Archive for the ‘Teradata Aster’ 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.



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/



03
Dec
By Steve Wooledge in Analytic platform, Analytics, Business analytics, TCO, Teradata Aster on December 3, 2012
   

Who do you believe in more – Santa Claus or Data Scientists? That’s the debate we’re having in New York City on Dec 12th at Big Analytics 2012. Due to the sold-out event this panel discussion will be simulcast live to dig a little deeper behind the hype.

Some believe that data scientists are a new breed of analytic professional that mergers mathematics, statistics, programming, visualization, and systems operations (and perhaps a little quantum mechanics and string theory for good measure) all in one. Others say that Data Scientists are simply data analysts who live in California.

Whatever you believe, the skills gap for “data scientists” and analytic professionals is real and not expected to close until 2018. Businesses see the light in terms of data-driven competitive advantage, but are they willing to fork out $300,000/yr for a person that can do data science magic? That’s what CIO Journal is reporting with the guidance that “CIOs need to make sure that they are hiring for these positions to solve legitimate business problems, and not just because everyone else is doing it too”.

Universities like Northwestern University have built programs and degrees in analytics to help close the gap. Technology vendors are bridging the gap to make new analytic techniques on big data tenable to a broader set of analysts in mainstream organizations. But is data science really new? What are businesses doing to unlock and monetize new insights? What skills do you need to be a “data scientist”? How can you close the gap? What should you be paying attention to?

Mike Gualtieri from Forrester Research will be moderating a panel to answer these questions and more with:

  • Geoff Guerdat, Director of Data Architecture, Gilt Groupe
  • Bill Franks, Chief Analytics Officer, Teradata
  • Bernard Blais, SAS
  • Jim Walker, Director of Product Marketing, Hortonworks

 

Join the discussion at 3:30 EST on Dec 12th where you can ask questions and follow the discussion thread on Twitter with #BARS12, or follow along on TweetChat at: http://tweetchat.com/room/BARS12

… it certainly beats sitting up all night with milk and cookies looking out for Santa!



17
Oct
   

“Big data” has always been a favorite subject of discussion among the Aster Data team. We’ve been talking about big data at least since 2009, long before the term became burning-hot. The big data hype has confused many organization (and vendors) in the market about the best technology or method to solve their analytical business problems.

However, our vision hasn’t changed: from the time we founded the company in 2005 to today where we are part of the Teradata family. Teradata Aster continues to lead the market with technology innovations and reference architectures which provide clear guidance and deliver significant business value to our customers

Today, we are pushing the limits of analytical technology once more, by launching the Teradata Aster Big Analytics Appliance. The Big Analytics Appliance is a unique machine that can help enterprises see their business in high-definition. By harnessing all existing and new data types in the enterprise, we enable organizations to leverage our powerful SQL-MapReduce framework and business-ready analytics & apps which solve specifics business problems in marketing attribution, fraud detection, graph analysis, pattern analysis, and much more. It unleashes the creativity of bright analysts to go discover new insights to help their organizations grow revenue and create sustainable competitive advantage.

So what is the Big Analytics Appliance? It’s five things in one box:

  1. Aster + Apache Hadoop (100% open source via the Hortonworks HDP distribution), fully integrated in one box
  2. ANSI-standard SQL and next-generation MapReduce, fully integrated
  3. More than 50 ready-to-use MapReduce  apps, to deliver immediate business value
  4. Full ecosystem connectivity for both Aster and Hadoop; with BI, ETL and other existing IT systems
  5. The latest-generation, most efficient hardware platform, specifically optimized for Aster, Hadoop, and Big Analytics

Loyal to our Stanford roots, the appliance comes in Cardinal-red color!

Teradata Aster Big Analytics Appliance

The Big Analytics Appliance packs a long list of essential and unique technologies, including:

  • SQL-MapReduce®,  industry’s only true SQL/MapReduce integration
  • SQL-H™, industry’s only ANSI-standard SQL and Hadoop integration
  • Teradata Viewpoint, the most advanced database monitoring platform now extended to Aster and Hadoop
  • Teradata TVI a very sophisticated hardware support and failure prevention software, now ported to Hadoop as well as to Aster
  • Infiniband network interconnect – makes ultra-high-performance connectivity between Aster and Hadoop, as well as scalability, a non-issue
  • Small factor disk drives and dense enclosures – make this appliance one of the most dense and space-efficient big data platforms in the market

And, of course, everything in this appliance is packaged, integrated, pre-tested and supported by Teradata – the most trusted brand in data management and analytics.

I also want to take a moment to talk about our Unified Data Architecture vision for the enterprise. When most vendors out there talk about big data at a very high level without explaining where it fits and how it relates with traditional technologies like data warehousing, we decided to do the hard work of figuring out how different technologies complement each other and for what purpose. The result of that was the diagram below that showcases how Teradata, Aster & Hadoop can work together in tandem to provide a complete data solution for enterprise environments:

Teradata Unified Data Architecture

We also went one step further and now have a matrix that explains what technology (or technologies) are more appropriate for what use case – given a workload/use case and a specific type of data. The result of that exercise is below:

Processing as a Function of Schema Requirements by Data Type

When To Use Which Technology? The best approach by workload and data type

If you want to know more about our Unified Data Architecture vision, read the whitepaper we co-authored with Hortonworks, or feel free to contact us and we’ll be happy to discuss with you this concept and how it’d fit into your environment.

Through tightly integrating Aster and Hadoop, the new Big Analytics Appliance addresses a large part of the Unified Data Architecture; and via the Teradata-Aster and Teradata-Hadoop connectors, Teradata now has all the necessary pieces to help enterprises extract the maximum business value from all their data and execute on their Big Data vision. At Aster, just like at Teradata, we are committed to continuously provide the best innovations to help our customers have the power to make the best decision possible.

P.S. If you want to try out Aster without ordering a full Aster box, we now allow you to download an Aster virtual appliance! Go give it a try: http://www.asterdata.com/AsterExpress



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.



26
Apr
By Paul Barrett in Digital Marketing, Social Media, Teradata Aster on April 26, 2012
   

What questions are important to you about Social Media?

John Lovett from Web Analytics Demystified just published a new white paper on Social Analytics. Lovett, who has written the book on Social Analytics (literally), lays out a compelling vision for Deeper Social Analytics for companies.  He clearly presents the value of companies to go beyond surface level analytics of likes, followers and friends and challenges the CMO to ask deeper and more important questions.

I love the three key questions presented in the paper that really hit the c-suite.  These are

  • What is the Audience?
  • What is the Activity?
  • What is the Action?

These 3 questions provide a framework to share social media initiatives with business leaders and strip away all the non-business related questions that become so distracting in understanding the impact of social media on the enterprise.

Although we are still in the early, Black and White TV stage of social analytics, Teradata Aster has been heavily influenced by our customers’ needs in the social space.  Customers like LinkedIn,  Gilt Groupe, MySpace, and Myzinga have redefined how consumers interact with each other through music, shopping, and content.  Attensity running on Aster promises to bring together big data and social analytics that starts to deliver on Mr. Lovett’s proposition of deeper social analytics.

Teradata’s strategy to marry big data analytics and marketing applications with its industry-leading database solutions is steeped with the concept of deeper analytics.  In social analytics, we have identified 10 key business questions that should be asked about every social post.   In a market where posts can go viral, impact brand, customer perception, and revenue, being able to quickly and effectively navigate deeper social analytics becomes a mission critical capability.

Beyond John’s questions, the 10 key questions are:

  • What was said?
  • What is it about? (ie,  product, service, brand, experience)
  • Is it a common sentiment?
  • What are the trends on this topic?
  • Who said it?
  • What is their value?
  • How engaged are they?
  • What is their influence?
  • How do I respond?
  • Was my response effective?

To effectively answer these questions CMO’s need a set of marketing technologies.  These include:

  • A social listening platform that analyzes social timelines for owned, embassy and public feeds.  These tools identify what was said, what it is about and if it is a common sentiment.
  • The second level demands a customer hub to understand who posting and what the customer relationship is with them and to measure the customer value of that relationship.
  • The third level requires social network analytics and the ability to find implicit and explicit social connections.  This helps illuminate how engaged customers are and their level of influence – or who influences them
  • The fourth level is where integrated marketing management and customer facing marketing applications come it.  Once you understand what was said, who said it and the potential impact – how do you respond?  Is it a one-on-one conversation, a social discussion or was a bigger issue identified that may result in marketing campaigns?

Are these the right business questions to ask?  What else do you want to know about social media posts in your business?



13
Apr
By Mayank Bawa in Analytics, Business analytics, Teradata Aster on April 13, 2012
   

We live in interesting times!

In the past 30 years, data was used to record business events and report on business events. Over the last 5 years, data has gotten closer to business. Now data is being used to record business events, report on business events as well as influence business events. We now realize that the more data we record, the more comprehensively data can influence business events.

Hence the excitement of “big data” – it is a business opportunity for each line of business – to influence business events to have favorable outcomes.

The responsibility for technologists is to provide the right platforms and tools to make influencing business easy and simple.

There are TWO relentless forces that are playing out in the big data space to which technology has to respond.

The first force is the diversity of data. As we record more data, we end up having different formats of data to manage. About 20% is relational, but we also have text, emails, PDF, Twitter feeds, Facebook profiles, social graphs, CDRs, Apache logs, JSON formats, …

The second force is the richness of analytics. As we influence more business, we end up having richer analytics to perform. About 20% is SQL, but we also have time series analysis, statistical analysis, geo-spatial analysis, graph analysis, sentiment analysis, entity extraction, …

Note that I am not saying MapReduce doesn’t have a diverse set of analytics to do: MapReduce is a way of programming to do analysis – time series, statistical, geo-spatial – each require different MapReduce programs to be written.

Today, the platforms and tools for big data are very complex. They expect lines of business owners to write programs to manage different forms of big data, to write sophisticated programs to analyze big data, to master the management and administration of big clusters and be self-sustaining in managing data quality. This last point is very important – data values change over time. We have to keep values consistent, otherwise our analysis will be wrong and our influence on business will be negative – garbage in, garbage out rule of computing.

As a result, big data is in danger of entering the DIY (do it yourself) space. A line of business is now expected to support big clusters = big administration = big programs = big friction = low influence.

We have to acknowledge these challenges as technologists. If we let big data solutions be a DIY solution, only pockets of enterprise will embrace big data – the rest of the non-technology savvy business leaders will be left out of the opportunity.

We have to simplify this equation. We need to enable line of business owners to benefit from big data a lot more easily. We have to make it simpler for business leaders to get from big data to big analytics.

Our goal, big data = small clusters = easy administration = big analytics = big influence.

This entails solving the following problems:

[1] Make platform and tools to be easier to use to manage and curate data. Otherwise, garbage in = garbage out, and you will get garbage analytics.

[2] Provide rich analytics functions out of the box. Each line of programming cuts your reachable audience by 50%.

[3] Provide tools to update or delete data. Otherwise, data consistency will drift away from truth as history accumulates.

[4] Provide applications to leverage data and find answers relevant to business. Otherwise the cost of DIY applications is too high to influence business – and won’t be done.

At Teradata Aster, we are continuing to lead the big data revolution. We have led the revolution for the past 5 years, and helped shape the market and technologies. We are convinced that the path to big data success is to connect it with Big Analytics in the coming 5 years.