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EDITOR’S NOTE VARIED NOSQL OPTIONS NEED CAREFUL WEIGHING, SORTING FIT BY FIT, NOSQL DATABASES VIE TO DISPLACE RDBMSes NOSQL JUST ONE PART OF IT MIX ON BIG DATA PROJECTS NoSQL Software Adds New Database Choices, Challenges They’re often a better fit for big data than mainstream relational technology is, but the diversity of NoSQL databases can be baffling. To avoid going in the wrong direction, you need to crack the code.

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EDITOR’S NOTE VARIED NoSQL OPTIONS NEED CAREFUL WEIGHING, SORTING

FIT BY FIT, NoSQL DATABASES VIE TO DISPLACE RDBMSes

NoSQL JUST ONE PART OF IT MIX ON BIG DATA PROJECTS

NoSQL Software Adds New Database Choices, ChallengesThey’re often a better fit for big data than mainstream relational technology is, but the diversity of NoSQL databases can be baffling. To avoid going in the wrong direction, you need to crack the code.

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Recognition Arrives for NoSQL—Adoption to Follow?

NoSQL databases, upstart technologies that offer more design flexibility than SQL-based relational software does, have started being accepted into the mainstream IT frater-nity. For example, Gartner included five NoSQL vendors when it plotted the top providers of operational database management systems in a Magic Quadrant report issued by the consult-ing company in late 2013. One of those ven-dors also made it into the ranks of leading data warehouse database developers in a similar report published in March 2014.

But NoSQL technology still hasn’t found a place in many user organizations. In a survey of IT and business professionals conducted by The Data Warehousing Institute in Novem-ber 2013, only 11% of the 538 respondents said their organizations were using NoSQL data-bases in their primary data warehouse archi-tectures. Another 24% said they planned to do so within three years—but that left 65% with

no adoption plans for NoSQL. Even on a survey of people with experience managing big data environments, done by TDWI earlier in 2013, just 32% of the 189 respondents said they had deployed NoSQL systems—the lowest adoption rate among six types of technology platforms.

In its report on operational databases, Gart-ner said that more of its clients were starting to use NoSQL products for specific purposes, such as running Web applications requiring high scalability. To help you decide if you have a use for NoSQL technologies, the three stories in this guide examine what they’re suited for. First, we assess the four primary categories of NoSQL databases. Next we explore NoSQL’s fit-for-purpose nature. We close by looking at the mix of data management platforms typi-cally needed to support big data applications. n

Craig StedmanExecutive Editor, SearchDataManagement

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Varied NoSQL Options Need Careful Weighing, Sorting

NoSQL databases are designed to address processing issues created by expanding data volumes and diversity, particularly in big data applications. But there’s no lack of either vol-ume or diversity in the NoSQL ranks, leaving IT and data managers with lots of alternatives to sort through when evaluating technology options.

“There are so many NoSQL databases today—I think we’re challenged by two or three on a daily basis,” quipped Michael Simone, global head of CitiData platform engineering at Citigroup Inc., during a presentation at the 2014 MongoDB World conference in New York. In reality, Citi currently has limited itself to using the MongoDB database as a NoSQL alterna-tive to relational software in a small number of applications, Simone said. But his joke pointed to the need for organizations considering NoSQL technologies to focus on finding the one that can best solve their application problems.

That starts with understanding the different types of NoSQL databases, which are broken down into four primary categories: document databases, key-value stores, wide column stores and graph databases. They all share some com-mon traits—most notably, support for more flexible and dynamic database designs than are feasible in SQL-based relational databases. But each NoSQL category is suited to particular uses, according to Gartner analyst Nick Heu-decker. In figuring out which way to go, he said, “you should ask yourself what kind of data you’re working with and how your applications are going to use that data.”

For example, document databases are often used in content management systems and to collect and process data from high-volume Web and mobile applications for uses such as appli-cation monitoring. Befitting their name, these databases store data elements in document-like structures, which can be simple sometimes

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to the point of being schema-less. MongoDB, CouchDB, Couchbase Server and MarkLogic are prominent examples of document databases.

Simone said Citi’s use of MongoDB origi-nated with application developers who were looking for a way to deal with data replication problems in an online financial application with a variety of data structures. The application was initially deployed on a relational database, but processing the data with that platform was slow and prone to errors. “It became clear that we couldn’t keep up with all the data formats coming from the data scientists,” he said.

A MORE DYNAMIC APPROACH

MongoDB’s support for dynamic schemas turned out to be a good fit for the rapidly evolving application, according to Simone. “We found that we could model everything that came at us,” he said. The modeling work also could be done much faster than with the relational approach: The developers built a pre-production model on MongoDB in just four months.

Key-value databases, such as Aerospike,

Redis and Riak, are the simplest form of NoSQL software; they pair unique keys with their associated values in data elements, with a goal of enabling ultrafast application perfor-mance against relatively simple data sets. “Key-value stores are incredibly lightweight,” said Joe Caserta, president of consulting and technical services provider Caserta Concepts. “We can do lookups in seconds.”

Flywheel Software Inc. uses Riak, developed by Basho Technologies, to run a mobile app that lets users hail taxis by tapping on their smartphones. Cuyler Jones, former chief archi-tect at Flywheel, said the database can scale to meet the company’s needs. Just as important is its high-availability nature and support for consistent data access times, added Jones, who now works at another startup.

All NoSQL databases share some com mon traits—most notably, support for more flexible database designs than are feasible in SQL databases.

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Wide column stores keep data in tables that can have very large numbers of columns, offer-ing the opportunity for high levels of perfor-mance and scalability in processing large data sets. Favored uses include Internet search and other large-scale Web applications as well as petabyte-level analytics apps; Accumulo, Cas-sandra and HBase are among the databases in the wide column category.

The column-based approach was a good match for a DNA matching application launched in 2012 by Ancestry.com, according to Jeremy Pollack, a development manager at the online provider of family history data. The Provo, Utah, company uses HBase in combina-tion with Hadoop to run DNA calculations that help customers trace their ethnic backgrounds and geographic origins and look for unknown relatives.

WONKS WANTED FOR DATABASE TUNING

Getting the desired performance from the database required considerable tuning and tweaking, said Pollack, who described HBase programming as a “wonky” process. “There are

a million buttons you can dial or tune,” he said. “You have to be willing to get your hands dirty.” But the NoSQL technology enables Ancestry to rapidly compare 700,000 data points in new and stored DNA samples to look for matching characteristics.

Graph databases, including InfiniteGraph and Neo4j, store related data elements in graph-like structures that exploit their associative qualities to power applications such as recom-mendation engines and social networks. For example, graph technology can be used to map the relationships between different people as well as their interests, said Alex Trofymenko, head of technology at HealthUnlocked, a Lon-don-based company that operates a website supporting user forums on different medical topics.

NoSQL technology enables Ancestry.com to rapidly compare 700,000 data points in new and stored DNA samples to look for matching characteristics.

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Trofymenko and his team use Neo4j, from Neo Technologies, to do such mappings. “We can get a lot of information in a graph data-base,” he said. “Say a user is very interested in diabetes, or exercise—you see it.” That’s important for a site that seeks to take millions of free-text searches, relate them to relevant health terms and build a data platform that helps users find information about possible treatment and assistance.

With the various technology options that the emergence of NoSQL software has added, the database selection process is very differ-ent than it was just a few years ago, when, in Caserta’s words, ‘‘you asked, ‘Should I go with Microsoft, Oracle or IBM?’ ”

The wider array of choices can be a good thing for user organizations—as long as they manage the process carefully and avoid going down the wrong database path. —Jack Vaughan

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SELECTION

Fit by Fit, NoSQL Databases Vie to Displace RDBMSes

Cassandra, HBase, MongoDB—they’re just a few of the many NoSQL databases look-ing to solve problems encountered by the rela-tional database management systems that have long ruled the IT roost. But the very variety that makes the NoSQL sector so vibrant can make comparing different products challenging for would-be users.

First, it’s reasonable to ask why NoSQL tech-nologies matter at all. The short answer is that large-scale distributed processing is taking hold in more applications, thus exposing some of the creaky flooring on which the RDBMS sits. In Web and enterprise applications alike, a new reality has been emerging: The relational database may not always be the best fit.

For example, relational software can be too expensive to scale out in widely distributed applications. It doesn’t easily adapt to new styles of data, such as the unstructured infor-mation that’s common in big data applications.

And it struggles with the massive data volumes coming from in-the-field sensors and Web server activity logs.

As IT managers and software developers have found more reasons to move work off of incumbent relational databases, what has emerged is a “fit for purpose” mentality—of the kind that was prevalent before the RDBMS became the all-purpose flour in the database server pantry. And the number of NoSQL data-base options developed to fit various purposes has grown greatly.

SOCIAL CLIMBER HAD BIG BACKER

Like some other NoSQL technologies, the Apache Cassandra database came about because of a big Web 2.0 fish—in this case, Facebook. It created Cassandra to enable users of the social network to search their inboxes. When the database was launched in 2008, it

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supported replication across geographically distributed data centers to quickly service the searches of as many as 100 million users.

Cassandra is a distributed key-value database that uses a wide column-store scheme and a peer-to-peer, shared-nothing architecture. Its design incorporates some of the characteris-tics of Google BigTable and Amazon Dynamo, two early and influential NoSQL databases. Along the way, Cassandra has gained support for MapReduce, as well as a SQL Server-like query language, triggers and lightweight trans-actions—all features commonly built into rela-tional databases.

Facebook eventually replaced its Cassandra-based search system with a Hadoop implemen-tation that includes HBase, another NoSQL database. But after the company released the software for open source development, a com-munity arose to carry it forward, and Cassandra became a top-level project at the Apache Soft-ware Foundation in 2010.

Cassandra represented a good fit for the needs of Internet Identity, said Jason Atlas, vice president of technology and engineering at the security services company in Tacoma,

Washington. Known as IID, the company had a rapidly growing database of IP addresses run-ning on a MySQL-based cluster. But for cost and other reasons, the relational MySQL path didn’t seem tenable going forward, according to Atlas.

IID was harvesting and collecting 600,000 unique IPv4 addresses and host names per week. Related metadata collections were also growing. “We started to see that we couldn’t store more than 30 days of information at one time,” Atlas said. “The problems largely revolved around scale.” He added that the IPv4 data “lent itself to a key-value approach,” which ultimately led IID to DataStax Enterprise, a commercial version of Cassandra.

ONWARD AND UPWARD

Cassandra was developed to run on commod-ity clusters, and its focus on scalability has borne fruit at IID: Atlas said the database is “coming as close to linear scaling” as any tech-nology he has seen. But he cautioned others who are looking to embrace NoSQL databases that it’s unwise to force-fit technologies into

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IT environments. “It’s always best to map the problem onto the solution,” he said.

Some NoSQL vendors are becoming house-hold names in database circles. For example, DataStax and a quartet of other NoSQL data-

base makers—Basho Technologies, Couch-Base, MarkLogic and MongoDB—were listed among the leading vendors of operational database management systems in a Gartner Magic Quadrant report published in October 2013. But there are dozens of NoSQL offerings in several distinct product categories—and

different databases in the same category were built to support different uses. It’s all a bit of a maze to navigate.

On Twitter, Gartner analyst Merv Adrian pointed to a Linux Journal reader poll compar-ing NoSQL databases. Adrian deadpanned: “In related news—do you prefer apples, cocktails or broccoli?” I tweeted that I understood his point. His response: “It’s useless—and mean-ingless—to compare ‘NoSQL’ products that are so wildly different in structure and intent.”

Atlas made a similar point. MongoDB and Cassandra are both called NoSQL databases but “have nothing to do with one another,” he said. “Their use cases are very different.”

Caveat emptor—if you aren’t careful in sort-ing out which NoSQL technology best fits the particular application you need to run, your organization may end up fit to be tied over its choice of software. —Jack Vaughan

“ It’s useless—and meaningless —to compare ‘NoSQL’ products that are so wildly different in structure and intent.”

—MERV ADRIAN, analyst at Gartner

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NoSQL Just One Part of IT Mix on Big Data Projects

When people think about big data tech-nologies, Hadoop and NoSQL databases are usually the first things that come to mind. But in many cases, big data environments are supported by a mix of data management plat-forms—and Hadoop clusters and NoSQL sys-tems aren’t the predominant ones being tapped by organizations.

For example, a survey conducted in 2013 by Enterprise Management Associates (EMA) and 9sight Consulting found that NoSQL and Hadoop ranked sixth and eighth, respectively, on a list of eight technology platforms being used as a part of big data projects. Traditional technologies—such as analytical databases, operational data stores and enterprise data warehouses—were deployed more broadly than the putative big data duo, according to the sur-vey of 259 IT and business professionals.

EMA analyst John Myers said a similar sur-vey the year before “validated the buzzwords

about big data: what it was, what it wasn’t.” By comparison, he added, the 2013 survey found an increasing number of organizations that were moving forward on projects and bringing big data tools and applications into their opera-tional workflows and processes.

Hadoop and NoSQL software are clearly part of the picture, Myers said, but they aren’t syn-onymous with big data. Only 16% of the survey respondents said they were using Hadoop; for NoSQL, it was 22%. To power their big data programs, many of the companies represented in the survey are creating what EMA calls a

Hadoop and NoSQL are not synonymous with big data. Many companies are using a blend of old and new technol-ogies for big data programs.

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hybrid data ecosystem, with a blend of old and new technologies. “So it’s not one platform to rule them all, so to speak,” he said, “but rather, how do you coordinate between a series of data management platforms to meet these challenges?”

RAPID RESPONSE REQUIRED

One of the big challenges, he noted, is meeting the need for speed in big data analytics appli-cations. According to Myers, the prominent applications turned up by the EMA-9sight survey included risk management and asset optimization—“things that are core in an oper-ational business.” Such uses don’t necessarily involve continuous real-time analytics, Myers said. But when data scientists, business ana-lysts and other end users run analytical queries, they “need to be able to hit the button and get that speed-of-response back.”

SumAll Inc., a marketing analytics services startup, faced just that issue with its clients. The New York company collects and analyzes large amounts of data about website traffic and social media advertising campaigns for

small businesses; it uses a cloud-based imple-mentation of MongoDB’s namesake NoSQL database to capture all the data, but the tech-nology wasn’t a good analytics platform, said Korey Lee, SumAll’s chief information officer. MapReduce-based queries “were taking hours, if not days, to run” on MongoDB.

The IT team first tried to export the data to a MySQL database for analysis, but Lee said that process also started taking too much time as the company collected more data. So in late 2013, SumAll turned to data warehouse soft-ware from vendor BitYota that supports SQL queries against non-SQL data. Lee said the software, also cloud-based, adds a mapping layer on top of MongoDB that enables SumAll to query its full store of data using familiar SQL tools.

GO YOUR OWN WAY

Other organizations might need to take dif-ferent approaches, though. In the big data era, enterprise architectures are no longer nice, neat and replicable from company to company, said William McKnight, president of McKnight

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Consulting Group. In fact, he uses the concept of a “no-reference architecture” in discussing the technologies that could be incorporated into a big data ecosystem. “Every company is different,” McKnight said. “Gone are the days

when a vendor or a consultant could walk into a shop with a laminated sheet of paper and say, ‘This is what everybody needs to do.’ ”

And McKnight agreed with Myers that in most cases, a big data environment requires a variety of technology platforms. “Everybody has a dirty sheet of paper right now with all

sorts of lines crisscrossing about data integra-tion,” he said. “The idea is just to keep moving it forward, though; keep moving it forward into a modern architecture that stores all data and serves it up to the user community.”

The traditional data warehouse still has a role to play in supporting basic reporting needs, McKnight said. But technologies such as columnar databases and in-memory process-ing systems might also be called for—the same for Hadoop and NoSQL software. According to McKnight, the latter are starting to find their way into most large companies—for prototyp-ing and proofs of concept, if not necessarily for production uses at this point. They have to work with other types of technologies, though.

Despite all the hoopla, Hadoop and NoSQL are “not going to do away with the data ware-house or relational databases in general.” —Craig Stedman

The traditional data ware- house still has a role to play in supporting basic reporting needs, but new technologies might also be called for.

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ABOUT

THE

AUTHORS

JACK VAUGHAN is news and site editor of SearchData Management. He covers topics such as big data manage-ment, data warehousing, databases and data integration. Vaughan previously was an editor for TechTarget’s SearchSOA, SearchVB, TheServerSide and SearchDomino websites. Email him at [email protected] and follow him on Twitter: @JackVaughanatTT.

CRAIG STEDMAN is an executive editor in TechTarget’s Business Applications and Architecture Media Group. Stedman oversees editorial processes and writes for SearchBusinessAnalytics and SearchDataManagement as well as the SearchOracle and SearchSQLServer websites. Email him at [email protected] and follow him on Twitter: @craigstedman.

NoSQL Software Adds New Database Choices, Challenges is a SearchDataManagement.com e-publication.

Scot Petersen | Editorial Director

Jason Sparapani | Managing Editor, E-Publications

Joe Hebert | Associate Managing Editor, E-Publications

Craig Stedman | Executive Editor

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