TechDogs-"Let’s Analyze In-Memory Analytics"

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Let’s Analyze In-Memory Analytics

By TechDogs Editorial Team

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Overview

Imagine you're working on a critical research project and the deadline is inching closer. You have gathered a ton of information that you must organize, categorize, analyze and present in a simple way. So, you start by creating an “evidence board” to understand the relation between the data – think of those crazy wall collages detectives make in murder mystery movies!

However, your research spans books, hand-written notes, bookmarked webpages, a few TechDogs’ PDFs, etc. As you start going through the information, you keep going back and forth between the bookshelf, the study table, your laptop and the evidence board. You need a more efficient workflow!

What if you had a magical desk that let you quickly grab any page from a book, any note or even a specific paragraph from a webpage without having to walk to the bookshelf or the laptop? Every piece of information is right there, instantly accessible to you!

While the magical desk is fictional, that’s pretty much how In-memory Analytics works. It is a lightning-quick virtual magic desk that keeps important data accessible, making your workflow much faster and easier. Read on to know how the magic happens!
TechDogs-"Let’s Analyze In-Memory Analytics" Navigate Straight Through The Data Maze
So, where were we? Yes, the magical desk – ahhh, how we wish we had one when we were pulling all-nighters for college projects! #Nostalgia

However, today you can have this magical desk in the form of In-memory Analytics. It is essentially a business intelligence methodology that uses memory storage (virtual) instead of hard disks (physical) for data querying and processing. As the information is positioned closer to the processing center, the time needed to access the data is significantly reduced. This leads to quicker data availability, computation and real-time analytics – talk about instant decision-making!

To understand how this technology works, along with its history, benefits, types and future, read on as we present a simple guide on In-memory Analytics.
 

So, What Is In-Memory Analytics?


Today, data is stored everywhere – from your smartphone to your IoT-based coffee machine. Yet, the volume and variety of data change depending on where it is stored. Your coffee machine saves preference data; your smartphone has contacts, images, videos, audio and a lot more saved on it.

In both cases, when someone needs to analyze the data as part of business intelligence, disk-based data or data that’s stored on physical disks is most commonly used. During analytics, the data on the hard disk moves through a network to the local system memory (also known as RAM or random-access memory), from where it moves to the CPU (central processing unit) for processing. #TooSlow

With In-memory Analytics, the queried data resides within the processing application’s random-access memory, leading to faster response times. This leads to a significant reduction in processing time for large datasets, such as those stored in data lakes and data warehouses. Moreover, In-memory Analytics reduces the need for data indexing, storing pre-aggregated data or aggregate tables that are inevitable with traditional methods of querying data.

So, how did we think of this time-saving invention? Read on to learn how In-memory Analytics was developed!
 

Evolution Of In-memory Analytics


TechDogs-"Evolution Of In-memory Analytics"-"A GIF Of The Flash"
The need for speed has always been critical in the business landscape, especially for data querying and analytics.

Back when the world was running on 32-bit operating systems, it could provide 4 GB of RAM which soon grew inadequate. With advancements in computing technologies, the newer 64-bit operating system could provide 1 terabyte (TB) or more memory. This made it possible to store larger volumes of data in a computer’s RAM for quick access during querying.

Despite the advent of data warehousing and big data, business intelligence experts and analysts faced the constant challenge of processing ever-increasing volumes of data in shorter durations. Soon, they realized the most significant factor in processing large volumes of data quicker was the speed at which the data could be accessed. With big data analytics stepping into the picture, businesses back then needed memory systems capable of storing massive volumes of data, so real-time processing and insights could be possible.

Hence, data warehousing architectures transitioned to massively parallel processing (MPP) designs that leveraged coordinated processing with multiple processors and dedicated memory. This led to the first In-memory Analytics database being developed in the late 1990s – and the rest, as they say, is history!

Today, we can cache (temporarily store) data equivalent to an entire data warehouse using In-memory Analytics. For instance, the ElastiCache for Redis cluster configuration by AWS can run workloads with up to 6.1 TB (terabytes) of in-memory capacity for a single cluster!

Here’s how In-memory Analytics works despite the large size of the data it stores.
 

How Does In-memory Analytics Work?


As we mentioned, In-memory Analytics leverages MPP (multiple parallel processing) to enable quick access to data for analytics. Here’s a breakdown of how this works:
 
  • Step 1 – Data Loading

    First off, the relevant data is loaded directly into a computer's RAM through In-memory Analytics. This mainly includes data that is frequently queried and needs to be accessible for business analytics at any given moment.

  • Step 2 – Query Execution

    As the data is already in the computer's memory, queries are executed directly and users do not need to wait for the data to be fetched from disk storage as it would be for traditional methods.

  • Step 3 – In-Memory Processing

    Analytical operations based on the queries input by the user are performed directly on the in-memory data with multiple parallel processing. This eliminates the need to fetch data from slower routes via databases and hard disks, resulting in quicker analytics even for data lakes and warehouses.

  • Step 4 – Real-Time Analysis

    In-memory Analytics enables real-time data querying and processing, leading to quicker analysis and insights. This allows BI analysts to work in real-time even when it comes to Big Data analytics or other larger databases.

  • Step 5 – Data Persistence

    While the primary analysis occurs in memory, some systems can send the results of the computation to a traditional storage system. This allows for long-term, persistent data retention and businesses need not leverage the In-memory Analytics each time.


While this is the process for In-memory Analytics, there are several types that differ slightly in their functionalities. Scroll on to learn what they are!
 

Types Of In-memory Analytics


TechDogs-"Types Of In-memory Analytics"-"A Meme About In-memory Analytics"  
  • In-Memory Database Systems (IMDB): In this type, entire datasets are loaded into RAM for real-time processing, enabling fast data retrieval and analytics without the need to read from disk.
  • In-Memory OLAP (Online Analytical Processing): In this type, OLAP cubes and multidimensional data are stored in memory for rapid querying and analysis, which speeds up complex analytical queries and interactive data exploration.
  • In-Memory Data Grids (IMDG): In this distributed, in-memory type, data is stored across a cluster for improved data access and scalability, enhancing performance for applications that need low-latency data access and processing.
  • In-Memory Streaming Analytics: This type enables real-time analysis of streaming data stored in-memory, leading to quick insights from live data sources and timely decision-making. 
  • In-Memory Text Analytics: This type can process and analyze large volumes of text data directly in-memory for natural language processing and sentiment analysis leading to faster text-based analysis tasks.

Now, no matter the type, the benefits of In-memory Analytics are impressive – take a look!
 

Advantages Of In-memory Analytics


Here's the lowdown on the numerous benefits that In-memory Analytics brings to the table. Make sure you keep them in-memory!
 
  • Blazing-fast Analytics

    With data stored in memory, querying, accessing and processing data is much faster compared to traditional methods of data analytics, despite the large size of data.

  • Higher Scalability

    As businesses grow, so does the data they collect. In-Memory Analytics is designed to scale and helps in handling the increasing volumes of data without investing in expensive hardware.

  • Real-Time Intelligence

    In-Memory Analytics enables businesses to make decisions in real-time, as it provides quicker analytics and insights.

  • All-round Flexibility

    In-memory Analytics can support a wide range of business intelligence tasks based on the needs of businesses – from simple data querying to complex big data processing.


Owing to these benefits, In-memory Analytics allows businesses to effectively use their enterprise data for strategic decision-making. For instance, financial businesses are using it to power instant risk assessment and fraud detection, while retailers are tracking customer behavior, allowing them to instantly tailor their offerings for an improved experience.

Yet, that’s just the trailer - there’s much more in store in the future!
 

What’s The Future Of In-memory Analytics?


TechDogs-"What’s The Future Of In-memory Analytics?"-"A Meme About People Confusing Tech For AI"
Nikita Ivanov, co-founder of GridGain Systems, a unified real-time data platform, envisioned In-memory Analytics to go mainstream. He stated in 2020 that, “When I started this journey close to 20 years ago, there was a general belief that in-memory computing will be a massive category, as has been with cloud compute.” While that obviously hasn’t happened yet, many businesses are optimistic about its future.

As the adoption of IoT grows, powered by the explosive capabilities of 5G wireless networks, we will witness a plethora of data sources emerging. This means businesses will need to step up their game to collect, organize and analyze these continuous data streams to improve customer experiences through data-driven insights. All of this will demand faster access to data at the same time that datasets explode in size.

Moreover, digital transformation will unleash massive quantities of data, making real-time data ingestion, processing and analysis critical for businesses. This is driving an evolution in the use cases of hybrid in-memory computing that merges online transactional processing (OLTP) and online analytical processing (OLAP) workloads. This is where In-memory Analytics will definitely be a game-changer in the future!
 

Conclusion


You already know that big data and data technologies are constantly changing the way businesses operate. The more data businesses have and the faster they have it, the better decisions they can make. As businesses want to streamline their data management to save time, money and effort, the ability to harness and analyze data quickly is becoming a business imperative. In-Memory Analytics stands tall here as the go-to solution to enable quicker access for data querying and analytics – unless someone invents a magic desk soon!

Frequently Asked Questions

What Is In-memory Analytics And How Does It Work?


In-Memory Analytics is a business intelligence methodology that stores data in virtual memory instead of physical disks, resulting in faster data access and processing. Traditional methods involve fetching data from disk storage, whereas In-Memory Analytics keeps queried data in the computer's RAM, enabling quicker response times and reducing processing time for large datasets.

What Are The Benefits Of Using In-memory Analytics For Businesses?


In-Memory Analytics offers several advantages, including blazing-fast analytics, higher scalability, real-time intelligence and all-round flexibility. With data stored in memory, businesses can access and process data more quickly, scale their operations without investing in expensive hardware, make real-time decisions and support various business intelligence tasks.

What Is The Future Outlook For In-memory Analytics?


The future of In-Memory Analytics looks promising, especially with the growing adoption of IoT and the need for faster access to data as datasets continue to expand. As businesses strive for real-time data ingestion, processing and analysis, In-Memory Analytics will play a vital role in providing quicker access to data for improved decision-making. It is expected to become a game-changer, particularly in hybrid in-memory computing that merges OLTP and OLAP workloads.

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