Introduction
My young daughter is very fond of elephants [1], as indeed am I, so I need to tread delicately here. I recent years, the world has been consumed with Big Data Fever [2] and this has been intimately entwined with Hadoop of yellow elephant fame. Clearly there are very many other products such as Apache [insert random word here] [3] which are part of the Big Data ecosystem, but it is Hadoop that has become synonymous with Big Data and indeed conflated with many of the other Big Data technologies.
I have seen some successful and innovative Big Data projects and there are clearly many benefits associated with the cluster of technologies that this term is used to describe. There are also any number of paeans to this new paradigm a mouse click, or finger touch, away [4]; indeed I have featured some myself in these pages [5]. However, what has struck me of late is that a few less positive articles have been appearing. I come to neither bury, nor praise Hadoop [6], but merely to reflect on this development. I will also touch on recent rumours that one of the Apache tribe [7], specifically Spark, may be seeking an amicable divorce from Hadoop proper [8].
In doing this, I am going to draw on two articles in particular. First Hadoop Is Falling by George Hill (@IE_George) on The Innovation Enterprise. Second The Hadoop Honeymoon is Over [9] by Martyn Richard Jones (@GoodStratTweet) on LinkedIn.
However, before I leap into analysing other people’s thoughts I will present some of my own [very basic] research, care of Google Trends.
Eine Kleine Nachtgoogling
Below I display two charts (larger versions are but a click away) tracking the volume of queries in the 2014-16 period for two terms: “hadoop” and “apache spark” [10]. On the assumption that California tends to lead trends more than it follows, I have focussed in on this part of the US.
| Note on axes: | On this blog I have occasionally spoken about the ability of images to conceal information as well as to reveal it [11]. Lest I am accused of making the same mistake, normalising both sets of data in the above graphs could give the misleading impression that the peak volume of queries for “hadoop” and “apache spark” are equivalent. This is not so. The maximum number of weekly queries for “apache spark” in the three years examined is just under a fifth of the maximum number of queries for “hadoop” [12]. So, applying a rather broad rule of thumb, people searched for “hadoop” around five times more often. However, it was not the absolute number of queries that I was interested in, but how these change over time, so I think the approach I have taken is justified. If I had not normalised, it would have been difficult to pick out the “apache spark” trend in a combined graph. |
The obvious inference to be drawn is that searches for Hadoop (in California at least) are declining and those for Spark are increasing; though maybe with a bit of a fall off in volume recently. Making a cast iron connection between trends in search and trends in industry is probably a mistake [13], but the discrepancies in the two trends are at least suggestive. In the Application Development Trends article I reference (note [8]) the author states:
The Spark momentum is so great that the technology — originally positioned as a replacement for MapReduce with added real-time capabilities and in-memory processing — could break free from the reins of the Hadoop universe and become its own independent tool.
This chimes with the AtScale findings I also reported here (note [5]), which included the observation that:
Organizations who have deployed Spark in production are 85% more likely to achieve value.
One conclusion (albeit a rather tentative one) could be that while Spark is on an upward trajectory and perhaps likely to step out of the Hadoop shadow, interest in Hadoop itself is at best plateauing and possibly declining. It is against this backdrop that I’ll now consider the two articles I introduced earlier.
Trouble with Trunks
In his article, George Hill begins by noting that:
[Hadoop] adoption appears to have more or less stagnated, leading even James Kobielus [@jameskobielus], Big Data Evangelist at IBM Analytics [14], to claim that “Hadoop declined more rapidly in 2016 from the big-data landscape than I expected” [15]
In search for a reasons behind this apparent stagnation, he hypothesises that:
[A] cause for concern is simply that one man’s big data is another man’s small data. Hadoop is designed for huge amounts of data, and as Kashif Saiyed [@rizkashif] wrote on KD Nuggets [16] “You don’t need Hadoop if you don’t really have a problem of huge data volumes in your enterprise, so hundreds of enterprises were hugely disappointed by their useless 2 to 10TB Hadoop clusters – Hadoop technology just doesn’t shine at this scale.”
Most companies do not currently have enough data to warrant a Hadoop rollout, but did so anyway because they felt they needed to keep up with the Joneses. After a few years of experimentation and working alongside genuine data scientists, they soon realize that their data works better in other technologies.
Martyn Richard Jones weighs in on this issue in more provocative style when he says:
Hadoop has grown, feature by feature, as a response to specific technical challenges in specific and somewhat peculiar businesses. When it all kicked off, the developers weren’t thinking about creating a new generic data management architecture, one for handling massive amounts of data. They were thinking of how to solve specific problems. Then it rather got out of hand, and the piecemeal scope grew like topsy as did the multifarious ways to address the product backlog.
and aligns himself with Kashif Saiyed’s comments by adding:
It also turns out that, in spite of the babbling of the usual suspects, Big Data is not for everyone, not everyone needs it, and even if some businesses benefit from analysing their data, they can do smaller Big Data using conventional rock-solid, high-performance and proven database technologies, well-architected and packaged technologies that are in wide use.
I have been around the data space long enough to have seen a number of technologies emerge, each of which was touted as solving all known problems. These included Executive Information Systems, Relational Databases, Enterprise Resource Planning, Data Warehouses, OLAP, Business Intelligence Suites and Customer Relationship Management systems. All are useful tools, I have successfully employed each of them, but at the end of the day, they are all technologies and technologies don’t sort out problems, people do [17]. Big Data enables us to address some new problems (and revisit some old ones) in novel ways and lets us do things we could not do before. However, it is no more a universal panacea than anything that has preceded it.
Big Data seems to have disappeared off of the Gartner hype cycle in 2016, perhaps as it is now viewed as having become mainstream. However, back in August 2015, it was heading downhill fast towards the rather cataclysmically named Trough of Disillusionment [18]. This reflects the unwavering fact that no technology ever lives up to its initial hype. Instead, after a period of being over-sold and an inevitable reaction to this, technologies settle down and begin to be actually useful. It seems that Gartner believes that Big Data has already gone through this rite of passage; they may well be correct in this assertion.
Hill references this himself in one of his closing comments, while ending on a more positive note:
[…] it is not the platform in itself that has caused the current issues. Instead it is perhaps the hype and association of Big Data that has done the real damage. Companies have adopted the platform without understanding it and then failed to get the right people or data to make it work properly, which has led to disillusionment and its apparent stagnation. There is still a huge amount of life in Hadoop, but people just need to understand it better.
For me there are loud and clear echos of other technologies “failing” in the past in what Hill says [19]. My experience in these other cases is that, while technologies may not have lived up to implausible initial claims, when they do genuinely fail, it is often for reasons that are all too human [20].
Summary
I had considered creating more balance in this article by adding a section making the case for the defence. I then realised that this was actually a pretty pointless exercise. Not because Hadoop is in terminal decline and denial of this would be indefensible. Not because it must be admitted that Big Data is over-hyped and under-delivers. Cases could be made that both of those statements are either false, or at least do not tell the whole story. However I think that arguments like these are the wrong things to focus on. Let me try to explain why.
Back in 2009 I wrote an article with the title A bad workman blames his [Business Intelligence] tools. This considered the all-too-prevalent practice in rock climbing and bouldering circles of buying the latest and greatest kit and assuming that performance gains would follow from this, as opposed to doing the hard work of training and practice (the same phenomenon occurs in other sports of course). I compared this to BI practitioners relying on technology as a crutch rather than focussing on four much more important things:
- Determining what information is necessary to drive key business decisions.
- Understanding the various data sources that are available and how they relate to each other.
- Transforming the data to meet the information needs.
- Managing the embedding of BI in the corporate culture.
I am often asked how relevant my heritage articles are to today’s world of analytics, data management, machine learning and AI. My reply is generally that what has changed is technology and little else [21]. This means that what was relevant back in 2009 remains relevant today; sometimes more so. The only area with a strong technological element in the list of four I cite above is number 3. I would agree that a lot has happened in the intervening years around how this piece can be effected. However, nothing has really changed in the other areas. We may call business questions use cases or user stories today, but they are the same thing. You still can’t really leverage data without attempting to understand it first. The need for good communication about data projects, high-quality education and strong follow-up is just as essential as it ever was.
Below I have taken the liberty of editing my own text, replacing the terms that were prevalent in data and information circles then, with the current ones.
Well if you want people to actually use analytics capabilities, it helps if the way that the technology operates is not a hindrance to this. Ideally the ease-of-use and intuitiveness of the analytical platform deployed should be a plus point for you. However, if you have the ultimate in data technology, but your analytics do not highlight areas that business people are interested in, do not provide information that influences actual decision-making, or contain numbers that are inaccurate, out-of-date, or unreconciled, then they will not be used.
I stand by these sentiments seven or eight years later. Over time the technology and terminology we use both change. I would argue that the essentials that determine success or failure seldom do.
Let’s take the undeniable hype cycle effect to one side. Let’s also discount overreaching claims that Hadoop and its related technologies are Swiss Army Knives, capable of dealing with any data situation. Let’s also set aside the string of technical objections that Martyn Richard Jones raises. My strong opinion is that when Hadoop (or Spark or the next great thing) fails, it will again most likely be a case of bad workmen blaming their tools; just as they did back in 2009.
Notes
| [1] |
As was Doug Cutting‘s son back in 2006. Rather than being yellow, my daughter’s favourite pachyderm is blue and called “Dee”, my wife and I have no idea why. |
| [2] |
WHO have described the Big Data Fever situation as follows:
|
| [3] |
Pick any one of: Cassandra, Flink, Flume, HBase, Hive, Impala, Kafka, Oozie, Phoenix, Pig, Spark, Sqoop, Storm and ZooKeeper. |
| [4] |
You could start with the LinkedIn Big Data Channel. |
| [5] |
Do any technologies grow up or do they only come of age? |
| [6] |
The evil that open-source frameworks do lives after them; The good is oft interred with their source code; So let it be with Hadoop. |
| [7] |
Perhaps not very respectful to Native American sensibilities, but hard to resist. No offence is intended. |
| [8] |
Spark Poised To Break from Hadoop, Move to Cloud, Survey Says, Application Development Trends. |
| [9] |
While functioning at the point that this article was originally written, it now appears that Martyn Richard Jones’s LinkedIn account has been suspended and the article I refer to is no longer available. The original URL was https://www.linkedin.com/pulse/hadoop-honeymoon-over-martyn-jones. I’m not sure what the issue is and whether or not the article may reappear at some later point. |
| [10] |
A couple of points here. As “spark” is a word in common usage, the qualifier of “apache” is necessary. On the contrary, “hadoop” is not a name that is used for much beyond yellow elephants and so no qualifier is required. I could have used “apache hadoop” as the comparator, but instances of this are less frequent than for just “hadoop”. For what it is worth, although the number of queries for “apache hadoop” are fewer, the trend over time is pretty much the same as for just “hadoop”. |
| [11] |
For example: |
| [12] |
18% to be precise. |
| [13] |
Though quite a few people make a nice living doing just that. |
| [14] |
“IBM Software” in the original article, corrected to “IBM Analytics” here. |
| [15] |
Big Data: Main Developments in 2016 and Key Trends in 2017, KD Nuggets. |
| [16] |
Why Not So Hadoop?, KD Nuggets. |
| [17] |
Though admittedly nowadays people sometimes sort problems by writing algorithms for machines to run, which then come up with the answer. |
| [18] |
Which has always felt to me that it should appear on a papyrus map next to a “here be dragons” legend. |
| [19] |
For example as in “Why Business Intelligence projects fail”. |
| [20] |
It’s worth counting how many of the risks I enumerate in 20 Risks that Beset Data Programmes are human-centric (hint: its a multiple of ten biger than 15 and smaller than 25). |
| [21] |
I might be tempted to answer a little differently when it comes to Artificial Intelligence. |
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