Archive for the ‘MapReduce’ Category

26
Nov
   

Speaking of ending things on a high note, New York City on December 6th will play host to the final event in the Big Analytics 2013 Roadshow series. Big Analytics 2013 New York is taking place at the Sheraton New York Hotel and Towers in the heart of Midtown on bustling 7th Avenue.

As we reflect on the illustrious journey of the Big Analytics 2013 Roadshow, kicking off in San Francisco, this year the Roadshow traveled through major international destinations including Atlanta, Dallas, Beijing, Tokyo, London and finally culminating at the Big Apple – it truly capsulated the appetite today for collecting, processing, understanding and analyzing data.

Big Analytics Atlanta 2013 photo

Big Analytics Roadshow 2013 stops in Atlanta

Drawing business & technical audiences across the globe, the roadshow afforded the attendees an opportunity to learn more about the convergence of technologies and methods like data science, digital marketing, data warehousing, Hadoop, and discovery platforms. Going beyond the “big data” hype, the event offered learning opportunities on how technologies and ideas combine to drive real business innovation. Our unyielding focus on results from data is truly what made the events so successful.

Continuing on with the rich lineage of delivering quality Big Data information, the New York event promises to pack tremendous amount of Big Data learning & education. The keynotes for the event include such industry luminaries as Dan Vesset, Program VP of Business Analytics at IDC, Tasso Argyros, Senior VP of Big Data at Teradata & Peter Lee, Senior VP of Tibco Software.

Photo of the Teradata Aster team in Dallas

Teradata team at the Dallas Big Analytics Roadshow

The keynotes will be followed by three tracks around Big Data Architecture, Data Science & Discovery & Data Driven Marketing. Each of these tracks will feature industry luminaries like Richard Winter of WinterCorp, John O’Brien of Radiant Advisors & John Lovett of Web Analytics Demystified. They will be joined by vendor presentations from Shaun Connolly of Hortonworks, Todd Talkington of Tableau & Brian Dirking of Alteryx.

As with every Big Analytics event, it presents an exciting opportunity to hear first hand from leading organizations like Comcast, Gilt Groupe & Meredith Corporation on how they are using Big Data Analytics & Discovery to deliver tremendous business value.

In summary, the event promises to be nothing less than the Oscars of Big Data and will bring together the who’s who of the Big Data industry. So, mark your calendars, pack your bags and get ready to attend the biggest Big Data event of the year.



12
Nov
   

I’ve been working in the analytics and database market for 12 years. One of the most interesting pieces of that journey has been seeing how the market is ever-shifting. Both the technology and business trends during these short 12 years have massively changed not only the tech landscape today, but also the future of evolution of analytic technology. From a “buzz” perspective, I’ve seen “corporate initiatives” and “big ideas” come and go. Everything from “e-business intelligence,” which was a popular term when I first started working at Business Objects in 2001, to corporate performance management (CPM) and “the balanced scorecard.” From business process management (BPM) to “big data”, and now the architectures and tools that everyone is talking about.

The one golden thread that ties each of these terms, ideas and innovations together is that each is aiming to solve the questions related to what we are today calling “big data.” At the core of it all, we are searching for the right way to enable the explosion of data and analytics that today’s organizations are faced with, to simply be harnessed and understood. People call this the “logical data warehouse”, “big data architecture”, “next-generation data architecture”, “modern data architecture”, “unified data architecture”, or (I just saw last week) “unified data platform”.  What is all the fuss about, and what is really new?  My goal in this post and the next few will be to explain how the customers I work with are attacking the “big data” problem. We call it the Teradata Unified Data Architecture, but whatever you call it, the goals and concepts remain the same.

Mark Beyer from Gartner is credited with coining the term “logical data warehouse” and there is an interesting story and explanation. A nice summary of the term is,

The logical data warehouse is the next significant evolution of information integration because it includes ALL of its progenitors and demands that each piece of previously proven engineering in the architecture should be used in its best and most appropriate place.  …

And

… The logical data warehouse will finally provide the information services platform for the applications of the highly competitive companies and organizations in the early 21st Century.”

The idea of this next-generation architecture is simple: When organizations put ALL of their data to work, they can make smarter decisions.

It sounds easy, but as data volumes and data types explode, so does the need for more tools in your toolbox to help make sense of it all. Within your toolbox, data is NOT all nails and you definitely need to be armed with more than a hammer.

In my view, enterprise data architectures are evolving to let organizations capture more data. The data was previously untapped because the hardware costs required to store and process the enormous amount of data was simply too big. However, the declining costs of hardware (thanks to Moore’s law) have opened the door for more data (types, volumes, etc.) and processing technologies to be successful. But no singular technology can be engineered and optimized for every dimension of analytic processing including scale, performance or concurrent workloads.

Thus, organizations are creating best-of-breed architectures by taking advantage of new technologies and workload-specific platforms such as MapReduce, Hadoop, MPP data warehouses, discovery platforms and event processing, and putting them together into, a seamless, transparent and powerful analytic environment. This modern enterprise architecture enables users to get deep business insights and allows ALL data to be available to an organization, creating competitive advantage while lowering the total system cost.

But why not just throw all your data into files and put a search engine like Google on top? Why not just build a data warehouse and extend it with support for “unstructured” data? Because, in the world of big data, the one-size-sits-all approach simply doesn’t work.

Different technologies are more efficient at solving different analytical or processing problems. To steal an analogy from Dave Schrader—a colleague of mine—it’s not unlike a hybrid car. The Toyota Prius can average 47 mpg with hybrid (gas and electric) vs. 24 mpg with a “typical” gas-only car – almost double! But you do not pay twice as much for the car.

How’d they do it? Toyota engineered a system that uses gas when I need to accelerate fast (and also to recharge the battery at the same time), electric mostly when driving around town, and braking to recharge the battery.

Three components integrated seamlessly – the driver doesn’t need to know how it works.  It is the same idea with the Teradata UDA, which is a hybrid architecture for extracting the most insights per unit of time – at least doubling your insight capabilities at reasonable cost. And, business users don’t need to know all of the gory details. Teradata builds analytic engines—much like the hybrid drive train Toyota builds— that are optimized and used in combinations with different ecosystem tools depending on customer preferences and requirements, within their overall data architecture.

In the case of the hybrid car, battery power and braking systems, which recharge the battery, are the “new innovations” combined with gas-powered engines. Similarly, there are several innovations in data management and analytics that are shaping the unified data architecture, such as discovery platforms and Hadoop. Each customer’s architecture is different depending on requirements and preferences, but the Teradata Unified Data Architecture recommends three core components that are key components in a comprehensive architecture – a data platform (often called “Data Lake”), a discovery platform and an integrated data warehouse. There are other components such as event processing, search, and streaming which can be used in data architectures, but I’ll focus on the three core areas in this blog post.

Data Lakes

In many ways, this is not unlike the operational data store we’ve seen between transactional systems and the data warehouse, but the data lake is bigger and less structured. Any file can be “dumped” in the lake with no attention to data integration or transformation. New technologies like Hadoop provide a file-based approach to capturing large amounts of data without requiring ETL in advance. This enables large-scale data processing for data refining, structuring, and exploring data prior to downstream analysis in workload-specific systems, which are used to discover new insights and then move those insights into business operations for use by hundreds of end-users and applications.

Discovery Platforms

Discovery platforms are a new workload-specific system that is optimized to perform multiple analytic techniques in a single workflow to combine SQL with statistics, MapReduce, graph, or text analysis to look at data from multiple perspectives. The goal is to ultimately provide more granular and accurate insights to users about their business. Discovery Platforms enable a faster investigative analytical process to find new patterns in data, identify different types fraud or consumer behavior that traditional data mining approaches may have missed.

Integrated Data Warehouses

With all the excitement about what’s new, companies quickly forget the value of consistent, integrated data for reuse across the enterprise. The integrated data warehouse has become a mission-critical operational system which is the point of value realization or “operationalization” for information. The data within a massively parallel data warehouse has been cleansed, and provides a consistent source of data for enterprise analytics. By integrating relevant data from across the entire organization, a couple key goals are achieved. First, they can answer the kind of sophisticated, impactful questions that require cross-functional analyses. Second, they can answer questions more completely by making relevant data available across all levels of the organization. Data lakes (Hadoop) and discovery platforms complement the data warehouse by enriching it with new data and new insights that can now be delivered to 1000’s of users and applications with consistent performance (i.e., they get the information they need quickly).

A critical part of incorporating these novel approaches to data management and analytics is putting new insights and technologies into production in reliable, secure and manageable ways for organizations.  Fundamentals of master data management, metadata, security, data lineage, integrated data and reuse all still apply!

The excitement of experimenting with new technologies is fading. More and more, our customers are asking us about ways to put the power of new systems (and the insights they provide) into large-scale operation and production. This requires unified system management and monitoring, intelligent query routing, metadata about incoming data and the transformations applied throughout the data processing and analytical process, and role-based security that respects and applies data privacy, encryption and other policies required. This is where I will spend a good bit of time on my next blog post.



15
Apr
   

About one year ago, Teradata Aster launched a powerful new way of integrating a database with Hadoop. With Aster SQL-H™, users of the Teradata Aster Discovery Platform got the ability to issue SQL and SQL-MapReduce® queries directly on Hadoop data as if that data had been in Aster all along. This level of simplicity and performance was unprecedented, and it enabled BI & SQL analysts that knew nothing about Hadoop to access Hadoop data and discover new information through Teradata Aster.

This innovation was not a one-off. Teradata has put forward the most complete vision for a data and analytics architecture in the 21st century. We call that the Unified Data Architecture™. The UDA combines Teradata, Teradata Aster & Hadoop into a best-of-breed, tightly integrated ecosystem of workload-specific platforms that provide customers the most powerful and cost-effective environment for their analytical needs. With Aster SQL-H™, Teradata provided a level of software integration between Aster & Hadoop that was, and still is, unchallenged in the industry.

Teradata Unified Data Architecture™ image

Teradata Unified Data Architecture™

Today, Teradata makes another leap in making its Unified Data Architecture™ vision a reality. We are announcing SQL-H™ for Teradata, bringing the best SQL engine for data warehousing and analytics to Hadoop. From now on, Enterprises that use Hadoop to store large amounts of data will be able to utilize Teradata’s analytics and data warehousing capabilities to directly query Hadoop data securely through ANSI standard SQL and BI tools by leveraging the open source Hortonworks HCatalog project. This is fundamentally the best and tightest integration between a data warehouse engine and Hadoop that exists in the market today. Let me explain why.

It is interesting to consider Teradata’s approach versus alternatives. If one wants to execute SQL on Hadoop, with the intent of building Data Warehouses out of Hadoop data, there are not many realistic options. Most databases have a very poor integration with Hadoop, and require Hadoop experts to manage the overall system – not a viable option for most Enterprises due to cost. SQL-H™ removes this requirement for Teradata/Hadoop deployments. Another “option” are the SQL-on-Hadoop tools that have started to emerge; but unfortunately, there are about a decade away from becoming sufficiently mature to handle true Data Warehousing workloads. Finally, the approach of taking a database and shoving it inside Hadoop has significant issues since it suffers from the worst of both worlds – Hadoop activity has to be limited so that it doesn’t disrupt the database, data is duplicated between HDFS and the database store, and performance of the database is less compared to a stand–alone version.

In contrast, a Teradata/Hadoop deployment with SQL-H™ offers the best of both worlds: unprecedented performance and reliability in the Teradata layer; seamless BI & SQL access to Hadoop data via SQL-H™; and it frees up Hadoop to perform data processing tasks at full efficiency.

Teradata is committed to being the strategic advisor of the Enterprise when it comes to Data Warehousing and Big Data. Through its Unified Data Architecture™ and today’s announcement on Teradata SQL-H™, it provides even more performance, flexibility and cost-effective options to Enterprises eager to use data as a competitive advantage.



20
Feb
   

Ever since Aster Data became part of Teradata a couple years ago, we have been fortunate to have the resources and focus to accelerate our rate of product innovation. In the past 8 months alone, we have led the market in deploying big analytics on Hadoop and introducing an ultra-fast appliance for discovering big data insights. Our focus is to provide the market with the best big data discovery platform; that is, the most efficient, cost-effective, and enterprise-friendly way to extract valuable business insights form massive piles of structured and unstructured data.

Today I am excited to announce another significant innovation that extends our lead in this direction. For the first time, we are introducing in-database, SQL-MapReduce-based visualization functions, as part of the Teradata Aster Discovery Platform 5.10 software release. These are functions that take the output of an analytical process (either SQL or MapReduce) and create an interactive data visualization that can be accessed directly from our platform through any web browser. There are several functions that we are introducing with today’s announcement, including functions that let you visualize flows of people or events, graphs, and arbitrary patterns. These functions complement your existing BI solution by extending the types of information you can visualize without adding the complexity of another BI deployment.

It did take some significant engineering effort and innovation from our field in working with customers to make a discovery platform produce in-database, in-process visualizations. So, why bother? Because these functions have three powerful characteristics: they are beautiful; powerful; and instant. Let me elaborate in reverse order.

Instant: the goal of a discovery platform like Aster’s is to accelerate the hypothesis –> analysis –> validation iteration process. One of the major big data challenges is that the data is so complex that you don’t even know what questions to ask. So you start with 10s or 100s of possible questions that you need to quickly implement and validate until you find the couple questions that extract the gold nuggets of information from the data. Besides analyzing the data, having access to instant visualizations can help data scientists and business analysts understand if they are down the right path of finding the insights they’re looking for. Being able to rapidly analyze and – now – visualize the insights in-process can rapidly accelerate the discovery cycle and save an analysts time and cost by more than 80% as has been recently validated.    

Powerful: Aster comes with a broad library of pre-built SQL-MapReduce functions. Some of the most powerful, like nPath, crunch terabytes of customer or event data and produce patterns of activity that yield significant insights in a single pass of the data, regardless of the complexity of the pattern or history being analyzed. In the past, visualizing these insights required a lot of work – even after the insight was generated. This is because there were no specialized visualization tools that could consume the insight as-is to produce the visualizations. Abstracting the insights in order to visualize them is sub-optimal since it is killing the ‘a-ha!’ moment. With today’s announcement, we provide analysts with the ability to natively visualize concepts such as a graph of interactions or patterns of customer behavior with no compromises and no additional effort!

Beautiful: We all know that numbers and data are only as good as the story that goes with them. By having access to instant, powerful and also aesthetically beautiful in-database visualizations, you can do justice to your insights and communicate them effectively to the rest of the organization, whether that means business clients, executives, or peer analysts.

In addition, with this announcement we are introducing four buckets of pre-built SQL-MapReduce functions, I.e. Java functions that can be accessed through a familiar SQL or BI interface. These buckets are Data Acquisition (connecting to external sources and acquiring data); Data Preparation (manipulate structured and unstructured data to quickly prepare for analysis); Data Analytics (everything from path and pattern analysis to statistics and marketing analytics); and Data Visualization (introduced today). This is the most powerful collection of big data tools available in the industry today, and we’re proud to provide them to our customers.

Teradata Aster Discovery Portfolio - figure 2

Teradata Aster Discovery Portfolio

Our belief is that our industry is still scratching the surface in terms of providing powerful analytical tools to enterprises that help them find more valuable insights, more quickly and more easily. With today’s launch, the Teradata Aster Discovery Platform reconfirms its lead as the most powerful and enterprise-friendly tool for big data analytics.



25
Jan
   

Last month in New York we completed the 4th and final event in the Big Analytics 2012 roadshow. This series of events shared ideas on practical ways to address the big data challenge in organizations and change the conversation from “technology” to “business value”. In New York alone, 500 people attended from across both business and IT and we closed out the event with two speaker panels. The data science panel was, in my opinion, one of the most engaging and interesting panels I’ve ever seen at an event like this. The topic was on whether organizations really need a data scientist (and what’s different about the skill set from other analytic professionals). Mike Gualtieri from Forrester Research did a great job leading & prodding the discussion.

Overall, these events were a great way to learn and network. The events had great speakers from cutting-edge companies, universities, and industry thought-leaders including LinkedIn, DJ Patil, Barnes & Noble, Razorfish, Gilt Groupe, eBay, Mike Gualtieri from Forrester Research, Wayne Eckerson, and Mohan Sawhney from Kellogg School of Management.

As an aside, I’ve long observed that there has been a historic disconnect between marketing groups and the IT organizations and data warehouses that they support. I noticed this first when I worked at Business Objects where very few reporting applications ever included Web clickstream data. The marketing department always used a separate tool or application like Web Side Story (now part of Adobe) to handle this. There is a bridge being built to connect these worlds – both in terms of technology which can handle web clickstream and other customer interactional data, but also new analytic techniques which make it easier for marketing/business analysts to understand their customers more intimately and better serve them a relevant experience.

We ran a survey at the events, and I wanted to share some top takeaways. The events were split into business and technical tracks with themes of “data science” and “digital marketing”. Thus, the survey data compares the responses from those who were more interested in technology than the business content, so we can compare their responses. The survey data includes responses from 507 people in San Francisco, 322 in Boston, 441 in Chicago, and 894 in New York City for a total of 2164 respondents.

You can get the full set of graphs here, but here are a couple of my own observations / conclusions in looking at the data:

1)      “Who is talking about big data analytics in your organization?” – IT and Marketing were by far the largest responses with nearly 60% of IT organizations and 43% of marketing departments talking about it. New York had slightly higher # of CIO’s and CEO’s talking about big data at 23 and 21%, respectively

 Survey Data: Figure 1

 

 

 

 

 

 

 

 

 

 

 

2)      “Where is big data analytics in your company” – Across all cities, “customer interactions in Web/social/mobile” was 62% – the biggest area of big data analytics. With all the hype around machine/sensor data, it was surprisingly only being discussed in 20% of organizations. Since web servers and mobile devices are machines, it would have been interesting to see how the “machine generated data” responses would have been if we had taken the more specific example of customer interactions away

 Survey Data: Figure 2

 

 

 

 

 

 

 

 

 

 

 

3)      This chart is a more detailed breakdown of the areas where big data analytics is found, broken down by city. NYC has a few more “other.” Some of the “other” answers in NYC included:

  1. Claims
  2. Client Data Cloud
  3. Development, and Data Center Systems
  4. Customer Solutions
  5. Data Protection
  6. Education
  7. Financial Transaction
  8. Healthcare data
  9. Investment Research
  10. Market Data
  11.  Predictive Analytics (sales and servicing)
  12. Research
  13. Risk management /analytics
  14. Security

 Survey Data: Figure 3

 

 

 

 

 

 

 

 

 

 

 

4)      “What are the Greatest Big Analytics Application Opportunities for Businesses Today? – on average, general “data discovery or data science” was highest at 72%, with “digital marketing optimization” as second with just under 60% of respondents. In New York, “fraud detection and prevention” at 39% was slightly higher than in other cities, perhaps tied to the # of financial institutions in attendance

 Survey Data: Figure 4

 

 

 

 

 

 

 

 

 

 

 

In summary, there are lots of applications for big data analytics, but having a discovery platform which supports iterative exploration of ALL types of data and can support both business/marketing analysts as well as savvy data scientists is important. The divide between business groups like marketing and IT are closing. Marketers are more technically savvy and the most demanding for analytic solutions which can harness the deluge of customer interaction data. They need to partner closely with IT to architect the right solutions which tackle “big analytics” and provide the right toolsets to give the self-service access to this information without always requiring developer or IT support.

We are planning to sponsor the Big Analytics roadshow again in 2013 and take it international, as well. If you attended the event and have feedback or requests for topics, please let us know. I hear that there will be a “call for papers” going out soon. You can view the speaker bios & presentations from the Big Analytics 2012 events for ideas.



18
Dec
   

It’s been about two months since Teradata launched the Aster Big Analytics Appliance and since then we have had the opportunity to showcase the appliance to various customers, prospects, partners, analysts, journalists etc. We are pleased to report that since the launch the appliance has already received the “Ventana Big Data Technology of the Year” award and has been well received by industry experts and customers alike.

Over the past two months, starting with the launch tweetchat, we have received numerous enqueries around the appliance and think now is a good time to answer the top 10 most frequently asked questions about the new Teradata Aster offering. Without further ado here are the top 10 questions and their answers:

WHAT IS THE TERADATA ASTER BIG ANALYTICS APPLIANCE?

The Aster Big Analytics Appliance is a powerful, ready to-run platform that is pre-configured and optimized specifically for big data storage and analysis. A purpose built, integrated hardware and software solution for analytics at big data scale, the appliance runs Teradata Aster patented SQL-MapReduce® and SQL-H technology on a time-tested, fully supported Teradata hardware platform. Depending on workload needs, it can be exclusively configured with Aster nodes, Hortonworks Data Platform (HDP) Hadoop nodes, or a mixture of Aster and Hadoop nodes. Additionally, integrated backup nodes are available for data protection and high availability

WHO WILL BENEFIT MOST BY DEPLOYING THE APPLIANCE?

The appliance is designed for organizations looking for a turnkey integrated hardware and software solution to store, manage and analyze structured and unstructured data (ie: multi-structured data formats). The appliance meets the needs of both departmental and enterprise-wide buyers and can scale linearly to support massive data volumes.

WHY DO I NEED THIS APPLIANCE?

This appliance can help you gain valuable insights from all of your multi-structured data. Using these insights, you can optimize business processes to reduce cost and better serve your customers. More importantly, these insights can help you innovate by identifying new markets, new products, new business models etc. For example, by using the appliance a telecommunications company can analyze multi-structured customer interaction data across multiple channels such as web, call center and retail stores to identify the path customers take to churn. This insight can be used proactively to increase customer retention and improve customer satisfaction.

WHAT’S UNIQUE ABOUT THE APPLIANCE?

The appliance is an industry first in tightly integrating SQL-MapReduce®, SQL-H and Apache Hadoop. The appliance delivers a tightly integrated hardware and software solution to store, manage and analyze big data. The appliance delivers integrated interfaces for analytics and administration, so all types of multi-structured data can be quickly and easily analyzed through SQL based interfaces. This means that you can continue to use your favorite BI tools and all existing skill sets while deploying new data management and analytics technologies like Hadoop and MapReduce. Furthermore, the appliance delivers enterprise class reliability to allow technologies like Hadoop to now be used for mission critical applications with stringent SLA requirements.

WHY DID TERADATA BRING ASTER & HADOOP TOGETHER?

With the Aster Big Analytics Appliance, we are not just putting Aster and Hadoop in the same box. The Aster Big Analytics Appliance is the industry’s first unified big analytics appliance, providing a powerful, ready to run big analytics and discovery platform that is pre-configured and optimized specifically for big data analysis. It provides intrinsic integration between the Aster Database and Apache Hadoop, and we believe that customers will benefit the most by having these two systems in the same appliance.

Teradata’s vision stems from the Unified Data Architecture. The Aster Big Analytics Appliance offers customers the flexibility to configure the appliance to meet their needs. Hadoop is best for capture, storing and refining multi-structured data in batch whereas Aster is a big analytics and discovery platform that helps derive new insights from all types of data. Hadoop is best for capture, storing and refining multi-structured data in batch. Depending on the customer’s needs, the appliance can be configured with all Aster nodes, all Hadoop nodes or a mix of the two.

WHAT SKILLS DO I NEED TO DEPLOY THE APPLIANCE?

The Aster Big Analytics appliance is an integrated hardware and software solution for big data analytics, storage, and management, which is also designed as a plug and play solution that does not require special skill sets.

DOES THE APPLIANCE MAKE DATA SCIENTISTS OR DATA ANALYSTS IRRELEVANT?

Absolutely not. By integrating the hardware and software in an easy to use solution and providing easy to use interfaces for administration and analytics, the appliance allows data scientists to spend more time analyzing data.

In fact, with this simplified solution, your data scientists and analysts are freed from the constraints of data storage and management and can now spend their time on value added insights generation that ultimately leads to a greater fulfillment of your organization’s end goals.

HOW IS THE APPLIANCE PRICED?

Teradata doesn’t disclose product pricing as part of its standard business operating procedures. However, independent research conducted by industry analyst Dr. Richard Hackathorn, president and founder, Bolder Technology Inc., confirms that on a TCO and Time-to-Value basis the appliance presents a more attractive option vs. commonly available do-it-yourself solutions. http://teradata.com/News-Releases/2012/Teradata-Big-Analytics-Appliance-Enables-New-Business-Insights-on–All-Enterprise-Data/

WHAT OTHER ASTER DEPLOYMENT OPTIONS ARE AVAILABLE?

Besides deploying via the appliance, customers can also acquire and deploy Aster as a software only solution on commodity hardware] or in a public cloud.

WHERE CAN I GET MORE INFORMATION?

You can learn more about the Big Analytics Appliance via http://asterdata.com/big-analytics-appliance/  – home to release information, news about the appliance, product info (data sheet, solution brief, demo) and Aster Express tutorials.

 

Join the conversation on Twitter for additional Q&A with our experts:

Manan Goel @manangoel | Teradata Aster @asterdata

 

For additional information please contact Teradata at http://www.teradata.com/contact-us/



12
Jun
   

Back in 2005, when we first founded Aster Data, our vision was to take some of the latest technology innovations – including MPP shared-nothing architectures; Linux-based commodity hardware; and novel analytical interfaces like Google’s MapReduce – and bring them to mainstream enterprises. This vision translated into a strategy focused not only on big data innovations, but also on delivering technologies that make big data viable for enterprise environments. SQL-MapReduce®, our industry-leading patented technology that combines standard SQL processing with a native MapReduce execution environment, is one example of how we make big data enterprise ready.

Today we have completed another major milestone on providing value to our customers by announcing a major innovation: Aster SQL-H™, a seamless way to execute SQL & SQL-MapReduce on Apache™ Hadoop™ data.

This is a significant step forward from what was state-of-the-art until yesterday. What was missing? A common DBMS-Hadoop connector operating at the physical layer. This means that getting data from Hadoop to a database required a Hadoop expert in the middle to do the data cleansing and the data type translation. If the data was not 100% clean (which is the case in most circumstances) a developer was needed to get it to a consistent, proper form. Besides wasting the valuable time of that expert, this process meant that business analysts couldn’t directly access and analyze data in Hadoop clusters. Other database connectors require duplicating the data into HDFS by using proprietary formats; a cumbersome and expensive approach by any measure.

SQL-H, an industry-first, solves all those problems.

First, we have integrated Aster’s metadata engine with Hadoop’s emerging metadata standard, HCatalog. This means that data stored in Hadoop using Pig, Hive & HBase can be “seen” in an Aster system as if they are just another Aster view. The business implication is that a business analyst using standard SQL or a BI tool can have full and seamless access to Hadoop data through the Aster’s standard ODBC/JDBC connector and Aster’s SQL engine. There is no need to have a human in the middle to translate the data or ensure its consistency; and no need to file tickets or call up experts to get the data the business needs. Everything happens transparently, seamlessly, and instantly. This is an industry first, since today all available Hadoop tools either do not provide standard SQL interfaces that are well optimized, do not provide native BI compatibility, or require manual data translation and movement from Hadoop to a third party system. None of these approaches are viable options for SQL & BI execution on Hadoop data, thus making it hard for enterprises to get value from Hadoop.

Secondly, SQL-H provides a high-performance, type-safe data connector, that can take a SQL or SQL-MapReduce query that involves Hadoop data, automatically select the minimum subset of data in Hadoop that is required for execution of the query, and run the query on the Aster system. The performance of running SQL and SQL-MapReduce analytics in Aster is significantly higher than Hadoop because (a) Aster can optimize data partitioning and distribution, thus reducing network transfers and overhead; (b) Aster’s engine can keep statistics about the data and use that to optimize execution of both SQL & MapReduce; (c) Aster’s SQL queries are cost-based-optimized which means that it can handle very complex SQL, including SQL produced by BI tools, very efficiently.

In addition, one can take advantage of SQL-H to apply the 50+ pre-build SQL-MapReduce apps that Teradata Aster provides on Hadoop data, thus doing big data analytics that are impossible to do in every other database without having to write a single line of Java MapReduce code! These apps include functions for path & pattern analysis, statistics, graph, text analysis, and more.

Teradata Aster is committed to groundbreaking product innovation as the key strategy in maintaining our #1 position in the big analytics market. SQL-H is another important step that we expect will make Hadoop and big data analytics much more palatable for enterprise environments, allowing business analysts, SQL power-users & BI tool users to analyze Hadoop data without having to learn about Hadoop interfaces and code.

If you want to find out more we’ll be talking about SQL-H at Hadoop Summit, on webcast taking place June 21st, at the upcoming Big Analytics 2012 events in Chicago & New York, and at the annual Teradata Partners event.



15
Mar
By Steve Wooledge in Analytics, MapReduce, Teradata Aster on March 15, 2012
   

Yesterday I presented at the Los Angeles Teradata User Group on the topic of “Data Science: Finding Patterns in Your Data More Quickly & Easily with MapReduce”. One point discussed was the common misnomer that big data is about volume, which is certainly part of the issue organizations are facing. However, the big story in big data is the complexity and additional processing required to make “unstructured” data actionable through analytics. This is where procedural frameworks like MapReduce can help. Here is a great post by Teradata’s own Bill Franks about unstructured data which helps describe the requirements unstructured data demands in the context of analytics.

As Franks notes, “the thought of using unstructured data really shouldn’t intimidate people as much as it often does.” Read more to learn why.

 



21
Feb
By Tasso Argyros in Analytic platform, Analytics, Analytics tech, Database, MapReduce on February 21, 2012
   

It has been about seven years since Aster Data was founded, four years since our industry-first Enteprise SQL-MapReduce implementation (first commercial MapReduce offering) and three years since our first Big Data Summit event (the first “Big Data” event in the industry as far as I know). During this whole time, we have witnessed our technology investments take off together with the Big Data market – just think how many people had never even heard the word MapReduce three years ago, and how many swear by it today!

As someone who was caught in the Big Data wave since 2005, I can tell you that the stage of the market has changed significantly during this time – and with it, the challenges that Enterprise customers face. A few years ago, customers were realizing the challenges that piles of new types of data were bringing – big volumes (terabytes to petabytes) and new, complex types (multi-structured data such as weblogs, text, customer interaction data); but at the same time, the opportunities that the new analytical interfaces, like MapReduce, were enabling. Fast forward to today and most enterprises are trying to put together their Big Data strategies and make sense of what the market has to offer – and as a result there is a lot of market noise and confusion: it is usually not clear what use cases apply to traditional technologies versus new; how to reconcile existing technologies with new investments; and what type of projects will they give them highest ROI versus a long and painful failure.

Teradata and Teradata Aster have a high interest in customers being successful with Big Data challenges and technologies, because we believe that the growth of the market will translate into growth for us. Given Teradata’s history in being the #1 strategic advisor to customers around data management and analytics, we only want to offer the best solutions to our customers. This includes our products –which are recognized by Gartner as leading technologies in Data Warehousing and Big Data analytics– but also our expertise helping customers how to use complementary solutions, like Hadoop, and making sure that the total solution works reliably and succeeds in tackling big business problems.

With this partnership, we are taking one more step towards this direction. So we are announcing three things:

1. Teradata and Hortonworks will work together to jointly solve big challenges for our customers. This is a win/win for customers and the industry.

2. Our intent to do joint R&D to make it easier for customers that use products from Teradata and Hadoop to utilize these products together. This is important because every enterprise will look to combine new technologies with existing investments, and there is plenty of opportunity to do better.

3. A set of reference architectures that combine Teradata and Hadoop products to accelerate the implementation of Big Data Big Data projects. We hope that this will be a starting point that will save enterprises time and money when they embark on Big Data projects.

We believe that all the above three points will translate into eliminating risks and unnecessary trial and error. We have enough collective experience to guide customers to avoid failed projects and traps. And by helping clear up some of the confusion in the big data market, we hope to accelerate its growth and the benefit to Enterprises that are looking to utilizing their data to become more competitive and efficient.



29
Sep
By Tasso Argyros in Analytic platform, Analytics, MapReduce on September 29, 2011
   

One of the great things about starting your own company (if you’re lucky and your company does well) is that you take part in the evolution of a whole new market, from its nascent days to its heyday. This was the case with Aster and the “Big Data” market. Back when we started Aster, in 2005, MPP systems that could store and analyze data using off-the-self servers was still a pretty new concept. I also recall in 2008, when we first came out with our native in-database MapReduce support — and our SQL-MapReduce® technology — we had to explain to most people what MapReduce even was. In 2009, we came out with the first Big Data event series — “Big Data Summit” — because we knew we were doing something new and wanted a term to describe it. “Big Data” caught on more than we had imagined back then, and the rest is history. Product innovation was at the core of Aster’s existence, and we kept pushing ourselves and our product to become the best platform for enterprise-class data analytics using both SQL and MapReduce as first class citizens on one analytic platform.

Today there is a lot of innovation in the big data market. However, we see a “chasm” between the SQL technologies—which are very enterprise-friendly—and the new wave of open source big data or “NoSQL” software which is used extensively by engineering organizations. In the middle is a very large number of enterprises trying to understand how they can use these new technologies to push their analytical capabilities beyond purely SQL, while at the same time utilizing their existing investments in technologies and people. This is the problem that Aster solves.

With last week’s announcement, the launch of our Teradata Aster MapReduce solutions which include Aster Database 5.0 software (formerly Aster nCluster) and our new Aster MapReduce Appliance, we bring to market the best answer for the organizations that are “caught in the middle.” Unlike SQL-only systems focused primarily on analyzing structured data, our database and appliance provide support for native MapReduce which enables a new generation of analytics, such as digital marketing optimization, social graph analysis, fraud detection based on customer behavior, etc. Our newly extended libraries of pre-built MapReduce analytical functions allows such applications to be developed with significantly less time and cost versus other MapReduce technologies. And, unlike other MapReduce-based systems, we offer full SQL support, integration with all major BI and ETL vendors and a data adaptor to EDW systems that allows enterprises to utilize existing tools and skills to bring big data analytics to their businesses. Finally, with our new appliance, we leverage Teradata’s strength and engineering to provide a proven and performance-optimized system for businesses to start analyzing untapped diverse data while cutting down on time, cost and frustration!

As we move forward, Aster is committed to being the leader in SQL and MapReduce analytics for multi-structured data. Having spent 6 years in this market, we believe that it’s not just the coolest technologies that will win, but the ones that make it easier for business analysts and data scientists within organizations to solve their business problems and innovate with analytics. With the launch of our new Teradata Aster solutions — including the revamped SQL-MapReduce interfaces and the new Aster MapReduce appliance—we are pushing the state of the art towards this direction (or as my marketing team likes to say – “bringing the science of data to the art of business”). :)