Artificial Intelligence (AI) is a topic that’s garnering a lot of interest in today’s digital world. Let’s understand the roles of database in the AI space, specifically analytical applications.
Analytics is a methodology to extract insights from data and AI contributes by injecting intelligence into processes. For instance, insights from transactional data can reveal that a certain group of people are buying product X, in a specific geography at a specific time. AI can leverage data from various other sources such as social media, historical information, etc., and can reveal how said group of people has a higher probability of buying product Y. Appropriate analytical application for these scenarios would be in the form of a recommendation engine or personalized communication.
The main blocks of the intelligent analytics process are Sense, Learn, Decide and Act. Let’s break down each process in detail. ‘Sense’ is about sourcing real-time/batch information from internal and external systems. Once the system senses incoming data, the process has to ‘learn’ based on these inputs. This involves understanding the subjects and environmental context to aid decision-making. The next step ‘decide’ involves comparing different possibilities and making the optimal decision. Finally, the system will ‘act’ by implementing decisions through process and technical engineering on new data.
Database’s role in AI Analytics
On the data side, each processing block will need some data storage space and processing capabilities. The AI database will help to concurrently ingest, explore, analyze and visualize fast-moving, complex data within milliseconds. The ultimate goal is to lower costs, generate new revenue, and integrate Machine Learning (ML) models so that businesses can make more efficient, data-driven decisions.
There are certain considerations that need to be kept in mind while selecting a database to manage the underlying data. Integration with existing software systems and AI applications, security, scalability, type of data (structured, semi-structured, unstructured), and frequency of data access are key parameters to be considered while selecting databases supporting AI analytics.
Which Database Should You Opt For - RDBMS or NoSQL?
Traditional relational databases (RDBMS) have several limitations when it comes to accommodating AI requirements. These databases are unable to scale out easily to accommodate huge data volume or to handle unstructured data and also lack a simplified high-availability mechanism.
On the other hand, non-relational databases (NoSQL) have played an integral role in the recent advancement of technology leveraging machine learning and deep learning as their primary technological components. The ability to collect and store large volumes of structured and unstructured data has provided deep learning with the raw material needed to improve predictions. Furthermore, these databases are highly scalable, which means for any additional resource requirement in the system, a cost-effective commodity hardware plug-in is all that is required. Non-relational databases are also highly available due to the default replication model.
How does NoSQL benefit AI ?
NoSQL brings several unique advantages to the table when it comes to handling AI applications:
- Flexible data model to handle the data structure modification load during the ‘Learn’ phase, and avoid costly schema migration.
- On-demand scalability to manage cumbersome data sets (during the Learn phase) and support any commodity machine that may be plugged in for additional power.
- High fault tolerance by way of the default replication model of NoSQL that helps avoid any disruption due to node failure.
- Seamless consistency and high availability during the Learn phase.
- Seamless integration through custom APIs for leading scripting languages (Python, Java, .Net, Node JS etc.). These APIs make database integration with the application layer seamless.
Achieving Superior Performance with Data Caching
Along with database, we also need to understand the data caching mechanism to optimize AI performance. To accelerate the iterative computational performance, it is common to cache learning data. We have multiple options to do this:
- Distributed caches (Memcached, Redis, MemSQL, GemFire)
- Data grids (Hazelcast, GigaSpaces, Ignite)
- Computing platforms (Apache Ignite, Oracle Exadata, Vertica)
- Hybrid databases i.e. Disk + Memory (Aerospike, MongoDB, CouchDB, MSSQL – in memory, Oracle – TimesTen)
Why NoSQL is Integral to Artificial Intelligence
Artificial Intelligence is being used in a variety of real-world applications today, working with a myriad data assets. To handle such vast information, NoSQL databases provide an effective mechanism for storage and retrieval of data. They also provide schema-less storage to support structured as well as unstructured data sources.
NoSQL is a worthy contender in the AI databases space where strong ACID capability is not the primary requirement. However, when making the move from traditional relational to non-relational databases, enterprises should be careful about the type of NoSQL databases to select since data type and data sources are the primary determining factors. With AI and ML, a plethora of opportunties await businesses. NoSQL aptly helps explore all of them by expanding to include data lakes, image processing, recommenders, Natural Language Processing (NLP) and much more.