What are the types of data analysis technologies available?
Below is a continuation of the transcript of the 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.
Mark Flaherty (MF): When you look at what kinds of data analysis technologies there are in the market, there are two types. They’re based on hardware and software architecture. There is a disk-based and a memory-based solution. And it’s really up to your own requirements for which one might be best suited to you. In some cases there is a hybrid solution combining both of these techniques. And we’ll talk about how InetSoft addresses these techniques.
Essentially the disk-based solution is usually a much more powerful, heavy duty one. It is used with a large data profile, where you are in the terabyte scale of raw data, and you’re looking to do aggregation to take advantage of cubes and summarization. In a lot of cases the disk-based approach requires some data modeling. That can be required in some cases of in-memory solutions, as well. So it’s not necessarily an either-or situation.
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Disk Based Analysis Solution
But in most cases the disk based solution requires some kind of data modeling, so that you can summarize and create aggregations. In a lot of cases disk-based solutions are little bit more inflexible in terms of changing what you’re looking to analyze, what you’re looking to aggregate, et cetera. Most commonly we see a lot of disk based solutions are powered by an OLAP engine. We’ll talk about what OLAP means in the context of data analysis.
In the memory-based environment, data access is much faster, because the information is cached and available through a fast I/O exchange. So you’ve got a very fast, speed-of-thought, kind of interactivity with the data. Whereas in the disk-based model, data access is slower due to the physics of the memory media used.
For in-memory only solutions, usually they are limited to workgroup-sized datasets where workgroups are departments or defined by a dozen or two users. In some cases there might be one terabyte of data used in a workgroup environment or just gigabytes of data. It typically doesn’t get into the multi-terabyte territory which is what we see for a large scale enterprise analytical environment.
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On the Fly Data Modeling
The modeling can be done via a graphical user interface, perhaps with on the fly modeling. Whereas with a disk-based solution, there tends to be more data summarization required to make the environment work properly.
Another feature of in-memory solutions is associative indexing. In some cases, it is defined at the user interface level, meaning I can assign a relationship between tables with a drag and drop tool, or in other cases it is defined automatically through some intelligent techniques built into the query engine. The data source environment can include relational or non-relational data sources, including flat files, spreadsheets, or almost any type of data source. This is another common feature of an in-memory BI solution.
Elastic Computing
Elastic computing refers to the ability of a cloud computing environment to dynamically scale computing resources up or down based on demand. This concept is foundational to cloud services, allowing businesses to efficiently manage their IT infrastructure without over-provisioning or underutilizing resources. Elastic computing ensures that organizations only pay for what they use, optimizing costs while maintaining high performance. Cloud providers like AWS, Microsoft Azure, and Google Cloud offer elastic computing solutions to handle fluctuating workloads seamlessly.
How Elastic Computing Works
Elastic computing relies on cloud-based infrastructure that can allocate and deallocate resources automatically. When an application experiences increased demand—such as during peak traffic hours—the system provisions additional processing power, memory, or storage. Conversely, when demand decreases, excess resources are released to minimize costs. This elasticity is achieved through virtualization and containerization, which allow multiple virtual machines or containers to be deployed and managed dynamically.
Key Benefits of Elastic Computing
One of the primary advantages of elastic computing is cost efficiency. Businesses no longer need to invest heavily in on-premises infrastructure that may remain underutilized for most of the time. Instead, they can scale their computing power based on actual needs. Another benefit is improved performance and reliability—applications remain responsive even during traffic spikes since additional resources are allocated instantly. Furthermore, elastic computing enhances operational agility, allowing companies to innovate and deploy new applications faster.
Use Cases of Elastic Computing
Elastic computing is widely used across various industries. E-commerce platforms, for instance, require elasticity to handle seasonal spikes in traffic, such as during Black Friday sales. Streaming services like Netflix and YouTube use elastic computing to adjust server capacity based on the number of active users. In the financial sector, banks and trading platforms rely on elasticity to process massive transaction volumes in real time. Scientific research and data analytics also benefit from elastic computing, as large-scale computations can be executed efficiently without permanent infrastructure costs.
Challenges and Considerations
Despite its benefits, elastic computing comes with challenges. Security and compliance are major concerns, as data stored in cloud environments needs robust encryption and access controls. Latency issues may arise if resources are scaled inefficiently, leading to temporary performance bottlenecks. Additionally, cost unpredictability can be a problem—if scaling is not properly managed, organizations may face unexpectedly high cloud bills. To mitigate these risks, businesses need well-defined policies and automation strategies for resource scaling.
The Future of Elastic Computing
As cloud computing continues to evolve, elastic computing is becoming more intelligent with the integration of AI and machine learning for predictive scaling. Future advancements may include serverless computing, where applications run entirely on demand without requiring infrastructure management. Additionally, edge computing is emerging as a complementary trend, allowing elastic computing to extend beyond centralized cloud data centers to distributed networks closer to end users. With these innovations, elastic computing will remain a crucial enabler of digital transformation for businesses worldwide.
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