Welcome!

Apache Authors: Carmen Gonzalez, Elizabeth White, Pat Romanski, Liz McMillan, Christopher Harrold

Related Topics: @CloudExpo, Linux Containers, Open Source Cloud, Apache, @DXWorldExpo

@CloudExpo: Article

Apache Spark vs. Hadoop | @CloudExpo #BigData #DevOps #Microservices

A choice of job styles

If you’re running Big Data applications, you’re going to want to look at some kind of distributed processing system. Hadoop is one of the best-known clustering systems, but how are you going to process all your data in a reasonable time frame? Apache Spark offers services that go beyond a standard MapReduce cluster.

A choice of job styles
MapReduce has become a standard, perhaps
the standard, for distributed file systems. While it’s a great system already, it’s really geared toward batch use, with jobs needing to queue for later output. This can severely hamper your flexibility. What if you want to explore some of your data? If it’s going to take all night, forget about it.

With Apache Spark, you can act on your data in whatever way you want. Want to look for interesting tidbits in your data? You can perform some quick queries. Want to run something you know will take a long time? You can use a batch job. Want to process your data streams in real time? You can do that too.

The biggest advantage of modern programming languages is their use of interactive shells. Sure, Lisp did that back in the ‘60s, but it was a long time before the kind of power to program interactively became available to the average programmer. With Python and Scala you can try out your ideas in real time and develop algorithms iteratively, without the time-consuming write/compile/test/debug cycle.

RDDs
The key to Spark’s flexibility is the Resilient Distributed Datasets, or RDDs. RDDs maintain a lineage of everything that’s done to your data. They’re fine-grained, keeping track of all changes that have been made from other transformations such as
map or join. This means that it’s possible to recover from failures by rebuilding from these transformations (which is why they’re called Resilient Distributed Datasets).

RDDs also represent data in memory, which is a lot faster than always pulling data off of disks—even with SSDs making their way into data centers. While having your data in memory might seem like a recipe for slow performance, Spark uses lazy evaluation, only making transformations on data when you specifically ask for the result. This is why you can get queries so quickly even on very large datasets.

You might have recognized the term “lazy evaluation” from functional programming languages like Haskell. RDDs are only loaded when specific actions produce some kind of output; for example, printing to a text file. You can have a complex query over your data, but it won’t actually be evaluated until you ask for it. And the query might only find a specific subset of your data instead of plowing through the whole thing. This lazy evaluation lets you create complex queries on large datasets without incurring a performance penalty.

RDDs are also immutable, which leads to greater protection against data loss even though they’re in memory. In case of an error, Spark can go back to the last part of an RDD’s lineage and recover from there rather than relying on a checkpoint-based system on a disk.

Spark and Hadoop, Not as Different as You Think
Speaking of disks, you might be wondering whether Spark replaces a Hadoop cluster. That’s really a false dichotomy. Hadoop and Spark work
together. While Spark provides the processing, Hadoop handles the actual storage and resource management. After all, you can’t store data in your memory forever.

With the combination of Spark and Hadoop in the same cluster, you can cut down on a lot of overhead in maintaining different clusters. This combined cluster will give you unlimited scale for Big Data operations.

Who’s Using Spark?
When you have your Big Data cluster in place, you’ll be able to do lots of interesting things. From genome sequencing analysis, to digital advertising to a major credit card company who uses Spark to match thousands of transactions at once
for possible fraud detection. Cisco does something similar with a cloud-based security product to spot possible hacking before it turns into a major data breach. Geneticists use it to match genes to new medicines.

Conclusion
Apache Spark builds on Hadoop and then goes beyond it by adding stream processing capabilities. The MapR distribution is the only one that offers everything you need right out of the box to enable real-time data processing.

For a more in-depth view into how Spark and Hadoop benefit from each other, read chapter four of the free interactive ebook: Getting Started with Apache Spark: From Inception to Production, by James A. Scott.

More Stories By Jim Scott

Jim has held positions running Operations, Engineering, Architecture and QA teams in the Consumer Packaged Goods, Digital Advertising, Digital Mapping, Chemical and Pharmaceutical industries. Jim has built systems that handle more than 50 billion transactions per day and his work with high-throughput computing at Dow Chemical was a precursor to more standardized big data concepts like Hadoop.

IoT & Smart Cities Stories
At CloudEXPO Silicon Valley, June 24-26, 2019, Digital Transformation (DX) is a major focus with expanded DevOpsSUMMIT and FinTechEXPO programs within the DXWorldEXPO agenda. Successful transformation requires a laser focus on being data-driven and on using all the tools available that enable transformation if they plan to survive over the long term. A total of 88% of Fortune 500 companies from a generation ago are now out of business. Only 12% still survive. Similar percentages are found throug...
Atmosera delivers modern cloud services that maximize the advantages of cloud-based infrastructures. Offering private, hybrid, and public cloud solutions, Atmosera works closely with customers to engineer, deploy, and operate cloud architectures with advanced services that deliver strategic business outcomes. Atmosera's expertise simplifies the process of cloud transformation and our 20+ years of experience managing complex IT environments provides our customers with the confidence and trust tha...
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...
At CloudEXPO Silicon Valley, June 24-26, 2019, Digital Transformation (DX) is a major focus with expanded DevOpsSUMMIT and FinTechEXPO programs within the DXWorldEXPO agenda. Successful transformation requires a laser focus on being data-driven and on using all the tools available that enable transformation if they plan to survive over the long term. A total of 88% of Fortune 500 companies from a generation ago are now out of business. Only 12% still survive. Similar percentages are found throug...
AI and machine learning disruption for Enterprises started happening in the areas such as IT operations management (ITOPs) and Cloud management and SaaS apps. In 2019 CIOs will see disruptive solutions for Cloud & Devops, AI/ML driven IT Ops and Cloud Ops. Customers want AI-driven multi-cloud operations for monitoring, detection, prevention of disruptions. Disruptions cause revenue loss, unhappy users, impacts brand reputation etc.
The Japan External Trade Organization (JETRO) is a non-profit organization that provides business support services to companies expanding to Japan. With the support of JETRO's dedicated staff, clients can incorporate their business; receive visa, immigration, and HR support; find dedicated office space; identify local government subsidies; get tailored market studies; and more.
As you know, enterprise IT conversation over the past year have often centered upon the open-source Kubernetes container orchestration system. In fact, Kubernetes has emerged as the key technology -- and even primary platform -- of cloud migrations for a wide variety of organizations. Kubernetes is critical to forward-looking enterprises that continue to push their IT infrastructures toward maximum functionality, scalability, and flexibility. As they do so, IT professionals are also embr...
As you know, enterprise IT conversation over the past year have often centered upon the open-source Kubernetes container orchestration system. In fact, Kubernetes has emerged as the key technology -- and even primary platform -- of cloud migrations for a wide variety of organizations. Kubernetes is critical to forward-looking enterprises that continue to push their IT infrastructures toward maximum functionality, scalability, and flexibility.
Today's workforce is trading their cubicles and corporate desktops in favor of an any-location, any-device work style. And as digital natives make up more and more of the modern workforce, the appetite for user-friendly, cloud-based services grows. The center of work is shifting to the user and to the cloud. But managing a proliferation of SaaS, web, and mobile apps running on any number of clouds and devices is unwieldy and increases security risks. Steve Wilson, Citrix Vice President of Cloud,...
When Enterprises started adopting Hadoop-based Big Data environments over the last ten years, they were mainly on-premise deployments. Organizations would spin up and manage large Hadoop clusters, where they would funnel exabytes or petabytes of unstructured data.However, over the last few years the economics of maintaining this enormous infrastructure compared with the elastic scalability of viable cloud options has changed this equation. The growth of cloud storage, cloud-managed big data e...