Welcome!

Microservices Expo Authors: Carmen Gonzalez, Derek Weeks, Elizabeth White, XebiaLabs Blog, Gordon Haff

Related Topics: @CloudExpo, Microservices Expo, Containers Expo Blog, Agile Computing, Apache

@CloudExpo: Article

The Big Data Revolution

The problem with the term Big Data is that it’s used in a lot of different ways

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.

More Stories By Rupert Tagnipes

Rupert Tagnipes is Senior Product Manager at GoGrid, with responsibility for managing and expanding the company's multiple product lines. His focus is on leveraging his technical background and industry knowledge to drive product innovation and increase adoption of the cloud.

He has extensive software product experience at Silicon Valley technology companies solving data analytics and cloud infrastructure problems for customers across a range of industries. Before joining GoGrid, he was a solutions architect at DASHbay, solving complex data analytics and business intelligence problems that leveraged cloud technologies for Internet companies. At Telephia / Nielsen, he was responsible for the technical development of its flagship wireless share measurement product. This product measures the market share of each carrier on a monthly basis and is an innovation in telecommunications data collection, analysis, and delivery. He earned his data chops at Informatica, developing a supply chain business analytics product that leveraged the company’s world-class ETL platform and next-generation business intelligence tools.

Comments (0)

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.


@MicroservicesExpo Stories
@DevOpsSummit at Cloud taking place June 6-8, 2017, at Javits Center, New York City, is co-located with the 20th International Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world. The widespread success of cloud computing is driving the DevOps revolution in enterprise IT. Now as never before, development teams must communicate and collaborate in a dynamic, 24/7/365 environment. There is no time to wait for long developm...
True Story. Over the past few years, Fannie Mae transformed the way in which they delivered software. Deploys increased from 1,200/month to 15,000/month. At the same time, productivity increased by 28% while reducing costs by 30%. But, how did they do it? During the All Day DevOps conference, over 13,500 practitioners from around the world to learn from their peers in the industry. Barry Snyder, Senior Manager of DevOps at Fannie Mae, was one of 57 practitioners who shared his real world journe...
Adding public cloud resources to an existing application can be a daunting process. The tools that you currently use to manage the software and hardware outside the cloud aren’t always the best tools to efficiently grow into the cloud. All of the major configuration management tools have cloud orchestration plugins that can be leveraged, but there are also cloud-native tools that can dramatically improve the efficiency of managing your application lifecycle. In his session at 18th Cloud Expo, ...
As 2016 approaches its end, the time to prepare for the year ahead is now! Following our own advice, we sat down with three XebiaLabs thought leaders–Andrew Phillips, Tim Buntel, and TJ Randall–and asked what they think the future has in store for the DevOps world. In 2017, we’ll see a new wave of “next gen platform” projects focused on container orchestration frameworks such as Kubernetes, and re-tooled PaaS platforms such as OpenShift or Cloud Foundry. Acceptance of the need for a cross-machi...
We call it DevOps but much of the time there’s a lot more discussion about the needs and concerns of developers than there is about other groups. There’s a focus on improved and less isolated developer workflows. There are many discussions around collaboration, continuous integration and delivery, issue tracking, source code control, code review, IDEs, and xPaaS – and all the tools that enable those things. Changes in developer practices may come up – such as developers taking ownership of code ...
Software development is a moving target. You have to keep your eye on trends in the tech space that haven’t even happened yet just to stay current. Consider what’s happened with augmented reality (AR) in this year alone. If you said you were working on an AR app in 2015, you might have gotten a lot of blank stares or jokes about Google Glass. Then Pokémon GO happened. Like AR, the trends listed below have been building steam for some time, but they’ll be taking off in surprising new directions b...
Containers have changed the mind of IT in DevOps. They enable developers to work with dev, test, stage and production environments identically. Containers provide the right abstraction for microservices and many cloud platforms have integrated them into deployment pipelines. DevOps and Containers together help companies to achieve their business goals faster and more effectively. In his session at DevOps Summit, Ruslan Synytsky, CEO and Co-founder of Jelastic, reviewed the current landscape of D...
When building DevOps or continuous delivery practices you can learn a great deal from others. What choices did they make, what practices did they put in place, and how did they connect the dots? At Sonatype, we pulled together a set of 21 reference architectures for folks building continuous delivery and DevOps practices using Docker. Why? After 3,000 DevOps professionals attended our webinar on "Continuous Integration using Docker" discussing just one reference architecture example, we recogn...
An overall theme of Cloud computing and the specific practices within it is fundamentally one of automation. The core value of technology is to continually automate low level procedures to free up people to work on more value add activities, ultimately leading to the utopian goal of full Autonomic Computing. For example a great way to define your plan for DevOps tool chain adoption is through this lens. In this TechTarget article they outline a simple maturity model for planning this.
The proper isolation of resources is essential for multi-tenant environments. The traditional approach to isolate resources is, however, rather heavyweight. In his session at 18th Cloud Expo, Igor Drobiazko, co-founder of elastic.io, drew upon his own experience with operating a Docker container-based infrastructure on a large scale and present a lightweight solution for resource isolation using microservices. He also discussed the implementation of microservices in data and application integrat...
In his General Session at DevOps Summit, Asaf Yigal, Co-Founder & VP of Product at Logz.io, will explore the value of Kibana 4 for log analysis and will give a real live, hands-on tutorial on how to set up Kibana 4 and get the most out of Apache log files. He will examine three use cases: IT operations, business intelligence, and security and compliance. This is a hands-on session that will require participants to bring their own laptops, and we will provide the rest.
Here’s a novel, but controversial statement, “it’s time for the CEO, COO, CIO to start to take joint responsibility for application platform decisions.” For too many years now technical meritocracy has led the decision-making for the business with regard to platform selection. This includes, but is not limited to, servers, operating systems, virtualization, cloud and application platforms. In many of these cases the decision has not worked in favor of the business with regard to agility and cost...
All organizations that did not originate this moment have a pre-existing culture as well as legacy technology and processes that can be more or less amenable to DevOps implementation. That organizational culture is influenced by the personalities and management styles of Executive Management, the wider culture in which the organization is situated, and the personalities of key team members at all levels of the organization. This culture and entrenched interests usually throw a wrench in the work...
As the race for the presidency heats up, IT leaders would do well to recall the famous catchphrase from Bill Clinton’s successful 1992 campaign against George H. W. Bush: “It’s the economy, stupid.” That catchphrase is important, because IT economics are important. Especially when it comes to cloud. Application performance management (APM) for the cloud may turn out to be as much about those economics as it is about customer experience.
When you focus on a journey from up-close, you look at your own technical and cultural history and how you changed it for the benefit of the customer. This was our starting point: too many integration issues, 13 SWP days and very long cycles. It was evident that in this fast-paced industry we could no longer afford this reality. We needed something that would take us beyond reducing the development lifecycles, CI and Agile methodologies. We made a fundamental difference, even changed our culture...
SYS-CON Events announced today that Dataloop.IO, an innovator in cloud IT-monitoring whose products help organizations save time and money, has been named “Bronze Sponsor” of SYS-CON's 20th International Cloud Expo®, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. Dataloop.IO is an emerging software company on the cutting edge of major IT-infrastructure trends including cloud computing and microservices. The company, founded in the UK but now based in San Fran...
Internet of @ThingsExpo, taking place June 6-8, 2017 at the Javits Center in New York City, New York, is co-located with the 20th International Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world. @ThingsExpo New York Call for Papers is now open.
The 20th International Cloud Expo has announced that its Call for Papers is open. Cloud Expo, to be held June 6-8, 2017, at the Javits Center in New York City, brings together Cloud Computing, Big Data, Internet of Things, DevOps, Containers, Microservices and WebRTC to one location. With cloud computing driving a higher percentage of enterprise IT budgets every year, it becomes increasingly important to plant your flag in this fast-expanding business opportunity. Submit your speaking proposal ...
Thanks to Docker, it becomes very easy to leverage containers to build, ship, and run any Linux application on any kind of infrastructure. Docker is particularly helpful for microservice architectures because their successful implementation relies on a fast, efficient deployment mechanism – which is precisely one of the features of Docker. Microservice architectures are therefore becoming more popular, and are increasingly seen as an interesting option even for smaller projects, instead of being...
@DevOpsSummit taking place June 6-8, 2017 at Javits Center, New York City, is co-located with the 20th International Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world. @DevOpsSummit at Cloud Expo New York Call for Papers is now open.