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
At CloudEXPO Silicon Valley, June 24-26, 2019, Digital Transformation (DX) is a major focus with expanded DevOpsSUMMIT and FinTechEXPO programs within the DXWorldEXPO agenda. Successful transformation requires a laser focus on being data-driven and on using all the tools available that enable transformation if they plan to survive over the long term. A total of 88% of Fortune 500 companies from a generation ago are now out of business. Only 12% still survive. Similar percentages are found throug...
AI and machine learning disruption for Enterprises started happening in the areas such as IT operations management (ITOPs) and Cloud management and SaaS apps. In 2019 CIOs will see disruptive solutions for Cloud & Devops, AI/ML driven IT Ops and Cloud Ops. Customers want AI-driven multi-cloud operations for monitoring, detection, prevention of disruptions. Disruptions cause revenue loss, unhappy users, impacts brand reputation etc.
Atmosera delivers modern cloud services that maximize the advantages of cloud-based infrastructures. Offering private, hybrid, and public cloud solutions, Atmosera works closely with customers to engineer, deploy, and operate cloud architectures with advanced services that deliver strategic business outcomes. Atmosera's expertise simplifies the process of cloud transformation and our 20+ years of experience managing complex IT environments provides our customers with the confidence and trust tha...
At CloudEXPO Silicon Valley, June 24-26, 2019, Digital Transformation (DX) is a major focus with expanded DevOpsSUMMIT and FinTechEXPO programs within the DXWorldEXPO agenda. Successful transformation requires a laser focus on being data-driven and on using all the tools available that enable transformation if they plan to survive over the long term. A total of 88% of Fortune 500 companies from a generation ago are now out of business. Only 12% still survive. Similar percentages are found throug...
As you know, enterprise IT conversation over the past year have often centered upon the open-source Kubernetes container orchestration system. In fact, Kubernetes has emerged as the key technology -- and even primary platform -- of cloud migrations for a wide variety of organizations. Kubernetes is critical to forward-looking enterprises that continue to push their IT infrastructures toward maximum functionality, scalability, and flexibility. As they do so, IT professionals are also embr...
The Japan External Trade Organization (JETRO) is a non-profit organization that provides business support services to companies expanding to Japan. With the support of JETRO's dedicated staff, clients can incorporate their business; receive visa, immigration, and HR support; find dedicated office space; identify local government subsidies; get tailored market studies; and more.
In his general session at 19th Cloud Expo, Manish Dixit, VP of Product and Engineering at Dice, discussed how Dice leverages data insights and tools to help both tech professionals and recruiters better understand how skills relate to each other and which skills are in high demand using interactive visualizations and salary indicator tools to maximize earning potential. Manish Dixit is VP of Product and Engineering at Dice. As the leader of the Product, Engineering and Data Sciences team at D...
As you know, enterprise IT conversation over the past year have often centered upon the open-source Kubernetes container orchestration system. In fact, Kubernetes has emerged as the key technology -- and even primary platform -- of cloud migrations for a wide variety of organizations. Kubernetes is critical to forward-looking enterprises that continue to push their IT infrastructures toward maximum functionality, scalability, and flexibility.
Today's workforce is trading their cubicles and corporate desktops in favor of an any-location, any-device work style. And as digital natives make up more and more of the modern workforce, the appetite for user-friendly, cloud-based services grows. The center of work is shifting to the user and to the cloud. But managing a proliferation of SaaS, web, and mobile apps running on any number of clouds and devices is unwieldy and increases security risks. Steve Wilson, Citrix Vice President of Cloud,...
When Enterprises started adopting Hadoop-based Big Data environments over the last ten years, they were mainly on-premise deployments. Organizations would spin up and manage large Hadoop clusters, where they would funnel exabytes or petabytes of unstructured data.However, over the last few years the economics of maintaining this enormous infrastructure compared with the elastic scalability of viable cloud options has changed this equation. The growth of cloud storage, cloud-managed big data e...