice for high-volume, low-value data that needs to be pre-aggregated before analysis. Content delivery networks Buffer and MaxCDN both leverage NoSQL tech for real-time analytics.
John is part of the Forbes Technology Council, an invitation-only organization comprised of elite CIOs, CTOs and technology executives.
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Data Intelligence On Complex, Modern Data. Without Moving Any Of It.
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Who Is Using SlamData?
Whitepaper: The Characteristics of NoSQL Analytics Systems
by John De Goes, CTO and Co-Founder of SlamData
Semistructured data, called NoSQL data in this paper, is growing at an unprecedented rate. This growth is fueled, in part, by the proliferation of web and mobile applications, APIs, event-oriented data, sensor data, machine learning, and the Internet of Things, all of which are disproportionately powered by NoSQL technologies and data models.
This paper carves out a single concern, by focusing on the system-level capabilities required to derive maximum analytic value from a generalized model of NoSQL data. This approach leads to eight well-defined, objective characteristics, which collectively form a precise capabilities-based definition of a NoSQL analytics system.
These capabilities are inextricably motivated by use cases, but other considerations are explicitly ignored. They are ignored not because they are unimportant (quite the contrary), but because they are orthogonal to the raw capabilities a system must possess to be capable of deriving analytic value from NoSQL data.
Table of Contents
- The Nature of NoSQL Data
- NoSQL Databases
- Big Data
- A Generic Data Model for NoSQL
- Approaches to NoSQL Analytics
- Coding & ETL
- Real-Time Analytics
- Relational Model Virtualization
- First-Class NoSQL Analytics
- Characteristics of NoSQL Analytics Systems
- Generic Data Model
- Isomorphic Data Model
- Unified Schema/Data
- Polymorphic Queries
- Dynamic Type Discovery & Conversion
- Structural Patterns