On-Premise Hadoop Just Got Easier With These 8 Hadoop-Optimized Systems

Enterprises agree that speedy deployment of big data Hadoop platforms has been critical to their success, especially as use cases expand and proliferate. However, deploying Hadoop systems is often difficult, especially when supporting complex workloads and dealing with hundreds of terabytes or petabytes of data. Architects need a considerable amount of time and effort to install, tune, and optimize Hadoop. Hadoop-optimized systems (aka appliances) make on-premises deployments virtually instant and blazing fast to boot. Unlike generic hardware infrastructure, Hadoop-optimized systems are preconfigured and integrated hardware and software components to deliver optimal performance and support various big data workloads. They also support one or many of the major distros such as Cloudera, Hortonworks, IBM BigInsights, and MapR.  As a result, organizations spend less time installing, tuning, troubleshooting, patching, upgrading, and dealing with integration- and scale-related issues.

Choose From Among 8 Hadoop-Optimized Systems Vendors

Noel Yuhanna and me published Forrester Wave: Big Data Hadoop-Optimized Systems, Q2 2016  where we evaluated 7 of the 8 options in the market. HP Enterprise's solution was not evaluated in this Wave, but Forrester also considers HPE a key player in the market for Hadoop-Optimized Systems along with the 7 vendors we did evaluate in the Wave. 

Read more

15 "True" Streaming Analytics Platforms For Real-Time Everything

Streaming Analytics Captures Real-Time Intelligence

Streaming AnalyticsMost enterprises aren't fully exploiting real-time streaming data that flows from IoT devices and mobile, web, and enterprise apps. Streaming analytics is essential for real-time insights and bringing real-time context to apps. Don't dismiss streaming analytics as a form of "traditional analytics" use for postmortem analysis. Far from it —  streaming analytics analyzes data right now, when it can be analyzed and put to good use to make applications of all kinds (including IoT) contextual and smarter. Forrester defines streaming analytics as:

Software that can filter, aggregate, enrich, and analyze a high throughput of data from multiple, disparate live data sources and in any data format to identify simple and complex patterns to provide applications with context to detect opportune situations, automate immediate actions, and dynamically adapt.

Forrester Wave: Big Data Streaming Analytics, Q1 2016

To help enterprises understand what commercial and open source options are available, Rowan Curran and I evaluated 15 streaming analytics vendors using Forrester's Wave methodology. Forrester clients can read the full report to understand the market category and see the detailed criteria, scores, and ranking of the vendors. Here is a summary of the 15 vendors solutions we evaluated listed in alphabetical order:

Read more

Hadoop Is Data's Darling For A Reason

Hadoop thoroughly disrupts the economics of data, analytics, and data-driven applications. That's cool because the unfortunate truth has been that the potential of most data lies dormant. On average, between 60% and 73% of all data within an enterprise goes unused for analytics. That's unacceptable in an age where deeper, actionable insights, especially about customers, are a competitive necessity. Enterprises are responding by adopting what Forrester calls "Hadoop and friends" (friends such as Spark and Kafka and others). Get Hadoop, but choose the distribution that is right for your enterprise.

Solid Choices All Around Make For Tough Choices

Forrester's evaluated five key Hadoop distributions from vendors: Cloudera, Hortonworks, IBM, MapR Technologies, and Pivotal Software. Forrester's evaluation of big data Hadoop distributions uncovered a market with four Leaders and one Strong Performer:

  • Cloudera, MapR Technologies, IBM, and Hortonworks are Leaders. Enterprise Hadoop is a market that is not even 10 years old, but Forrester estimates that 100% of all large enterprises will adopt it (Hadoop and related technologies such as Spark) for big data analytics within the next two years. The stakes are exceedingly high for the pure-play distribution vendors Cloudera, Hortonworks, and MapR Technologies, which have all of their eggs in the Hadoop basket. Currently, there is no absolute winner in the market; each of the vendors focuses on key features such as security, scale, integration, governance, and performance critical for enterprise adoption.

Read more

The Predictive Modeling Process Using Machine Learning

Predictive analytics uses statistical and machine learning algorithms to find aptterns in data that might predict similar outcomes in the future. Check out this less than 3 minute, fun and fruity video to understand the six steps of predictive modeling.  For tools that use machine learning to build predictive models, Forrester clients can read The Forrester Wave: Big Data Predictive Analytics Solutions, Q2 2015 and A Machine Learning Primer For BT Professionals.

Apache Spark's Marriage To Hadoop Will Be Bigger Than Kim And Kanye

  • Apache Spark is an open source cluster computing platform designed to process big data as efficiently as possible. Sound familiar? That's what Hadoop is designed to do. However, these are distinctly different, but complementary, platforms. Hadoop is designed to process large volumes of data that lives in an Hadoop distributed file system (HDFS). Spark is also designed to process large volumes of data, but much more efficiently than MapReduce, in part, by caching data in-memory. But, to say that Spark is just an in-memory data processing platform is a gross oversimplification and a common misconception. It also has a unique development framework that simplifies the development and efficiency of data processing jobs. You'll often hear Hadoop and Spark mentioned in the same breath. That's because, although they are independent platforms in their own right, they have an evolving, symbiotic relationship. Application development and delivery professionals (AD&D) must understand the key differences and synergies between this next-generation cluster-computing power couple to make informed decisions about their big data strategy and investments. Forrester clients can read the full report explaining the difference and synergies here: Apache Spark Is Powerful And Promising
Read more

Forrester’s Hadoop Predictions 2015

Hadoop adoption and innovation is moving forward at a fast pace, playing a critical role in today's data economy. But, how fast and far will Hadoop go heading into 2015? 
 
Prediction 1: Hadooponomics makes enterprise adoption mandatory. The jury is in. Hadoop has been found not guilty of being an over-hyped open source platform. Hadoop has proven real enterprise value in any number of use cases including data lakes, traditional and advanced analytics, ETL-less ETL, active-archive, and even some transactional applications. All these use cases are powered by what Forrester calls “Hadooponomics” — its ability to linearly scale both data storage and data processing.
 
What it means: The remaining minority of dazed and confused CIOs will make Hadoop a priority for 2015.
 
Predictions 2 and 3: Forrester clients can read the full text of all 8 Hadoop Predictions.
 
Read more

What Qualities Do Great Enterprise Application Developers Possess?

What are you doing on October 16th and 17th? That's when Forrester's Forum for Application Development & Delivery Professionals will be held in Chicago. Join us this year for lively session, networking, and discussions about building software that powers your business. The agenda is hot including a session from me on The Unstoppable Momentum Of Hadoop and guest speaker from McDonald's on How McDonald's Plans To Leverage Its New Digital Platform To Revolutionaize Customer Experiences.

We have lots of fun at these events too. Check out this video of last year's event where we grabbed both clients and analysts and asked them an important, and to some, philosophical question: What Makes A Great Application Developer? See if you'd answer the same way.

Three Ways Mobile Apps Are Better With Contextual Sensor Data

Watch Forrester Researcher Rowan Curran explain how sensors in mobile devices and remote sensors can uniquely enable three new tiers app functionality. Also, be sure to download the full report: Use Sensors To Take Apps To The Next Level of Customer Engagement

Apps Are Blind — Use Sensors To Make Them See

Most apps are dead boring. Sensors can help add some zing. Sensors are data collectors that measure physical properties of the real-world such as location, pressure, humidity, touch, voice, and much more. You can find sensors just about anywhere these days, most obviously in mobile devices that have accelerometers, GPS, microphones, and more. There is also the Internet of Things (IoT) that refers to the proliferation of Internet connected and accessible sensors expanding into every corner of humanity. But, most applications barely use them to the fullest extent possible. Data from sensors can help make your apps predictive to impress customers, make workers more efficient, and boost your career as an application developer.

Read more

TechnoPolitics Podcast: If You Love Your Data, Should You Set It Free?

Living in an increasingly software-mediated world, consumers are more conscious of the value of their data and concerned over its protection and stewardship. At the same time, companies realize that integration of their internal data with external partners is what will elevate personalization, contextualization, predictive apps, and customer service to the level demanded in the age of the customer.

Forrester Senior Analyst Fatemeh Khatibloo urges firms to share some of their data with other firms to drive contextually appropriate knowledge about customers. The result: A more complete view of customers that each sharing firm would not have on their own. In this episode of TechnoPolitics hosted by Rowan Curran, Fatemeh describes the rewards of adaptive intelligence and how firms can use it to gain competitive advantage.

Listen here:

 

Read more