Welcome!

Microservices Expo Authors: Liz McMillan, Elizabeth White, Carmen Gonzalez, Pat Romanski, Sematext Blog

Related Topics: @CloudExpo, Microservices Expo, Containers Expo Blog, Agile Computing, Release Management , Cloud Security

@CloudExpo: Article

Lessons Learned from Real-World Big Data Implementations

The value of Big Data is in the insights that the data can provide

In the past few weeks I visited several Cloud and Big Data conferences that provided me with a lot of insight. Some people only consider the technology side of Big Data technologies like Hadoop or Cassandra. The real driver however is a different one. Business analysts have discovered Big Data technologies as a way to leverage tons of existing data and ask questions about customer behavior and all sorts relationships to drive business strategy. By doing that they are pushing their IT departments to run ever bigger Hadoop environments and ever faster real-time systems.

What's interesting from a technical side is that ad-hoc analytics on existing data is allowed to take some time. However ad-hoc implies people waiting for an answer, meaning we are talking about minutes and not hours. Another interesting insight is that Hadoop environments are never static or standalone. Most companies take in new data on a continuous basis via technologies like flume. This means Hadoop MapReduce jobs need to be able to keep up with the data flow, either by adding more hardware or by optimizing them.

There are multiple drivers to Big Data (actually there are a lot) but the two most important ones are these: Analytics and Technical Need for Speed. Let's look at some of those and the resulting takeaways.

The Value Is in the Insight Not the Volume
The value of Big Data is in the insights that the data can provide, not the sheer volume of it. The reason that more and more companies are keeping all of their log and transaction data is that they want to gain those insights. The sheer size of the data is rather an obstacle to this goal and has been for a long time. With Big Data technologies this value can be harnessed.

Don't Forget That Data Analysts Are People Too
Ad-hoc analytics doesn't have to be instant, but must not take hours either. It was interesting to see that time to result on ad-hoc analytics is considered important. This is because people are doing those queries, and people don't like to wait for hours. But even more important is that business analytics is often an iterative process. Ask a question, check the answer, refine or change the question. Hours long MapReduce jobs are prohibitive to this process.

New Data Is Coming in All the Time
Big Data environments are constantly fed new data. This is not really big news, but I was still surprised by the constant reiteration of this fact. The constant data growth means that ad-hoc queries get either slower over time or need to work on samples. To remedy this, companies are writing, scrubbing and categorizing MapReduce jobs. These jobs basically strip out all the unimportant stuff and put cleansed, streamline easy-to-access data into new files. Instead of executing analytics against raw files, the analyst works on a cleansed data set. The implications are that scrubbing jobs need to be maintained all the time (as data input is changing over time) and they need to be able to keep up with the velocity of the input. MapReduce is not allowed to run for hours, but needs to be quick and iterative.

Big Data Is Not Cheap
While it sounds obvious, it's something that's not talked about by the vendors unless specifically asked. Hadoop requires a lot of hardware and a lot of expertise. Especially the expertise is hard to come by as of yet. While hardware might be cheap (you don't need expensive boxes for Hadoop) the bigger the environment the higher the operational costs. That operational cost is the reason some Hadoop vendors exist on services alone and also why customers are demanding better monitoring and management solutions.

Data Must Be Accessible at Low Latencies to Provide Value
One very interesting fact is that most early adopters that use Hadoop for analytics use it for ad-hoc analytics and not as a traditional warehouse. They use MapReduce to do the heavy lifting that is usually reserved for ETL jobs and put the resulting dimensions in existing data warehouses or into a NoSQL solution like HBase, Cassandra or MongoDB. These solutions provide low latency access semantics and are then integrated in the transactional application world, e.g. to provide recommendations to the end users.

This does not absolve them from optimizing their Hadoop environment where they can, but it gives them the much needed real time access that Hadoop so far does not provide. This also makes for additional complexity that needs to be maintained and monitored.

NoSQL Solutions Need Management and Monitoring as Well
NoSQL solutions are most often used to provide low latency databases with failover and horizontal scaling characteristics. As expected, practitioners quickly run into new issues like distribution and wrong access patterns. Most NoSQL solutions lack sophisticated monitoring or performance analysis tools and require experts instead. Fortunately several companies are working on providing those tools and some APM vendors work hard to support NoSQL databases similar to normal databases. This is emphasized by another interesting finding: With a fast and scalable data storage, the application itself quickly becomes the response time and scaling bottleneck.

Applications Using NoSQL Technologies Are More Complex
Most NoSQL solutions surrender more complex logic like joins in order to achieve horizontally scalable data distribution. That logic is moved to the application - arguably this is where it should be anyway. NoSQL solutions require data to be stored in a query access optimized way - de-normalization is the key. The flip side of storing data multiple times and the need to keep it in sync on updates, is that the storage logic again becomes more complex. More application logic usually means less performance.

My conclusion as a performance engineer is relatively clear: Big Data requires Performance Management and Monitoring Tools to fulfill its promise in a cost effective and timely manner. Here are some suggestions on what you should think about when you start a Big Data project.

  1. Large Hadoop environments are hard to manage and operate. Without automation in terms of deployment, operations, monitoring and root cause analysis they quickly become unmanageable. Make sure to have a monitoring solution in place that informs you pro-actively of any infrastructure or software issues that would affect your operation. It needs to give you an easy way to pinpoint the root cause.
  2. The easiest way to identify new performance issues is to detect and analyze change. Adopt a life cycle and 24/7 production APM approach. It will enable you to notice changes in data and compute distribution over time. In addition a life cycle approach will allow you to immediately pin point any negative changes introduced by a new software release.
  3. Don't just throw more and more hardware at the problem. While you can use cheaper hardware for Hadoop, it's still cost. But more than that you have to consider the operational drag. Every node you add will make traditional log based analysis more complicated. Instead ensure that you have an APM solution in place that lets you understand and optimize MapReduce jobs at their core and reduce both the time and resources it takes to run them.
  4. Your Hadoop cluster is no island, but will always be connected in some form or the other to a real time or at least transactional system. Make sure that you have a monitoring solution in place that can support both.

NoSQL applications tend to have more complex logic. The very performance and scalability of the store depends on correct data access and data distribution. An good monitoring solution allows you to monitor and optimize that additional complexity with ease; it also enables you to understand how your application access the data and how that access is distributed across your NoSQL cluster in your production system. The best way to ensure a scalable and fast NoSQL store is to ensure optimal distribution and access patterns.

Conclusion
Big Data is still very much an emerging technology and its promises are huge. But in order to deliver on those promises it must be cost and time effective to those that harness its value - The Business and not just technology experts.

More Stories By Michael Kopp

Michael Kopp has over 12 years of experience as an architect and developer in the Enterprise Java space. Before coming to CompuwareAPM dynaTrace he was the Chief Architect at GoldenSource, a major player in the EDM space. In 2009 he joined dynaTrace as a technology strategist in the center of excellence. He specializes application performance management in large scale production environments with special focus on virtualized and cloud environments. His current focus is how to effectively leverage BigData Solutions and how these technologies impact and change the application landscape.

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
Get deep visibility into the performance of your databases and expert advice for performance optimization and tuning. You can't get application performance without database performance. Give everyone on the team a comprehensive view of how every aspect of the system affects performance across SQL database operations, host server and OS, virtualization resources and storage I/O. Quickly find bottlenecks and troubleshoot complex problems.
@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.
In his session at 19th Cloud Expo, Claude Remillard, Principal Program Manager in Developer Division at Microsoft, contrasted how his team used config as code and immutable patterns for continuous delivery of microservices and apps to the cloud. He showed how the immutable patterns helps developers do away with most of the complexity of config as code-enabling scenarios such as rollback, zero downtime upgrades with far greater simplicity. He also demoed building immutable pipelines in the cloud ...
In IT, we sometimes coin terms for things before we know exactly what they are and how they’ll be used. The resulting terms may capture a common set of aspirations and goals – as “cloud” did broadly for on-demand, self-service, and flexible computing. But such a term can also lump together diverse and even competing practices, technologies, and priorities to the point where important distinctions are glossed over and lost.
Without lifecycle traceability and visibility across the tool chain, stakeholders from Planning-to-Ops have limited insight and answers to who, what, when, why and how across the DevOps lifecycle. This impacts the ability to deliver high quality software at the needed velocity to drive positive business outcomes. In his session at @DevOpsSummit 19th Cloud Expo, Eric Robertson, General Manager at CollabNet, showed how customers are able to achieve a level of transparency that enables everyone fro...
Monitoring of Docker environments is challenging. Why? Because each container typically runs a single process, has its own environment, utilizes virtual networks, or has various methods of managing storage. Traditional monitoring solutions take metrics from each server and applications they run. These servers and applications running on them are typically very static, with very long uptimes. Docker deployments are different: a set of containers may run many applications, all sharing the resource...
Join Impiger for their featured webinar: ‘Cloud Computing: A Roadmap to Modern Software Delivery’ on November 10, 2016, at 12:00 pm CST. Very few companies have not experienced some impact to their IT delivery due to the evolution of cloud computing. This webinar is not about deciding whether you should entertain moving some or all of your IT to the cloud, but rather, a detailed look under the hood to help IT professionals understand how cloud adoption has evolved and what trends will impact th...
Information technology is an industry that has always experienced change, and the dramatic change sweeping across the industry today could not be truthfully described as the first time we've seen such widespread change impacting customer investments. However, the rate of the change, and the potential outcomes from today's digital transformation has the distinct potential to separate the industry into two camps: Organizations that see the change coming, embrace it, and successful leverage it; and...
You have great SaaS business app ideas. You want to turn your idea quickly into a functional and engaging proof of concept. You need to be able to modify it to meet customers' needs, and you need to deliver a complete and secure SaaS application. How could you achieve all the above and yet avoid unforeseen IT requirements that add unnecessary cost and complexity? You also want your app to be responsive in any device at any time. In his session at 19th Cloud Expo, Mark Allen, General Manager of...
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 ...
"Dice has been around for the last 20 years. We have been helping tech professionals find new jobs and career opportunities," explained Manish Dixit, VP of Product and Engineering at Dice, in this SYS-CON.tv interview at 19th Cloud Expo, held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA.
Rapid innovation, changing business landscapes, and new IT demands force businesses to make changes quickly. In the eyes of many, containers are at the brink of becoming a pervasive technology in enterprise IT to accelerate application delivery. In this presentation, attendees learned about the: The transformation of IT to a DevOps, microservices, and container-based architecture What are containers and how DevOps practices can operate in a container-based environment A demonstration of how ...
Application transformation and DevOps practices are two sides of the same coin. Enterprises that want to capture value faster, need to deliver value faster – time value of money principle. To do that enterprises need to build cloud-native apps as microservices by empowering teams to build, ship, and run in production. In his session at @DevOpsSummit at 19th Cloud Expo, Neil Gehani, senior product manager at HPE, discussed what every business should plan for how to structure their teams to delive...
Without lifecycle traceability and visibility across the tool chain, stakeholders from Planning-to-Ops have limited insight and answers to who, what, when, why and how across the DevOps lifecycle. This impacts the ability to deliver high quality software at the needed velocity to drive positive business outcomes. In his general session at @DevOpsSummit at 19th Cloud Expo, Phil Hombledal, Solution Architect at CollabNet, discussed how customers are able to achieve a level of transparency that e...
IT leaders face a monumental challenge. They must figure out how to sort through the cacophony of new technologies, buzzwords, and industry hype to find the right digital path forward for their organizations. And they simply cannot afford to fail. Those organizations that are fastest to the right digital path will be the ones that win. The path forward, however, is strewn with the legacy of decisions made long ago — often before any of the current leadership team assumed their roles. While it’s ...
Kubernetes is a new and revolutionary open-sourced system for managing containers across multiple hosts in a cluster. Ansible is a simple IT automation tool for just about any requirement for reproducible environments. In his session at @DevOpsSummit at 18th Cloud Expo, Patrick Galbraith, a principal engineer at HPE, discussed how to build a fully functional Kubernetes cluster on a number of virtual machines or bare-metal hosts. Also included will be a brief demonstration of running a Galera MyS...
As we enter the final week before the 19th International Cloud Expo | @ThingsExpo in Santa Clara, CA, it's time for me to reflect on six big topics that will be important during the show. Hybrid Cloud: This general-purpose term seems to provide a comfort zone for many enterprise IT managers. It sounds reassuring to be able to work with one of the major public-cloud providers like AWS or Microsoft Azure while still maintaining an on-site presence.
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...
Between 2005 and 2020, data volumes will grow by a factor of 300 – enough data to stack CDs from the earth to the moon 162 times. This has come to be known as the ‘big data’ phenomenon. Unfortunately, traditional approaches to handling, storing and analyzing data aren’t adequate at this scale: they’re too costly, slow and physically cumbersome to keep up. Fortunately, in response a new breed of technology has emerged that is cheaper, faster and more scalable. Yet, in meeting these new needs they...
Logs are continuous digital records of events generated by all components of your software stack – and they’re everywhere – your networks, servers, applications, containers and cloud infrastructure just to name a few. The data logs provide are like an X-ray for your IT infrastructure. Without logs, this lack of visibility creates operational challenges for managing modern applications that drive today’s digital businesses.