Better Together: The Symbiotic Relationship of Big Data and AI
While the expression "Data is the New Oil" has its shortcomings, it underscores the growing importance of data in contemporary enterprises. A more accurate analogy might frame data as "the new energy," emphasizing its integral role in powering today's organizations. Simply put, it is nearly impossible to operate a business today without effectively utilizing data and data analytics.
Similarly, artificial intelligence (AI) has firmly established itself as a critical component of modern business operations. The relationship between AI and Big Data is symbiotic in that AI relies on large quantities of data for its capabilities and enables rapid analysis of massive datasets. This interdependence illustrates the profound utility of both technologies when integrated into business processes.
In this blog, find out how Big Data and AI work together, where open source technologies fit in, and some of the challenges that must be overcome to leverage Big Data and AI impactfully and cost-effectively.
AI and Big Data: Basic Definitions and Distinctions
The term "AI" has a complex and multifaceted history, which I briefly touched on in this previous blog. People use AI, sometimes inaccurately, to describe many different kinds of technologies. The AI currently dominating headlines — Large Language Models (LLMs) — is relevant to the scope of this blog, but it's important to note that there other AI methodologies and technologies that belong in a conversation about AI and Big Data.
Similarly, as early as 2011, the term “Big Data” was starting to show signs of conceptual aging. Dana Boyd and Kate Crawford aptly remarked at the time,
"There is little doubt that the quantities of data now available are indeed large, but that's not the most relevant characteristic of this new data ecosystem."
In other words, while Big Data refers to large datasets, the term encompasses modern data practices as distinctive from traditional transactional models.
At OpenLogic, we categorize the Big Data stack into four primary functional areas:
- Big Data Frameworks: Examples include Apache Hadoop and Apache Spark.
- Databases: Technologies such as Apache Cassandra, MongoDB/FerretDB, and JanusGraph.
- Stream Processing Platforms: Apache Kafka, Apache Pulsar, and Apache Druid.
- Orchestration and Resource Management: Tools like Kubernetes and YARN.
These categories, while useful, are not rigid and should be viewed as flexible groupings. For instance, Apache Kafka is not limited to stream processing. Similarly, Apache Hive, often associated with Apache Hadoop, can also be configured to use MySQL as a storage backend. Additionally, Apache Spark can operate in conjunction with Hadoop, further blurring these boundaries.
Despite these overlaps, this categorization provides a practical framework for understanding the vast and diverse landscape of open source Big Data infrastructure software.
Back to topAI Enhancing Data Analysis
Data on its own, no matter how much you have, isn’t particularly useful. You need to analyze it efficiently. AI can help with this.
AI can help automate and enhance data analysis workflows, taking over time-consuming processes like data extraction, transformation, and loading (ETL). It can clean data, detect anomalies, and even fill in missing values faster than any manual process.
More advanced AI techniques take this even further. They allow enterprises to:
- Interpret customer feedback in natural language.
- Cluster and segment users based on behavioral data.
- Generate predictive models that forecast everything from sales to equipment failure.
Real-world examples are everywhere:
- In retail, predictive analytics helps anticipate customer needs and optimize inventory.
- In cybersecurity, AI detects anomalies that could signal threats or breaches.
- In IT, machine learning models identify root causes of performance degradation before they impact end users.
The Synergy Between Big Data and AI
When Big Data and AI work together, the result is more than the sum of its parts. Big Data feeds AI with information. AI, in turn, extracts value from that information at scale. Together, they’re transforming how organizations operate.
Here are just a few examples of the synergy at work:
- Customer Personalization: By analyzing user data in real-time, businesses can tailor offerings, increase engagement, and drive conversions.
- Supply Chain Optimization: AI-powered analytics can predict demand, flag disruptions, and streamline logistics using vast historical and real-time data.
- Targeted Marketing: AI algorithms can process user behavior and tailor campaigns in a way that manual segmentation never could.
Across industries, we’re seeing this play out:
- In e-commerce, dynamic pricing and personalized recommendations are made possible by AI trained on customer data.
- In healthcare, patient records and diagnostic imaging feed AI tools that assist with diagnostics and treatment plans.
- In financial services, AI detects fraud, assesses risk, and even powers automated advisors.
The Role of Open Source in Big Data and AI
As with so many technologies before it, the open source ecosystem is a key enabler for both Big Data and AI. Open source tools are shaping the enterprise adoption curve, offering flexibility, scalability, and the power of transparent, community-driven development.
Why is open source so important here?
- Flexibility: Open source allows for deep customization to fit enterprise use cases.
- Cost-Effectiveness: Large-scale AI and Big Data implementations can get expensive quickly. Open source reduces licensing costs and avoids vendor lock-in.
- Community Innovation: Projects evolve rapidly with contributions from global developers and researchers.
AI use cases for Cassandra, Kafka, and Spark were already covered in this blog, so this time, let's focus on Kubernetes, JanusGraph, MongoDB/FerretDB, Pulsar, and Hadoop. It is also worth mentioning that popular AI frameworks like TensorFlow and PyTorch are open source and becoming increasingly popular, especially among large enterprises.
Kubernetes
You will not normally find Kubernetes on a list of Big Data tools, but Big Data operations using Kubernetes has been a topic of discussion since at least 2018. Spark has worked with Kubernetes natively for some time, and Kubernetes now has beta batch and AI/ML features that some organizations are now using in production.
JanusGraph
JanusGraph is a scalable graph database. While graph databases are perceived by some to not be as closely related to AI as vector databases, this (mis)perception has nothing to do with how useful graph databases are for AI workloads. It's due to the fact that vector databases are being built into multi-purpose databases; for example, Apache Cassandra and PostgreSQL both have vector database components. However, graphs can also be presented as vectors. Case in point: Neo4j has been talking about graphs as vectors since 2019.
JanusGraph joined the Linux Foundation’s AI & Data arm in 2021. JanusGraph 1.0 not coming out until late 2023 may have held it back some in the market, but now big players like Microsoft are writing about using it for AI workloads.
MongoDB/FerretDB
Many people probably think of MongoDB as a web database, and not Big Data, but Openlogic has been talking about MongoDB as part of the Big Data ecosystem for at least six years. It should be noted, though, that MongoDB has not been open source since late 2018. MongoDB is now source available, which limits what users can do with it without paying a license fee to MongoDB.
FerretDB has risen as an open source alternative to MongoDB. In their words, "[FerretDB] is a proxy that converts MongoDB 5.0+ wire protocol queries to SQL and uses PostgreSQL with DocumentDB extension as a database engine." The AI connection for FerretDB was strengthened with the recent 2.0 GA announcement that specifically highlighted "Vector search support for AI-driven use cases".
Apache Pulsar
Pulsar can be used to build real-time AI pipelines. Some think of Pulsar as a new technology and while it is younger than, for instance, Kafka, it has been talked about publicly since 2015 and the first public release was in 2016. As a distributed messaging system that recently added some streaming functionality, Pulsar shares some capabilities with both Kafka and RabbitMQ. Again, the AI use case for Kafka was previously covered in this blog and the use case is exactly the same for Pulsar (though the technical details differ).
Apache Hadoop
For many, Hadoop is synonymous with Big Data. Although it has been around for a long time, it is still a robust technology and its ecosystem has evolved significantly. Hadoop often gets compared to Spark, but they are not opposing frameworks; they just have different strengths. Understanding the nuances between Spark vs. Hadoop is an important step for enterprises assembling pieces of their Big Data puzzle.
While Hadoop is not AI-native in the way SparkML is, you can use GPUs in YARN, the cluster resource management tool in Hadoop. Rest assured, there are plenty of teams working to keep Hadoop compatible with market trends. That is the power of open source: No one is waiting for the Hadoop vendor to get Hadoop ready for AI because Hadoop users are already making that happen. As this Next Platform articlefrom 2021 states, "Hadoop is not bad or slow or expensive or not adaptable, of course. It’s possible to keep all the scale-out benefits that come with it and maintain the locality and sharing capabilities with other interfaces."
Back to topChallenges in Big Data and AI
Despite the opportunities, integrating Big Data and AI comes with challenges. Some of the most common include:
- Data Privacy and Compliance: Regulations like GDPR and HIPAA require strict data handling protocols. Enterprises must implement governance frameworks that ensure compliance while enabling innovation.
- Bias and Data Quality: Poor data quality can lead to biased algorithms. Ensuring diverse, clean, and representative training data is critical to building trustworthy AI.
- Infrastructure and Talent: Running AI and Big Data workloads at scale demands powerful infrastructure and skilled professionals — both of which come at a premium.
Let's explore these in a little more depth. The first two relate to what data gets included. There is always going to be some data you will not want in your models. Some data may need to be excluded in order to stay compliant with data privacy regulations. You also might want less data simply because more data can mean lower-quality data and all data is contextual. Imagine a dataset that has all purchase data for the entire planet, but you do not currently do business in Africa or Asia. The data from Africa and Asia might be pristine, but it is probably useless.
The solution here, while potentially difficult to implement, is conceptually simple. You just need a robust data governance framework with access controls that you follow. This can stop the data from ever getting in the data set.
Solving for infrastructure and talent can best be addressed by:
- Investing in scalable, open source-based infrastructure.
- Upskilling internal teams or partnering with third parties like OpenLogic.
Final Thoughts
The convergence of Big Data and AI isn’t a passing trend — it’s the foundation of next-gen enterprise IT. Looking ahead, several trends are poised to shape the future:
- Edge AI: Bringing AI to the edge enables real-time data processing closer to where it’s generated—reducing latency and opening up new use cases in IoT, manufacturing, and more.
- Augmented Analytics: These tools democratize insights by enabling business users to interact with data using natural language and guided discovery.
- Ethical AI: As adoption grows, so does the focus on transparency, fairness, and accountability in AI models.
For enterprises, this means a continued emphasis on innovation, agility, and smart decision-making. With the right tools — many of them open source — you can unlock real value from your data, power new AI-driven services, and future-proof your digital strategy.
OpenLogic Is Your Trusted OSS Advisor
Whether you're scaling your infrastructure, choosing the right open source stack, or navigating the complexities of Big Data and AI integration, OpenLogic can help. Talk to an Enterprise Architects today to start putting our expertise to work for your business.
