It needed a low-code app development framework that would enable it to develop highly customizable HCM applications without writing code. “They can even register and download AWS Service Organization Control compliance reports on their own.”. The data structure for these databases consists of vertices or nodes, and direct links called edges. Plus, the effort of managing those databases was a burden on the next gen HCM staff. Amazon Neptune easily builds queries that efficiently navigate highly connected datasets, enabling the next gen HCM team to build applications that use ADP’s wealth of data to answer complex workplace questions for a variety of use cases. Click here to return to Amazon Web Services homepage, Provided Service Organization Control and General Data Protection Regulation compliance. AWS addresses many common use cases by providing standalone IAM policies that are created and administered by AWS. Join thousands of aspiring developers and DevOps enthusiasts Take a look, Secure Your APIs with Firebase + AWS API Gateway, Basic commands to interact with the Bitcoin Core RPC console. Or, check out some AWS Neptune Tutorials on YouTube. Coding Bootcamps: What Do New Graduates Earn? These follow the general pricing for AWS On Demand instances. The following endpoints are available from a Neptune DB cluster. We have, Click here to return to Amazon Web Services homepage, Quick start Documentation for Amazon Neptune, Getting started Documentation for Amazon Neptune, Practical Gremlin: An Apache TinkerPop Tutorial, Working Backwards to your Graph Data Model and Queries with Amazon Neptune, example code for visualizing your Neptune graph, application to ETL IMDB into Neptune using AWS Glue to try out graph queries to find "Six Degrees of Kevin Bacon". Graph databases like AWS Neptune were created to address the limitations of relational databases, and offer an efficient way to work with complex data.

There are only two data types: vertices and edges, both of which have properties stored as key-value pairs. They have advantages over relational databases for use cases like social networking, recommendation engines, and fraud detection, where you need to create relationships between data and quickly query these relationships. Global human capital management (HCM) solutions provider Automatic Data Processing (ADP) recently took a bold step as part of its strategy to be on the “innovation offensive.” ADP’s digital transformation unit, Lifion, launched ADP’s next gen HCM, a system designed to model a dynamic modern workplace where work gets done in teams rather than traditional hierarchal structures. “It has given us broader use cases in our current features for customers that we never thought we could have accomplished in our early products,” says Jain. “We have more than 200 microservices,” Masud explains, “and each domain has its own data storage needs. Neptune supports Property Graph and W3C’s RDF.

It was first announced at AWS re:Invent 2017, and made generally available in May 2018. With the fully managed Amazon Neptune, ADP eliminated database licensing, reduced Amazon EC2 costs, and enabled its team to focus on core business operations rather than database maintenance.

You can load data in CSV or RDF formats and begin writing graph queries with Apache TinkerPop Gremlin or SPARQL. “We like app-level encryption in addition to database-level encryption,” says Masud. “Today there are many types of workplace structures, and traditional HCM systems have not embraced that,” says Zaid Masud, chief architect for ADP’s next gen HCM.

Having already used Amazon Web Services (AWS) for self-managed database provisioning, the company looked to AWS for a fully managed database solution, and it found one in Amazon Neptune, a fast, reliable graph database that makes it easy to build and run applications that work with highly connected datasets. “Graph databases naturally represent your structure in the way it’s designed or visualized, which enables us to build much more dynamic queries.”. example application showing how to use AWS AppSync GraphQL and Amazon Neptune. As you get started, the AWS Neptune docs are a great resource. “We expect an increase in reliability and availability with Amazon Neptune, which means that we’re running less of an exposure risk.”, Amazon Neptune has multiple levels of security, including encryption at rest, which is important in securing next gen HCM’s sensitive data, such as personally identifiable information. “We’re very comfortable telling our customers that we are on AWS,” says Masud. ADP’s next gen HCM is exploring multitenancy using Amazon Neptune to better represent the structure of its customer data: currently, each customer has its own isolated graph. All rights reserved. AWS Neptune Use Cases Use cases for the AWS graph database and other similar offerings include: Machine learning, such as intelligent image … Follow us on Twitter and Facebook and join our Facebook Group . SQL queries to navigate this data would require nested queries and complex joins that quickly become unwieldy, and the queries would not perform well as your data size grows over time. “With Amazon Neptune, sensitive data is already encrypted before it gets to the database, and then it’s encrypted again at rest.” Amazon Neptune also satisfies end users’ requirements for compliance with Service Organization Control and General Data Protection Regulation. Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides secure, resizable compute capacity in the cloud. It’s also highly available and shows up in multiple availability zones. Founded in 1949, ADP designs cutting-edge products, premium services, and exceptional experiences informed by data for HR, talent, time management, benefits, and payroll that enable people to reach their full potential. If you'd like to migrate to Amazon Neptune, there's a re:Invent 2018 presentation on Migrating to Amazon Neptune.

“Writing a query, plugging in criteria, and viewing a list of employees who qualify are things that Amazon Neptune is very well suited for,” says Lucky Jain, engineering manager, next gen HCM.
A graph database is a database optimized to store and process highly connected data — in short, it’s about relationships. “Spending less time on those things significantly improves our time to market.” ADP also avoids paying for database licensing and reduces spending on Amazon EC2. Data transfer — you are charged per GB for data transferred in and out of AWS Neptune. This is very similar to Aurora, the relational database from Amazon, in its architecture and availability. Querying relational databases is challenging and required the next gen HCM team to denormalize the data, or add redundant data, in order to speed retrieval, which wasn’t efficient. Unlike AWS Neptune, Neo4j is open source. The relationships are stored as first order citizens of the data model. “One of the really powerful things about our low-code app development platform is that it enables you to build no-code graph traversal queries,” adds Masud. If you already have your data in a graph model, it’s easy to get started with Amazon Neptune.

They have advantages over relational databases for use cases like social networking, recommendation engines, and fraud detection, where you need to create relationships between data and quickly query these relationships. As per usual, increasing the retention period will cost more. There are no tables, columns, or rows — it feels like a NoSQL database. Graph databases, like Amazon Neptune, are purpose-built to store and navigate relationships.

“We felt that Amazon Neptune was a slam dunk because our application was already using these open standards,” says Masud. Since Amazon Neptune supports open Graph APIs and query languages, we link to external documentation for Gremlin and SPARQL.

An endpoint is a URL that contains a host address and a port.
Masud says AWS has been very responsive to ADP’s needs: “AWS has a way of getting things out to market quickly and then refining that and iterating over that. Neptune uses graph structures such as nodes (data entities), edges (relationships), and properties to represent and store data. The Dynamic Teams features of ADP’s next gen HCM aim to help organizations break out of the traditional workplace hierarchy by taking team members out of silos, improving engagement and performance, and creating a culture of connectivity. While there are several languages available in the graph database market, Cypher is widely known by now. You would need multiple tables with multiple foreign keys. Which Works Better For You — Golang Or Node.js? ADP offers HCM software solutions for automating payroll, core human resources (HR), talent management, benefits, and workforce management for companies ranging from small businesses to global corporations. Neo4j is a graph database that has been rated most popular by mindshare and adoption. In contrast, a graph database offered ADP’s next gen HCM the agile storage it needed. The collection of databases included relational databases like multimaster MySQL clusters, distributed key-value stores, and column family stores. Getting Started with Amazon Neptune Graph databases, like Amazon Neptune, are purpose-built to store and navigate relationships. Now staff can focus on cloud infrastructure and site reliability engineering operations, enabling ADP’s next gen HCM to further grow its platform without adding additional staff.

Once you’re on board, make sure you have cost control as a priority. “So many things must be customized at every level to account for client-specific needs, regional needs, and compliance needs.” ADP’s next gen HCM also wanted a graph database with open-source standards, which would help the team avoid lock-in. The core of Neptune is a purpose-built, high-performance graph database engine optimized for storing billions of relationships and querying the graph with milliseconds latency. But most importantly, it was able to use the purpose-built graph database to power complex queries and deliver to its customers advanced HR applications that it wouldn’t have been able to otherwise. It also needed to move away from a relational database to a graph database. The next gen HCM team had a number of requirements for a graph database in addition to a fully managed service. Neptune’s interactive performance at scale effectively enables a broad set of graph use cases.

This purpose-built, high-performance graph database engine is optimized for storing billions of relationships and querying the graph with milliseconds latency.

If you'd like to understand how to view your data as a graph, there's a re:Invent 2018 presentation on Working Backwards to your Graph Data Model and Queries with Amazon Neptune along with sample code available on Github. Building such a solution required a database that could seamlessly manage an extensive network of complex data points. You can do this using the graph models’ query languages: Apache TinkerPop Gremlin and SPARQL.

AWS Neptune is fully managed, which means that database management tasks like hardware provisioning, software patching, setup, configuration, and backups are taken care of for you. Version 1.0 was released in February 2010. A graph database is fundamentally different from SQL.

The AWS Neptune cost calculation depends on a few factors: For this, as with most AWS services, pricing is confusing and difficult to predict. You can use these to build your own web of data sets that you care about, and build networks across the data sets in the way that makes sense for your data, not with arbitrary presets. Instantly get access to the AWS Free Tier. To join our community Slack ️ and read our weekly Faun topics ️, click here⬇, Medium’s largest and most followed independent DevOps publication. Use cases for such highly-connected data include social networking, restaurant recommendations, retail fraud detection, knowledge graphs, life sciences, and network & IT ops. ParkMyCloud can now park Neptune databases to ensure you’re only paying for what you’re actually using.

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