|By Elad Israeli||
|October 7, 2010 10:25 AM EDT||
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:
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.’
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 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.
SYS-CON Events announced today that HPM Networks will exhibit at the 17th International Cloud Expo®, which will take place on November 3–5, 2015, at the Santa Clara Convention Center in Santa Clara, CA. For 20 years, HPM Networks has been integrating technology solutions that solve complex business challenges. HPM Networks has designed solutions for both SMB and enterprise customers throughout the San Francisco Bay Area.
Aug. 2, 2015 05:45 PM EDT Reads: 505
For IoT to grow as quickly as analyst firms’ project, a lot is going to fall on developers to quickly bring applications to market. But the lack of a standard development platform threatens to slow growth and make application development more time consuming and costly, much like we’ve seen in the mobile space. In his session at @ThingsExpo, Mike Weiner, Product Manager of the Omega DevCloud with KORE Telematics Inc., discussed the evolving requirements for developers as IoT matures and conducted a live demonstration of how quickly application development can happen when the need to comply wit...
Aug. 2, 2015 11:15 AM EDT Reads: 354
The Internet of Everything (IoE) brings together people, process, data and things to make networked connections more relevant and valuable than ever before – transforming information into knowledge and knowledge into wisdom. IoE creates new capabilities, richer experiences, and unprecedented opportunities to improve business and government operations, decision making and mission support capabilities.
Aug. 1, 2015 10:00 AM EDT Reads: 335
Explosive growth in connected devices. Enormous amounts of data for collection and analysis. Critical use of data for split-second decision making and actionable information. All three are factors in making the Internet of Things a reality. Yet, any one factor would have an IT organization pondering its infrastructure strategy. How should your organization enhance its IT framework to enable an Internet of Things implementation? In his session at @ThingsExpo, James Kirkland, Red Hat's Chief Architect for the Internet of Things and Intelligent Systems, described how to revolutionize your archit...
Jul. 30, 2015 07:30 PM EDT Reads: 1,421
MuleSoft has announced the findings of its 2015 Connectivity Benchmark Report on the adoption and business impact of APIs. The findings suggest traditional businesses are quickly evolving into "composable enterprises" built out of hundreds of connected software services, applications and devices. Most are embracing the Internet of Things (IoT) and microservices technologies like Docker. A majority are integrating wearables, like smart watches, and more than half plan to generate revenue with APIs within the next year.
Jul. 30, 2015 02:30 PM EDT Reads: 140
Growth hacking is common for startups to make unheard-of progress in building their business. Career Hacks can help Geek Girls and those who support them (yes, that's you too, Dad!) to excel in this typically male-dominated world. Get ready to learn the facts: Is there a bias against women in the tech / developer communities? Why are women 50% of the workforce, but hold only 24% of the STEM or IT positions? Some beginnings of what to do about it! In her Opening Keynote at 16th Cloud Expo, Sandy Carter, IBM General Manager Cloud Ecosystem and Developers, and a Social Business Evangelist, d...
Jul. 30, 2015 12:00 PM EDT Reads: 2,079
In his keynote at 16th Cloud Expo, Rodney Rogers, CEO of Virtustream, discussed the evolution of the company from inception to its recent acquisition by EMC – including personal insights, lessons learned (and some WTF moments) along the way. Learn how Virtustream’s unique approach of combining the economics and elasticity of the consumer cloud model with proper performance, application automation and security into a platform became a breakout success with enterprise customers and a natural fit for the EMC Federation.
Jul. 30, 2015 09:00 AM EDT Reads: 2,173
The Internet of Things is not only adding billions of sensors and billions of terabytes to the Internet. It is also forcing a fundamental change in the way we envision Information Technology. For the first time, more data is being created by devices at the edge of the Internet rather than from centralized systems. What does this mean for today's IT professional? In this Power Panel at @ThingsExpo, moderated by Conference Chair Roger Strukhoff, panelists addressed this very serious issue of profound change in the industry.
Jul. 29, 2015 03:00 PM EDT Reads: 1,299
Discussions about cloud computing are evolving into discussions about enterprise IT in general. As enterprises increasingly migrate toward their own unique clouds, new issues such as the use of containers and microservices emerge to keep things interesting. In this Power Panel at 16th Cloud Expo, moderated by Conference Chair Roger Strukhoff, panelists addressed the state of cloud computing today, and what enterprise IT professionals need to know about how the latest topics and trends affect their organization.
Jul. 29, 2015 02:00 PM EDT Reads: 1,205
It is one thing to build single industrial IoT applications, but what will it take to build the Smart Cities and truly society-changing applications of the future? The technology won’t be the problem, it will be the number of parties that need to work together and be aligned in their motivation to succeed. In his session at @ThingsExpo, Jason Mondanaro, Director, Product Management at Metanga, discussed how you can plan to cooperate, partner, and form lasting all-star teams to change the world and it starts with business models and monetization strategies.
Jul. 28, 2015 04:30 PM EDT Reads: 1,776
Converging digital disruptions is creating a major sea change - Cisco calls this the Internet of Everything (IoE). IoE is the network connection of People, Process, Data and Things, fueled by Cloud, Mobile, Social, Analytics and Security, and it represents a $19Trillion value-at-stake over the next 10 years. In her keynote at @ThingsExpo, Manjula Talreja, VP of Cisco Consulting Services, discussed IoE and the enormous opportunities it provides to public and private firms alike. She will share what businesses must do to thrive in the IoE economy, citing examples from several industry sectors.
Jul. 28, 2015 11:00 AM EDT Reads: 2,049
There will be 150 billion connected devices by 2020. New digital businesses have already disrupted value chains across every industry. APIs are at the center of the digital business. You need to understand what assets you have that can be exposed digitally, what their digital value chain is, and how to create an effective business model around that value chain to compete in this economy. No enterprise can be complacent and not engage in the digital economy. Learn how to be the disruptor and not the disruptee.
Jul. 27, 2015 10:00 AM EDT Reads: 2,044
Akana has released Envision, an enhanced API analytics platform that helps enterprises mine critical insights across their digital eco-systems, understand their customers and partners and offer value-added personalized services. “In today’s digital economy, data-driven insights are proving to be a key differentiator for businesses. Understanding the data that is being tunneled through their APIs and how it can be used to optimize their business and operations is of paramount importance,” said Alistair Farquharson, CTO of Akana.
Jul. 27, 2015 09:00 AM EDT Reads: 334
Business as usual for IT is evolving into a "Make or Buy" decision on a service-by-service conversation with input from the LOBs. How does your organization move forward with cloud? In his general session at 16th Cloud Expo, Paul Maravei, Regional Sales Manager, Hybrid Cloud and Managed Services at Cisco, discusses how Cisco and its partners offer a market-leading portfolio and ecosystem of cloud infrastructure and application services that allow you to uniquely and securely combine cloud business applications and services across multiple cloud delivery models.
Jul. 27, 2015 08:00 AM EDT Reads: 1,910
The enterprise market will drive IoT device adoption over the next five years. In his session at @ThingsExpo, John Greenough, an analyst at BI Intelligence, division of Business Insider, analyzed how companies will adopt IoT products and the associated cost of adopting those products. John Greenough is the lead analyst covering the Internet of Things for BI Intelligence- Business Insider’s paid research service. Numerous IoT companies have cited his analysis of the IoT. Prior to joining BI Intelligence, he worked analyzing bank technology for Corporate Insight and The Clearing House Payment...
Jul. 26, 2015 09:00 PM EDT Reads: 1,588
"Optimal Design is a technology integration and product development firm that specializes in connecting devices to the cloud," stated Joe Wascow, Co-Founder & CMO of Optimal Design, in this SYS-CON.tv interview at @ThingsExpo, held June 9-11, 2015, at the Javits Center in New York City.
Jul. 25, 2015 02:00 PM EDT Reads: 404
SYS-CON Events announced today that CommVault has been named “Bronze Sponsor” of SYS-CON's 17th International Cloud Expo®, which will take place on November 3–5, 2015, at the Santa Clara Convention Center in Santa Clara, CA. A singular vision – a belief in a better way to address current and future data management needs – guides CommVault in the development of Singular Information Management® solutions for high-performance data protection, universal availability and simplified management of data on complex storage networks. CommVault's exclusive single-platform architecture gives companies unp...
Jul. 25, 2015 01:00 PM EDT Reads: 1,973
Electric Cloud and Arynga have announced a product integration partnership that will bring Continuous Delivery solutions to the automotive Internet-of-Things (IoT) market. The joint solution will help automotive manufacturers, OEMs and system integrators adopt DevOps automation and Continuous Delivery practices that reduce software build and release cycle times within the complex and specific parameters of embedded and IoT software systems.
Jul. 25, 2015 12:15 PM EDT Reads: 486
"ciqada is a combined platform of hardware modules and server products that lets people take their existing devices or new devices and lets them be accessible over the Internet for their users," noted Geoff Engelstein of ciqada, a division of Mars International, in this SYS-CON.tv interview at @ThingsExpo, held June 9-11, 2015, at the Javits Center in New York City.
Jul. 25, 2015 12:00 PM EDT Reads: 1,553
Internet of Things is moving from being a hype to a reality. Experts estimate that internet connected cars will grow to 152 million, while over 100 million internet connected wireless light bulbs and lamps will be operational by 2020. These and many other intriguing statistics highlight the importance of Internet powered devices and how market penetration is going to multiply many times over in the next few years.
Jul. 25, 2015 09:00 AM EDT Reads: 1,502