Take one major trend spanning the business and technology worlds, add countless vendors and consultants hoping to cash in, and what do you get? A whole lot of buzzwords with unclear definitions.
In the world of big data, the surrounding hype has spawned a brand-new lingo. Need a little clarity? Read on for a glossary of sorts highlighting some of the main data types you should understand.
1. Fast data
The shining star in this constellation of terms is "fast data," which is popping up with increasing frequency. It refers to "data whose utility is going to decline over time," said Tony Baer, a principal analyst at Ovum who says he coined the term back in 2012.
It's things like Twitter feeds and streaming data that need to be captured and analyzed in real time, enabling immediate decisions and responses. A capital markets trading firm may rely on it for conducting algorithmic or high-frequency trades.
"Fast data can refer to a few things: fast ingest, fast streaming, fast preparation, fast analytics, fast user response," said Nik Rouda, a senior analyst with Enterprise Strategy Group. It's "mostly marketing hype," but it "shows the need for performance in a variety of ways."
Increased bandwidth, commodity hardware, declining memory prices and real-time analytics have all contributed to the rise of fast data, Baer said.
2. Slow data
At the opposite end of the spectrum is "slow data," or data that might trickle in at a comparatively leisurely pace, warranting less-frequent analysis. Baer points to a device that monitors ocean tides as an example -- for most purposes, real-time updates aren't needed.
In general, this kind of data is better-suited for capture in a data lake and subsequent batch processing.
3. Small data
"Small data" is "anything that fits on one laptop," said Gregory Piatetsky-Shapiro, president of analytics consultancy KDnuggets.
Essentially, the term recognizes the fact that "a lot of analysis is still done on one or a few data sources, on a laptop, using lightweight apps -- sometimes even just Excel," Rouda said.
4. Medium data
As for "medium data," well, it's in between.
When you're talking about many petabytes of data, that's big data, and you'd likely use technologies such as Hadoop and MapReduce to analyze it, Baer said. But "most analytic problems don't involve petabytes," he added. When analyses involve data on a more intermediate scale, that's medium data, and you'd likely use Apache Spark.
5. Dark data
"Dark data" is typically data that gets overlooked and underused.
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