Archive for the ‘Analytic platform’ Category

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.



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”). :)



03
Aug
By Mayank Bawa in Analytic platform on August 3, 2011
   

The world of big data will benefit tremendously from a hybrid big data platform. Teradata’s Aster Data nCluster provides such a hybrid big data platform.

It enables multi-structured data to be stored natively in the database. Therefore, we can store relational data as tables with rows and columns. We can store PDF documents as PDF documents, HTML pages as HTML pages – and the same with Java objects, JPG files, Word documents, GIS data, and others.

It enables multi-structured data to be automatically (dynamically) interpreted natively in the database. For example, we can process PDF data to retrieve the various text blocks in that document, HTML pages to retrieve its content, and JPG files to render images or extract features. In other words, we can interact with the data in its native form to leverage the structure inherent in the stored data.

The final piece is that it enables a human or application user to step across the different structures seamlessly. For example, you can write a query that:

  1. Identifies your valuable customers by analyzing payment history table
  2. Analyzes and interprets customer sentiment by analyzing logs of customer calls
  3. Builds a decision tree to determine the most common problem detected in customer logs
  4. Builds a linear regression model to predict the loss in revenue that can be prevented by solving customers’ problem and the cost of acquiring net new customers to overcome the losses

This can all be done in one workflow and one session. Impressive?

We live in interesting times. The future is opening up in front of us.



28
Jul
By Mayank Bawa in Analytic platform, Analytics on July 28, 2011
   

I wrote earlier that data is structured in multiple forms. In fact, it is the structure of data that allows applications to handle it “automatically” - as an automaton, i.e., programmatically – rather than relying on humans to handle it “semantically”.

Thus a search engine can search for words, propose completion of partially typed words, do spell checking, and suggest grammar corrections “automatically”.

In the last 30 years, we’ve built specialized systems to handle each data structure differently at scale. We index a large corpus of documents in a dedicated search engine for searches, we arrange lots of words in a publishing framework to compose documents, we store relational data in a RDBMS to do reporting, we store emails in an e-discovery platform to identify emails that satisfy a certain pattern, we build and store cubes in a MOLAP engine to do interactive analysis, and so on.

Each such system is a silo – it imposes a particular structure on big data, and then it leverages that structure to do its tasks efficiently at scale.

The silo approach imposes fragmentation of data assets. It is expensive to maintain these silos. It is inefficient for humans and programs to master these silos – they have to learn the nuances of each silo to become an expert in exploiting it. As a result, we have all kinds of data administrators – a cube expert, a text expert, a spreadsheet expert, and so on.

The state of data fragmentation reminds me of the “dedicated function machines” that pre-dated the “Personal Computer”. We used to have electronic type-writers that would create documents, calculators that would calculate formulae, fax machines that would transmit documents, even tax machines that would calculate taxes. All of these machines were booted to relic-status at a museum by a general-purpose computer – the functions were ported on top of its computing framework and the data was stored in its file system. The unity of all of these functions and its data on the general-purpose computer gave rise to “integration” benefits. It made tasks easier: we can now fill our tax forms in (structured form-based) PDF documents, do tax calculations, and file taxes by transmitting the document - all on one platform. Our productivity has gone up. Indeed, the assimilation of data is leading to net new tasks that were not possible before. We can let programs search for previous year’s filings, read the entries, and populate this year’s forms from previous year’s filing to minimize data-entry errors.

We have the same opportunity in front of us now in the field of big data. For too long, have we relegated functions that work on big data to isolated “dedicated function machines.” These dedicated function machines are bad because they are not “open.” Data in a search engine can only be “searched” - it cannot be analyzed for sentiments or plagiarism or edited to insert or remove references. The data is the same, but each of these tasks requires a “dedicated function machine.”

We have the option to build a general purpose machine for big data – a multi-structured big data platform – that allows multiple structures of data to co-exist on a single platform that is flexible enough to perform multiple functions on data.

Such a platform, for example, would allow us to analyze structured payments data to identify our valuable customers, interpret sentiments of calls they made to us, analyze the most common problem across negative sentiment interactions, and predict the loss in revenue that can be prevented by solving that problem and the cost of acquiring net new customers to overcome the losses. Without a multi-structure big data platform, the above workflow is a 12-18 month cycle performed by a cross-functional team of “dedicated function experts” (CFO group, Customer Support group, Products group, Marketing group) – a bureaucratic mess of project management that produces results too expensively, too infrequently and too inaccurately, making simplifying assumptions at each step as they cannot agree on even basic metrics.

An open “Multi-Structured Big Data Platform” would be hugely enabling and open up vast efficiency and functionality that we can’t imagine today.



13
Jun
By Mayank Bawa in Analytic platform on June 13, 2011
   

The “big data” world is a product of exploding applications. The number of applications that are generating data has just gone through the roof. The number of applications that are being written to consume the generated data is also growing rapidly. Each application wants to produce and consume data in a structure that is most efficient for its own use.  As Gartner points out in a recent report on big data[1], “Too much information is a storage issue, certainly, but too much information is also a massive analysis issue.”

In our data-driven economy, business models are being created (and destroyed) and shifting based on the ability to compete on data and analytics. The winners realize the advantage of having platforms,  that allow data to be stored in multiple structures and (more importantly) allow data to be processed in multiple structures. This allows companies to more easily 1) harness and 2) quickly process ALL of the data about their business to better understand customers, behaviors, and opportunities/threats in the market. We call this “multi-structured” data, which has been a topic of discussion lately with IDC Research (where we first saw the term referenced) and other industry analysts. It is also the upcoming topic of a webcast we’re doing with the IDC on June 15th.

To us, multi-structured data means “a variety of data formats and types.” This could include any data “structured” or “unstructured”  - “relational” or “non-relational”. Curt Monash has blogged about naming such data Poly-structured or Multi-structured. At the core is the ability for an analytic platform to both 1) store and 2) process a diversity of formats in the most efficient means possible.

Handling Multi-structured Data

We in the industry use the term “structured” data to mean “relational” data. And data that is not “relational” is called “unstructured” or “semi-structured.”

Unfortunately, this definition lumps text, csv, pdf, doc, mpeg, jpeg, html, log files as unstructured data. Clearly, all of these forms of data have an implicit “structure” to them!

My first observation is that Relational is one way of manifesting the data. Text is another way of expressing the data - Jpeg, gif, bmp and other formats are structured forms of expressing images. For example, (Mayank, Aster Data, San Carlos, 6/1/2011) is a relational row stored in a table (Name, Company Visited, City Visited, Date Visited) – the same data can be expressed in text as “Mayank visited Aster Data, based in San Carlos, on June 1, 2011.” A geo-tagged photograph of Mayank entering the Aster Data office in San Carlos on June 1, 2011 will also capture the same information.

My second observation is that “structure” of data is what makes applications understand the data and know what to do with it. For example, a SQL-based application can issue the right SQL queries to process its logic; an image viewer can interpret JPG/GIF/BMP files to interpret the data; a text-engine can parse subject-object-verbs to interpret the data; etc.

Each application leverages the structure of data to do its processing in the most efficient manner. Thus, search engines recognize the white-space structure in English and can build inverted indexes on words to do fast searches. Relational engines recognize row headers and tuple boundaries to build indexes that can be used to retrieve selected rows very quickly. And so on.

My third observation is that each application produces data in a structure that is most efficient for its use. Thus, applications produce logs; cameras produce images; business applications produce relational rows; Web content engines produce HTML pages; etc. It is very hard to “Transform” data from one structure to the other. ETL tools have their hands full in just doing transformations from a relational schema to another relational schema. And semantic engines have a hard time “transforming” text to relational forms. All such “across structure” transforms are lost in the information.

Relational databases handle relational structure and relational processing very efficiently, but they are severely limiting in their capabilities to store and process other structures (e.g., text, xml, jpg, pdf, doc). In these engines, relations are a first-class citizen; every other structure is a distant second-class citizen.

Hadoop is exciting in the “Big Data” world because it doesn’t pre-suppose any structure. Data in any structure can be stored in plain files. Applications can read the files and build their own structures on the fly. It is liberating. However, it is not efficient – precisely because it reduces all data to its base form of files and robs the data of its structure - the structure that would allow for efficient processing or storage by applications! Each application has to redo its work from scratch.

What would it take for a platform to treat multiple structures of data as first class citizens? How could it natively support each format, yet provide a unified way to express queries or analytic logic at the end-user level to as to abstract away the complexity/diversity of the data and provide insights more quickly?  It’d be liberating as well as efficient!


[1] “’Big Data’ Is Only the Beginning of Extreme Information Management”. Gartner Research, April 7, 2011



02
Feb
By Tasso Argyros in Analytic platform, Analytics on February 2, 2011
   

In my previous post, I spoke about how strongly I feel that this is the year that the analytic platform will become its own distinct and unique category.  As the market as a whole realizes the value of integrated data and process management, in-database applications and in-database analytics, the “analytic platform”, or “analytic computing system”, or “data analytics server” (pick your name) will gain even more momentum, reaching critical mass this year.

In this process, you will see significant movement from vendors, first in their marketing collateral (as it is always the case for followers in a technology space) and then scrambling to cover their product gaps in the 5 categories that define a true analytic platform that I mentioned in Part I of 2011: - The Year of the Analytics Platform.

What took Aster Data 6+ years to build is impossible to be done overnight, or over a few releases (side note: if you are interested in software product development and haven’t read the Mythical Man-Month, now is a good time – it’s an all-time classic and explains this point very clearly), and especially if the fundamental architecture is not there from day one.

But the momentum for the analytic platform category is there and, at this point, is irreversible. Part of this powerful trend is derived from the central place that analytics is taking in the enterprise and government. Analytics today is not a luxury, but a necessity for competitiveness. Every industry today is thinking how to employ analytics to better understand its customers, cut costs, and increase revenues. For example, companies in the financial services sector, a fiercely competitive space, want to use the wealth of data they have to become more relevant to their customers, increase customer satisfaction and retention rates. Governments’ use of data and analytics is one of few last resorts against terrorism and cyber threats. In retail, the advent of Internet, social networks, and globalization has increased competition and reduced margins. Using analytics to understand cross-channel behavior and preferences of consumers improves the returns of marketing campaigns and optimizes product pricing and placement, and can make the difference between red and black ink at the bottom of the balance sheet. Read the rest of this entry »



26
Jan
By Tasso Argyros in Analytic platform, Analytics, Database, MapReduce on January 26, 2011
   

When we kicked off Aster Data back in 2005, we envisioned building a product that would advance the state of the art in data management in two areas; (1) size and diversity of data and (2) depth of insight/analytics. My co-founders and I quickly realized that building just another database wouldn’t cut it. With yet-another-database, even if we enabled companies to more cost-effectively manage large data sizes, it was not going to be enough given the explosion in diverse data types and the massive need to process all of it. So we set out to build a new platform that would solve these challenges - what’s now commonly known as the ‘Big Data’ challenge.

Fast forward to 2008 when Aster Data led the way in putting massive parallel processing inside a MPP database, using MapReduce, to advance how you process massive amounts of diverse data. While this was fully aligned with our vision for managing hoards of diverse data and allowing deep data processing in a single platform, most thought it was intriguing but couldn’t quite see the light in terms of where the future was going. At one point, we thought of naming our product XAP – “extreme analytic platform” or “extreme analytic processing” as that’s what it was designed to do from day one. However, we thought better of it since we thought we would have to educate people too much on what an “analytic platform” was and how it was different from a traditional DBMS for data warehousing. Since we also were serving the data architects in organizations as well as the front-line business that demands better, faster analytics, we needed to use terminology that resonated with both.

Then, in the fall of 2009, with our flagship product Aster Data nCluster 4.0, we made further strides in running advanced analytics inside the database by including all the built-in application services (e.g. like dynamic WLM, backup, monitoring, etc) to go with it. At that time, we referred to it as a Data-Application Server - which our customers quickly started calling a Data-Analytics Server.  I remember when analyst Jim Kobielus at Forrester said,

“It’s really innovative and I don’t use those terms lightly. Moving application logic into the data warehousing environment is ‘a logical next step’.”

And others saying,

“The platform takes a different approach from traditional data warehouses, DBMS and data analytics solutions by housing data and applications together in one system, fully parallelizing both. This eradicates the need for movements of massive amounts of data and the problems with latency and restricted access that creates.”

What they started to fully appreciate and realize is that big data is not just about storing hoards of data, but rather, cracking the code on how to process all of it in deep ways, at blazing fast speeds. Read the rest of this entry »