Welcome!

Microservices Expo Authors: Yeshim Deniz, XebiaLabs Blog, AppNeta Blog, Elizabeth White, Kong Yang

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
Cloud Expo, Inc. has announced today that Aruna Ravichandran, vice president of DevOps Product and Solutions Marketing at CA Technologies, has been named co-conference chair of DevOps at Cloud Expo 2017. The @DevOpsSummit at Cloud Expo New York will take place on June 6-8, 2017, at the Javits Center in New York City, New York, and @DevOpsSummit at Cloud Expo Silicon Valley will take place Oct. 31-Nov. 2, 2017, at the Santa Clara Convention Center in Santa Clara, CA.
Developers want to create better apps faster. Static clouds are giving way to scalable systems, with dynamic resource allocation and application monitoring. You won't hear that chant from users on any picket line, but helping developers to create better apps faster is the mission of Lee Atchison, principal cloud architect and advocate at New Relic Inc., based in San Francisco. His singular job is to understand and drive the industry in the areas of cloud architecture, microservices, scalability ...
Back in February of 2017, Andrew Clay Schafer of Pivotal tweeted the following: “seriously tho, the whole software industry is stuck on deployment when we desperately need architecture and telemetry.” Intrigue in a 140 characters. For me, I hear Andrew saying, “we’re jumping to step 5 before we’ve successfully completed steps 1-4.”
This recent research on cloud computing from the Register delves a little deeper than many of the "We're all adopting cloud!" surveys we've seen. They found that meaningful cloud adoption and the idea of the cloud-first enterprise are still not reality for many businesses. The Register's stats also show a more gradual cloud deployment trend over the past five years, not any sort of explosion. One important takeaway is that coherence across internal and external clouds is essential for IT right n...
Enterprise architects are increasingly adopting multi-cloud strategies as they seek to utilize existing data center assets, leverage the advantages of cloud computing and avoid cloud vendor lock-in. This requires a globally aware traffic management strategy that can monitor infrastructure health across data centers and end-user experience globally, while responding to control changes and system specification at the speed of today’s DevOps teams. In his session at 20th Cloud Expo, Josh Gray, Chie...
To more closely examine the variety of ways in which IT departments around the world are integrating cloud services, and the effect hybrid IT has had on their organizations and IT job roles, SolarWinds recently released the SolarWinds IT Trends Report 2017: Portrait of a Hybrid Organization. This annual study consists of survey-based research that explores significant trends, developments, and movements related to and directly affecting IT and IT professionals.
Is your application too difficult to manage? Do changes take dozens of developers hundreds of hours to execute, and frequently result in downtime across all your site’s functions? It sounds like you have a monolith! A monolith is one of the three main software architectures that define most applications. Whether you’ve intentionally set out to create a monolith or not, it’s worth at least weighing the pros and cons of the different architectural approaches and deciding which one makes the most s...
Software as a service (SaaS), one of the earliest and most successful cloud services, has reached mainstream status. According to Cisco, by 2019 more than four-fifths (83 percent) of all data center traffic will be based in the cloud, up from 65 percent today. The majority of this traffic will be applications. Businesses of all sizes are adopting a variety of SaaS-based services – everything from collaboration tools to mission-critical commerce-oriented applications. The rise in SaaS usage has m...
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...
We'd all like to fulfill that "find a job you love and you'll never work a day in your life" cliché. But in reality, every job (even if it's our dream job) comes with its downsides. For you, the constant fight against shadow IT might get on your last nerves. For your developer coworkers, infrastructure management is the roadblock that stands in the way of focusing on coding. As you watch more and more applications and processes move to the cloud, technology is coming to developers' rescue-most r...
2016 has been an amazing year for Docker and the container industry. We had 3 major releases of Docker engine this year , and tremendous increase in usage. The community has been following along and contributing amazing Docker resources to help you learn and get hands-on experience. Here’s some of the top read and viewed content for the year. Of course releases are always really popular, particularly when they fit requests we had from the community.
Keeping pace with advancements in software delivery processes and tooling is taxing even for the most proficient organizations. Point tools, platforms, open source and the increasing adoption of private and public cloud services requires strong engineering rigor – all in the face of developer demands to use the tools of choice. As Agile has settled in as a mainstream practice, now DevOps has emerged as the next wave to improve software delivery speed and output. To make DevOps work, organization...
Today we can collect lots and lots of performance data. We build beautiful dashboards and even have fancy query languages to access and transform the data. Still performance data is a secret language only a couple of people understand. The more business becomes digital the more stakeholders are interested in this data including how it relates to business. Some of these people have never used a monitoring tool before. They have a question on their mind like “How is my application doing” but no id...
In large enterprises, environment provisioning and server provisioning account for a significant portion of the operations team's time. This often leaves users frustrated while they wait for these services. For instance, server provisioning can take several days and sometimes even weeks. At the same time, digital transformation means the need for server and environment provisioning is constantly growing. Organizations are adopting agile methodologies and software teams are increasing the speed ...
Even for the most seasoned IT pros, the cloud is complicated. It can be difficult just to wrap your head around the many terms and acronyms that make up the cloud dictionary-not to mention actually mastering the technology. Unfortunately, complicated cloud terms are often combined to the point that their meanings are lost in a sea of conflicting opinions. Two terms that are used interchangeably (but shouldn't be) are hybrid cloud and multicloud. If you want to be the cloud expert your company ne...
In his general session at 19th Cloud Expo, Manish Dixit, VP of Product and Engineering at Dice, discussed how Dice leverages data insights and tools to help both tech professionals and recruiters better understand how skills relate to each other and which skills are in high demand using interactive visualizations and salary indicator tools to maximize earning potential. Manish Dixit is VP of Product and Engineering at Dice. As the leader of the Product, Engineering and Data Sciences team at D...
In his session at 20th Cloud Expo, Scott Davis, CTO of Embotics, will discuss how automation can provide the dynamic management required to cost-effectively deliver microservices and container solutions at scale. He will discuss how flexible automation is the key to effectively bridging and seamlessly coordinating both IT and developer needs for component orchestration across disparate clouds – an increasingly important requirement at today’s multi-cloud enterprise.
SYS-CON Events announced today that CollabNet, a global leader in enterprise software development, release automation and DevOps solutions, will be a Bronze Sponsor of SYS-CON's 20th International Cloud Expo®, taking place from June 6-8, 2017, at the Javits Center in New York City, NY. CollabNet offers a broad range of solutions with the mission of helping modern organizations deliver quality software at speed. The company’s latest innovation, the DevOps Lifecycle Manager (DLM), supports Value S...
The human body is the most complex machine ever created! With a complex network of interconnected organs, millions of cells and the most advanced processor, human body is the most automated system in this planet. In this article, we will draw comparisons between working of a human body to that of a datacenter. We will learn how self-defense and self-healing capabilities of our human body is similar to firewalls and intelligent monitoring capabilities in our datacenters. We will draw parallels b...
Cloud adoption is often driven by a desire to increase efficiency, boost agility and save money. All too often, however, the reality involves unpredictable cost spikes and lack of oversight due to resource limitations. In his session at 20th Cloud Expo, Joe Kinsella, CTO and Founder of CloudHealth Technologies, will tackle the question: “How do you build a fully optimized cloud?” He will examine: Why TCO is critical to achieving cloud success – and why attendees should be thinking holisticall...