|By Rupert Tagnipes||
|July 6, 2012 03:30 PM EDT||
For many years, companies collected data from various sources that often found its way into relational databases like Oracle and MySQL. However, the rise of the Internet, Web 2.0, and recently social media began an enormous increase in the amount of data created as well as in the type of data. No longer was data relegated to types that easily fit into standard data fields. Instead, it now came in the form of photos, geographic information, chats, Twitter feeds, and emails. The age of Big Data is upon us.
Big Data Beginnings
A study by IDC titled "The Digital Universe Decade" projects a 45-fold increase in annual data by 2020. In 2010, the amount of digital information was 1.2 zettabytes (1 zettabyte equals 1 trillion gigabytes). To put that in perspective, the equivalent of 1.2 zettabytes is a full-length episode of "24" running continuously for 125 million years, according to IDC. That's a lot of data. More important, this data has to go somewhere, and IDC's report projects that by 2020, more than one-third of all digital information created annually will either live in or pass through the cloud. With all this data being created, the challenge will be how to collect, store, and analyze what it means.
Business intelligence (BI) systems have always had to deal with large data sets. Typically the strategy was to pull in "atomic" data at the lowest level of granularity, then aggregate the information to a consumable format for end users. In fact, it was preferable to have a lot of data because you could also drill-down from the aggregation layer to get at the more detailed information, as needed.
In other words, large data sets have been around a long time. And there have been many attempts at trying to manage, wrangle, and tame the onslaught of data being generated from everywhere. But it wasn't until Jeffrey Dean and Sanjay Ghemawat of Google Labs wrote their influential paper on MapReduce in 2003 that Big Data really started to take shape. Google has had to deal with large amounts of raw data (such as crawled documents and web request logs) that needed to be analyzed in a timely manner. Creating MapReduce was their way of being able to abstract the compute parallelization, distribution of data, fault tolerance, and load balancing from developers so they could focus on expressing the computations necessary to analyze the data. This seminal paper reportedly inspired Doug Cutting to develop an open-source implementation of the MapReduce framework called "Hadoop," which was named after his son's toy elephant. Yahoo famously embraced this implementation after hiring Cutting in 2004. Yahoo continued to build upon this technology and first used Hadoop in production in 2008 for its search "webmap," which was an index of all known webpages and all the metadata needed to search them.
One of the key characteristics of Hadoop was that it could run on commodity hardware and automatically distribute jobs. By its nature, it is designed to be fault tolerant so jobs aren't impacted by the failure of a single node. According to an article in Wired magazine about Yahoo's use of Hadoop, "Hadoop could ‘map' tasks across a cluster of machines, splitting them into tiny sub-tasks, before ‘reducing' the results into one master calculation." Soon after, companies like eBay and Facebook were adopting the technology and implementing it internally. Reportedly, Facebook has the largest Hadoop cluster in the world, currently at 30 petabytes (PB).
Although early adopters of Hadoop and other Big Data technologies tended to form around the Internet, social media, and ad networks, Big Data solutions are intended to be general-purpose tools. With most companies now integrating social media into their offerings, the amount of data created internally combined with those extracted externally will only increase. This is an indication that companies from all industries will need to start investigating how to implement Big Data technologies to make use of all this data they're collecting and creating.
Making Sense of Big Data
The problem with the term Big Data is that it's used in a lot of different ways. One definition is that Big Data is any data set that is too large for on-hand data management tools. According to Martin Wattenberg, a scientist at IBM, "The real yardstick ... is how it [Big Data] compares with a natural human limit, like the sum total of all the words that you'll hear in your lifetime." Essentially, what makes something Big Data is that it:
- Is at a large scale (petabytes, not gigabytes)
- Has high velocity (frequently polled, generated, or collected)
- Is unstructured (not only from a relational database)
Collecting that data is a solvable problem, but making sense of it, (particularly in real time), is the challenge that technology tries to solve. This new type of technology is often listed under the title of NoSQL (or Not Only SQL) and includes distributed databases that are a departure from relational databases like Oracle and MySQL. These systems are specifically designed to be able to parallelize compute, distribute data, and create fault tolerance on a large cluster of servers. Some examples of NoSQL projects and software are Cassandra, Hadoop, Membase, MongoDB, and Riak.
The techniques vary, but there is a definite distinction between SQL relational databases and their NoSQL brethren. Most notably, NoSQL systems share the following characteristics:
- Do not use SQL as their primary query language
- May not require fixed table schemas
- May not give full ACID guarantees (Atomicity, Consistency, Isolation, Durability)
- Scale horizontally
Because of the lack of ACID, NoSQL is used when performance and real-time results are more important than consistency. For example, if a company wants to update its website in real time based on an analysis of the behaviors of a particular user interaction with the site, it will most likely turn to NoSQL technologies to solve this use case.
However, this shortcoming doesn't mean relational databases are going away. In fact, it's likely that in larger implementations, NoSQL and SQL will function together. Just as NoSQL was designed to solve a particular use case, so do relational databases solve theirs. Relational databases excel at organizing structured data and are the standard for serving up ad-hoc analytics and BI reporting. In fact, Apache Hadoop even has a separate project called Sqoop that is designed to link Hadoop with structured data stores. Most likely, those who implement NoSQL will maintain their relational databases for legacy systems and for reporting off their NoSQL clusters.
Big Data Moves to the Cloud
The early adopters of Big Data tended to be companies with capital budgets that could be invested into dedicated data centers. However, with the incredible increase in the amount of data generated, collected, and analyzed, smaller companies can take advantage of the cloud and off-load the hardware management to those vendors. Two traits that many of these NoSQL solutions have in common make them a seemingly natural fit for the cloud: One is that the nodes are distributed, and the second is that they run on commodity hardware. The cloud is designed for horizontal scaling and often built on low-cost, commodity hardware, especially at the infrastructure-as-service (IaaS) layer, where customers simply need infrastructure and have the application expertise to build and configure their own Big Data application (whether it is with Hadoop, Cassandra, or any number of products).
Not all clouds are built the same, however. One of the design elements you should look for is the ability for each virtual server in the Big Data cluster to be deployed on different nodes. Although the servers are all on the same private VLAN, ensuring that each server is on different hardware solves for two problems: (1) all the traffic and processing aren't hitting the same hardware, and (2) the cluster is protected against hardware failure because all the servers are distributed. Whether or not the architecture is assuming a name node and data node construct or a Ring design, this setup ensures performance and reliability. In addition, the option of using local storage on the virtual machine and a high-performance network will reduce latency and improve performance.
Given what most users are trying to achieve with Big Data applications-large-scale data sets, large-scale analysis, often in real time-performance is a key factor. Depending on the problem to be solved, users can also leverage a hybrid implementation that combines both virtual and dedicated servers. This setup offers maximum flexibility that balances the elastic, scalable nature of virtual machines with the single-tenancy of dedicated servers. Big Data projects don't happen in a vacuum: Although a NoSQL database can leverage dedicated servers, the app or web servers that present the results of the analysis to end users or that are used to add additional functionality like log file processing can easily be added to as many virtual machines as needed to meet demand. In addition, using the cloud means that users won't need to invest in expensive equipment, pay for power and connectivity, or hire additional resources to maintain hardware. Users simply pay for the infrastructure they need and can scale it as desired over time. The ability to scale up or down to match demand (and to pay only for the infrastructure you actually use) is one of the values of using the cloud for Big Data.
Conclusion: Succeeding with Big Data
With whatever solution you select, you should also take into account the nature of the application and where you'll want to house the processing and the output. The amount of data you collect, analyze, and present will only increase over time. The advantage will go to companies that can collect and analyze this data quickly and efficiently, allowing them to react instantly to customer sentiment and to changing trends in the ever-quickening pace of business. Make sure to select the right infrastructure vendor who can match your performance criteria and has the capacity to grow with you as your data and application needs increase to match the changing demands of your business.
With microservices, SOA and distributed architectures becoming more popular, it is becoming increasingly harder to keep track of where time is spent in a distributed application when trying to diagnose performance problems. Distributed tracing systems attempt to address this problem by following application requests across service boundaries, persisting metadata along the way that provide context for fine-grained performance monitoring.
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