Databases Reference
In-Depth Information
The HFlame compelling argument is the common data analysis framework for both
offline and real-time massively parallel data analysis, which essentially means no new
storage, no new data processing semantics, and leveraging existing high-level abstraction
languages like Pig and Hive. For Hadoop users, real-time streaming analysis with HFlame
requires absolutely zero investment into new infrastructure and no new API/tools to learn.
Use Case: Real-Time Analysis of Machine Generated Data
(Log Processing)
Machine data (or data exhaust) is produced all the time by nearly every software
application and electronic device. The applications, servers, network devices, sensors,
browsers, desktop and laptop computers, mobile devices, and various other systems
deployed to support operations are continuously generating information relating to their
status and activities.
Machine data is generated by both machine-to-machine (M2M) as well as
human-to-machine (H2M) interactions. Machine data in is generated in a multitude
of formats and structures, as each software application or hardware device records and
creates machine data associated with their specific use. Machine data also varies among
vendors and even within the same vendor across product types, families, and models.
The figure below illustrates the type of machine data created and the business and IT
insights that can be derived when a single web visitor makes a purchase in a typical
e-commerce environment shown in Figure
8-7
.
Figure 8-7.
Machine-generated data and business impacts
