Russell Jurney

Building analytics products at scale requires a deep investment in people, machines, and time. How can you be sure you’re building the right models that people will pay for? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Spark.

Using lightweight tools such as Python, PySpark, Elastic MapReduce, MongoDB, ElasticSearch, Doc2vec, Deep Learning, D3.js, Leaflet, Docker and Heroku, your team will create an agile… Show more

Building analytics products at scale requires a deep investment in people, machines, and time. How can you be sure you’re building the right models that people will pay for? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Spark.

Using lightweight tools such as Python, PySpark, Elastic MapReduce, MongoDB, ElasticSearch, Doc2vec, Deep Learning, D3.js, Leaflet, Docker and Heroku, your team will create an agile environment for exploring data, starting with an example application to mine flight data into an analytic product. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working applications.

* Create analytics applications by using the Agile Data Science development methodology
* Build value from your data in a series of agile sprints, using the data-value pyramid
* Learn how to build and deploy predictive analytics using Kafka and Spark Streaming
* Extract features for statistical models from a single dataset
* Visualize data with charts, and expose different aspects through interactive reports
* Use historical data to predict the future via classification and regression
* Translate predictions into actions
* Get feedback from users after each sprint to keep your project on track Show less

With Early Release ebooks, you get books in their earliest form—the author's raw and unedited content as he or she writes—so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters as they're written, and the final ebook bundle.

To help you answer big data questions, this unique guide shows you how to use simple, fun, and elegant tools leveraging Apache Hadoop. You'll learn… Show more

With Early Release ebooks, you get books in their earliest form—the author's raw and unedited content as he or she writes—so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters as they're written, and the final ebook bundle.

To help you answer big data questions, this unique guide shows you how to use simple, fun, and elegant tools leveraging Apache Hadoop. You'll learn how to break problems into efficient data transformations to meet most of your analysis needs.

This book uses real data and real problems to illustrate patterns found across knowledge domains. It equips you with a fundamental toolkit for performing statistical summaries, text mining, spatial and time-series analysis, and light machine learning. For those working in an elastic cloud environment, you'll learn superpowers that make exploratory analytics especially efficient.

* Learn from detailed example programs that apply Hadoop to interesting problems in context
* Gain advice and best practices for efficient software development
* Discover how to think at scale by understanding how data must flow through the cluster to effect transformations
* Identify the tuning knobs that matter, and rules-of-thumb to know when they're needed
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Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop.

Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach… Show more

Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop.

Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps.

* Create analytics applications by using the agile big data development methodology
* Build value from your data in a series of agile sprints, using the data-value stack
* Gain insight by using several data structures to extract multiple features from a single dataset
* Visualize data with charts, and expose different aspects through interactive reports
* Use historical data to predict the future, and translate predictions into action
* Get feedback from users after each sprint to keep your project on track
Show less

Jurney takes on the question of how many people you need to start a data product team. He draws out the ideal roles for such a team including: customer, market strategist, deal maker, product manager, experience designer, interaction designer, Web developer, data hacker and researcher.

What is Silicon Valley?

An economic cluster. A network of networks, rich in financial and social capital spanning every area of technology, all focused on developing and commercializing new technologies.

The Integrated Circuit solved the Tyranny of Numbers and unleashed Moore’s law, enabling a computerized, networked society.  It did so with the considerable overhead of patent licensing and litigation.  MapReduce is solving the Tyranny of Threads, enabling any company to process data at scale in parallel to extract real value from our most abundant and underutilized resource: information.  It is doing it in the open, through free and open-source software, through the Apache Foundation, Hadoop… Show more

The Integrated Circuit solved the Tyranny of Numbers and unleashed Moore’s law, enabling a computerized, networked society.  It did so with the considerable overhead of patent licensing and litigation.  MapReduce is solving the Tyranny of Threads, enabling any company to process data at scale in parallel to extract real value from our most abundant and underutilized resource: information.  It is doing it in the open, through free and open-source software, through the Apache Foundation, Hadoop and its sub-projects.  We’ve gotten more efficient organizationally this time around. Show less

We are constrained in our strategies by what we imagine possible.
MapReduce and cloud computing open broad possibilities and business
opportunities by placing a usable supercomputer by the hour in the
hands of every startup that wants one. There is no problem which you
lack the processing power to solve, its just a question of whether the
hourly cost is profitable. That's a profound change from being bound to
one machine. As a result of this shift, smaller companies can… Show more

We are constrained in our strategies by what we imagine possible.
MapReduce and cloud computing open broad possibilities and business
opportunities by placing a usable supercomputer by the hour in the
hands of every startup that wants one. There is no problem which you
lack the processing power to solve, its just a question of whether the
hourly cost is profitable. That's a profound change from being bound to
one machine. As a result of this shift, smaller companies can attack
'bigger' problems without a large up-front investment in hardware or
software infrastructure.

A new renaissance in computing is coming that will be comparable to
the business adoption of the personal computer and VisiCalc, and
MapReduce will drive it. Show less

To discover the shape and structure of the big data market, the San Francisco-based startup Relato took a unique approach to market research and created the first fully data-driven market report. Company CEO Russell Jurney and his team collected and analyzed raw data from a variety of sources to reveal a boatload of business insights about the big data space. This exceptional report is now available for free download.
Using data analytic techniques such as social network analysis (SNA)… Show more

To discover the shape and structure of the big data market, the San Francisco-based startup Relato took a unique approach to market research and created the first fully data-driven market report. Company CEO Russell Jurney and his team collected and analyzed raw data from a variety of sources to reveal a boatload of business insights about the big data space. This exceptional report is now available for free download.
Using data analytic techniques such as social network analysis (SNA), Relato exposed the vast and complex partnership network that exists among tens of thousands of unique big data vendors. The dataset Relato collected is centered around Cloudera, Hortonworks, and MapR, the major platform vendors of Hadoop, the primary force behind this market.

From this snowball sample, a 2-hop network, the Relato team was able to answer several questions, including:

* Who are the major players in the big data market?
* Which is the leading Hadoop vendor?
* What sectors are included in this market and how do they relate?
* Which among the thousands of partnerships are most important?
* Who’s doing business with whom?

Metrics used in this report are also visible in Relato’s interactive web application, via a link in the report, which walks you through the insights step-by-step. Show less