Data is everywhere, but unlocking its value is often a complex task. Today, the emergence of AI makes this important job even more critical, further highlighting the difficulty of managing relevant data. In many ways, data analytics has already faced these challenges long before the rise of Artificial Intelligence (AI). That’s why businesses everywhere spend millions of dollars capturing, managing, and analyzing data.
Now, this same problem is playing out with AI. Like data analytics, AI requires quality, relevant data to train its models for accurate output. Without this data, AI is doomed to provide limited value to businesses. The two technologies now face similar problems, and both will benefit from a similar solution.
The gold rush for AI value
The world is experiencing a gold rush in AI, but there’s a problem. Without a good data architecture to easily access, test, and feed AI models with relevant business data, the true value of AI cannot be unlocked. This is the same problem that data analytics continues to face. Together, this creates a situation where businesses are starving for the right data.
What’s more, the problem is growing. Assuming projected demand keeps pace with expectations, the problem of serving relevant data will only become more widespread and costly.
AI challenges and opportunities
But, where there are challenges, there is also business opportunity. McKinsey reports that generative AI could add $2.6 trillion in annual corporate value through productivity increases, new revenue streams, and AI-led product enhancements across industries. Meanwhile, Bloomberg projects that the generative AI market could reach $1.3 trillion by 2032.
To fuel this growth, new vendors are emerging to commoditize critical tooling for businesses to be successful with AI. There are two main reasons for this. First, since the release of OpenAI’s ChatGPT in 2022, the number of AI models under development has rapidly increased, and with each release, they are getting better and more efficient. Second, AI hardware advancements are no longer exclusive to the top tier enterprises, with production increasing from Nvidia, AMD, Intel, and others. Over time, this will expand the reach of AI data architecture into all businesses. The data problem continues to persist, but not for much longer.
3 problems facing data analytics and AI
As businesses rush to capitalize on AI, it becomes important to consider the data architecture that feeds it. Specifically, the three main problems facing analytics and AI data architecture:
1. How do we retain distributed data without data fiefdoms?
Most businesses store data across multiple locations or systems rather than a single server or storage unit. In fact, the single source of data is a short-lived reality realized by very few organizations. This can happen for many reasons, and not all of them are unintentional. For instance, data may be purposefully left on-premises to meet regulatory or compliance requirements or to manage risk.
More problematically, this generally leads to data silos. In this case, data storage becomes so fragmented that discovering, accessing, and querying relevant data becomes nearly impossible. This situation often leads to low-quality data, which harms relevance and impedes downstream insights. This is true of data analytics, and it is also true of AI. Like analytics, traditional distributed data models lower the efficacy of the AI models they are meant to train.
2. How do we ensure data can be accessed in a timely manner?
Your most recent data is not only the most timely, but it’s also the most relevant. Because of this, it’s also some of the best data when powering analytics or AI. However, analyzing data at speed is difficult. It must be efficiently ingested into a data lake, transformed, and loaded into the target system. The silos that often plague data architecture make this process even slower. Solving this problem has been the core focus of data engineering for decades.
3. How do we safely make data shareable?
Too often, data remains cut off from the people who need it. This problem happens for many reasons, from the type of data architecture used to the nature of compliance requirements. The nature of your data architecture plays a large role here. Securely sharing data may not be easy; in most cases, it represents a pipedream.
Whether you think of a data warehouse, data lake, or conventional data lakehouse, each of these traditional architectures takes power away from the people accessing data. Since these people typically need data to feed analytic or AI models, this directly impacts the viability of insights. What’s needed is a new approach that packages data in a more user-friendly way to help make the data pipeline safe and accessible.
The old data architectures have failed
If you’re experiencing any of these conditions, you already face challenges with your data analytics. You may also be experiencing similar issues training your AI models. In both cases, your data architecture is holding you back from delivering the necessary insights faster and more accurately.
However, data collection is not a new problem. Data warehouses have been attempting to solve the three problems above for 40 years with limited success. If you reach even further back in time, humans have been trying to solve the problem of storing and leveraging high-quality data for millennia. Although the storage media has drastically changed over the years, the basic analytics problem has not.
The challenge isn’t just with data warehouses, it extends across the three major architecture patterns.
1. Data warehouse
Data warehouses are the bedrock of traditional data architecture. They use a data centralization approach to achieve significant performance, but they come at a high cost. When we think of data warehouses feeding analytics of AI use cases, rising costs become prohibitive for many businesses and slow the uptake of relevant data – this is especially true for data applications where margin optimization is critical.
2. Data lake
Due to their use of object storage, data lakes are a more cost-effective storage solution than data warehouses, particularly for certain data structures. However, many data lakes struggle to perform in the way demanded by data analytics and AI. Additionally, data discoverability can be a key issue for data lakes, hindering data relevance. In these scenarios, you can inadvertently create your own data swamp.
3. Conventional data lakehouse
Data lakehouses are the newest addition to the data architecture family. They combine the best aspects of data warehouses–performance–with the cost-effectiveness of data lakes. However, not all data lakehouses are created equally. You need the right lakehouse to operate effectively at scale. For example, many lakehouse solutions limit data access. Some do not access on-premises data, while others operate only in the cloud. They also introduce proprietary mechanisms into your data architecture. This creates opportunities for vendor lock-in, negating the inherent potential of the lakehouse to be open.
Why an open hybrid lakehouse is the solution
The solution to the problem of relevant data is found in the open hybrid lakehouse. This data architecture unlocks the openness of a lakehouse, offering the best of all possible worlds. An open hybrid lakehouse combines the scale of the cloud, the control of on-premises solutions, and an open foundation at data and query layers. The result is a data architecture based on openness, versatility, and change.
There are five foundational pillars of an open hybrid lakehouse:
1. Icehouse
The open hybrid lakehouse uses an Icehouse architecture combining Apache Iceberg and Trino. This central engine provides superior performance capable of serving the needs of data analytics and AI. An open hybrid lakehouse uses an Icehouse architecture at its core and applies it to data held in the cloud and on-premises.
2. Access to data in the lake or lakehouse
An open hybrid lakehouse makes the data lake, whether in the cloud or on-premises, the center of data gravity. It also puts data ownership back in the hands of businesses by using open file and table formats.
3. Access to data around the lake or lakehouse
At the same time, you also have access to data outside or around the lake. Data federation allows you to discover and access data in warehouses, relational databases, applications, or NoSQL stores. The location of this data does not matter. An open hybrid lakehouse can access data either in the cloud or on-premises.
4. Near real-time ingestion
The open hybrid lakehouse employs near real-time ingestion for streaming data. This overcomes the challenge of leveraging the freshest data when needed. Today, businesses are becoming more agile and responsive to dynamic business conditions. This means that access to recent, relevant data is increasingly important. Access must also match the massive scale required by analytics and AI, and be highly optimized for price and performance.
5. Data products
The open hybrid lakehouse also addresses the packaging and sharing of data. This is achieved using data products, which are purpose-built datasets for business use cases. They can be governed and shared with fine-grained access control rules. Data products are an easy way to feed analytics and AI applications, reduce the complexity of data pipelines, and create a hub of trusted data assets that can be leveraged by multiple teams, solving the third major problem of traditional data architectures.
3 ways that the open hybrid lakehouse helps you deliver analytics and AI
The open hybrid lakehouse solves the data architecture challenges required by analytics and AI. It lets you quickly discover and access relevant business data wherever it lives, ingest that data in near real-time at a massive scale, run industry-leading price-performant queries, and package and share data securely using data products.
When evolving your data architecture towards an open hybrid lakehouse, there are three requirements that you want to stand firmly by:
1. Maintain data control
An open hybrid lakehouse based on modern table formats and SQL allows you to maintain control of your data. This approach gives you options and allows you to plan for the future. You can begin by taking advantage of the cloud data lakehouse using an Icehouse architecture to improve cost-performance dynamics; you can offload data from a warehouse or leave it where it is; you can access it in the cloud or on-premises. As you move away from proprietary storage and SQL, the shift allows you to adapt to changing economic and regulatory environments across on-premises and cloud solutions.
2. Accept the reality of decentralized data
Unlike other data architectures, a hybrid data lakehouse doesn’t fight data decentralization–it embraces it. You get all the advantages of a cloud data lake-centric approach based on an Icehouse architecture, but you can also use data federation to integrate siloed disparate data sources and unlock hidden value. Unlike other data lakehouses, this approach is also open, unlocking all your data in and around the lake, uniting all your data, and avoiding proprietary vendor lock-in.
As you continue to build your data lake as the center of data gravity, you retain the flexibility to discover, access, and use all of your decentralized data. For analytics and AI, this means that new data sources can always be added to old ones, improving data quality.
3. Use a near real-time data architecture
In an increasingly dynamic business environment, optimizing the latest data allows you to take advantage of market opportunities or mitigate risk during times of crisis. The open hybrid lakehouse is built to provide rapid access with near real-time data ingestion to feed both analytics and AI models.
The open hybrid data lakehouse is the data architecture best suited to serving data analytics and AI together. As businesses begin to pivot towards realizing the hunger for relevant data that these twin technologies demand, selecting a data architecture to meet this challenge becomes paramount.
Every day, we help businesses take this journey, unlocking their cloud and on-premises data to power modern analytics and improve their AI to be better tailored to the uniqueness of their business. Using an Open Hybrid Lakehouse makes this easy. It allows you to discover, access, govern, analyze, and share your data the way you need. Try Starburst Galaxy today to see how it can upgrade your data stack for the AI era.
Want to know more about how analytics and AI are starving for data? Check out the video below.
