Welcome!

Microservices Expo Authors: Olivier Huynh Van, Yeshim Deniz, Elizabeth White, Liz McMillan, Sematext Blog

Related Topics: @CloudExpo, Microservices Expo, Containers Expo Blog, Agile Computing, Apache, Cloud Security

@CloudExpo: Article

Arrival of Big Data Opens Up a New Range of Analytics

It's happening: Hadoop and SQL worlds are converging

With Strata, IBM IOD, and Teradata Partners conferences all occurring this week, it’s not surprising that this is a big week for Hadoop-related announcements. The common thread of announcements is essentially, “We know that Hadoop is not known for performance, but we’re getting better at it, and we’re going to make it look more like SQL.” In essence, Hadoop and SQL worlds are converging, and you’re going to be able to perform interactive BI analytics on it.

The opportunity and challenge of Big Data from new platforms such as Hadoop is that it opens a new range of analytics. On one hand, Big Data analytics have updated and revived programmatic access to data, which happened to be the norm prior to the advent of SQL. There are plenty of scenarios where taking programmatic approaches are far more efficient, such as dealing with time series data or graph analysis to map many-to-many relationships.

It also leverages in-memory data grids such as Oracle Coherence, IBM WebSphere eXtreme Scale, GigaSpaces and others, and, where programmatic development (usually in Java) proved more efficient for accessing highly changeable data for web applications where traditional paths to the database would have been I/O-constrained. Conversely Advanced SQL platforms such as Greenplum and Teradata Aster have provided support for MapReduce-like programming because, even with structured data, sometimes using a Java programmatic framework is a more efficient way to rapidly slice through volumes of data.

But when you talk analytics, you can’t simply write off the legions of SQL developers that populate enterprise IT shops.

Until now, Hadoop has not until now been for the SQL-minded. The initial path was, find someone to do data exploration inside Hadoop, but once you’re ready to do repeatable analysis, ETL (or ELT) it into a SQL data warehouse. That’s been the pattern with Oracle Big Data Appliance (use Oracle loader and data integration tools), and most Advanced SQL platforms; most data integration tools provide Hadoop connectors that spawn their own MapReduce programs to ferry data out of Hadoop. Some integration tool providers, like Informatica, offer tools to automate parsing of Hadoop data. Teradata Aster and Hortonworks have been talking up the potentials of HCatalog, in actuality an enhanced version of Hive with RESTful interfaces, cost optimizers, and so on, to provide a more SQL friendly view of data residing inside Hadoop.

But when you talk analytics, you can’t simply write off the legions of SQL developers that populate enterprise IT shops. And beneath the veneer of chaos, there is an implicit order to most so-called “unstructured” data that is within the reach programmatic transformation approaches that in the long run could likely be automated or packaged inside a tool.

At Ovum, we have long believed that for Big Data to crossover to the mainstream enterprise, that it must become a first-class citizen with IT and the data center. The early pattern of skunk works projects, led by elite, highly specialized teams of software engineers from Internet firms to solve Internet-style problems (e.g., ad placement, search optimization, customer online experience, etc.) are not the problems of mainstream enterprises. And neither is the model of recruiting high-priced talent to work exclusively on Hadoop sustainable for most organizations; such staffing models are not sustainable for mainstream enterprises. It means that Big Data must be consumable by the mainstream of SQL developers.

Making Hadoop more SQL-like is hardly new

Hive and Pig became Apache Hadoop projects because of the need for SQL-like metadata management and data transformation languages, respectively; HBase emerged because of the need for a table store to provide a more interactive face – although as a very sparse, rudimentary column store, does not provide the efficiency of an optimized SQL database (or the extreme performance of some columnar variants). Sqoop in turn provides a way to pipeline SQL data into Hadoop, a use case that will grow more common as organizations look to Hadoop to provide scalable and cheaper storage than commercial SQL. While these Hadoop subprojects that did not exactly make Hadoop look like SQL, they provided building blocks from which many of this week’s announcements leverage.

Progress marches on

One train of thought is that if Hadoop can look more like a SQL database, more operations could be performed inside Hadoop. That’s the theme behind Informatica’s long-awaited enhancement of its PowerCenter transformation tool to work natively inside Hadoop. Until now, PowerCenter could extract data from Hadoop, but the extracts would have to be moved to a staging server where the transformation would be performed for loading to the familiar SQL data warehouse target. The new offering, PowerCenter Big Data Edition, now supports an ELT pattern that uses the power of MapReduce processes inside Hadoop to perform transformations. The significance is that PowerCenter users now have a choice: load the transformed data to HBase, or continue loading to SQL.

There is growing support for packaging Hadoop inside a common hardware appliance with Advanced SQL. EMC Greenplum was the first out of gate with DCA (Data Computing Appliance) that bundles its own distribution of Apache Hadoop (not to be confused with Greenplum MR, a software only product that is accompanied by a MapR Hadoop distro).

Teradata Aster has just joined the fray with Big Analytics Appliance, bundling the Hortonworks Data Platform Hadoop; this move was hardly surprising given their growing partnership around HCatalog, an enhancement of the SQL-like Hive metadata layer of Hadoop that adds features such as a cost optimizer and RESTful interfaces that make the metadata accessible without the need to learn MapReduce or Java. With HCatalog, data inside Hadoop looks like another Aster data table.

Not coincidentally, there is a growing array of analytic tools that are designed to execute natively inside Hadoop. For now they are from emerging players like Datameer (providing a spreadsheet-like metaphor; which just announced an app store-like marketplace for developers), Karmasphere (providing an application develop tool for Hadoop analytic apps), or a more recent entry, Platfora (which caches subsets of Hadoop data in memory with an optimized, high performance fractal index).

Yet, even with Hadoop analytic tooling, there will still be a desire to disguise Hadoop as a SQL data store, and not just for data mapping purposes.

Yet, even with Hadoop analytic tooling, there will still be a desire to disguise Hadoop as a SQL data store, and not just for data mapping purposes. Hadapt has been promoting a variant where it squeezes SQL tables inside HDFS file structures – not exactly a no-brainer as it must shoehorn tables into a file system with arbitrary data block sizes. Hadapt’s approach sounds like the converse of object-relational stores, but in this case, it is dealing with a physical rather than a logical impedance mismatch.

Hadapt promotes the ability to query Hadoop directly using SQL. Now, so does Cloudera. It has just announced Impala, a SQL-based alternative to MapReduce for querying the SQL-like Hive metadata store, supporting most but not all forms of SQL processing (based on SQL 92; Impala lacks triggers, which Cloudera deems low priority). Both Impala and MapReduce rely on parallel processing, but that’s where the similarity ends. MapReduce is a blunt instrument, requiring Java or other programming languages; it splits a job into multiple, concurrently, pipelined tasks where, at each step along the way, reads data, processes it, and writes it back to disk and then passes it to the next task.

Conversely, Impala takes a shared nothing, MPP approach to processing SQL jobs against Hive; using HDFS, Cloudera claims roughly 4x performance against MapReduce; if the data is in HBase, Cloudera claims performance multiples up to a factor of 30. For now, Impala only supports row-based views, but with columnar (on Cloudera’s roadmap), performance could double. Cloudera plans to release a real-time query (RTQ) offering that, in effect, is a commercially supported version of Impala.

By contrast, Teradata Aster and Hortonworks promote a SQL MapReduce approach that leverages HCatalog, an incubating Apache project that is a superset of Hive that Cloudera does not currently include in its roadmap. For now, Cloudera claims bragging rights for performance with Impala; over time, Teradata Aster will promote the manageability of its single appliance, and with the appliance has the opportunity to counter with hardware optimization.

The road to SQL/programmatic convergence

Either way – and this is of interest only to purists – any SQL extension to Hadoop will be outside the Hadoop project. But again, that’s an argument for purists. What’s more important to enterprises is getting the right tool for the job – whether it is the flexibility of SQL or raw power of programmatic approaches.

SQL convergence is the next major battleground for Hadoop. Cloudera is for now shunning HCatalog, an approach backed by Hortonworks and partner Teradata Aster. The open question is whether Hortonworks can instigate a stampede of third parties to overcome Cloudera’s resistance. It appears that beyond Hive, the SQL face of Hadoop will become a vendor-differentiated layer.

Part of conversion will involve a mix of cross-training and tooling automation. Savvy SQL developers will cross train to pick up some of the Java- or Java-like programmatic frameworks that will be emerging. Tooling will help lower the bar, reducing the degree of specialized skills necessary.

And for programming frameworks, in the long run, MapReduce won’t be the only game in town. It will always be useful for large-scale jobs requiring brute force, parallel, sequential processing. But the emerging YARN framework, which deconstructs MapReduce to generalize the resource management function, will provide the management umbrella for ensuring that different frameworks don’t crash into one another by trying to grab the same resources. But YARN is not yet ready for primetime – for now it only supports the batch job pattern of MapReduce. And that means that YARN is not yet ready for Impala or vice versa.

Either way – and this is of interest only to purists – any SQL extension to Hadoop will be outside the Hadoop project. But again, that’s an argument for purists.

Of course, mainstreaming Hadoop – and Big Data platforms in general – is more than just a matter of making it all look like SQL. Big Data platforms must be manageable and operable by the people who are already in IT; they will need some new skills and grow accustomed to some new practices (like exploratory analytics), but the new platforms must also look and act familiar enough. Not all announcements this week were about SQL; for instance, MapR is throwing a gauntlet to the Apache usual suspects by extending its management umbrella beyond the proprietary NFS-compatible file system that is its core IP to the MapReduce framework and HBase, making a similar promise of high performance.

On the horizon, EMC Isilon and NetApp are proposing alternatives promising a more efficient file system but at the “cost” of separating the storage from the analytic processing. And at some point, the Hadoop vendor community will have to come to grips with capacity utilization issues, because in the mainstream enterprise world, no CFO will approve the purchase of large clusters or grids that get only 10 – 15 percent utilization. Keep an eye on VMware’s Project Serengeti.

They must be good citizens in data centers that need to maximize resource (e.g., virtualization, optimized storage); must comply with existing data stewardship policies and practices; and must fully support existing enterprise data and platform security practices. These are all topics for another day.

You may also be interested in:

More Stories By Tony Baer

Tony Baer is Principal Analyst with Ovum, leading Ovum’s research on the software lifecycle. Working in concert with other members of Ovum’s software group, his research covers the full lifecycle from design and development to deployment and management. Areas of focus include application lifecycle management, software development methodologies (including agile), SOA, IT service management/ITIL, and IT management/governance.

Baer has been a noted authority on software development platforms and integration architecture for nearly 20 years. Prior to joining Ovum, he was an independent analyst whose company ‘onStrategies’ delivered software development and integration tools to vendors with technology assessment and market positioning services. He also led Computerwire’s CIO Agenda and Computer Finance end-user best practices research services.

Follow him on Twitter @TonyBaer or read his blog site www.onstrategies.com/blog.

@MicroservicesExpo Stories
Large enterprises today are juggling an enormous variety of network equipment. Business users are asking for specific network throughput guarantees when it comes to their critical applications, legal departments require compliance with mandated regulatory frameworks, and operations are asked to do more with shrinking budgets. All these requirements do not easily align with existing network architectures; hence, network operators are continuously faced with a slew of granular parameter change req...
24Notion is full-service global creative digital marketing, technology and lifestyle agency that combines strategic ideas with customized tactical execution. With a broad understand of the art of traditional marketing, new media, communications and social influence, 24Notion uniquely understands how to connect your brand strategy with the right consumer. 24Notion ranked #12 on Corporate Social Responsibility - Book of List.
Whether they’re located in a public, private, or hybrid cloud environment, cloud technologies are constantly evolving. While the innovation is exciting, the end mission of delivering business value and rapidly producing incremental product features is paramount. In his session at @DevOpsSummit at 19th Cloud Expo, Kiran Chitturi, CTO Architect at Sungard AS, will discuss DevOps culture, its evolution of frameworks and technologies, and how it is achieving maturity. He will also cover various st...
Information technology is an industry that has always experienced change, and the dramatic change sweeping across the industry today could not be truthfully described as the first time we've seen such widespread change impacting customer investments. However, the rate of the change, and the potential outcomes from today's digital transformation has the distinct potential to separate the industry into two camps: Organizations that see the change coming, embrace it, and successful leverage it; and...
SYS-CON Events announced today that Sheng Liang to Keynote at SYS-CON's 19th Cloud Expo, which will take place on November 1-3, 2016 at the Santa Clara Convention Center in Santa Clara, California.
Video experiences should be unique and exciting! But that doesn’t mean you need to patch all the pieces yourself. Users demand rich and engaging experiences and new ways to connect with you. But creating robust video applications at scale can be complicated, time-consuming and expensive. In his session at @ThingsExpo, Zohar Babin, Vice President of Platform, Ecosystem and Community at Kaltura, will discuss how VPaaS enables you to move fast, creating scalable video experiences that reach your ...
In his General Session at DevOps Summit, Asaf Yigal, Co-Founder & VP of Product at Logz.io, will explore the value of Kibana 4 for log analysis and will give a real live, hands-on tutorial on how to set up Kibana 4 and get the most out of Apache log files. He will examine three use cases: IT operations, business intelligence, and security and compliance. This is a hands-on session that will require participants to bring their own laptops, and we will provide the rest.
Monitoring of Docker environments is challenging. Why? Because each container typically runs a single process, has its own environment, utilizes virtual networks, or has various methods of managing storage. Traditional monitoring solutions take metrics from each server and applications they run. These servers and applications running on them are typically very static, with very long uptimes. Docker deployments are different: a set of containers may run many applications, all sharing the resource...
In this strange new world where more and more power is drawn from business technology, companies are effectively straddling two paths on the road to innovation and transformation into digital enterprises. The first path is the heritage trail – with “legacy” technology forming the background. Here, extant technologies are transformed by core IT teams to provide more API-driven approaches. Legacy systems can restrict companies that are transitioning into digital enterprises. To truly become a lea...
Internet of @ThingsExpo, taking place November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA, is co-located with 19th Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading industry players in the world. The Internet of Things (IoT) is the most profound change in personal and enterprise IT since the creation of the Worldwide Web more than 20 years ago. All major researchers estimate there will be tens of billions devices - comp...
About a year ago we tuned into “the need for speed” and how a concept like "serverless computing” was increasingly catering to this. We are now a year further and the term “serverless” is taking on unexpected proportions. With some even seeing it as the successor to cloud in general or at least as a successor to the clouds’ poorer cousin in terms of revenue, hype and adoption: PaaS. The question we need to ask is whether this constitutes an example of Hype Hopping: to effortlessly pivot to the ...
Just over a week ago I received a long and loud sustained applause for a presentation I delivered at this year’s Cloud Expo in Santa Clara. I was extremely pleased with the turnout and had some very good conversations with many of the attendees. Over the next few days I had many more meaningful conversations and was not only happy with the results but also learned a few new things. Here is everything I learned in those three days distilled into three short points.
SYS-CON Events announced today the Enterprise IoT Bootcamp, being held November 1-2, 2016, in conjunction with 19th Cloud Expo | @ThingsExpo at the Santa Clara Convention Center in Santa Clara, CA. Combined with real-world scenarios and use cases, the Enterprise IoT Bootcamp is not just based on presentations but with hands-on demos and detailed walkthroughs. We will introduce you to a variety of real world use cases prototyped using Arduino, Raspberry Pi, BeagleBone, Spark, and Intel Edison. Y...
With the rise of Docker, Kubernetes, and other container technologies, the growth of microservices has skyrocketed among dev teams looking to innovate on a faster release cycle. This has enabled teams to finally realize their DevOps goals to ship and iterate quickly in a continuous delivery model. Why containers are growing in popularity is no surprise — they’re extremely easy to spin up or down, but come with an unforeseen issue. However, without the right foresight, DevOps and IT teams may lo...
DevOps at Cloud Expo – being held November 1-3, 2016, at the Santa Clara Convention Center in Santa Clara, CA – announces that its Call for Papers is open. Born out of proven success in agile development, cloud computing, and process automation, DevOps is a macro trend you cannot afford to miss. From showcase success stories from early adopters and web-scale businesses, DevOps is expanding to organizations of all sizes, including the world's largest enterprises – and delivering real results. Am...
Much of the value of DevOps comes from a (renewed) focus on measurement, sharing, and continuous feedback loops. In increasingly complex DevOps workflows and environments, and especially in larger, regulated, or more crystallized organizations, these core concepts become even more critical. In his session at @DevOpsSummit at 18th Cloud Expo, Andi Mann, Chief Technology Advocate at Splunk, showed how, by focusing on 'metrics that matter,' you can provide objective, transparent, and meaningful f...
Digitization is driving a fundamental change in society that is transforming the way businesses work with their customers, their supply chains and their people. Digital transformation leverages DevOps best practices, such as Agile Parallel Development, Continuous Delivery and Agile Operations to capitalize on opportunities and create competitive differentiation in the application economy. However, information security has been notably absent from the DevOps movement. Speed doesn’t have to negat...
With online viewership and sales growing rapidly, enterprises are interested in understanding how they analyze performance to positively impact business metrics. Deeper insight into the user experience is needed to understand why conversions are dropping and/or bounce rates are increasing or, preferably, to understand what has been helping these metrics improve. The digital performance management industry has evolved as application performance management companies have broadened their scope beyo...
While DevOps promises a better and tighter integration among an organization’s development and operation teams and transforms an application life cycle into a continual deployment, Chef and Azure together provides a speedy, cost-effective and highly scalable vehicle for realizing the business values of this transformation. In his session at @DevOpsSummit at 19th Cloud Expo, Yung Chou, a Technology Evangelist at Microsoft, will present a unique opportunity to witness how Chef and Azure work tog...
Your business relies on your applications and your employees to stay in business. Whether you develop apps or manage business critical apps that help fuel your business, what happens when users experience sluggish performance? You and all technical teams across the organization – application, network, operations, among others, as well as, those outside the organization, like ISPs and third-party providers – are called in to solve the problem.