Hire the Best Graph Databases Specialists

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Waqas A.

Lahore, Pakistan

$25/hr
5.0
92 jobs

As an AWS Advanced Tier Partner and Claude Partner, we help startups and enterprises design, build, deploy, and scale reliable AI solutions. โœ… AWS Advanced Tier Partner โœ… Claude Partner โœ… Enterprise AI Specialists โœ… Production-Ready AI Solutions โœ… Secure & Scalable Architecture โœ… Strong AI + Cloud Engineering Team ---------------------------------- Common Use Cases ---------------------------------- Automating legacy business workflows using GenAI and AI Agents Designing and building custom MCP (Model Context Protocol) servers Connecting legacy databases to MCP servers Building custom MCP tools for enterprise applications Connecting CRMs, ERPs, APIs, and internal systems to AI Developing AI agents that can read, reason, and take actions Building multi-agent systems for complex business workflows Creating enterprise AI copilots for employees Building customer support AI assistants Developing Retrieval-Augmented Generation (RAG) applications Integrating AI with existing SaaS products Automating document processing, extraction, and classification Building AI-powered search across enterprise knowledge Creating voice AI agents and AI calling systems Modernizing enterprise applications with Amazon Bedrock, Claude, and OpenAI Building secure AI APIs and backend services Deploying scalable AI solutions on AWS ------------------------- Expertise ------------------------- AI Workflow Automation Automating legacy workflows using GenAI AI-powered business process automation Intelligent workflow orchestration Human-in-the-loop AI systems Agentic AI Production-ready AI Agents Multi-Agent Architectures AI Copilots Autonomous task execution Tool-using AI Agents Planning and reasoning workflows MCP (Model Context Protocol) Designing and building MCP Servers Building custom MCP Tools Connecting legacy databases to MCP Connecting CRMs, ERPs, APIs, and enterprise systems through MCP Secure enterprise MCP deployments Enterprise AI Integration Internal AI assistants Customer support AI Enterprise knowledge assistants AI integrated into existing applications RAG (Retrieval-Augmented Generation) Document Intelligence Intelligent Document Processing Contract extraction Invoice processing Medical document analysis Legal document processing PDF understanding Document classification and indexing Reliable AI Engineering AI Evaluation frameworks Prompt Engineering AI Guardrails AI Testing Reliable AI system design Continuous AI improvement AI Observability AI tracing Agent monitoring Workflow debugging Performance monitoring Production observability Cost optimization Production Deployment Amazon Bedrock implementations Claude integration OpenAI integration AWS deployment Scalable cloud architecture Production AI infrastructure Technologies Model Context Protocol (MCP) Claude Amazon Bedrock OpenAI Gemini LangGraph LangChain LangSmith CrewAI Python FastAPI Node.js AWS Lambda ECS EKS Docker Kubernetes Terraform Pinecone PostgreSQL MongoDB Redis n8n Whether you need to automate legacy workflows, build MCP servers, design Agentic AI systems, integrate AI into enterprise software, or deploy production-grade AI on AWS, we can help turn your AI vision into a reliable, scalable solution. Let's build AI that doesn't just answer questionsโ€”it gets work done.

  • AI Bot
  • AI Agent Development
  • AI App Development
  • AI Model Integration
  • AI Model Development
  • AWS Glue
  • AI Image Generator
  • AI Video Generator
  • AI-Generated Voice-Over
  • Web Application Development
  • Mobile App Development
  • Amazon Bedrock
  • Amazon SageMaker
  • Amazon Lex
  • Amazon Comprehend
Ashish S.

Delhi, India

$20/hr
4.9
154 jobs

๐€๐ˆ-๐ƒ๐‘๐ˆ๐•๐„๐ ๐ƒ๐€๐“๐€ ๐„๐๐†๐ˆ๐๐„๐„๐‘ ๐€๐”๐“๐Ž๐Œ๐€๐“๐ˆ๐Ž๐ ๐€๐‘๐‚๐‡๐ˆ๐“๐„๐‚๐“ ๐ƒ๐„๐‚๐ˆ๐’๐ˆ๐Ž๐ ๐ˆ๐๐“๐„๐‹๐‹๐ˆ๐†๐„๐๐‚๐„ ๐’๐๐„๐‚๐ˆ๐€๐‹๐ˆ๐’๐“ โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• I design systems where data is not just stored or visualized โ€” it is processed, understood, and acted upon through AI and automation. With 9+ years of experience, I have consistently delivered scalable, high-impact solutions across enterprise and startup ecosystems. โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• 1. CORE CAPABILITIES โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ Data Engineering Design and implementation of ETL/ELT pipelines Batch and real-time data processing architectures AI Systems and LLM Applications Conversational AI, RAG-based systems, intelligent automation Business Intelligence Decision-centric dashboards and analytical reporting Workflow Automation Event-driven systems using APIs, orchestration tools, and integrations Microsoft Power Platform Power Apps, Power Automate, Dynamics 365 enterprise solutions โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• 2. SIGNATURE SOLUTIONS โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ 1 Data Engineering Python, SQL, PySpark 2 Business Intelligence Power BI, Tableau, Looker Studio 3 Data Platforms Snowflake, BigQuery 4 Cloud Infrastructure Azure, AWS, Google Cloud Platform 5 Application Layer FastAPI, React 6 AI and LLM Ecosystem LLMs, LangChain, OpenAI, Claude 7 Automation and Orchestration n8n, API integrations, workflow systems 8 DevOps and Deployment Docker, Kubernetes, Airflow, DBT 9 Microsoft Power Platform Power Apps, Power Automate, Dynamics 365, MS Fabric โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• 5. FOCUS AREAS โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ 1 Data Engineering 2 AI Automation 3 Power Platform Solutions 4 LLM Applications 5 Decision Intelligence 6 Data Migration โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• โ€œData becomes valuable only when systems are designed to act on it.โ€

  • Tableau
  • SQL
  • Python
  • R
  • Microsoft Power BI
  • Dashboard
  • Sentiment Analysis
  • Looker Studio
  • Embedded Application
  • DevOps Engineering
  • Data Analytics & Visualization Software
  • Amazon ECS for Kubernetes
Dylan S.

Quito, Ecuador

$15/hr
4.9
127 jobs

4+ years building BI end-to-end Infrastructure. I sit with the ambiguity, ask what your team hasn't, and translate fuzzy business goals into a clear data model and KPI framework that holds up under scrutiny. ๐Ÿง  ๐–๐ก๐š๐ญ ๐ˆ ๐›๐ซ๐ข๐ง๐  ๐ญ๐จ ๐ฒ๐จ๐ฎ๐ซ ๐ญ๐ž๐š๐ฆ ๐Ÿ”น Ambiguity โ†’ Clarity: I define the right questions and metrics before building anything, so you measure what matters, not what's easy. ๐Ÿ”น Semantic & Data Modeling: Star schemas, slowly-changing dimensions, reconciled grain, single source of truth ๐Ÿ”น Scalable Architecture: Pipelines and models designed for "Day 2", maintainable, cost-aware, documented, built to grow instead of breaking at scale. ๐Ÿ”น Context Integration: I unify Stripe, Shopify, Zoho, GA4, Ads platforms, etc. and CRMs into one coherent ecosystem that mirrors how your business actually operates. ๐Ÿ”น Narrative & Decision Design: Every visual answers a business question and points to an action, analysis a CEO can act on in seconds. โš™๏ธ ๐—ง๐—ฒ๐—ฐ๐—ต๐—ป๐—ถ๐—ฐ๐—ฎ๐—น ๐—ง๐—ผ๐—ผ๐—น๐—ฏ๐—ผ๐˜… ๐Ÿ”น BI & Viz: Power BI, Looker Studio, Zoho Analytics, Metabase, Apache Superset, Domo, Databox ๐Ÿ”น Modeling & Query: SQL, DAX, Power Query (M), Python, dbt, Excel ๐Ÿ”น Warehousing: BigQuery, Cloud SQL, Snowflake, SQL Server, MySQL, Firebase ๐Ÿ”น Pipelines & ETL: Make, n8n, Fivetran, Apps Script, Cloud Run, Windsor AI, Coupler IO, Supermetrics ๐Ÿ”น Sources: Stripe, Shopify, Zoho CRM, GA4, Google/Meta/TikTok Ads, Search Console, etc. ๐Ÿ”น AI layer (the plus): OpenAI & Claude APIs, Claude Code, Codex, conversational analytics, anomaly detection, predictive models, and agent workflows for automated briefings & alerts ๐Ÿš€ ๐—ช๐—ต๐—ฎ๐˜ ๐—ฆ๐—ฒ๐˜๐˜€ ๐— ๐—ฒ ๐—”๐—ฝ๐—ฎ๐—ฟ๐˜ ๐Ÿ”น Business-first, not tool-first: I start with your objectives and constraints, not your dataset. ๐Ÿ”น End-to-end ownership: From auditing sources and defining KPIs to modeling, building, and publishing, I run the full lifecycle. ๐Ÿ”น Transparency: You own your data and infrastructure. I document every step, so nothing is a black box. ๐Ÿ”น AI as leverage, not a gimmick: I automate the repetitive parts so the strategic parts get more of my attention. โญ ๐—ช๐—ต๐˜† ๐—–๐—น๐—ถ๐—ฒ๐—ป๐˜๐˜€ ๐—ฆ๐˜๐—ฎ๐˜† ๐Ÿ”น Reliable communication and clear expectations , no micromanagement needed. ๐Ÿ”น Insights in plain language, for technical and non-technical stakeholders alike. ๐Ÿ”น Proactive: I diagnose and recommend, not just deliver charts. ๐Ÿ”น Consistent delivery, on time, with care. ๐Ÿ’ก ๐—Ÿ๐—ฒ๐˜'๐˜€ ๐—•๐˜‚๐—ถ๐—น๐—ฑ ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐——๐—ฒ๐—ฐ๐—ถ๐˜€๐—ถ๐—ผ๐—ป ๐—ฆ๐˜†๐˜€๐˜๐—ฒ๐—บ If your business generates data but not clarity, let's fix that. I'll design the model, architecture, and dissemination layer that turn your numbers into a durable competitive advantage, clear, scalable, and genuinely yours.

  • Looker Studio
  • Microsoft Power BI
  • Zoho Analytics
  • Metabase
  • Apache Superset
  • Domo
  • Microsoft Excel
  • BigQuery
  • Google Cloud Platform
  • Cloud Run
  • SQL
  • Python
  • JavaScript
  • Google Apps Script
  • Microsoft Power Automate
  • Data Analysis
  • Data Visualization
  • Data Engineering
  • ETL Pipeline
  • Data Warehousing & ETL Software
Nghi L.

Ho Chi Minh City, Vietnam

$25/hr
5.0
53 jobs

โฐ Available 24/7 โ€“ Long-term & High-impact Projects Hi, Iโ€™m Nghi, a Senior Data Engineer and Data Architect with a strong backend foundation, now focused on building high-performance analytics platforms, explainable data pipelines, and production-grade cloud architectures. I help companies transform unreliable, slow, or opaque data systems into scalable, well-documented, and business-trustworthy platforms. ๐Ÿง  WHAT I SPECIALIZE IN ๐Ÿ—๏ธ Data Architecture & Platform Design - Designing modern lakehouse & warehouse architectures - dbt-first analytics engineering with testing, freshness & lineage - Event-driven and batch hybrid pipelines - Data quality frameworks & SLA monitoring - Customer-facing data explainability systems Tools: dbt, Dagster, Airflow, Spark, Kafka, Snowflake, BigQuery, Redshift, PostgreSQL, DuckDB, ClickHouse โšก Database Performance Engineering - If your queries are slow, costs are high, or dashboards lag, This is my zone - Query plan analysis & index strategies - Warehouse cost optimization (Snowflake, BigQuery, Redshift) - OLTP & OLAP performance tuning - High-concurrency workload design ๐Ÿ”„ Reverse ETL & Operational Analytics - Syncing analytics back to CRMs & internal tools - Building real-time metrics pipelines - Feature-store style transformations ๐Ÿ•ท๏ธ Enterprise-grade Web Data Extraction - I donโ€™t just scrape pages, I build durable data acquisition systems: - Complex ASP.NET, JS-heavy, authenticated & paginated systems - Anti-bot bypassing & failure-recovery pipelines - Headless browser automation + async scraping - Real-estate, finance, campaign-finance & marketplace platforms โ˜๏ธ Cloud Infrastructure - AWS | Azure | GCP - EMR / Dataproc / Glue / Dataflow / Synapse / BigQuery / Redshift - Terraform-based deployments - Cost-aware architectures - Kubernetes + Dockerized data services ๐Ÿงช What You Get Working With Me โœ”๏ธ Production-ready pipelines โœ”๏ธ Clean, testable dbt models โœ”๏ธ Well-documented architecture diagrams โœ”๏ธ Transparent data logic for non-technical stakeholders โœ”๏ธ Systems that scale beyond MVP โœ”๏ธ Honest advice and not over-engineering ๐Ÿ† Ideal Projects ๐Ÿ‘ Data warehouse migrations ๐Ÿ‘ Broken pipelines that need debugging & stabilization ๐Ÿ‘ Analytics platforms that lack trust or explainability ๐Ÿ‘ Performance bottlenecks costing thousands per month ๐Ÿ‘ Long-term data platform ownership โฃ๏ธ Why Clients Stay Long-Term ๐Ÿ€Clear communication ๐Ÿ€ Business-first thinking ๐Ÿ€ No black-box systems ๐Ÿ€ I build systems others can maintain ๐Ÿ‡ป๐Ÿ‡ณ๐Ÿ‡ป๐Ÿ‡ณ๐Ÿ‡ป๐Ÿ‡ณ๐Ÿ‡ป๐Ÿ‡ณ If your data platform feels fragile, slow, or impossible to explain to customers, I can fix that. Letโ€™s make your data system something you can confidently stand behind.

  • Python
  • Data Scraping
  • ETL
  • Data Visualization
  • SQL Programming
  • Microsoft Azure
  • Amazon Web Services
  • Web Development
  • Database Administration
  • NoSQL Database
  • Google Cloud Platform
  • Apache Airflow
  • dbt
  • Analytics
Elo O.

Lagos, Nigeria

$25/hr
5.0
5 jobs

Tired of workflows that break, integrations that don't sync, and manual work eating 20+ hours every week? I build automation systems that combine no-code platforms with custom API integrations and scripting; delivering solutions that save you 15-30 hours weekly and scale with your business. 150+ projects delivered for agencies, SaaS companies, eCommerce brands, and service businesses since 2019. Fully certified in Airtable (Builder, Admin, AI App Builder), Monday (Work Management, Workflow), Make (Basic through Advanced), and Zapier (18 certifications). Unlike typical no-code specialists, I handle both visual builders AND custom code when platforms hit their limits. ๐—ช๐—ต๐—ฎ๐˜ ๐—œ ๐—•๐˜‚๐—ถ๐—น๐—ฑ: ๐Ÿ”ท Airtable & Monday Systems โ€“ CRM builds, operations databases, custom dashboards, complex migrations (10K+ records), workspace redesigns ๐Ÿ”ท Make, Zapier & N8N Automation โ€“ Multi-platform workflows, data sync, client onboarding, lead routing, error-handled scenarios that run 24/7 ๐Ÿ”ท API Integration & Custom Scripts โ€“ Python/JavaScript solutions when no-code tools can't cut it, REST API connections, webhook implementations, platform extensions ๐Ÿ”ท AI-Powered Workflows โ€“ ChatGPT API integrations, intelligent routing, automated data processing, AI agents for repetitive tasks ๐Ÿ”ท Softr Apps & Client Portals โ€“ Turn databases into branded client portals, booking systems, internal dashboards ๐Ÿ”ท Data Migration & Cleanup โ€“ Platform migrations, database restructuring, validation systems, complete documentation ๐—ฅ๐—ฒ๐—ฐ๐—ฒ๐—ป๐˜ ๐—ช๐—ถ๐—ป๐˜€: - Events rental company โ€“ Complete Monday workspace overhaul + integrations with booking, accounting, and staff platforms. Eliminated double-entry across 4 systems. - Shoe manufacturer โ€“ Built order and inventory management system with automated QR code generation and tracking. Cut fulfilment errors by 85%. - Influencer agency โ€“ Centralised 50+ influencer boards into a unified Monday workspace with automated sync. Reduced management overhead from 15 hours to 2 hours weekly. - Events planning firm โ€“ Airtable system managing 50 locations and 1,000+ annual events with automated scheduling and resource allocation. Delivered @65% boost in event planning turnaround. - eCommerce business โ€“ Two-way Monday-Shopify sync via Make. Real-time inventory, order tracking, and fulfilment automation. Saved 10+ hours of weekly work and boosted order processing accuracy by 40%. ๐—ช๐—ต๐˜† ๐—–๐—น๐—ถ๐—ฒ๐—ป๐˜๐˜€ ๐—–๐—ต๐—ผ๐—ผ๐˜€๐—ฒ ๐— ๐—ฒ: โœ… Technical depth beyond standard no-code work (API integration, Python, JavaScript) โœ… Fully certified across all major platformsโ€”not just self-taught โœ… Systems built for handoff with complete docs, training, and error handling โœ… Fast communication, clear explanations, projects delivered on time ๐—ง๐˜†๐—ฝ๐—ถ๐—ฐ๐—ฎ๐—น ๐—ฅ๐—ฒ๐˜€๐˜‚๐—น๐˜๐˜€: - 15-30 hours saved weekly on manual tasks - 70-85% reduction in data entry errors - Complete tool stack integrationโ€”everything syncs automatically - Scalable systems that grow with your team - Real-time dashboards for faster decisions ๐Ÿ“ฉ Ready to automate your operations? Send me your biggest workflow challenge. I'll respond with a specific solution approach within 24 hours. Click "Invite to Job" or message me directly. KEYWORDS: Airtable Expert | Monday Expert | Make Automation | Zapier Integration | N8N | API Integration | Python Automation | JavaScript | Workflow Automation | CRM Development | Softr Builder | AI Automation | Data Migration | Process Automation | Custom Integration

  • Make.com
  • Zapier
  • Airtable
  • Automation
  • ChatGPT
  • API Integration
  • No-Code Development
  • CRM Automation
  • JSON
  • GraphQL
  • Database Design
  • HighLevel
  • Google Sheets
  • Automated Workflow
  • n8n
Akash S.

Delhi, India

$15/hr
4.6
10 jobs

Experienced Software Engineer Greetings! I am a passionate and results-driven Software Engineer with 4 years of hands-on experience in crafting robust and user-friendly web applications. My expertise lies in the following technologies: Frontend: Next.js, React.js, TypeScript, JavaScript, HTML, CSS Backend: Node.js, Express.js, tRPC, GraphQL, Prisma Databases: MongoDB, PostgreSQL DevOps: Docker, Docker Compose Version Control: Git, GitHub Cloud Services: AWS, GCP, Azure ๐Ÿ’ก Why Choose Me: โ€ข Innovative Solutions: My curiosity drives me to solve complex problems with creative and efficient solutions. โ€ข Collaborative Approach: A team player with effective communication skills for seamless collaboration. โ€ข Adaptability: Quick learner, always eager to stay updated with the latest industry trends. โ€ข Quality Assurance: Committed to delivering high-quality code through rigorous testing. ๐Ÿ”ง Key Skills: Next.js/React.js: Building responsive and dynamic user interfaces. Node.js/Express.js/tRPC/GraphQL: Creating scalable and efficient server-side applications. Database Management: Proficient in working with MongoDB and PostgreSQL. DevOps and Containers: Experience with Docker and Docker Compose for containerization. Version Control: Skilled in using Git and GitHub for collaborative development. Cloud Integration: Skilled with AWS, GCP, and Azure for cloud-based solutions. ๐Ÿ“ฌ Let's Connect: If you are looking for a dedicated and skilled Engineer, let's connect and discuss how I can bring your ideas to life.

  • TypeScript
  • GraphQL
  • MongoDB
  • PostgreSQL
  • Docker
  • Next.js
  • React
  • Node.js
  • Websockets
  • Docker Compose
  • Firebase
  • Amazon EC2
  • Amazon S3
  • OpenAPI
  • CSS 3

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Don't just take our word for it

The Importance of Graph Databases in an Increasingly Connected World

What are graph databases, and what specific data-related challenges are they designed to meet? Not all data is created equalโ€”and databases have evolved to meet varying demands on data. Before we can dive into what graph databases are, we must first review the more common data technologies that are out there.

SQL, NoSQL, and the pros of each

First, we have structured data that fits neatly into the rows and columns of tables. This is the domain of relational databases, well suited to things like phone books where each entry shares the same properties. Relational databases have served as the organized brains of structured data-based software for decades, and they still play an important role. (If youโ€™re not familiar with how relational databases work, check out our great explainer on the topic.) Relational databases are highly structured and easy to query with a language like SQL, but they have limitations when it comes to unstructured data. Theyโ€™re neat, tidy, and straightforward, but they require developers and their data to be strictly structured too.

Not all data, however, is that easily organized. Unstructured data like IoT sensor data, social sharing, photos, location-based information, online activity, and usage metrics canโ€™t be neatly broken down, which makes rigid tables out of the question. Instead, to coherently group unstructured data together, NoSQL databases trade tables for document files, which are sort of like file folders that help to categorize related data. Imagine the data for a single blog post, which contains tags, photos, edits, comments, and links, grouped together in a doc file.

NoSQL databases such as MongoDB provide fast, scalable solutions for unstructured data. PostgreSQL is another solution, a SQL database that can support more exotic data types than a purely relational database.

SQL and NoSQL database solutions work well in many scenarios. But when demands for connected data grow more complex, their efficiency can be tested. Straightforward queries and isolated data are not always whatโ€™s behind the rich, data-driven experiences weโ€™ve come to expectโ€”the IoT, social networking sites, location-based marketing and navigation, enterprise-grade analytics, product suggestions on eCommerce sites, etc. Interconnected, hierarchical data makes our connected world (and sometimes, our businesses) possible, and itโ€™s not easy to pull off.

This shifts the focus from data alone to the relationships between our data. And thatโ€™s where graph databases come in.

Graph Theory: the story behind graph databases

In 1736, Swiss mathematician and engineer Leonhard Euler used graph theory to prove that there was no solution to the historic math problem Seven Bridges of Kรถnigsberg. If youโ€™re not familiar with the problem, hereโ€™s a quick explanation: Kรถnigsberg, a city in Prussia (now Kaliningrad in Russia), is split into four parts by a river. Connecting the city are seven bridges. The problem was to come up with a walking route that would take a person across each bridge just once. It ended up being impossible, but in the process of trying to solve it, Euler came up with a simplified way of looking at it.

Euler drew attention to the fact that the graphical representation of the problem could be simplified as much as possible using only nodes and connecting lines (or graphs or edges), all without affecting the outcome of the problem. The city sections are abstracted into nodes because they donโ€™t have any effect on the outcome, and the bridges take center stage. This gave way to a new theoryโ€”the graph theoryโ€”and subsequently, a new way of abstracting and structuring databases. The emphasis is on the relationships among the nodes, which helps to simplify connected data.

Streamlining connected data with graph databases

Abstraction and relationships are the heart of graph databases. Through this, they offer an alternative view of and methods for handling and processing complex connected information.

Letโ€™s go back to relational databases to see how they handle connected data. As we know, relational databases consist of tables of entries connected to one another by keys. A relational database can pull related data with foreign keys, JOIN tables and operations, and map-reduce processing. The more many-to-many relationships you need, the more tables youโ€™ll have to create, and the more JOIN operations will be necessary in a single SQL statementโ€”data-scientist speak for complex and inefficient with lots of extra noise. It requires a ton of computing power and memory to pull off and can slow performance exponentially.

The four basics behind a graph database:

  • Nodes: The primary data elements
  • Relationships: How two nodes are connected
    • Nodes may have multiple relationships
  • Properties: Attributes of a node or of a relationship
  • Labels: How nodes are described and grouped together as sets
    • Nodes may have multiple labels
    • Labels get indexed and optimized, making it easier for them to be quickly located

Graph databases shift the focus of their data models to the relationships, which makes retrieving complex data structures much easier. By abstracting nodes and relationships into one structure, theyโ€™re a little like next-gen relational databases that put relationships above the data alone. Rather than the multistep process described above, graph databases allow developers to build sophisticated data models in a much simpler, faster wayโ€”with fewer tables, and sometimes even with only one operation.

The where and how of graph databases

The capabilities of graph databases make them perfectly suited to enterprise data, connected experiences, and data-heavy applicationsโ€”think machine learning, AI, fraud detection, social media sites such as Facebook, which uses the GraphQL language to query data, and recommendation engines behind immersive sites such as Airbnb, TripAdvisor, and Amazon, to name a few. In part two, weโ€™ll look at how social networking applications, in particular, can leverage graph databases to handle the complexity of relationshipsโ€”both between people and between data.

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