Welcome!

Weblogic Authors: Yeshim Deniz, Elizabeth White, Michael Meiner, Michael Bushong, Avi Rosenthal

Related Topics: Microservices Expo, Industrial IoT

Microservices Expo: Article

The In-Memory Technologies Behind Business Intelligence Software

Understanding the in-memory technologies that are used in Business Intelligence software

If you follow trends in the business intelligence (BI) space, you'll notice that many analysts, independent bloggers and BI vendors talk about in-memory technology.

There are technical differences that separate one in-memory technology from another, some of which are listed on Boris Evelson's blog.

Some of the items on Boris' list are just as applicable to BI technologies that are not in-memory (‘Incremental updates', for example), but there is one item that merits much deeper discussion. Boris calls this characteristic ‘Memory Swapping' and describes it as, What the (BI) vendor's approach is for handling models that are larger than what fits into a single memory space.

Understanding Memory Swapping
The fundamental idea of in-memory BI technology is the ability to perform real-time calculations without having to perform slow disk operations during the execution of a query. For more details on this, visit my article describing how in-memory technology works.

Obviously, in order to perform calculations on data completely in memory, all the relevant data must reside in memory, i.e., in the computer's RAM. So the questions are: 1) how does the data get there? and 2) how long does it stay there?

These are probably the most important aspects of in-memory technology, as they have great implications on the BI solution as a whole.

Pure In-Memory Technology
Pure in-memory technologies are the class of in-memory technologies that load the entire data model into RAM before a single query can be executed by users. An example of a BI product which utilizes such a technology is QlikView.

QlikView's technology is described as "associative technology." That is a fancy way of saying that QlikView uses a simple tabular data model which is stored entirely in memory. For QlikView, much like any other pure in-memory technology, compression is very important. Compressing the data well makes it possible to hold more data inside a fixed amount of RAM

Pure in-memory technologies which do not compress the data they store in memory are usually quite useless for BI. They either handle amounts of data too small to extract interesting information from, or they break too often.

With or without compression, the fact remains that pure in-memory BI solutions become useless when RAM runs out for the entire data model, even if you're only looking to work with limited portions of it at any one time.

Just-In-Time In-Memory Technology
Just-In-Time In-Memory (or JIT In-Memory) technology only loads the portion of the data into RAM required for a particular query, on demand. An example of a BI product which utilizes this type of technology is SiSense.

Note: The term JIT is borrowed from Just-In-Time compilation, which is a method to improve the runtime performance of computer programs.

JIT in-memory technology involves a smart caching engine that loads selected data into RAM and releases it according to usage patterns.

This approach has obvious advantages:

  1. You have access to far more data than can fit in RAM at any one time
  2. It is easier to have a shared cache for multiple users
  3. It is easier to build solutions that are distributed across several machines

However, since JIT In-Memory loads data on demand, an obvious question arises: Won't the disk reads introduce unbearable performance issues?

The answer would be yes, if the data model used is tabular (as they are in RDBMSs such as SQL Server and Oracle, or pure in-memory technologies such as QlikView), but scalable JIT In-Memory solutions rely on a columnar database instead of a tabular database.

This fundamental ability of columnar databases to access only particular fields, or parts of fields, is what makes JIT In-Memory so powerful. In fact, the impact of columnar database technology on in-memory technology is so great, that many confuse the two.

The combination of JIT In-Memory technology and a columnar database structure delivers the performance of pure in-memory BI technology with the scalability of disk-based models, and is thus an ideal technological basis for large-scale and/or rapidly-growing BI data stores.


The ElastiCube Chronicles - Business Intelligence Blog

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.

IoT & Smart Cities Stories
IoT is rapidly becoming mainstream as more and more investments are made into the platforms and technology. As this movement continues to expand and gain momentum it creates a massive wall of noise that can be difficult to sift through. Unfortunately, this inevitably makes IoT less approachable for people to get started with and can hamper efforts to integrate this key technology into your own portfolio. There are so many connected products already in place today with many hundreds more on the h...
Digital Transformation is much more than a buzzword. The radical shift to digital mechanisms for almost every process is evident across all industries and verticals. This is often especially true in financial services, where the legacy environment is many times unable to keep up with the rapidly shifting demands of the consumer. The constant pressure to provide complete, omnichannel delivery of customer-facing solutions to meet both regulatory and customer demands is putting enormous pressure on...
Business professionals no longer wonder if they'll migrate to the cloud; it's now a matter of when. The cloud environment has proved to be a major force in transitioning to an agile business model that enables quick decisions and fast implementation that solidify customer relationships. And when the cloud is combined with the power of cognitive computing, it drives innovation and transformation that achieves astounding competitive advantage.
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by ...
Charles Araujo is an industry analyst, internationally recognized authority on the Digital Enterprise and author of The Quantum Age of IT: Why Everything You Know About IT is About to Change. As Principal Analyst with Intellyx, he writes, speaks and advises organizations on how to navigate through this time of disruption. He is also the founder of The Institute for Digital Transformation and a sought after keynote speaker. He has been a regular contributor to both InformationWeek and CIO Insight...
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
Early Bird Registration Discount Expires on August 31, 2018 Conference Registration Link ▸ HERE. Pick from all 200 sessions in all 10 tracks, plus 22 Keynotes & General Sessions! Lunch is served two days. EXPIRES AUGUST 31, 2018. Ticket prices: ($1,295-Aug 31) ($1,495-Oct 31) ($1,995-Nov 12) ($2,500-Walk-in)
Andrew Keys is Co-Founder of ConsenSys Enterprise. He comes to ConsenSys Enterprise with capital markets, technology and entrepreneurial experience. Previously, he worked for UBS investment bank in equities analysis. Later, he was responsible for the creation and distribution of life settlement products to hedge funds and investment banks. After, he co-founded a revenue cycle management company where he learned about Bitcoin and eventually Ethereal. Andrew's role at ConsenSys Enterprise is a mul...
Nicolas Fierro is CEO of MIMIR Blockchain Solutions. He is a programmer, technologist, and operations dev who has worked with Ethereum and blockchain since 2014. His knowledge in blockchain dates to when he performed dev ops services to the Ethereum Foundation as one the privileged few developers to work with the original core team in Switzerland.
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...