The end of single PLM database architecture is coming

August 5, 2014

PLM-distributed-cloud-database-architecture

The complexity of PLM implementations is growing. We have more data to manage. We need to process information faster. In addition to that, cloud solutions are changing the underlining technological landscape. PLM vendors are not building software to be distributed on CD-ROMs and installed by IT on corporate servers anymore. Vendors are moving towards different types of cloud (private and public) and selling subscriptions (not perpetual licenses). For vendors it means operating data centers, optimize data flow, cost and maintenance.

How to implement future cloud architecture? This question is coming to the focus and, obviously, raising lots of debates. Infoworld cloud computing article The right cloud for the job: multi-cloud database processing speaks about how cloud computing is influencing what is the core of every PDM and PLM system – database technology. Main message is to move towards distributed database architecture. What does it mean? I’m sure you are familiar with MapReduce approach. So, simply put, the opportunity of cloud infrastructure to bring multiple servers and run parallel queries is real these days. The following passage speaks about the idea of how to optimize data processing workload by leveraging cloud infrastructure:

In the emerging multicloud approach, the data-processing workloads run on the cloud services that best match the needs of the workload. That current push toward multicloud architectures provides the ability to place workloads on the public or private cloud services that best fit the needs of the workloads. This also provides the ability to run the workload on the cloud service that is most cost-efficient.

For example, when processing a query, the client that launches the database query may reside on a managed service provider. However, it may make the request to many server instances on the Amazon Web Services public cloud service. It could also manage a transactional database on the Microsoft Azure cloud. Moreover, it could store the results of the database request on a local OpenStack private cloud. You get the idea.

However, not so fast and not so simple. What works for web giants might not work for enterprise data management solutions. The absolute majority of PLM systems are leveraging single RDBMS architecture. This is fundamental underlining architectural approach. Most of these solutions are using "scale up" architecture to achieve data capacity and performance level. Horizontal scale of PLM solutions today is mostly limited to leverage database replication tech. PLM implementations are mission critical for many companies. To change that would be not so simple.

So, why PLM vendors might consider to make a change and to think about new database architectures? I can see few reasons – the amount of data is growing; companies are getting even more distributed; design anywhere, build anywhere philosophy comes into real life. The cost of infrastructure and data services becomes very important. In the same time for all companies performance is an absolute imperative – slow enterprise data management solutions is a thing in the past. To optimize workload and data processing is an opportunity for large PLM vendors as well as small startups.

What is my conclusion? Today, large PLM implementations are signaling about reaching technological and product limits. It means existing platforms are achieving a possible peak of complexity, scale and cost. To make the next leap, PLM vendors will have to re-think underlining architecture, to manage data differently and optimize cost of infrastructure. Data management architecture is the first to be considered. Which means end of existing "single database" architectures. Just my thoughts…

Best, Oleg


PLM Scale and Some Internet Factoids

December 22, 2012

The scalability of enterprise systems is an interesting topic. Enterprise IT usually keeps the story about scalability of systems close to their chest. It involves data centers, databases, channels, networks, latency, and many other aspects that allows you to tune your enterprise PLM. And I know, it was absolutely true for existing enterprise PDM and PLM.

The situation is different nowadays. Last 10 years of web development and internet established a new level of scale. The amount of data and user activities web and social networks can handle is going much beyond typical enterprise deployments. The following AronoldIT factoid article captured my attention earlier this week. I don’t know if these numbers are accurate. But knowing that Gangnam style fist video just hit 1B Youtube views, I can easy believe that.

Every minute more than 1,649,305 tweets get shared.
Every minute more than 3,472,225 photos get added to Facebook.
Every minute more than 2,060 brand new blogs are created.
Every minute more than 52,488 minutes of video are added to YouTube.
Every minute more than 31,510 new articles are created by an online newspaper.
Every minute more than 3,645,833,340 new spam emails are delivered online.

What is my conclusion? The consumer web and social media introduced a completely different perspective of scale, capacity and system performance. Enterprise PLM vendors and IT service companies need to start paying attention. The technological gap consumer systems are developing these days can easy outperform existing enterprise PDM and PLM deployments. Important. Just my thoughts…

Best, Oleg

Image courtesy of [ddpavumba] / FreeDigitalPhotos.net


How Big Is Product Lifecycle Data?

July 6, 2010

Product-related data is one of the most important aspects of any PLM implementation. When you talk about PLM implementation, the topic of product-related data (or IP) is very often becomes a center of the conversation.  There are multiple sources of this type of data in the organization. In my view, one of the PLM goals is to have a control of this data and provide tools to manage the overall lifecycle. One of the PLM implementation challenges is to provide wide support for product-related data. The topic I want to discuss is related the ability of PLM product to handle full scope of this product lifecycle data.

I read the article Oracle, SAP working on Exadata support. The core of this conversation is about how to scale up and provide extensive support for big data handling in the organization. Have a read of this article and make you opinion. Mine is simple – both Oracle and SAP understood the size of the potential problem (data size). They are working in multiple directions to find a solution for data sizing in transactional enterprise application. Should PLM care? This is a very good question in my view…

PLM and Product Lifecycle Data Problem
One of the challenges PLM is having for many years is getting control of product-related data. My observation shows that product-related data is not completely controlled by PLM systems in the majority of PLM implementations. Even with a very successful PLM implementation, data is scattered between multiple data sources and PLM is only one of them. In addition to that, product-related data can be located in the diverse set of applications used for product development.

Product Data, Size and PLM value
The full value of Product Lifecycle Management is directly dependent on how what scope of product-related data is covered by PLM. The wider scope can maximize PLM value for organizations. With all current developments, PLM is looking on starting from design to manufacturing strategies and development of social-oriented application, sizing can easily become one of the potential bottlenecks related to the ability to support large scope of data.

What is my conclusion? I think, to understand sizing of product lifecycle data is important in order to build right operational and strategic plans related to data management. Data is growing fast. Future PLM implementation can suffer from problems related to data sizing. How to scale up PLM implementation in terms of size can be one of the most important questions in the future. Just my thought…

Best, Oleg

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