What is In-Memory Analysis?

Below is a continuation of the transcript of a Webinar hosted by InetSoft on the topic of OLAP, the basics of what it is and an explanation of some of the technology terms related to, what OLAP's benefits are, and what the choices in OLAP technologies and OLAP tools are. The speaker is Mark Flaherty, CMO at InetSoft.

What is MDX – Multi-dimensional Expression?

The power and the strength of OLAP are based on its query language, which is called MDX. You could think of it simply as a series of SQL statements that are combined together to operate as a unit. So it has a very SQL-like semantic, and commonly you will find that if you view an MDX statement, it is a series of mini SQL statements that are all working together.

So this allows for a more powerful way to do SQL based analysis against your environment, and it’s also easier to do. If you use MDX, it is a much easier technique to apply to your data analysis than if you used straight SQL. Some of the power that you can get from an OLAP-based configuration comes from some of the algorithms, the statistical analysis that you can do that is built into the technology.

A couple of examples of those are the ability to do prior period analysis. So for example in the highlighted chart you could compare the first month of Q2 with the entire month prior. So this is one way you could spot trends or anomalies between two different periods of time that are of non-equal duration.

The other technique that is built in using MDX is parallel period analysis, where you are looking at one period of time against another period of time. Certainly this can be performed using traditional SQL, but with the power of MDX and the fact that this is built in, the ability to perform this sort of analysis is much easier and purpose built into the technology. There is also the asymmetric analysis where maybe I am trying to look at the month of February versus the average of all of the months of last year. So this a technique that is built into the way that MDX works, and that is one of the common reasons why a lot of database analysts prefer the MDX language over traditional SQL based techniques.

InetSoft's Style Intelligence can perform many of the common OLAP analysis functions. For instance you can do linear regressions and standard deviations, ranking and filtering by hierarchy, conditional calculations, and more. For instance, you can do complex charting that takes into account multiple groupings. When you are thinking OLAP and InetSoft capabilities, you should be thinking pre-built analytical functions that are supportive of the MDX language.

How Is In-Memory Technology Related to Elastic Computing?

In-memory technology and elastic computing are closely related concepts that together enable modern data processing, analytics, and cloud-based computing solutions. Both technologies contribute to high-performance computing by optimizing how data is processed, stored, and scaled to meet changing demands.

1. Understanding In-Memory Technology

In-memory technology refers to the practice of storing and processing data in RAM (Random Access Memory) rather than traditional disk-based storage. This approach significantly accelerates data retrieval and computational speed because RAM is much faster than hard drives or even solid-state drives (SSDs). In-memory databases, such as SAP HANA, Apache Ignite, and Redis, allow businesses to analyze massive datasets in real-time without the delays caused by reading and writing to disk storage.

2. Understanding Elastic Computing

Elastic computing is a cloud computing concept that enables automatic scaling of computing resources (such as CPU, memory, and storage) based on demand. Cloud providers like AWS, Azure, and Google Cloud offer elastic computing to ensure that applications and services can dynamically expand or contract resource allocation as workloads fluctuate. This prevents over-provisioning and optimizes cost-efficiency.

3. The Relationship Between In-Memory Technology and Elastic Computing

In-memory technology plays a crucial role in elastic computing environments by supporting fast, real-time data processing. Since elastic computing adjusts computing resources dynamically, it often works in tandem with in-memory computing to ensure that data-intensive applications remain performant under variable workloads. Here's how they interact:

• Dynamic Scaling of In-Memory Databases: Elastic computing allows in-memory databases to scale horizontally (adding more nodes) or vertically (adding more RAM/CPU) to handle increased data processing demands. This is especially useful for applications that require real-time analytics, such as fraud detection, stock market trading, and online gaming.

• Optimized Resource Utilization: By leveraging elastic computing, businesses can allocate in-memory resources efficiently. For instance, an e-commerce platform experiencing high traffic during a sale event can temporarily increase its in-memory database capacity to handle peak loads and then scale down when demand decreases.

• Cloud-Native In-Memory Solutions: Many in-memory databases and caching systems, such as Amazon ElastiCache (Redis/Memcached) and Google Cloud Memorystore, are designed for elastic computing environments. These services automatically adjust memory allocation based on workload demands, ensuring cost-effective performance.

• Real-Time Data Processing in Big Data and AI: Machine learning (ML) models and big data analytics often require in-memory processing for speed. Elastic computing enables businesses to scale in-memory processing clusters up or down as needed, providing computational power for real-time insights without excessive cost.

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