Big data-Top Emerging Big Data Technologies

Big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis,and visualization. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to "spot business trends, determine quality of research, prevent diseases, link legal citations, combat crime, and determine real-time roadway traffic conditions.

Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process the data within a tolerable elapsed time.Big data sizes are a constantly moving target, as of 2012 ranging from a few dozen terabytes to many petabytes of data in a single data set. The target moves due to constant improvement in traditional DBMS technology as well as new databases like NoSQL and their ability to handle larger amounts of data. With this difficulty, new platforms of "big data" tools are being developed to handle various aspects of large quantities of data.

Big data is data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn’t fit the strictures of your database architectures. To gain value from this data, you must choose an alternative way to process it.

The hot IT buzzword of 2012, big data has become viable as cost-effective approaches have emerged to tame the volume, velocity and variability of massive data. Within this data lie valuable patterns and information, previously hidden because of the amount of work required to extract them. To leading corporations, such as Walmart or Google, this power has been in reach for some time, but at fantastic cost. Today’s commodity hardware, cloud architectures and open source software bring big data processing into the reach of the less well-resourced. Big data processing is eminently feasible for even the small garage startups, who can cheaply rent server time in the cloud.

The value of big data to an organization falls into two categories: analytical use, and enabling new products. Big data analytics can reveal insights hidden previously by data too costly to process, such as peer influence among customers, revealed by analyzing shoppers’ transactions, social and geographical data. Being able to process every item of data in reasonable time removes the troublesome need for sampling and promotes an investigative approach to data, in contrast to the somewhat static nature of running predetermined reports.

The past decade’s successful web startups are prime examples of big data used as an enabler of new products and services. For example, by combining a large number of signals from a user’s actions and those of their friends, Facebook has been able to craft a highly personalized user experience and create a new kind of advertising business. It’s no coincidence that the lion’s share of ideas and tools underpinning big data have emerged from Google, Yahoo, Amazon and Facebook.

The emergence of big data into the enterprise brings with it a necessary counterpart: agility. Successfully exploiting the value in big data requires experimentation and exploration. Whether creating new products or looking for ways to gain competitive advantage, the job calls for curiosity and an entrepreneurial outlook.

What does big data look like?

As a catch-all term, “big data” can be pretty nebulous, in the same way that the term “cloud” covers diverse technologies. Input data to big data systems could be chatter from social networks, web server logs, traffic flow sensors, satellite imagery, broadcast audio streams, banking transactions, MP3s of rock music, the content of web pages, scans of government documents, GPS trails, telemetry from automobiles, financial market data, the list goes on. Are these all really the same thing?

To clarify matters, the three Vs of volume, velocity and variety are commonly used to characterize different aspects of big data. They’re a helpful lens through which to view and understand the nature of the data and the software platforms available to exploit them. Most probably you will contend with each of the Vs to one degree or another.

Volume

The benefit gained from the ability to process large amounts of information is the main attraction of big data analytics. Having more data beats out having better models: simple bits of math can be unreasonably effective given large amounts of data. If you could run that forecast taking into account 300 factors rather than 6, could you predict demand better?

This volume presents the most immediate challenge to conventional IT structures. It calls for scalable storage, and a distributed approach to querying. Many companies already have large amounts of archived data, perhaps in the form of logs, but not the capacity to process it.

Assuming that the volumes of data are larger than those conventional relational database infrastructures can cope with, processing options break down broadly into a choice between massively parallel processing architectures — data warehouses or databases such as Greenplum — and Apache Hadoop-based solutions. This choice is often informed by the degree to which the one of the other “Vs” — variety — comes into play. Typically, data warehousing approaches involve predetermined schemas, suiting a regular and slowly evolving dataset. Apache Hadoop, on the other hand, places no conditions on the structure of the data it can process.

At its core, Hadoop is a platform for distributing computing problems across a number of servers. First developed and released as open source by Yahoo, it implements the MapReduce approach pioneered by Google in compiling its search indexes. Hadoop’s MapReduce involves distributing a dataset among multiple servers and operating on the data: the “map” stage. The partial results are then recombined: the “reduce” stage.

To store data, Hadoop utilizes its own distributed filesystem, HDFS, which makes data available to multiple computing nodes. A typical Hadoop usage pattern involves three stages:

• loading data into HDFS,
• MapReduce operations, and
• retrieving results from HDFS.

This process is by nature a batch operation, suited for analytical or non-interactive computing tasks. Because of this, Hadoop is not itself a database or data warehouse solution, but can act as an analytical adjunct to one.

One of the most well-known Hadoop users is Facebook, whose model follows this pattern. A MySQL database stores the core data. This is then reflected into Hadoop, where computations occur, such as creating recommendations for you based on your friends’ interests. Facebook then transfers the results back into MySQL, for use in pages served to users.

Velocity

The importance of data’s velocity — the increasing rate at which data flows into an organization — has followed a similar pattern to that of volume. Problems previously restricted to segments of industry are now presenting themselves in a much broader setting. Specialized companies such as financial traders have long turned systems that cope with fast moving data to their advantage. Now it’s our turn.

Why is that so? The Internet and mobile era means that the way we deliver and consume products and services is increasingly instrumented, generating a data flow back to the provider. Online retailers are able to compile large histories of customers’ every click and interaction: not just the final sales. Those who are able to quickly utilize that information, by recommending additional purchases, for instance, gain competitive advantage. The smartphone era increases again the rate of data inflow, as consumers carry with them a streaming source of geolocated imagery and audio data.

It’s not just the velocity of the incoming data that’s the issue: it’s possible to stream fast-moving data into bulk storage for later batch processing, for example. The importance lies in the speed of the feedback loop, taking data from input through to decision. A commercial from IBM makes the point that you wouldn’t cross the road if all you had was a five-minute old snapshot of traffic location. There are times when you simply won’t be able to wait for a report to run or a Hadoop job to complete.

Industry terminology for such fast-moving data tends to be either “streaming data,” or “complex event processing.” This latter term was more established in product categories before streaming processing data gained more widespread relevance, and seems likely to diminish in favor of streaming.

There are two main reasons to consider streaming processing. The first is when the input data are too fast to store in their entirety: in order to keep storage requirements practical some level of analysis must occur as the data streams in. At the extreme end of the scale, the Large Hadron Collider at CERN generates so much data that scientists must discard the overwhelming majority of it — hoping hard they’ve not thrown away anything useful. The second reason to consider streaming is where the application mandates immediate response to the data. Thanks to the rise of mobile applications and online gaming this is an increasingly common situation.

Product categories for handling streaming data divide into established proprietary products such as IBM’sInfoSphere Streams, and the less-polished and still emergent open source frameworks originating in the web industry: Twitter’s Storm, and Yahoo S4.

As mentioned above, it’s not just about input data. The velocity of a system’s outputs can matter too. The tighter the feedback loop, the greater the competitive advantage. The results might go directly into a product, such as Facebook’s recommendations, or into dashboards used to drive decision-making.

It’s this need for speed, particularly on the web, that has driven the development of key-value stores and columnar databases, optimized for the fast retrieval of precomputed information. These databases form part of an umbrella category known as NoSQL, used when relational models aren’t the right fit.

Variety

Rarely does data present itself in a form perfectly ordered and ready for processing. A common theme in big data systems is that the source data is diverse, and doesn’t fall into neat relational structures. It could be text from social networks, image data, a raw feed directly from a sensor source. None of these things come ready for integration into an application.

Even on the web, where computer-to-computer communication ought to bring some guarantees, the reality of data is messy. Different browsers send different data, users withhold information, they may be using differing software versions or vendors to communicate with you. And you can bet that if part of the process involves a human, there will be error and inconsistency.

A common use of big data processing is to take unstructured data and extract ordered meaning, for consumption either by humans or as a structured input to an application. One such example is entity resolution, the process of determining exactly what a name refers to. Is this city London, England, or London, Texas? By the time your business logic gets to it, you don’t want to be guessing.

The process of moving from source data to processed application data involves the loss of information. When you tidy up, you end up throwing stuff away. This underlines a principle of big data: when you can, keep everything. There may well be useful signals in the bits you throw away. If you lose the source data, there’s no going back.

Despite the popularity and well understood nature of relational databases, it is not the case that they should always be the destination for data, even when tidied up. Certain data types suit certain classes of database better. For instance, documents encoded as XML are most versatile when stored in a dedicated XML store such as MarkLogic. Social network relations are graphs by nature, and graph databases such as Neo4J make operations on them simpler and more efficient.

Even where there’s not a radical data type mismatch, a disadvantage of the relational database is the static nature of its schemas. In an agile, exploratory environment, the results of computations will evolve with the detection and extraction of more signals. Semi-structured NoSQL databases meet this need for flexibility: they provide enough structure to organize data, but do not require the exact schema of the data before storing it.

In practice

We have explored the nature of big data, and surveyed the landscape of big data from a high level. As usual, when it comes to deployment there are dimensions to consider over and above tool selection.

Cloud or in-house?

The majority of big data solutions are now provided in three forms: software-only, as an appliance or cloud-based. Decisions between which route to take will depend, among other things, on issues of data locality, privacy and regulation, human resources and project requirements. Many organizations opt for a hybrid solution: using on-demand cloud resources to supplement in-house deployments.

Big data is big

It is a fundamental fact that data that is too big to process conventionally is also too big to transport anywhere. IT is undergoing an inversion of priorities: it’s the program that needs to move, not the data. If you want to analyze data from the U.S. Census, it’s a lot easier to run your code on Amazon’s web services platform, whichhosts such data locally, and won’t cost you time or money to transfer it.

Even if the data isn’t too big to move, locality can still be an issue, especially with rapidly updating data. Financial trading systems crowd into data centers to get the fastest connection to source data, because that millisecond difference in processing time equates to competitive advantage.

Big data is messy

It’s not all about infrastructure. Big data practitioners consistently report that 80% of the effort involved in dealing with data is cleaning it up in the first place, as Pete Warden observes in his Big Data Glossary: “I probably spend more time turning messy source data into something usable than I do on the rest of the data analysis process combined.”

Because of the high cost of data acquisition and cleaning, it’s worth considering what you actually need to source yourself. Data marketplaces are a means of obtaining common data, and you are often able to contribute improvements back. Quality can of course be variable, but will increasingly be a benchmark on which data marketplaces compete.

Culture

The phenomenon of big data is closely tied to the emergence of data science, a discipline that combines math, programming and scientific instinct. Benefiting from big data means investing in teams with this skillset, and surrounding them with an organizational willingness to understand and use data for advantage.

In his report, “Building Data Science Teams,” D.J. Patil characterizes data scientists as having the following qualities:

• Technical expertise: the best data scientists typically have deep expertise in some scientific discipline.
• Curiosity: a desire to go beneath the surface and discover and distill a problem down into a very clear set of hypotheses that can be tested.
• Storytelling: the ability to use data to tell a story and to be able to communicate it effectively.
• Cleverness: the ability to look at a problem in different, creative ways.

The far-reaching nature of big data analytics projects can have uncomfortable aspects: data must be broken out of silos in order to be mined, and the organization must learn how to communicate and interpet the results of analysis.

Those skills of storytelling and cleverness are the gateway factors that ultimately dictate whether the benefits of analytical labors are absorbed by an organization. The art and practice of visualizing data is becoming ever more important in bridging the human-computer gap to mediate analytical insight in a meaningful way.

Know where you want to go

Finally, remember that big data is no panacea. You can find patterns and clues in your data, but then what? Christer Johnson, IBM’s leader for advanced analytics in North America, gives this advice to businesses starting out with big data: first, decide what problem you want to solve.

If you pick a real business problem, such as how you can change your advertising strategy to increase spend per customer, it will guide your implementation. While big data work benefits from an enterprising spirit, it also benefits strongly from a concrete goal.

Examples include Big Science, RFID, sensor networks, social networks, big social data analysis (due to the social data revolution), Internet documents, Internet search indexing, call detail records, astronomy, atmospheric science, genomics, biogeochemical, biological, and other complex and often interdisciplinary scientific research, military surveillance, forecasting drive times for new home buyers, medical records, photography archives, video archives, and large-scale e-commerce.

Top  Emerging Big Data Technologies

Big data is big business – ask anyone in the data center industry (or enterprise in general) and chances are fairly high that you’ll get an earful. Though it’s existed in some form for quite some time, we’re just now starting to realize the true power that’s hidden just below the surface – the marketing and organizational insights that can be garnered with the capacity to analyze it. The trouble lies in reaching that capacity – and the massive strain it’s going to put on data centers and businesses the world over. Big data is, by its very nature, an incredibly broad concept, one which spans a wide array of industries, technologies, and organizations.

Databases

Database technology, Kaur said, is undergoing a significant evolution as a result of the big data craze. This is primarily out of necessity: “traditional, row-oriented databases are excellent for online transaction processing with high updates speeds, but they fall short on query performance as the data volumes grow and as data becomes more unstructured.”

Column-oriented databases solve this problem, Kaur explained. This is due to how they store data – the focus on columns allows for “huge data compression and very fast query times,” though at the cost of considerably slowed update times.

A switch from rows to columns isn’t the only way databases are changing, either. Kaur also brought up Schema-less (NoSQL) databases in the interview. These databases address unstructured data through increased performance as a result of scalability and distributed processing, while doing away with many conventional database restrictions, such as read-write consistency.

MapReduce

There’s one word to describe why the MapReduce programming model has gained so much traction as a result of big data: scalability. MapReduce allows organizations to easily and efficiently co-ordinate distributed computing operations, running distributed programs in parallel between thousands of servers or server clusters. It accomplishes this by enabling database managers to simply make use of the “Map” and “Reduce” functions – which convert and combine data-sets between them – while the MapReduce infrastructure itself handles the program and management.

Hadoop

MapReduce in and of itself has gained a great deal of ground in the data market, but one implementation in particular stands out: Hadoop. It is by far the most popular implementation of the model, and the fact that it’s completely open-source is a huge plus for many IT professionals and data center management officials. It’s flexible, powerful, and easy to implement; designed from the ground up to manage large volumes of fluctuating data.

Hive

Another technology pointed out by Kaur as particularly vital where big data is concerned was Hive, a “SQL-Like bridge” which alows conventional Business Intelligence applications to run queries against a Hadoop cluster.” Like Hadoop itself, Hive is open-source; originally developed by Facebook to address its own big data concerns. Hive’s strength lies in the fact that it makes Hadoop considerably more familiar and easy-to-use for professionals voiced only in traditional Business Intelligence – it bridges the gap between BI and Big Data.

PIG

Kaur points out Pig as another emergent technology; “another bridge that tries to bring Hadoop closer to the realities of developers and business users, similar to Hive.” Where the difference lies is that PIG uses a “Perl-Like” language instead of an “SQL-Like” language, allowing it to execute queries over data stored on a Hadoop cluster. Like Hive and Hadoop, it’s fully open-source.

WibiData

WibiData is yet another technology which attempts to bridge the disparity between big data and more traditional analytics (I’m noticing a trend here). Built on top of HBase, WibiData combines web analytics with the power of Hadoop. It’s easy to see why such a technology has caught on, as it’s fairly rare these days to see a web master who hasn’t dealt with unstructured data at least in passing. To them, WibiData is probably a breath of fresh air; a solution which allows them to more easily work with their user data and respond to user behavior in real-time with better content, better decisions, and better communication.

PLATFORA

“Perhaps the greatest limitation of Hadoop,” explained Kaul, “is that it’s a very low-level implementation of MapReduce, requiring extensive developer knowledge to operate.” Platfora attempts to address this issue by turning user queries into Hadoop jobs automatically. Essentially, this allows virtually anyone to access the power of Hadoop for their unstructured data needs. Again, it’s easy to see why this technology has taken off – not everyone who requires analysis of unstructured data has the knowledge base to use Hadoop on its own.

Better Storage

Storage technologies – and, by association, vendors and OEMs – have been exerting a great deal of influence on the data industry over the past several years. This influence is only going to become more intense as data volumes grow ever larger and the demand for efficient, effective, and low-cost storage techniques and technologies becomes higher and higher. Of all the entries on this list, this is probably the one which has the most direct impact on the data center – after all, where do you think all this unstructured data is being crunched?

SkyTree

SkyTree is a predictive analytics platform which utilizes machine learning to tackle the issue of Big Data, allowing for automatic data exploration which stands head and shoulders above more conventional methods (which, by and large, simply do not work). Again, it’s quite clear why it’s gaining so much ground – it’s easy to use, efficient, and incredibly effective, to boot.

The Cloud

Last, but certainly not least, we have cloud computing as a whole. As I’m sure you’ve all noticed, many of the emerging technologies on this list are at least partially based in the cloud. That’s no accident – as the need for better analytics becomes ever more dire, I feel that more and more organizations are going to start flocking to the Cloud, as they realize the true value the technology holds for them in storage, management, and analysis of unstructured data.