Gaining better access to graph and blockchain data within SQL databases

By | 4 minute read | September 23, 2020

Two of the most innovative technologies to emerge in recent years are graph and blockchain. Graph is particularly good at tracking relationships between data while blockchain keeps a record of transactions in a decentralized way that mitigates the risk of retroactive alteration. However, those wanting to use SQL to query the data stored using each technology have found it difficult if not impossible to achieve. Fortunately, there is a now a solution.

As a key component of The AI Ladder™, data management solutions make an organization’s data simple and accessible, helping drive AI activities and insights. The ability to use SQL querying for graph and blockchain technologies helps achieve this goal with a commonly understood code base. As a result, graph and blockchain data can be incorporated more thoroughly into business processes.

Databases like IBM® Db2® that are built for AI and include these capabilities deliver the most value when powered by AI to optimize and improve query use as well. Additional details can be found in the eBook, Db2 – The AI Database, which covers 8 key capabilities that fall into these categories. Graph and blockchain are two of the built for AI features — explored in greater depth below — that empower developers with tools to create successful AI initiatives.

Modeling complex relationships with graph and SQL

Graph’s previous lack of compatibility with transactional systems led to less-than-optimal workarounds to gain access to graph functionality. Because some data had to be extracted and transferred from relational systems to graph databases, data duplication introduced potential for error and misalignment. The transfer process slowed the speed at which insights could be revealed and wasted users’ valuable time.

By natively integrating graph functionality with relational data and SQL, a SQL database allows graph applications to run directly from relational data, enabling the SQL engine to query graph data directly. In practice, this reduces or eliminates the duplication of data so that insights emerge faster without an ETL process. Graphs can also be used on relational data to provide a different level of insight than standard SQL analytics. In addition, graphs can be updated in real-time for transaction processing and analytics.

Graph integration is highly valuable for a number of industries.

  • Retailers can use real-time graph functionality to better understand returns of a particular item across multiple locations and update the transactional database immediately so that it is no longer sold.
  • Insurers can link multiple databases and surface relationships between claims to better determine instances where fraud might be occurring.
  • Telecommunications providers can use graph functionality to assess the status of networks and determine which customers may be affected by maintenance or outages.

These are just a few examples, but all industries can benefit from the ability to better understand relationships within their data and immediately take action.

Analyzing blockchain data natively

Highly compressed and designed to avoid alteration, blockchain has not lent itself to easy analysis. Ad hoc reporting solutions could be created to extract blockchain data, but a simpler solution integrated with existing information architectures is a better option. The Db2 Blockchain Connector allows the blockchain data store to be connected to an existing Db2 database.

In doing so, the valuable information previously locked in blockchain becomes open for analysis – being presented as a relational table in the database. Moreover, this data can be combined with myriad other data sources connected to the database so that blockchain information can be analyzed contextually. And data can be used within AI apps, allowing them to operate with more complete data sets.

These new and combined analyses are invaluable. For example, the ability to extract details on container locations throughout shipment can be cross-referenced with weather data to give a more robust picture of why a particular order or group of orders may have been delayed or damaged in transit. These analyses can be executed while maintaining the integrity of the blockchain for future review. Without the blockchain connector these insights may remain untapped within the blockchain data.

Readying your data management for graph and blockchain

SQL-supported integration of graph and blockchain data can provide the distinct advantages necessary to outpace competitors on the Journey to AI. Both are available features in Db2 whether deployed on-premises, in the cloud, or as part of IBM Cloud Pak® for Data, which is built on the Red Hat® OpenShift® Container Platform.

To learn more about graph, blockchain, and six additional features in Db2 designed to help you succeed on the Journey to AI read our latest eBook, Db2 – The AI Database. It has more information on all of the capabilities that make Db2 built for and powered by AI.

 

 

 

 

 

 

 

 

 

 

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