Data Mesh compared to traditional Data Warehouses

In partnership with

Dear Data Professional…

One architectural paradigm in 21st Century data management continues to stand out: Data Mesh. Gaining significant traction over the last decade in particular, it represents a significant leap forward compared to tradition data warehousing approaches. This week we wanted to explore…

How does Data Mesh stack up against traditional Data Warehouse architectures?

Imagine a vast reservoir of data, where all the information for a business is stored in a single, centralised repository. Historically this approach has been applied as data is collected, transformed, and stored creating monolithic systems. In contrast, Data Mesh is like a network of smaller interconnected ponds connected by canals, where each pool of information represents a domain-specific data repository. Generally speaking this decentralised approach allows for greater flexibility, scalability, and agility in managing different pools of data.

The key differences between Data Mesh and traditional data architectures lie in their approach to data ownership, governance, and management. In a traditional data warehouse architecture, where data is owned and managed by a one central team. With Data Mesh, data ownership is distributed to domain-specific teams. The teams can draw on the supply from their pond as and when they need to. Offering up greater autonomy and accountability, as each team is responsible for their own data. This means data is treated as a product and made available to users through a standardised interface. This approach enables faster access to relevant data, improving business agility and decision-making, just like providing access to water across a series distributed reservoirs of water.

The Data Mesh approach arguably means your business has the ability to handle more complexity and scale more effectively. Traditional data architectures often struggle with scalability, as the centralised system becomes increasingly complex and difficult to maintain. In contrast, Data Mesh is designed to handle complexity and scale with a decentralised architecture, allowing for the easy integration of new data sources and domains. This means each domain team can manage their own data and respond to changing business needs without affecting the entire system. To summarise in a “bake-off” format here are three takeaways:

Architectural Approach

Data Mesh promotes a decentralised, domain-oriented architecture. Each business domain owns and manages its data, treating it as a product. This means domain-specific teams to handle data independently. Vs. Traditional Data Warehouses where there is a centralised architecture. Data from various sources is collected, transformed, and stored in a single repository. This can lead to bottlenecks and scalability issues as data volumes grow.

Data Ownership and Governance

Data Mesh distributes data ownership and governance to different teams. Each team is responsible for data quality, security, and compliance within their domain. Leading to more direct accountability and faster decisions. Vs. Traditional Data Warehouses that centralise data ownership and governance, typically by a dedicated data team. Often leading to slower responsiveness and a disconnect between data producers and consumers.

Data Quality and Trust

Data Mesh focuses on data quality within individual domains, potentially leading to improved data trustworthiness. Individual teams are incentivised to maintain high data standards since they directly benefit from the data's accuracy and relevance. Vs. Traditional Data Warehouses where quality is managed centrally, which can be effective but may require additional processes to ensure consistency and accuracy across the entire dataset.

There are some viable options in the data warehousing world such as Data Vault, that provides the best of both worlds. For more on this come on over the the Data Innovators Exchange where this week we feature the pro’s and con’s of Data Vault vs. Data Mesh for agile data management.

AI Strategies & tools that will skyrocket your Marketing ROI by 50% 🚀

You don’t realize it yet, but AI has massive potential for you as a marketer.

This free 3-hour Masterclass on AI & ChatGPT (worth $399) will help you become a master of 20+ AI tools & prompting techniques. Join it now for $0

This is for you if you work in any vertical of marketing– writing, designer, campaign managing, influencer marketing, growth marketing, etc.

Ready to shock your team with a 10x boost in revenue & campaign performance? 🚀

Get it now for absolutely free! 🎁

You will join 1 Million+ people who have taken this masterclass to learn how to:

• Create 100+ content pieces for reels,blogs, from one single long form video

• Put data tracking & reporting for your campaigns on autopilot

• Do predictive analysis and optimize your marketing campaigns for better results

• Personalize customer experiences by leveraging the power of AI

You’ll wish you knew about this FREE AI masterclass sooner (Btw, it’s rated at 9.8/10 ⭐)

Register & save your seat now! (valid for next 24 hours only!)

In a recent piece by CNBC they examine heavy demands the current 8,000 worldwide data centres that power AI will place not just on energy demand, but water consumption and carbon emissions.

So just how can Data Engineers can reduce the environmental impact of AI?

Each of us in our own way can play a part in meeting global Emissions Reduction Targets. As we engineer the use of LLM’s and utilise enormous data sets to provide insights, there are ways to reduce the environmental impact that we have. Here are few of the strategies to consider as you architect, deploy and manage systems that rely on enormous data sets.

1. Energy-Efficient Training and Inference

Training and inference for LLMs are energy-intensive processes. Adopting energy-efficient training methods with these two techniques can help:

Model Distillation reduces the size of the model without significantly compromising performance. For instance, DistilBERT is a smaller, faster, and more energy-efficient version of BERT, achieving similar results with fewer resources.

Hardware Utilisation: Energy-efficient hardware, such as specialised AI chips like the SpiNNaker2, which emulates biological neural networks, can significantly reduce power consumption during training and inference.

2. Carbon-Aware Computing

Implementing carbon-aware computing practices can help align AI workloads with periods of low-carbon production intensity:

Time-shifting by scheduling training and inference tasks during times when renewable energy is more available, you can reduce their carbon footprint. For example, leveraging historical carbon intensity data from tools like WattTime can optimize workloads to avoid peak carbon periods. And Geographical Load-Shifting distributes computational tasks across data centres in regions with higher availability of renewable energy can also reduce emissions. This approach ensures that the energy used for AI operations comes from greener sources.

3. Optimized Data Management

Efficient data management practices can minimise the footprint of data storage and processing:

Implementing effective data compression techniques reduces the amount of storage required, thereby lowering energy consumption for data storage and retrieval. Instead of retraining entire models, incremental learning techniques allow models to update with new data, reducing the computational load and associated energy use.

4. Sustainable Data Center Practices

Data centers utilising Renewable Energy, such as solar or wind power, can significantly cut down carbon emissions. Couple with Advanced Cooling Techniques that use less water and energy, can help manage the heat generated by high-performance computing systems more efficiently.

That’s a wrap for this week.

For more on these topics check out the Data Radio Show - on YouTube and where you get your podcasts.