An Overview Impact 

Integrating cloud data warehousing with business intelligence can result in improved decision-making, increased operational efficiency, better understanding of customer behaviour, and enhanced competitive advantage.

This is why we are not only building on the foundational understanding of data warehousing as caption in our previous article (Data warehousing wonders: a leverage for business value in 2024) but also exploring the cutting-edge advancements and practical applications of its cloud-based solutions, making it highly relevant for businesses looking to maximize their data-driven decision-making capabilities in 2025.

What is Cloud Data Warehouse?

Databases hosted on the cloud is referred to as cloud data warehouse.

Data warehousing and cloud data warehousing share the fundamental goal of storing and analyzing large volumes of data.

It can integrate data from various sources, including structured and unstructured data, for the purpose of reporting and analyzing.

One of its major advantage over the on-premise data warehousing is that it can be accessed anywhere its users resides provided there is Internet Connection

However, cloud data warehousing differs significantly over traditional data warehousing. Here are seven major concepts:

7 Basic Concepts of Migrating into Cloud Storage

It is important you know that we are focusing more on the “how modern cloud-based data warehousing solutions are transforming the landscape of business intelligence (BI) and analytics”, providing organizations with –

1. Greater Flexibility: Adapt to changing business needs without a need for significant capital investment
2. Scalability: Based on the amount of data and the number of queries, it can scale up and down
3. Performance: It provide high-performance query execution and data processing
4. Storage: Storage of cloud data is in highly durable manner that accommodate different access pattern
5. Data Integration: On-premise databases allows the ingestion of data, IoT devices and third-party applications into cloud storage with 100% safety.
6. Reduced Maintenance: The cloud provider handles infrastructure maintenance, updates, and scaling.
7. Advanced Features: Access to advanced analytics, machine learning, and artificial intelligence tools.

The above concepts form the foundation of cloud data warehousing, enabling organizations to leverage their data more effectively and efficiently.

Top 4 Cloud Data-Warehousing Providers in 2025

Cloud providers handle maintenance, upgrades, and security, reducing the burden on in-house IT teams.

1. Amazon Redshift: A fast, fully managed data warehouse service that makes it simple and cost-effective to analyze all your data using standard SQL and existing business intelligence tools.
2. Google BigQuery: A fully managed, serverless data warehouse that enables super-fast SQL queries using the processing power of Google’s infrastructure.
3. Microsoft Azure Synapse Analytics: An analytics service that brings together big data and data warehousing.
4. Snowflake: A cloud-based data warehousing company that offers a fully managed service with separate storage and compute, enabling independent scaling.

Use Cases of Cloud Warehouse of all time

• Business Intelligence and Reporting: Aggregating data from various sources for comprehensive reporting and analysis.
• Customer Analytics: Analyzing customer behavior to improve marketing strategies and customer satisfaction.
• Financial Analysis: Conducting financial analysis and forecasting to support business decisions.
• IoT Data Processing: Ingesting and analyzing data from IoT devices for real-time insights and monitoring. 

Now, let’s try to consider some advantages which guarantee more insight of cloud data warehousing solutions as against traditional data warehousing with the followings

1. On-premise infrastructure/cloud-based infrastructure

On-Premise: deployed using an organization’s own hardware/software infrastructure with a fixed capacity, based on the physical hardware resources available.

Cloud-based are hosted on cloud platforms provided by vendors and offer scalable capacity, adjust resources dynamically based on current demands.

2. Fixed Capacity/Scalable Capacity

On-Premise: Needs additional hardware that is time consuming/expensive. They are static in nature with difficulty in achieving urgent data-volume expansion

Cloud-based: It can scale resources up and down to meet changing demands, workload but ensuring optimal performance and cost efficiency.

3. Fixed cost/Variable cost

On-premise: Cost are fixed with its usage notwithstanding, therefore incurring more expenses based on proposed needs.

Cloud-based: A pay as you go model of resources engaged. Cost reduction is possible during scale down of resources.

4. In-house management/Vendor’s management

On-premise is not cost effective thereby, in-house IT teams is needed to manage resources, backups and eradicate security breach. 

Cloud-based: Maintenance, updates, Security is sorely handled by cloud providers with less burdens on the IT teams.

5. Limited accessibility/Global accessibility:

On-premise: Data access is limited due to Organization’s local networks which cannot be used remotely. Integration of data from multiple sources is almost impossible.

Cloud-based: Working remotely is 100% possible from anywhere globally with access to internet connection.

Compatibility and Security 

Data is replicated across multiple regions thereby provide high availability and reduce the risk of data loss.

Also, the cloud providers offer automated backup and disaster recovery solutions which ensure data quick restoration in the event of a failure.

Again, structured and unstructured data are offered scalable storage solutions for easy storage and management of large dataset.

Here is what to expect when you migrate from Traditional Data warehousing into Cloud Data warehousing.

1. Seamless Integration: Cloud solutions offer robust data integration services that can easily ingest and process data from various sources, including structured, semi-structured, and unstructured data.
2. Data Protection: Cloud platforms implement robust security measures, including encryption, access controls, and auditing, to protect sensitive data.

CONCEPT OF LOUDNESS WAR

The “Loudness War” in digital sound mix describes the trend of increasing the overall loudness of music productions and recordings.

This means that The Loudness war focuses on loudness over “Dynamic Range”, often time at the expense of quality sound.

While Dynamic Range as caption is the difference between the quietness and loudest parts of a track.

But the misconception is that compressing this range allow producers to make music sound louder that it often results in a loss of depth, clarity and emotional impact.

adversely, compression also allow quieter sounds to be heard more clearly while avoiding distortion in louder sound.

Also, Compression can make music sound fuller, but excessive compression can lead to distortion and a loss of dynamics.

Please know that Compression is the technique that levels out peaks and valleys in audio, making the track sound “louder” on average.

LOUDNESS BY COMPRESSION VS SOFTNESS BY COMPRESSION

In digital mixing, engineers choose between these compression concept depending on the desired style and emotional impact of the mix.

Let’s understand the two (2) contrasting concepts of using compression in digital audio mix.

1. Loudness by Compression

   It refers to the perceived volume of sound by listeners, often increased through dynamic range compression of an audio track without increasing the peak levels.

This technique is commonly used in genres that require impactful, punchy mixes (like pop, rock, and EDM), where energy and loudness are prioritized.

The phenomenon became prominent in the late 1990s and early 2000s, as advances in digital audio production tools allowed engineers to push the volume of recordings to the limit.

Why do you need Loudness by compression?

By making quieter elements of a track more audible, compression ensures that details are present in the mix. Instruments and vocals do not get lost, which can make a mix sound fuller and more cohesive.

Why you do not need Loudness by compression!

Continuous exposure to highly compressed music can lead to “listener fatigue.” When all parts of a song are pushed to sound equally loud, the ear can become overwhelmed, making it tiring to listen for extended periods.

2. Softness by Compression

   It is when you apply more subtly to maintain a natural, smooth feel in the audio by controlling only the loudest peaks without heavily compressing the overall signal.

This method preserves more of the natural dynamics and character of the sound.

The softness technique is often used in genres where a relaxed, open dynamic range is preferred such as jazz, classical, and acoustic music, where listeners appreciate the delicate nuances of each instrument.

The Beauty of using Softness by compression IN LOUDNESS WAR

Softness by compression keeps the original dynamics intact, allowing softer and louder parts to breathe and improved sound quality and clarity.

Why do you have to look away from Softness by DYNAMIC compression?

In a complex or dense mix, quieter elements in a track can get lost if the track lacks strong compression.

This will make some parts of your track less audible on lower-quality speakers

Yes, there are both advantages and disadvantages to using both approaches in digital mixing.

They are both popular techniques, especially in modern music but you have to trade with care to reach out to your audience the best form to desire more of your mix.

Let’s take a ride into its

1. Root cause;
2. Feedback mechanisms of Loudness War and;
3. Best remedies to avoid Loudness war

ROOT CAUSE OF THE LOUDNESS WAR

1. Misguided perception of Quality Sound: Music producers realised that louder tracks stood out more on the radio and music charts.
2. Competitive Market Pressures: As CDs and digital music became the primary mediums for music consumption, producers and record labels began competing for attention.  A louder song would stand out more, particularly in radio and streaming playlists, encouraging labels to prioritise loudness to catch listeners’ ears.
3. Audio Tools: Likes of limiters and compressors which gives rooms for engineers to increase perceived loudness without noticeable clipping.

PLEASE NOTE: Dynamic compression – The main tested way to increase the overall volume of recordings.

5 FEEDBACK MECHANISMS THAT BEST CURE THE LOUDNESS WAR

1. Audiophiles –

As music listeners became more aware of the loudness war, it resulted to pushback, particularly from audiophiles and music critics who value sound quality over sheer loudness.

2. Streaming and Normalization –

Some platforms automatically adjust playback levels to avoid jumps in loudness between tracks, reducing the incentive to make songs artificially louder.

Example includes Spotify, Apple Music, and YouTube. Etc. This has encouraged some producers to focus on sound quality rather than loudness.

3. Artist and Producer Shifts –

Artists and producers in genres; jazz and classical, embraced greater dynamic range in recordings as a mark of quality.

This approach has gained traction even in mainstream pop, with more producers prioritizing for clarity and depth.

4. Technical Advancements –

Enhanced digital audio technologies and better mastering plugins give producers more control over how sound translates across different listening environments, enabling quality production without excessive loudness.

5. Listener Experience –

This mechanism promoted advocacy for balancing ensuring tracks are engaging without causing discomfort/tiring the listener over time.

2 BEST REMEDIES TO IMPROVE LOUDNESS WAR FOR CLEARER AND QUALITY SOUND.

Interestingly, modern streaming platforms like Spotify and Apple Music as mentioned above now try to normalize the loudness of tracks.

But, excessive loud tracks are not automatically favoured, potentially signalling the end of Loudness War.

Some mastering engineers and artists are pushing for a return to greater dynamic range, advocating for sound quality over sheer volume.

Audiophiles and producers alike are now rethinking this approach, focusing on delivering a more balanced, high-quality sound. Therefore, the remedies include;

1. Digital Audio Workstations (DAWs): The rise of  DAWs and other digital tools allowed engineers to manipulate audio with precision.

This made it easier to push tracks to higher loudness levels without necessarily clipping or distorting.

2. Listener Preferences and Formats: It enables streaming platforms became popular from high-quality headphones to phone speakers.

Introduction to AI in Sound Engineering

AI is poised to revolutionise sound engineering, offering tools that enhance creativity, improve efficiency, and democratise access to high-quality production.

Today, AI-driven tools and technologies are the best shot in transforming how sound is created, mixed, and mastered digitally, leading to unprecedented levels of efficiency, creativity, and precision in our sound and music production.

Therefore, our intention is to help you explore the needs on the future of digitalised mix through the lens of Artificial Intelligence (AI), pointing to the followings;

• Its Historical context with importance of maintaining the human element
• Relevance of AI in sound and music production
• Transformative potentials of AI in sound engineering
• Resources for effective deployment of AI in sound and music production is explained
• Examples of AI Solutions that is frequently used in Live Concerts and Events
• Practical Tips to implement AI solutions in your sound and music production; Simpler than you could think
• My Thought on AI in sound engineering

However, if you desire to push the boundaries of what is possible in advanced digitalised mix by powering AI tools and models, exploring the Resources for Effective Deployment of AI in sound engineering should be your goal.

But I am quite sure you may not grab all the idea from this article unless you first visit our previous article on Sound Engineering: Tips for mastering digitalized mix which will broaden your knowledge on the need and beauty of a digital mix.

Its Historical Context in Sound Engineering

The journey of sound and music production has been marked by significant technological advancements that have revolutionised the industry.

From the era of analog recording to the digital revolution and now the integration of AI, each of these phases has brought unique innovations in its own way.

Understanding this historical context is crucial in appreciating the current landscape [AI] and maintaining the essential human element [that is, despite its digital capabilities, successful production still required human creativity and judgment] in music production.

• The Analog process which was highly manual, requiring skilled engineers and musicians to achieve the desired sound with integral collaboration often working in the studio.
• Next, the introduction of digital sound and music production brought about a paradigm shift. Digital Audio Workstations (DAWs) like Pro Tools emerged and some other tools

The Digital technology allowed for precise editing, layering, and manipulation of sound.

• Now, the emergence of AI technologies began to be integrated into music production tools and processes. BUT HOW?

1. AI-driven plugins and software gives room for advanced functionalities such as automated mixing, mastering, and sound design.
2. Musicians, sound engineers and music producers are adapting to the new tools, incorporating digital techniques while striving to retain the organic feel of music.

Relevance of AI in Sound and Music Production

First, it is necessary to open up a broader perspective to you our reader on the future of sound engineering with AI, making it highly relevant and informative thereby delving into the latest trends and technologies

Some of its unwavering relevance are;

1. Contemporary theme of digitalisation that provide insights into the cutting-edge advancements that are transforming the field of sound engineering
2. With AI’s tools, there is so much enhancement in sound and music production for mastering digitalised mixes
3. AI opens new possibilities for artists and producers e.g. Amper Music platforms that sets parameters, aiding a more robust creative process for all
4. AI allows musicians and sound engineers to focus more on the artistic aspects of their work.
5. Its impact on the music industry will spontaneously grow, fostering innovation and expanding the horizons to more advanced digital productions.
6. AI helps in generating chord progressions, melodies, and harmonies, expanding creative possibilities.

6 AI’s Transformative Potential in Sound and Music Production

1. Automated audio editing and correction.
2. AI can assist in live sound settings, adapting to acoustical changes and optimising audio in real-time.
3. AI can analyze and adjust levels ensuring a polished mix with little of human intervention
4. AI is used in interactive art installations to process and generate sound in response to audience movement and interaction
5. It is accessible to novice producers, enabling high-quality production with less technical expertise.
6. Preservation of audio quality without degradation over time.

5 Major Resources for Effective Deployment of AI in Sound and Music Production

By leveraging these resources to be discussed here, musicians, sound engineers and music producers can effectively deploy AI in sound and music production, enhancing creativity, automation, and quality in the industry. The resources are as follows;

1. AI Frameworks and Libraries: Are software tools that provide developers with pre-built functions and structures.

They efficiently build, train, and deploy artificial intelligence models for various applications. E.g TensorFlow, PyTorch, Keras

2. Datasets and Data Sources: They are audio recordings and associated metadata used to train AI models for tasks such as audio classification, sound synthesis, and music generation. E.g. MIDI files

3. Cloud Computing Services: Available in the delivering of computing resources like sound and music software, AI tools over the internet.

E.g. AWS AI services, Google cloud AI platform, Microsoft Azure AI.

4. AI-powered Tools and Software: Application programs equipped with AI capabilities, designed to automate tasks, perform complex operations like music composition.

E.g Open AI Jukebox, LANDR, AIVA

5. Community and Educational Resources: These are necessary for integrating AI in sound engineering.

Why?

They provide essential knowledge, collaborative opportunities, skill development, and access to tools and ethical guidelines, fostering innovation and effective application of AI technologies.

E.g. AI and music conferences (ISMIR), online courses and tutorials (Coursera, edX, Udacity), GitHub repositories

Moreover, it is been proven that one of the primary reasons for integrating AI into your sound and music production is to transform sound engineering, thereby making complex tasks more efficient and opening up new creative possibilities.

SOME Examples of AI Solutions being used in Live Concerts and Events

• Google’s WaveNet developed by DeepMind – A speech and audio recognition software for identifying, isolating and processing speech elements.

• OpenAI’s MuseNet – An AI model that helps generate music in various styles and instruments. It therefore uses deep learning to compose original pieces that is generated

• iZotope’s Ozone – A Recognized AI-powered mastering software. It automatically analyses audio tracks and suggest adjustments to equalization, compression, and other effects to achieve a professionally polished sound mix

Practical Tips – How to implement AI in sound engineering.

1. Input and Analysis: The audio file is input into the AI system, which performs speech recognition and source separation to identify and isolate different elements.
2. Processing: The isolated elements are processed individually with AI-driven tools for noise reduction, EQ, and compression.
3. Mixing: The processed elements are recombined, with AI providing real-time adjustments to maintain balance and clarity.
4. Output: The final mix is exported with metadata tags generated by the AI for easy reference and further processing.

MY THOUGHT ON AI IN SOUND ENGINEERING

By understanding and embracing these technologies, sound engineers can leverage on AI, the faster your accomplishment, fulfillment and achievements thereby ensuring that you stay at the forefront of an ever-evolving industry.