Big Data

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DevOps for Big Data

Classical DevOps was all about creating velocity between business requirements and needs—the developers writing the code, the systems that actually embody the code and solve the business problems—and it was all around processes like Agile, continuous integration, and continuous delivery. That is all well and good, but for big data, there is another big component which is this performance aspect. We believe that performance needs to be a first-level player in DevOps for big data. Continue reading this article at DBTA

I am not very technical. How are big data and DevOps related?

The big data team includes data scientists and big data engineers who in current practices might be integrated into distinct product teams or otherwise isolated in their own analytics department. Under devops, the data scientists and big data engineers develop analytical models and algorithms in python or scala and store those in a version control system. That code is tested and automatically integrated into the main code branch then deployed and monitored in production. So, devops ensures that the process of CI/CD is unified across all of the engineering organization, including the big data team. Continue reading this thread at Quora

Extending Agile and DevOps to Big Data

With a scalable and flexible infrastructure platform like BlueData, IT organizations can quickly spin up virtual Hadoop or Spark clusters within minutes — in a multi-tenant virtualized environment that can be shared across different business units. Their data scientists and analysts can fail fast and iterate quickly. But more importantly, they can deliver the Big Data value their business expects and needs when they need it. Continue reading this article at BlueData

DevOps for Big Data

Chad says that from his personal historical background, he sees Hadoop being used mainly for web search and Machine Learning for ad queries. When it comes to Pepperdata users, production involvement in Big Data initiatives is huge with 50 million Hadoop and Spark jobs running every year with the number growing as the amount of data grows. Continue reading this article at DZone

Feedback loops: The confluence of DevOps and big data

By hosting the high-caliber analytics engines in the cloud, even small companies and individuals can obtain astonishing insights to aid their weighty decisions. Near real-time feedback loops are transforming slow, batch assessments of essential data into live streaming of reaction and prediction services. Affordable automation of actionable analysis accompanies the democratization trend. Continue reading this article at TechBeacon

The Colliding and Complementary Worlds of DevOps, Big Data and Data Management

At the heart of innovations across markets like IoT and mobile, and industries such as retail, banking and healthcare, is data. Refreshingly, data is also increasingly understood as the currency that drives the value of the companies that use it optimally. As companies continue to migrate off legacy systems in favor of platforms designed to support today’s application needs, they must also plan accordingly to ensure issues around scale and security are fully considered and addressed. These are top-of-mind issues for DevOps teams, and a focus on the entire application lifecycle is key to modern data management. The right planning has big upside and the risks related to lost or compromised data are far too great to ignore. Continue reading this article at Data Center Knowledge

Your Big Data Strategy Needs DevOps

Extracting accurate and meaningful answers from big data is tough. It’s often made more challenging given the way big data software developers and IT operations lack coordination in many enterprises. Even though an IT organization may practice sound DevOps strategies for other supported applications, big data projects often remain siloed for a variety of reasons. Continue reading this article at InformationWeek

Three Little Words For Payday: Big, Data, DevOps

But, DevOps isn’t red hot everywhere. Mid-level pros are getting $83,000 in Bloomington and $89,000 in Hartford, while senior-level DevOps pros are earning $99,000 and $104,000 respectively.

I suppose you do want to know what you might make in Silicon Valley, just for the sake of comparison. For example, senior-level staffers with DW/BI, Hadoop, software architecture, DevOps, Python, Javascript, PHP, and Ruby skills all are listed at the $182K-and-change level. That will buy plenty of cheeseburgers. Continue reading this article at InformationWeek


To gain visibility into these dynamic and complex architectures, IT Ops and DevOps teams extensively deploy monitoring mechanisms across the IT stack, from the application software down to the physical equipment. Henceforth, the volume of monitoring data has increased greatly and will continue to expand as environments scale. Given this data deluge, ITOA technologies are in high demand because they can make sense of the Big Data generated by IT itself. They can automate the analysis, then quickly turn this information into useful and actionable insights which can help IT support work in a more efficient manner. Continue reading this article at Dataconomy

Extending Agile and DevOps to Big Data

Typically, to build an enterprise-grade application, multiple teams would work independently on the components of the application. When all the individual building and testing is done, the pieces are combined and tested together. There’d be issues (usually) and the pieces would go back to the teams for rework, more testing, etc., and this can happen multiple times. Finally the application is handed off to the operations team to stage and deploy it into product, a process that can take months. Continue reading this article at Inside Big Data

Devops can take data science to the next level

However, most data scientists are, at heart, statistical analysts. While conducting their deep data explorations, they may not be focusing on their downstream production performance of the analytic models they build and refine. If the regression, neural-network, or natural language processing algorithms they’ve incorporated don’t scale under heavy loads, the models may have to be scrapped or significantly reworked before they can be considered production-ready. Continue reading this article at InfoWorld

DevOps Analytics Tool Offered by Big Data Specialist

DevOps integrations — Perspica now supports major DevOps platforms including Docker, Flume, OpenTSDB, Redis, Zookeeper, Kafka, Collectd, MongoDB and Loggly.
Time machine — Perspica’s Incident Replay is a time machine for performance data, logs and topology. It provides DevOps personnel with the ability to play back the impact of the latest code release on application behavior and identify the root cause of any application performance degradation. Continue reading this article at ADT Mag

How to monitor the apocalypse using big data DevOps

Last month the Bulletin of Atomic Scientists ticked the Doomsday Clock thirty seconds closer to the apocalypse. The clock, an index of potential global threats maintained since 1947 by a respected consortium of scientists, enumerates a number of existential threats like climate change, nuclear war, artificial intelligence, and of course, Donald Trump. Continue reading this article at TechRepublic

Open source big data and DevOps tools: A fast path to analytics applications

Meanwhile, LinkedIn developed Pinot, a distributed OLAP datastore with real-time scalable analytics, as well as Taiga, which relies on the Kafka messaging system and Hadoop YARN to conduct distributed stream processing. EMC and VMware spun off Pivotal GemFire, Greenplum Database, and HAWQ, part of the two large companies’ data suite that help build data-driven applications, create analytics-enabled databases, and use SQL analytics in Hadoop. Continue reading this article at Tech Pro

Big Data and DevOps

Helping data analysts and everyone else on the DevOps team understand one another is easier, by the way, if you take advantage of data integration and automation tools like the ones from Syncsort. They streamline time-consuming data migration and translation processes, and help ensure better data quality so that your IT staff can focus their energy on what matters most – like deriving value from data – instead of on time-draining, tedious processes. Continue reading this article at Syncsort

Data engineering needs DevOps to navigate big data ecosystem

For big data engineering to continue to move forward, teams will need to very seriously pursue the tenets of DevOps, or what some now call DataOps — especially the principles that call for data engineers and IT architects to take responsibility for moving innovative ideas into production. As always, a zest to make it new will help, too. Continue reading this article at Tech Target

Why Big Data Strategies Need DevOps

The operations section of the company must learn about the ways analytics models are implemented and gain more profound knowledge on big data platforms. Also, as opposed to data engineers, analytics professionals perceive themselves as social engineers, so they must learn some new things as well. Continue reading this article at DataFloq

Pepperdata Integrates Performance into DevOps for Big Data

Application Profiler is based on the open source Dr. Elephant project originally created by LinkedIn Corporation. Application Profiler and Dr. Elephant help improve Hadoop and Spark developer productivity and increase cluster efficiency by making clear recommendations on how to tune workloads and configurations. Application Profiler delivers the capabilities of Dr. Elephant, but as a simple to adopt SaaS offering that is very easy to deploy and use. Application Profiler supports Spark and MapReduce on all standard Hadoop distributions: Cloudera, Hortonworks, MapR, IBM and Apache. Continue reading this article at Market Wired

The Role of Big Data Performance in DevOps Production

“Other companies like Cloudera, MapR, and Hortonworks have tools for some of this stuff, but what we find in our customer base is that those tools don’t provide the fine-grained information that we provide, and they don’t scale.” Because Pepperdata’s products scale to thousands of nodes, Munshi says they can, “Supply the headroom that’s needed, but also the detail, and we can do that simultaneously,” which means customers can use Pepperdata’s tools with existing Big Data software. Continue reading this article at Dataversity

AWS Cloud Migration, DevOps, Big Data and IOT Solutions Provider stackArmor is Now APN Advanced Partner

stackArmor also announced that it has achieved AWS Service Delivery Partner status for AWS GovCloud and Amazon DynamoDB, and has become a member of the AWS Public Sector Partner Program. The AWS Service Delivery Program identifies APN Partners that have passed technical reviews, and have a validated track record of customer success with specific AWS services. The AWS Public Sector Partner Program is designed for APN Partners with solutions and experience to help deliver government, education, and nonprofit customer missions around the world leveraging the AWS Cloud. Continue reading this press release at PR Newswire

Location data is now part of ‘big data’ in IBM Cognos

In simpler words, every organization needs to associate customer locations with specific location-based risk information. Mater Data location and Geoenrichment data achieve it in a simple and accurate straight-through-process. Now organizations can focus on accessing risks and preparing data. Using Geoenrichment module, Master Location Data adds unique pbKey to each address in order to simplify linking to various Pitney Bowes and their datasets to unlock the rich attributable information. Mater Data Location answers questions at both Big picture location like network optimization and detailed questions like flood risks. Continue reading this article at IBM developerWorks

Let’s Be Clear: DevOps and the Agile Approach

Kanban, another framework based on lean principles, can really help realize the benefits of DevOps by visualizing the collaboration between development teams and different operation teams. A Kanban board visually defines the sequence of work (SDLC) and the teams responsible for that work. Continue reading this article at TeraData Combines Open Source, Cloud, Big Data and Machine Learning for DevOps and SRE

Logz tells its customers what’s going on with their software applications. It offers an enhanced version of the open source ELK stack which combines an enterprise search engine with log analytics and visualization tools. On top of ELK, it has developed Cognitive Insights, an artificial intelligence platform that detects overlooked and critical events and provides the user with actionable data about context, severity, relevance, and recommended next steps. Continue reading this article at What’s The Big Data


The multiple steps in a DevOps process are called pipelines. There are different variations for the name of the pipes, but they are all similar to the ones in figure 2. It covers the key steps of the software development life cycle.

Design – Create – Merge – Build – Bind – Deliver – Deploy

Continue reading this article at Everything About Data

DevOps & Big Data: Interview with John Sumser

“In 2006, the vast majority said there will never be a driverless car, because nobody can handle the data required to make a left hand turn into oncoming traffic. And about 15 minutes later, Google’s prototype was running on the highway 101 between San Jose and San Francisco. That’s the world we inhabit, where the impossible becomes real in a tighter span than you could ever imagine.” Continue reading this article at Stackify

Bridging the Gap Between Data Science and DevOps

If you keep reading Airbnb’s publication, another aspect of ‘DevOps thinking’ emerges: a relentless focus on customer experience. By this, I don’t simply mean that the work done by the Airbnb engineers is specifically informed by a desire to improve customer experiences; that’s obvious. Instead, it’s the sense that tools through which internal collaboration and decision making take place should actually be similar to a customer experience. They need to be elegant, engaging, and intuitive. This doesn’t mean seeing every relationship as purely transactional, based on some perverse logic of self-interest, but rather having a deeper respect for how people interact and share ideas. If DevOps is an agile methodology that bridges the gap between development and operations, it can also help to bridge the gap between data and operations. Continue reading this article at Packt

A data platform for data scientists, DevOps, and developers

There’s a good diagram that I use for reference for our architecture (see below). The infrastructure can be a public or a private cloud where TAP is ultimately deployed. On top of that, we use CDH, which is Cloudera’s distribution of Hadoop, to add a level of data management. We then build on that with an ingestion layer, which makes available several services, like Kafka, Gear Pump, RabbitMQ, depending on the data you’re ingesting or streaming into the platform. Continue reading this article at OReilly

DevOps For Data Science: Why Analytics Ops Is Key To Value

It may be a stretch to call data science commonplace, but the question “what’s next” is often heard with regard to analytics. And then the conversation often turns straight to Artificial Intelligence and deep learning. Instead, a tough love review of the current reality may be in order. Continue reading this article at Forbes

The 9 Key steps to implement Big Data DevOps

Per Gene Kim(author of The Phoenix Project): DevOps is set of cultural norms and technical practices that enable this fast flow of work from dev through test through operations while preserving world class reliability. Continue reading this article at Dataottam

Why DevOps, Big Data and IoT go hand-in-hand

All these sensors and software constantly sends input to servers, API’s and so fourth, meaning that large amounts of data will be collected from around the World – data from your fridge ends up with fridge-data from – likely – the other side of the World. To process and analyze patterns, and make suggestions, recommendations, etc., Big Data technologies, ie. often large database systems based on NoSQL, are scaled up and down in a cloud somewhere, possibly AWS, Azure or OpenStack. Continue reading this article at CIMA

DevOps For Data Science: Why Analytics Ops Is Key To Value

Analytics Ops is the difference between focusing on resource-intensive one-off victories and having a constant, adaptable source of nourishment. To get there, companies will require cross-functional teams with the right software and discipline to enable data scientists, engineers, product managers and domain experts to all work together to create a continuous cycle that drives value to the business. Continue reading this article at ThinkBig

2017 DevOps Predictions

According to reviewers on IT Central Station, it’s clear that Big Data and metrics are expected to play an increased role in promoting DevOps cultures in 2017. IT Central Station community members have explained that their businesses, and particularly enterprises, are looking to generate even more data and analytics surrounding every facet of their applications. This includes all the historical data a company can generate, metrics regarding the topology of an application, all data surrounding service disruptions, location information, information comparing application types and categories, and more. Continue reading this article at DevOps Digest

Driving Big Data Innovation on AWS – ISV Highlights, April 2017

Alteryx integrates with Amazon Redshift and provides support for Amazon Aurora and Amazon S3. Using Alteryx on AWS, users can blend data stored in the AWS Cloud, such as data stored in Redshift, with data from other sources using Alteryx’s advanced analytic workflow. Earlier this year, the company virtualized its Alteryx Server platform to make it easy for users to deploy on AWS through the AWS Marketplace. “Organizations can deploy our Alteryx Server platform in the AWS Cloud within minutes, while maintaining the enterprise-class security and scalability of our popular on-premises solution. This gives organizations a choice for how they want to quickly share critical business insights with others in their organization,” explains Laurent. Continue reading this article at Amazon

Cloudwick Launches Big Loop, The First Big Data Collaboration Platform

Cloudwick Big Loop is designed to support clusters on-premise or in the cloud – Amazon Web Services (AWS), Google Compute, Microsoft Azure, or Rackspace. Cloudwick Big Loop is included as part of Cloudwick’s industry leading DevOps big data support services for Cassandra, Hadoop and Spark. Continue reading this article at Cloudwick

Big Data and DevOps – Using Flexible Development Models to Maximize Data

There are a few major challenges that come with big data, but one of the greatest is what to do with the trends, key metrics and other information that is gleaned from a large-scale analytics program. While some robust apps and services may integrate with your backend to allow big data systems to feed conclusions directly to end users, chances are that you will face a need to create custom solutions – whether by altering existing apps or developing proprietary apps from scratch – to make the most of big data. Continue reading this article at BCM One

ITDC 2017: What to Expect from the Development and DevOps Track

With AI, DevOps, Mobile and Big Data being prominent, a full stack developer must upgrade his professional skillset to be an expert in at least two of the above, at the very least. These professional enhancements are a welcome change and running behind may not be the best thing for the career. Continue reading this post at IT/Dev Connections

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