Welcome!

AJAX & REA Authors: RealWire News Distribution, Harald Zeitlhofer

Related Topics: SOA & WOA

SOA & WOA: Article

In-Memory BI Is Not the Future, It’s the Past

Why the current in-memory BI hype can be misleading.

In recent times, one of the most popular subjects related to the field of Business Intelligence (BI) has been In-memory BI technology. The subject gained popularity largely due to the success of QlikTech, provider of the in-memory-based QlikView BI product. Following QlikTech’s lead, many other BI vendors have jumped on the in-memory “hype wagon,” including the software giant, Microsoft, which has been aggressively marketing PowerPivot, their own in-memory database engine.

The increasing hype surrounding in-memory BI has caused BI consultants, analysts and even vendors to spew out endless articles, blog posts and white papers on the subject, many of which have also gone the extra mile to describe in-memory technology as the future of business intelligence, the death blow to the data warehouse and the swan song of OLAP technology. I find one of these in my inbox every couple of weeks.

Just so it is clear - the concept of in-memory business intelligence is not new. It has been around for many years. The only reason it became widely known recently is because it wasn’t feasible before 64-bit computing became commonly available. Before 64-bit processors, the maximum amount of RAM a computer could utilize was barely 4GB, which is hardly enough to accommodate even the simplest of multi-user BI solutions. Only when 64-bit systems became cheap enough did it became possible to consider in-memory technology as a practical option for BI.

The success of QlikTech and the relentless activities of Microsoft’s marketing machine have managed to confuse many in terms of what role in-memory technology plays in BI implementations. And that is why many of the articles out there, which are written by marketers or market analysts who are not proficient in the internal workings of database technology (and assume their readers aren’t either), are usually filled with inaccuracies and, in many cases, pure nonsense.

The purpose of this article is to put both in-memory and disk-based BI technologies in perspective, explain the differences between them and finally lay out, in simple terms, why disk-based BI technology isn’t on its way to extinction. Rather, disk-based BI technology is evolving into something that will significantly limit the use of in-memory technology in typical BI implementations.

But before we get to that, for the sake of those who are not very familiar with in-memory BI technology, here’s a brief introduction to the topic.

Disk and RAM
Generally speaking, your computer has two types of data storage mechanisms – disk (often called a hard disk) and RAM (random access memory). The important differences between them (for this discussion) are outlined in the following table:

Disk RAM
Abundant Scarce
Slower Faster
Cheap Expensive
Long-term Short-term

Most modern computers have 15-100 times more available disk storage than they do RAM. My laptop, for example, has 8GB of RAM and 300GB of available disk space. However, reading data from disk is much slower than reading the same data from RAM. This is one of the reasons why 1GB of RAM costs approximately 320 times that of 1GB of disk space.

Another important distinction is what happens to the data when the computer is powered down: data stored on disk is unaffected (which is why your saved documents are still there the next time you turn on your computer), but data residing in RAM is instantly lost. So, while you don’t have to re-create your disk-stored Microsoft Word documents after a reboot, you do have to re-load the operating system, re-launch the word processor and reload your document. This is because applications and their internal data are partly, if not entirely, stored in RAM while they are running.

Disk-based Databases and In-memory Databases
Now that we have a general idea of what the basic differences between disk and RAM are, what are the differences between disk-based and in-memory databases? Well, all data is always kept on hard disks (so that they are saved even when the power goes down). When we talk about whether a database is disk-based or in-memory, we are talking about where the data resides while it is actively being queried by an application: with disk-based databases, the data is queried while stored on disk and with in-memory databases, the data being queried is first loaded into RAM.

Disk-based databases are engineered to efficiently query data residing on the hard drive. At a very basic level, these databases assume that the entire data cannot fit inside the relatively small amount of RAM available and therefore must have very efficient disk reads in order for queries to be returned within a reasonable time frame. The engineers of such databases have the benefit of unlimited storage, but must face the challenges of relying on relatively slow disk operations.

On the other hand, in-memory databases work under the opposite assumption that the data can, in fact, fit entirely inside the RAM. The engineers of in-memory databases benefit from utilizing the fastest storage system a computer has (RAM), but have much less of it at their disposal.

That is the fundamental trade-off in disk-based and in-memory technologies: faster reads and limited amounts of data versus slower reads and practically unlimited amounts of data. These are two critical considerations for business intelligence applications, as it is important both to have fast query response times and to have access to as much data as possible.

The Data Challenge
A business intelligence solution (almost) always has a single data store at its center. This data store is usually called a database, data warehouse, data mart or OLAP cube. This is where the data that can be queried by the BI application is stored.

The challenges in creating this data store using traditional disk-based technologies is what gave in-memory technology its 15 minutes (ok, maybe 30 minutes) of fame. Having the entire data model stored inside RAM allowed bypassing some of the challenges encountered by their disk-based counterparts, namely the issue of query response times or ‘slow queries.’

Disk-based BI
When saying ‘traditional disk-based’ technologies, we typically mean relational database management systems (RDBMS) such as SQL Server, Oracle, MySQL and many others. It’s true that having a BI solution perform well using these types of databases as their backbone is far more challenging than simply shoving the entire data model into RAM, where performance gains would be immediate due to the fact RAM is so much faster than disk.

It’s commonly thought that relational databases are too slow for BI queries over data in (or close to) its raw form due to the fact they are disk-based. The truth is, however, that it’s because of how they use the disk and how often they use it.

Relational databases were designed with transactional processing in mind. But having a database be able to support high-performance insertions and updates of transactions (i.e., rows in a table) as well as properly accommodating the types of queries typically executed in BI solutions (e.g., aggregating, grouping, joining) is impossible. These are two mutually-exclusive engineering goals, that is to say they require completely different architectures at the very core. You simply can’t use the same approach to ideally achieve both.

In addition, the standard query language used to extract transactions from relational databases (SQL) is syntactically designed for the efficient fetching of rows, while rare are the cases in BI where you would need to scan or retrieve an entire row of data. It is nearly impossible to formulate an efficient BI query using SQL syntax.

So while relational databases are great as the backbone of operational applications such as CRM, ERP or Web sites, where transactions are frequently and simultaneously inserted, they are a poor choice for supporting analytic applications which usually involve simultaneous retrieval of partial rows along with heavy calculations.

In-memory BI
In-memory databases approach the querying problem by loading the entire dataset into RAM. In so doing, they remove the need to access the disk to run queries, thus gaining an immediate and substantial performance advantage (simply because scanning data in RAM is orders of magnitude faster than reading it from disk). Some of these databases introduce additional optimizations which further improve performance. Most of them also employ compression techniques to represent even more data in the same amount of RAM.

Regardless of what fancy footwork is used with an in-memory database, storing the entire dataset in RAM has a serious implication: the amount of data you can query with in-memory technology is limited by the amount of free RAM available, and there will always be much less available RAM than available disk space.

The bottom line is that this limited memory space means that the quality and effectiveness of your BI application will be hindered: the more historical data to which you have access and/or the more fields you can query, the better analysis, insight and, well, intelligence you can get.

You could add more and more RAM, but then the hardware you require becomes exponentially more expensive. The fact that 64-bit computers are cheap and can theoretically support unlimited amounts of RAM does not mean they actually do in practice. A standard desktop-class (read: cheap) computer with standard hardware physically supports up to 12GB of RAM today. If you need more, you can move on to a different class of computer which costs about twice as much and will allow you up to 64GB. Beyond 64GB, you can no longer use what is categorized as a personal computer but will require a full-blown server which brings you into very expensive computing territory.

It is also important to understand that the amount of RAM you need is not only affected by the amount of data you have, but also by the number of people simultaneously querying it. Having 5-10 people using the same in-memory BI application could easily double the amount of RAM required for intermediate calculations that need to be performed to generate the query results. A key success factor in most BI solutions is having a large number of users, so you need to tread carefully when considering in-memory technology for real-world BI. Otherwise, your hardware costs may spiral beyond what you are willing or able to spend (today, or in the future as your needs increase).

There are other implications to having your data model stored in memory, such as having to re-load it from disk to RAM every time the computer reboots and not being able to use the computer for anything other than the particular data model you’re using because its RAM is all used up.

A Note about QlikView and PowerPivot In-memory Technologies
QlikTech is the most active in-memory BI player out there so their QlikView in-memory technology is worth addressing in its own right. It has been repeatedly described as “unique, patented associative technology” but, in fact, there is nothing “associative” about QlikView’s in-memory technology. QlikView uses a simple tabular data model, stored entirely in-memory, with basic token-based compression applied to it. In QlikView’s case, the word associative relates to the functionality of its user interface, not how the data model is physically stored. Associative databases are a completely different beast and have nothing in common with QlikView’s technology.

PowerPivot uses a similar concept, but is engineered somewhat differently due to the fact it’s meant to be used largely within Excel. In this respect, PowerPivot relies on a columnar approach to storage that is better suited for the types of calculations conducted in Excel 2010, as well as for compression. Quality of compression is a significant differentiator between in-memory technologies as better compression means that you can store more data in the same amount RAM (i.e., more data is available for users to query). In its current version, however, PowerPivot is still very limited in the amounts of data it supports and requires a ridiculous amount of RAM.

The Present and Future Technologies
The destiny of BI lies in technologies that leverage the respective benefits of both disk-based and in-memory technologies to deliver fast query responses and extensive multi-user access without monstrous hardware requirements. Obviously, these technologies cannot be based on relational databases, but they must also not be designed to assume a massive amount of RAM, which is a very scarce resource.

These types of technologies are not theoretical anymore and are already utilized by businesses worldwide. Some are designed to distribute different portions of complex queries across multiple cheaper computers (this is a good option for cloud-based BI systems) and some are designed to take advantage of 21st-century hardware (multi-core architectures, upgraded CPU cache sizes, etc.) to extract more juice from off-the-shelf computers.

A Final Note: ElastiCube Technology
The technology developed by the company I co-founded, SiSense, belongs to the latter category. That is, SiSense utilizes technology which combines the best of disk-based and in-memory solutions, essentially eliminating the downsides of each. SiSense’s BI product, Prism, enables a standard PC to deliver a much wider variety of BI solutions, even when very large amounts of data, large numbers of users and/or large numbers of data sources are involved, as is the case in typical BI projects.

When we began our research at SiSense, our technological assumption was that it is possible to achieve in-memory-class query response times, even for hundreds of users simultaneously accessing massive data sets, while keeping the data (mostly) stored on disk. The result of our hybrid disk-based/in-memory technology is a BI solution based on what we now call ElastiCube, after which this blog is named. You can read more about this technological approach, which we call Just-in-Time In-memory Processing, at our BI Software Evolved technology page.

More Stories By Elad Israeli

Elad Israeli is co-founder of business intelligence software company, SiSense. SiSense has developed Prism, a next-generation business intelligence platform based on its own, unique ElastiCube BI technology. Elad is responsible for driving the vision and strategy of SiSense’s unique BI products. Before co-founding SiSense, Elad served as a Product Manager at global IT services firm Ness Technologies (NASDAQ: NSTC). Previously, Elad was a Product Manager at Anysoft and, before that, he co-founded and led technology development at BiSense, a BI technology company.

Cloud Expo Latest Stories
14th International Cloud Expo, held on June 10–12, 2014 at the Javits Center in New York City, featured three content-packed days with a rich array of sessions about the business and technical value of cloud computing, Internet of Things, Big Data, and DevOps led by exceptional speakers from every sector of the IT ecosystem. The Cloud Expo series is the fastest-growing Enterprise IT event in the past 10 years, devoted to every aspect of delivering massively scalable enterprise IT as a service.
As more applications and services move "to the cloud" (public or on-premise) cloud environments are increasingly adopting and building out traditional enterprise features. This in turn is enabling and encouraging cloud adoption from enterprise users. In many ways the definition is blurring as features like continuous operation, geo-distribution or on-demand capacity become the norm. NuoDB is involved in both building enterprise software and using enterprise cloud capabilities. In his session at 15th Cloud Expo, Seth Proctor, CTO at NuoDB, Inc., will discuss the experiences from building, deploying and using enterprise services and suggest some ways to approach moving enterprise applications into a cloud model.
Until recently, many organizations required specialized departments to perform mapping and geospatial analysis, and they used Esri on-premise solutions for that work. In his session at 15th Cloud Expo, Dave Peters, author of the Esri Press book Building a GIS, System Architecture Design Strategies for Managers, will discuss how Esri has successfully included the cloud as a fully integrated SaaS expansion of the ArcGIS mapping platform. Organizations that have incorporated Esri cloud-based applications and content within their business models are reaping huge benefits by directly leveraging cloud-based mapping and analysis capabilities within their existing enterprise investments. The ArcGIS mapping platform includes cloud-based content management and information resources to more widely, efficiently, and affordably deliver real-time actionable information and analysis capabilities to your organization.
In his session at 15th Cloud Expo, Mark Hinkle, Senior Director, Open Source Solutions at Citrix Systems Inc., will provide overview of the open source software that can be used to deploy and manage a cloud computing environment. He will include information on storage, networking(e.g., OpenDaylight) and compute virtualization (Xen, KVM, LXC) and the orchestration(Apache CloudStack, OpenStack) of the three to build their own cloud services. Speaker Bio: Mark Hinkle is the Senior Director, Open Source Solutions, at Citrix Systems Inc. He joined Citrix as a result of their July 2011 acquisition of Cloud.com where he was their Vice President of Community. He is currently responsible for Citrix open source efforts around the open source cloud computing platform, Apache CloudStack and the Xen Hypervisor. Previously he was the VP of Community at Zenoss Inc., a producer of the open source application, server, and network management software, where he grew the Zenoss Core project to over 10...
Almost everyone sees the potential of Internet of Things but how can businesses truly unlock that potential. The key will be in the ability to discover business insight in the midst of an ocean of Big Data generated from billions of embedded devices via Systems of Discover. Businesses will also need to ensure that they can sustain that insight by leveraging the cloud for global reach, scale and elasticity. In his session at Internet of @ThingsExpo, Mac Devine, Distinguished Engineer at IBM, will discuss bringing these three elements together via Systems of Discover.
Cloud and Big Data present unique dilemmas: embracing the benefits of these new technologies while maintaining the security of your organization’s assets. When an outside party owns, controls and manages your infrastructure and computational resources, how can you be assured that sensitive data remains private and secure? How do you best protect data in mixed use cloud and big data infrastructure sets? Can you still satisfy the full range of reporting, compliance and regulatory requirements? In his session at 15th Cloud Expo, Derek Tumulak, Vice President of Product Management at Vormetric, will discuss how to address data security in cloud and Big Data environments so that your organization isn’t next week’s data breach headline.
The cloud is everywhere and growing, and with it SaaS has become an accepted means for software delivery. SaaS is more than just a technology, it is a thriving business model estimated to be worth around $53 billion dollars by 2015, according to IDC. The question is – how do you build and scale a profitable SaaS business model? In his session at 15th Cloud Expo, Jason Cumberland, Vice President, SaaS Solutions at Dimension Data, will give the audience an understanding of common mistakes businesses make when transitioning to SaaS; how to avoid them; and how to build a profitable and scalable SaaS business.
SYS-CON Events announced today that Gridstore™, the leader in software-defined storage (SDS) purpose-built for Windows Servers and Hyper-V, will exhibit at SYS-CON's 15th International Cloud Expo®, which will take place on November 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA. Gridstore™ is the leader in software-defined storage purpose built for virtualization that is designed to accelerate applications in virtualized environments. Using its patented Server-Side Virtual Controller™ Technology (SVCT) to eliminate the I/O blender effect and accelerate applications Gridstore delivers vmOptimized™ Storage that self-optimizes to each application or VM across both virtual and physical environments. Leveraging a grid architecture, Gridstore delivers the first end-to-end storage QoS to ensure the most important App or VM performance is never compromised. The storage grid, that uses Gridstore’s performance optimized nodes or capacity optimized nodes, starts with as few a...
SYS-CON Events announced today that Solgenia, the global market leader in Cloud Collaboration and Cloud Infrastructure software solutions, will exhibit at SYS-CON's 15th International Cloud Expo®, which will take place on November 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA. Solgenia is the global market leader in Cloud Collaboration and Cloud Infrastructure software solutions. Designed to “Bridge the Gap” between personal and professional social, mobile and cloud user experiences, our solutions help large and medium-sized organizations dramatically improve productivity, reduce collaboration costs, and increase the overall enterprise value by bringing collaboration and infrastructure solutions to the cloud.
Cloud computing started a technology revolution; now DevOps is driving that revolution forward. By enabling new approaches to service delivery, cloud and DevOps together are delivering even greater speed, agility, and efficiency. No wonder leading innovators are adopting DevOps and cloud together! In his session at DevOps Summit, Andi Mann, Vice President of Strategic Solutions at CA Technologies, will explore the synergies in these two approaches, with practical tips, techniques, research data, war stories, case studies, and recommendations.
Enterprises require the performance, agility and on-demand access of the public cloud, and the management, security and compatibility of the private cloud. The solution? In his session at 15th Cloud Expo, Simone Brunozzi, VP and Chief Technologist(global role) for VMware, will explore how to unlock the power of the hybrid cloud and the steps to get there. He'll discuss the challenges that conventional approaches to both public and private cloud computing, and outline the tough decisions that must be made to accelerate the journey to the hybrid cloud. As part of the transition, an Infrastructure-as-a-Service model will enable enterprise IT to build services beyond their data center while owning what gets moved, when to move it, and for how long. IT can then move forward on what matters most to the organization that it supports – availability, agility and efficiency.
Every healthy ecosystem is diverse. This is especially true in cloud ecosystems, where portability and interoperability are more important than old enterprise models of proprietary ownership. In his session at 15th Cloud Expo, Mark Baker, Server Product Manager at Canonical/Ubuntu, will discuss how single vendors used to take the lead in creating and delivering technology, but in a cloud economy, where users want tools of their preference, when and where they need them, it makes no sense.
The 15th International Cloud Expo has just expanded its conference program, to bring together Cloud Computing, APM, APIs, Security, Big Data, Internet of Things, DevOps and WebRTC at one location. Cloud Expo is the single show where delegates and technology vendors can meet to experience and discuss the entire world of the cloud. With cloud computing driving a higher percentage of enterprise IT budgets every year, it becomes increasingly important to learn about the latest technology developments and solutions.
SYS-CON Events announced today that Bsquare Corporation, a leading enabler of smart connected systems, has been named “Bronze Sponsor” of SYS-CON's Internet of @ThingsExpo, which will take place on November 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA. Bsquare is a global leader of embedded software solutions. We enable smart connected systems at the device level and beyond that millions use every day and provide actionable data solutions for the growing Internet of Things (IoT) market. We empower our world-class customers with our products, services and solutions to achieve innovation and success.
SYS-CON Events announced today that NuoDB, Inc., the leader in webscale distributed database technology, has been named “Bronze Sponsor” of SYS-CON's 15th International Cloud Expo®, which will take place on November 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA. NuoDB was launched in 2010 by industry-renowned database architect Jim Starkey and accomplished software CEO Barry Morris to deliver a webscale distributed database management system that is specifically designed for the cloud and the modern datacenter.