Amazon Web Services (AWS) announced its entry to the graph database market at its AWS reINVENT summit in Seattle in November last year. It turned out to be a notable statement for a few reasons: it was the first graph database by the company (it offers a variety of relational and NoSQL databases as an agency ). However, it also shone a rather bright light onto a database group that’s frequently been considered niche, complex and expensive.
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Neptune is in trailer until it reaches general accessibility, but we expect that to happen shortly. So should you be bothered?
A graph database is one that uses graph structures to permit the data to be queried, using the concepts of nodes, edges and properties to represent and store data. The key concept is the fact that the graph immediately records the connections between various data items in the database. Since the graph links related items directly, it implies those that have a relationship with one another can often be recovered in one operation.
In relational databases, there aren’t any such immediate connections between related items as data is stored in rows and columns. To make a relationship between different elements developers need to write a’combine’. But joins can become excruciating and affect database performance.
The qualities of graph databases enable the straightforward and speedy retrieval of complex hierarchical arrangements that would be more challenging or even prohibitively time tested to model in relational databases.
The small disadvantage with graph databases is that they cannot easily be queried using the de facto querying language for relational databases, Structured Query Language (SQL). Not only that, but at the graph database planet there isn’t yet an equal de facto query language — there are a number of industry standard languages but there is very likely to be a shakeout of a number of these as graph databases become more popular and also a very clear winner maybe emerges.
Amazon says it constructed Neptune specifically for the cloud, which has its pluses and minuses. The downside is that there is not an on-premises version. The benefit though is that because of its economies of scale AWS tends to have the ability to provide decent value subscriptions. Much like additional AWS managed services Amazon Neptune is highly accessible, using read replicas, point-in-time retrieval, constant backup to Amazon S3, and replication across AWS Availability Zones.
It can save billions of connections and the graph could be queried with milliseconds latency. Neptune supports encryption at rest and in transit. As for that thorny problem of which query languages to support, AWS has hedged its bets with the choice of Apache Tinkerpop Gremlin or even SPARQL (Microsoft’s cloud graph that offers, Azure CosmosDB, supports Gremlin or even Gremlin-compatible languages like Apache Spark GraphX).
I would have liked to see the addition to both of Cypher, a language created by graph database leader Neo4j, as we think it has rather widespread adoption. Neo4j given this to the openCypher Project at 2015 and in Addition to Neo4j it has supported in SAP HANA Graph, Redis and AgensGraph databases.
Use cases and early adopters
Early adopters of Neptune are very likely to be present AWS users that have any or all their data in the cloud : AWS currently provides a range of databases such as relational and NoSQL choices.
Amazon envisages that Neptune will power graph use cases like recommendation motors, fraud detection, knowledge graphs, drug discovery, and network security. Safety is possibly the most typical area where graph databases have been pushed into action, but they’re also utilised in logistics, supply chain management, master data management, life sciences, e-commerce as well as the hospitality market.
Companies having a play Neptune in trailer include AstraZeneca, Thomson Reuters, Siemens, and the Financial Industry Regulatory Authority (FINRA). Amazon was looking into the way that it can use it in order to improve its own Amazon Alexa program.
I think AWS’ move into the graph database space is important for the industry. It will make it easier than ever for people to have a drama with a graph database . Using Neptune, you do not need to be concerned about hardware adware, software patching, installation, setup, or copies.
It is not that there are not other graph-as-a-service offerings, but few have the advantage of AWS. With so many companies currently having at least some of the data about AWS, this is a chance to see what a graph database can do for you.
There are too many graph databases to mention all of them here, but here is a selection of companies large and small (in alphabetical order) to add to all those mentioned previously. Most provide some type of pre-production free trial, which means it is possible to kick off the tyres before you jump right in.
Have you got any experience of using databases? I would want to hear your thoughts in the comments section.