Note – This is a post authored by Rob Rowe, Sr. Manager, MDM Product Marketing, Software AG. I just helped in co-authoring the part about taming big data since I have already written on the topic before.

We’ve all heard about big data and there are numerous definitions of it. Suffice to say, it’s a lot of data from both inside the organization (e.g. ERP, CRM, application logs, etc) and outside the organization (e.g. Twitter, Facebook, and other social media, videos, news feeds, mobile apps, smart meters or other devices, etc). Whatever big data means to you, the real challenge is harnessing all this data and making sense out of it to provide real business value.

Enterprises today focus on application integration as data grows at an explosive rate everywhere. In order to tame such big data growth, traditional integration techniques may need to be complemented with newer design patterns such as in-memory data management platforms. An earlier blog post on this details this approach using three use cases.

With the explosion of data everywhere, Master Data Management (MDM) also becomes a critical point of observation. However, MDM works hand-in-hand with Big Data and is enabled by integration. There are two sides of this which we will explain. The first is using master data to provide “grounding” for the data, such as positively identifying the source of a social media stream. So for example, someone writes on Facebook that they are unsatisfied with a certain product. As it happens, it is YOUR product!  But, identifying this requires a Master Product repository. You also want to know who this person is so that you can investigate and rectify the situation.  This requires a Customer Master. You want to maintain brand integrity and customer satisfaction levels. So if this person is one of your customers, and they “liked” you on Facebook, perhaps to win a prize or receive a discount, then you not only have their “standard” customer profile information, you also have the supplemental information of their Facebook name. They are already in your database and you know who they are. You can contact them and resolve the issue, ideally in real-time, before your customer loyalty or your brand reputation is tarnished.

The second side of this is that big data can provide valuable information which can supplement the data that you’re already mastering. Yes, it works both ways. For example, let’s take a utility company that is mastering the location data of their smart meters, as well as the product data associated with the meters, transformers, and all the other important assets of the business. From the (big) data, integrated with the real-time analysis tools, it is discovered that a smart meter is no longer reporting its data. Because you are mastering location data, you know where this meter is, you know what type of meter it is, and you know what customer is affected. The call logs can verify whether the customer has already called in a loss of service. Having all this data allow the utility company to dispatch someone close to the troubles location, investigate the problem, and resolve it quickly, perhaps even before the resident comes home from work.

So as you can see, big data, master data, and integration together can produce valuable insight and real business value. Want to know more? Attend our webinar entitled “Taming Big Data and MDM with Integration” on May 15, 2013. You can register at http://www.softwareag.com/corporate/res/webinars/innovation/default.asp

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Business Intelligence is a thing of the past. Companies used to look at their past performance and understand their mistakes or achievements. In today’s world of tweets and posts, even news agencies can’t keep up with the speed at which information is traveling across the world. Enterprises need to be extremely proactive rather than being reactive. So, a few years ago, the concept of Social Business Intelligence was introduced. There is abundant information available in all the petabytes and zetabytes of social feeds across all social networks. The ability to single out the right channel, to kill unnecessary noise and to make sense of the remaining social information into actionable metrics is called Social Business Intelligence. For example, a retail company monitoring specific social feeds of its consumers can understand that they actually prefer its newly introduced product in red more than blue. Using this data, it can instantaneously prepare its supply chain to ship its next batch of products in red rather than blue.

However, there are inherent challenges with gathering such social data. To start with, social data is not structured. Consider a tweet such as this – “Love the fit of #Gap’s new #slimfit #jeans. Zipper not smooth”. How do you associate a metric with such a tweet? Is it a positive tweet or a negative tweet? How do we classify this tweet to make predictive intelligence from this? To address this, we have many tools out there that perform “Sentiment Analysis” on such social data. This type of analysis assigns weights and scores to social data and makes the data more meaningful for Social Business Intelligence dashboards.

There has also been another trend in the last couple of years – an increase in adoption of social media platforms within enterprises. This can span just within employees or even with customers and partners. These tools help in keeping collaborative teams closer and well-informed. And, this is where I will introduce Social Business Intelligence 2.0. The social feeds have grown to gargantuan proportions that they have created silos of Big Data by themselves. Next, the information in such social data is not useful freestanding on its own. They need to be cross-referenced with other data stores in the enterprise such as Product Master, Customer Master, Pricing database, Support Center application etc. This is the way that coherent information is created from such chaos.

Let me give you an example of such a Social Business Intelligence 2.0 scenario. Customer X logs in a support ticket about a system failure in one of their servers. The support system instantly posts a note about that ticket into the corporate social feed under the support channel. Since a few stakeholders such as the Account Manager, Systems Engineer and on-site consultant have subscribed to this specific customer’s posts, they get notifications about the post and are aware that the customer has an issue. They can immediately call up the customer expressing their interest in addressing the issue ASAP. This increases the level of customer satisfaction as well. But, what has also happened behind the scenes is that there were three other similar issues (with the same component) raised by three different customers in the past 1 week. But, those were not trouble tickets but just social posts in support forums. The correlation is made between the various social posts and a social notification is sent out to the relevant R&D team automatically. Members of that team see that post, understand the problem and release a fix immediately addressing the issue. Next, an automatic notification goes to each of those affected customers’ technical contacts indicating the availability of this fix, which addresses their problem.

Yes, I know that sounds like a fairy tale and hardly ever happens that way in any company. But, that is not wholly true. There are quite a lot of companies that are leading the charge on such initiatives and making great strides in bringing such solutions to reality. What I want you to focus on is the underlying technology to make this happen. First, yes, you need a social platform that can primarily connect everyone together. Also, the APIs of this social platform should be open enough that it can be called from other applications or from the integration platform. Second, you will need a correlation engine or a Complex Event Processing (CEP) engine that can associate disjoint data and identify patterns in them. The third most critical thing you need in such an environment is an integration backbone or an Enterprise Service Bus (ESB). This will be the glue that connects all of the moving parts in this environment together.

An ESB within a collaborative enterprise helps in enabling Social Business Intelligence 2.0 by –

• Understand and parse structured / unstructured data from various social sources
• Integrate social data from various sources into a correlation engine or a sentiment analysis engine
• Connect up with various Master data sources and lookup databases within the enterprise. This will help in classifying social data into relevant taxonomies.
• Integrate support systems, R&D systems etc with social data streams so that automatic posts can be sent into appropriate channels based on incoming / inferred data
• Pump all metrics and social analytics data into dashboards to create real-time actionable views for relevant stakeholders

An ESB can also leverage complementary technology such as in-memory management solutions to process Big Data effectively. Social Business Intelligence 2.0 is already here. I urge you to start considering your integration backbone / ESB as an effective platform to launch it within your enterprise today.

Until next time, Ciao!

The post Social Business Intelligence 2.0: Driven by Integration appeared first on Reality Check.

In my role, I have the privilege of meeting with exceptionally bright customers to hear what they are doing. Most of these customers tell me that they are witnessing the effects of the nexus of 4 forces – Mobile, Social, Cloud and Big Data. A lot has been written about the nexus of the four forces – Mobile, Social, Big Data, and cloud. But, I think more importantly it’s not just that the forces exist, but what are these combined forces creating?

From what our customers are seeing, we believe these forces are creating proliferation of 3 key assets: Data, Services and Devices. It is this proliferation that is pushing integration to be the key focus for all new applications.

Data Proliferation

Big Data is everywhere and the newest and probably hottest area in the marketplace but integrating this big data is key to deriving business value. Data integration can be as “simple” as creating a single view of the customer – basically creating a single logical record of the customer data across the enterprise so that all interactions with the customer are consistent, and all relevant data is considered as part of that interaction. As one of our customers who is in the pediatric healthcare business put it, this single view isn’t a nice to have but it saves lives by ensuring all drug interactions are known and factored in from their pharmaceutical, surgical and ER systems. But, data integration can be much more complex, involving statistical pattern matching, algorithmic correlation and producing a model that predicts whether a particular transaction is safe or fraudulent. No matter what the application, if you are talking about data proliferation you probably need some form of integration to bring this all together.

Services Proliferation

My prior blog entry on “Big Services”  describes this area, but consider a relatively simple transaction of shipping a product from your favorite online retail site – there is probably dozens of services that need to be integrated together to provide a seamless experience – Customer data from one or more services, product data from another service, logistics data from the warehouse, preference list from a predictive analytics service, shipping time from the shipping service, credit verification from the card issuer, etc. This is a very simplistic view since products usually come from one or more partners, and the logistics and product availability is derived from warehouses across the globe. Net net, though, is that integration of these systems is key to ensuring that the customer has a great experience.

Device Proliferation

Much has been said about Bring your own Device (BYOD) and the need to have applications that address iOS, Android, Windows, web, and the multitude of screen sizes, but the real device proliferation is around the “Internet of Things”. Integration of the information these devices create is a key enablers to new applications. Consider another customer of ours who is in the recycling business. They integrate GPS data with telephony data, and create an application where customers can view where the recycling truck is and alert the customer when their recycling container has been picked up via a text message. Again, it is the integration that makes this application interesting and compelling.

What This Means for You

Integration is the application, and it is only through integration of existing assets that new compelling applications can be created that provide differentiated value. You need to have a plan and partner that takes the proliferation of data, devices and services into account and allows innovation through integration, only then will you truly have applications that take your business to the next level. Conversely. I challenge you to find the application that has no integration!

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Get this mix right, though, and loyalty pays huge dividends in increasing the lifetime value of a customer, lower acquisition cost through referrals and a positive brand association that creates new business opportunities.

Online retailers, like Zappos, Nordstrom and Expedia, are leading examples of a customer-focused culture that has helped them attract & retain customers and further grow their business via word-of-mouth marketing. The best way to enrich customer relationships is by making customers feel special in every interaction they have with your company. This can be accomplished by following these three maxims for customer delight:

1. Personalize the shopping experience
This includes not only personalizing the products offered to customers but also specially tailored content and promotions. Research shows that personalization can deliver five to eight times the ROI on marketing spend and lift sales 10% or more.

2. Acknowledge and reward customer loyalty
Send customers thank you notes with special offers to let them know that you appreciate their loyalty. This will drive repeat purchases, reduce customer churn, grow word-of-mouth marketing and increase customer lifetime value.

3. Invest in the right technology solutions
In-Memory Data Management (IMDM) solutions are the perfect fit to add velocity to your customer experience while enabling personalization, loyalty based rewards, fraud management and predictive customer analytics, all in real-time. Choose technology that can seamlessly integrate with your existing environment and make the job of enriching your customer relationships a lot easier.

What makes you a loyal customer to your favorite brands? Share your secrets on why you love some brands and not others.

(This blog was originally written on the Real-Time Big Data Blog and has been republished on Reality Check)

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Regardless of the industry, size or company, the “four forces” are enabling businesses to transform processes and technology platforms in fundamental ways. These forces are driving new business models, new sales channels, and new ways to attract and engage with customers. By leveraging these technology trends, organizations can provide better insight into the business to plan more effectively and better predict everything from demand to the delivery of goods and services.

But the question is, how do you adopt the latest technology trends to innovate quickly, while limiting the impact on your existing investments and IT infrastructure?

Integration is key.

Why Integration? Because the best approach for adopting new technology trends is to leverage your existing business logic, IT systems and services. And integration makes it possible to leverage these investments while allowing  you to adopt new technology trends quickly, with less risk and reduced redundancy in data and logic. Businesses have made tremendous investments in their current IT infrastructure and want to protect and maintain these investments by limiting potentially disruptive or risky initiatives in order to adopt the latest trends.

Register for one or all of a series of webinars that explore this topic. Whether your organization is adopting one or all of the 4 forces or developing new technologies for mobile applications, public APIs, enterprise social collaboration, big data analytics or addressing data quality, partner onboarding, or process automation, this series of webinars will provide insight into how you can drive “Innovation through Integration.”

The post Innovation through Integration: It’s back and more relevant than ever appeared first on Reality Check.

Introducing Big Data’s cousin Big Services. What you never heard of Big Services? Well Big Services is perhaps a little shy. OK being a little tongue-in-cheek here but services are proliferating very very fast. And the business opportunity is just as big as taming Big Data.

Services are not a new concept; they started as design guidelines to creating reusable services, evolved to Service-Oriented-Architecture (SOA), and the now to API management. What is amazing is that we are in the age of service proliferation – by one measure public services that can be accessed over the internet are doubling every year and that excludes the hundreds of thousands of services inside the enterprise. Add to this the whole new suites of Software-as-a-Service (SaaS) offerings it becomes a nightmare to even know how many services are out there.

The sheer volume of services is one reason we could call them “Big Services”, but it is the complexity of the different service interfaces, issues of access and control, monitoring, lifecycle management, governance and access that make them really Big Headache Services!

Consider SalesForce.com which has one API (well that seems easy enough) but has a 1,000 page document describing how to access it (OK not so easy). Workday’s opposite approach of publishing 100’s of API’s with 6 or 7 versions. Each WSDL and schema is unique, with different data types (which many need translation form one API to the next), different dependencies (ordering of API calls make a big difference to the end results) and different security and access requirements.

In order to keep track of these Big Services (Volume and complexity) is no easy task. There is a need for software to help you do this. The software needs to provide repository services and a level of abstraction that ensures you work at solving the business problem at hand and not the complexity of the services call. Naturally the software needs to take care of error handling (like what to do if the service is unavailable), monitoring (like checking if the API is available and the average time per call), lifecycle management (like which version of the service to call) and a host of other service security and dependency checking needs. It comes down to whether you have time to build this software of buy it from a trusted partner.

Big Services are creating Big Opportunities: Online services such as Google, Facebook, Netflix and eBay are handling billions of application programming interface (API) calls per day and some companies are accounting for billions of dollars in revenue per year via API links to their services, according to John Musser, founder of ProgrammableWeb. Enterprises are finding that exposing their systems to the world as services create amazing opportunities, just like Best Buy who allows shopping and fulfillment via their API’s, or the EPA which serves up hundreds of self-service APIs that handle air quality to UV index monitoring, or 7-Eleven who exposed coffee purchase API information to determine the results of the election. It isn’t a matter of if, but a matter of when you expose your services to the self-service world, but if you don’t you will miss this BIG services opportunity. Naturally exposing internal systems as services creates its own set of Big Services Headache – stopping denial-of-service attacks, securing the service, publishing the WSDL and documenting the interfaces, and providing access to these services across the mobile and web world.

How will you take advantage of this Big Services opportunity, and provide your business with Big returns?

The post Big Data Meet Big Services appeared first on Reality Check.

To capture the Big Data opportunity, manufacturers are quickly moving data out of slow disk-bound databases and into ultra-fast machine memory. Here are 10 ways that manufacturers can benefit from in-memory data management:

1.  Product design & development
Easily experiment with different design options for products by instantly manipulating large design files held in-memory.
2.  Planning & forecasting
Use terabytes of multiple data sets and historical information, in memory, to develop more accurate plans and forecasts.
3.  Contracting
Flag contract and service-level agreement (SLA) violations in real time.
4.  Procurement
Highlight best and worst performing vendors by comparing performance against a set of KPIs.
5.  Supply Chain
Optimize sourcing of raw materials from multiple vendors and track inventory positions in real time, even across multiple geographies and company divisions.
6.  Operations
Intelligently parse data from sensors and telematics to avoid breakdowns, conduct preventive maintenance, and improve manufacturing output.
7.  Marketing
Understand and refine marketing campaigns based on social feeds, customer reviews, and buying patterns.
8.  Logistics
Optimize truck routing in real time to boost delivery speed while reducing fuel costs.
9.  Customer Service
Delight customers with fast service by keeping profiles and account histories in memory.
10.  Maintenance
Gather real-time sensor data from shipped products to quickly gain intelligence about what causes breakdowns.

Simply put, in-memory computing is a big competitive differentiator for manufacturing companies. Have you moved your manufacturing business in-memory?

The post 10 Reasons Why Manufacturing Is Moving In-Memory appeared first on Reality Check.

Infrastructure has always been a significant problem but companies seem to be handling it quite well with cloud-based models and virtualized servers. Now, that brings about its own set of challenges from an integration problem. But, that is not the topic of this post.

There is an even bigger challenge at stake today – Data. Companies are growing at the rate of 120% their data volume year-over-year. With such explosive growth rates of data, companies are battling the classic challenges of latency, data handling costs, real-time access, analytics and something real hot today – BIG DATA!

An ESB, as we know, is fundamental to a large and growing infrastructure. And, an ESB is also critical to address most of the data challenges above. However, not every ESB in the market has the capability to solve such issues. As a consumer, do NOT be misled by terms such as “high-performance” or “highly scalable”. Every vendor loves to use those terms lavishly. So, to clearly distinguish the ones that can actually deliver from the ones that are just claiming so, you need to understand what is it that makes an ESB so scalable to solve such BIG data problems.

The root problem of all the key data challenges is the sheer volume of it. If the ESB needs to cater data back and forth between various applications, it needs to have the capacity to handle such bulk loads of data in memory. Yes, you may have heard about design patterns that can handle streams of bulk data so as to not overload the ESB. However, those patterns may not be applicable in a variety of use cases where you need the bulk of the data to be available in totality. I will discuss such key scenarios below.

But, before we get to the scenarios, let us understand one thing clearly. To process such large volumes of data (> 10 GB) in real-time, ESBs need a lot of memory to work with but they do not have the capability to manage all that memory by themselves. It is not the function of an ESB. However, very few of the top notch ESBs can actually work hand-in-hand with an in-memory database (IMDB) or an in-memory data management platform to handle such complex scenarios easily. Some even come bundled with an IMDB. An IMDB is different than the traditional disk-oriented relational databases in that all of the data resides in the main memory of the machine where the IMDB is installed. The IMDB is based on an optimized algorithm that executes much faster in memory and so, data retrieval / storage have no noticeable latency. The main advantage of an in-memory database is that you are no longer delayed by costly database reads/writes and are not tied down by disk latency issues.

Scenarios to explain how an ESB can scale up and scale out with an in-memory database –

• Mainframe or AS/400 data access too expensive and poor response – There are many companies that have adopted a legacy system as their system-of-record. In which case, every single data update or read has to go to the legacy system. This is not only time-consuming and expensive but also hurts IT agility when it comes to rapidly changing business needs. Bringing in an ESB in between can solve some of the point-to-point connectivity issues and ease up on onboarding / off-boarding of other systems in the environment. However, if you are still going to the legacy system for every single query / update, then having the ESB is not helpful here. Now, bring in an IMDB and turn on the ESB’s capability to work with it. You can now bring in a significantly large amount of the most-commonly used legacy data into the IMDB using the ESB and save up on a lot of time going back and forth to the legacy system. The IMDB will take care of updating the legacy system asynchronously with its incremental updates. The ESB can now serve up any application with information from the legacy system, which is now available readily in memory. This is truly a “high-performance” promise.
• In-memory analytics of diverse data sources – This is similar to the above use case, only that the data in this case is mostly read-only and also might come from multiple data sources. The most important criterion is that the related data from such diverse sources needs to be available for real-time analysis. Again, you have to imagine real huge volumes of data (Hundreds of GBs of data). This scenario is very common in the Finance industry (Credit card fraud detection) or in Healthcare (Patient telemetric data). The question about which analytics tool to use is not relevant here since the key challenge is to bring in all these data points collectively in real-time with very little or no latency. The ESB can naturally connect with all these data sources and pull in such data effectively. However, to deposit all that information in real-time for the analytics tool to read, it will leverage the IMDB.
• Large file processing – When IT has to process a large file (> 10 GB in size), it is usually done as a nightly job so as to not impact the performance of any system or database during regular hours. However, there are scenarios when we need to process a large file like that during regular business hours. Also, the other requirement for this scenario might also be that you may need a really large chunk (if not all) of that data to be available in memory for parsing, validation and cross-reference reasons. Again, the ESB may have native capabilities to parse that file format very easily. But, to enable the ESB to read such a large chunk of the file in one shot, an IMDB is necessary. This scenario can be quite common in the Retail space (Initial Product Master load), High-tech manufacturing (Test data, supplier files etc) or in the Financial industry (Trades).

So to sum it up, when you ask an ESB vendor on how their product scales up and if they answer – “Throw in more memory” at it, question that immediately. As you now know, an ESB by itself cannot manage all that memory. It is constrained by whatever JVM or CLR constraints it is allowed. Even if your architect thinks that they can tweak the JVM parameters to use up more extended memory, remind them that the garbage collection (GC) process only takes longer and slows down the ESB even more when more memory is included. You need more sophisticated technology like IMDB to allow for very large main memory access, storage and clean-up. IMDBs have their own GC management and hence, do not rely on native JVM GC techniques. As a result, they are very fast.

Go ahead and power up your ESB. Build up an Application Integration Strategy. Give your ESB a shot in the arm by pairing it with an IMDB, if you run into any of the above mentioned scenarios or one where you see a need for such technology. When you get into more Big Data scenarios, this approach discussed here (as well as in this other post by a colleague), may serve as a possible solution pattern to that problem but may not be the entire solution. There may be other challenges that you may encounter that you typically do not see in your day-to-day use cases within your industry vertical. Topic for a different day! Write to me about some of the big data use cases that you have encountered.

Sayonara!!

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According to IDC, for “the past several years, the IT industry’s transition to the 3rd Platform, built on mobile computing, cloud services, social networking, and big data analytics technologies, has dominated the annual Predictions… For 2013, IDC predicts the transition to the 3rd Platform will shift into high gear as the industry accelerates past the exploration phase and into full-blown, high stakes competition.”

Yes folks, the time is now. Gartner echoes the same beliefs. “Mr. Cearley said these strategic technologies are emerging amidst a nexus of converging forces – social, mobile, cloud and information. Although these forces are innovative and disruptive on their own, together they are revolutionizing business and society, disrupting old business models and creating new leaders. As such, the Nexus of Forces is the basis of the technology platform of the future.”

And our customers are ready for change too. We recently polled over 800 ProcessWorld attendees and the survey confirms that Big Data, Cloud, Mobile and Collaborative technologies will show adoption rates of over 20% in the next 24 months. The simultaneous adoption of these technologies is significantly changing the way enterprises do business and accelerating the reaction of enterprises to changing customer and market behavior. Asked to identify technologies that will open major business opportunities, over 20% of responders see the management of Big Data and collaborative work practices as important technologies, over 30% see Cloud and mobile applications driving new business and over 40% see BPM as the key technology in the digital enterprise.

At ProcessWorld 2012 Software AG announced a major update to its webMethods and ARIS business process product suites focusing on integrating Big Data with Business Processes and the Cloud. The validity of this strategy has been fully confirmed by this survey. “Driving the adoption of these technologies is the need for faster, better informed business decisions”, stated Software AG CTO, Wolfram Jost. “But the real-time data, rapid scalability and fast decision making enabled by these technologies are of little use if companies still take months or years to implement business process change. The market will not wait, By integrating business processes with Big Data, the Cloud and both Mobile and Collaborative applications we are giving enterprises an uninterrupted rapid response service to changing market conditions”.

ProcessWorld attendees primarily saw the integration of on and off premise application integration as the main driver for Cloud adoption. “Rapid scalability is key in the light of the survey’s findings”, said Wolfram Jost. “And rapid integration of existing on-premise infrastructure with Cloud based applications is a significant feature of webMethods 9.0. We developed both webMethods 9.0 and ARIS 9.0 in close cooperation with major customers and this survey shows that we are right on track”.

The impact of these technologies is huge. Organizations can save billions of dollars a year and dramatically increase their operating profit by adopting and implementing these strategic technologies. Is your enterprise ready for the new wave of cloud, mobile, social and big data? Where does your organization see the most value in the 4 forces?

The post Big Data, Cloud, and Process Management Are Driving the Digital Enterprise appeared first on Reality Check.

As a result, more and more online gaming companies are turning to in-memory computing. Ditching disk-bound databases, they’re storing game objects and event data in ultra-fast RAM because — thanks to plummeting RAM prices — there’s no longer an excuse for forcing players to endure the annoyance of waiting for games to talk to a traditional database. In fact, at Terracotta, we’re seeing online gaming companies use BigMemory, our flagship in-memory data  solution, to address three of their biggest Big Data challenges:

1. Better, faster gameplay: Great games personalize their experiences based on a player’s preferences and gaming history. They also deliver complex interactions between the player, the environment, and other players. Top game makers are now using BigMemory to manage the exploding volume of data associated with these objects in machine memory, virtually eliminating slow disk interactions and greatly improving performance.

2. More realistic graphics: Online game makers traditionally relied on caching to deliver in-game graphics. Unfortunately, caching keeps only a small subset of a game’s landscape in memory to be served up quickly. Gaming companies have turned to BigMemory to store ALL of their maps and frames — terabytes of data — in memory for instant access. The result is dynamic frame generation based on events and player actions, adding freshness and excitement each time a game is played. .

3. Platform portability (with Java): Java is attractive to game makers because it’s easily ported to any JVM-supported OS, but Java has not traditionally been the preferred choice for gaming engines due to performance concerns, especially around garbage collection. With BigMemory, garbage collection performance problems are a thing of the past, and game makers can finally take full advantage of Java’s ubiquity. An in-memory, Java-based development environment greatly reduces development costs while opening up new revenue streams on previously unsupported platforms.

Terracotta’s flagship in-memory data solution, BigMemory, has been successfully deployed by several global online gaming companies to deliver seamless gaming experiences with lightning-fast performance. If you think your game and/or gaming platform might benefit from in-memory technology, please send me a note at gagan@terracotta.org or write a comment below. Also, check out this video — and beware: When you start using BigMemory, it can drive competitors crazy!

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