Graph Lakehouse Features and Benefits

Graph Lakehouse is a native, massively parallel processing (MPP) graph OLAP database, built to deliver hyperfast advanced analytics at big data scale. This topic provides details about the key Graph Lakehouse features and the benefits that they provide.

Native Graph Database

Graph Lakehouse is built to handle graph workloads throughout the computing stack, from the query language to the database and memory management engine, and the file system. Data is stored in native graph format whether it is on disk or in memory. Graph Lakehouse's use of the organic graph model avoids the overhead that non-native graph databases employ for simulating graph traversal and reformatting data on disk. Graph Lakehouse processes queries faster, scales better, and runs efficiently on hardware, virtual, or cloud platforms.

Massively Parallel Processing

Graph Lakehouse is a massively parallel processing (MPP) graph database. Its compressed in-memory and on disk data storage and MPP design provides extremely fast data loading, real-time updates, and interactive analytics on huge amounts of data. For more information, see Graph Lakehouse Architecture.

Performance at Scale

Graph Lakehouse scales with your needs by distributing graph data across cluster nodes and processing queries in parallel on all nodes. Because of Graph Lakehouse's MPP and fast intra-cluster network implementation, load and query performance increases as the data and cluster size grow.

Graph OLAP Technology and Multi-Graph Support

Unlike transaction-oriented graph databases, Graph Lakehouse is a modern enterprise Graph Online Analytics Processing (GOLAP) database that enables users to interactively view, analyze, and update graph data. Graph Lakehouse provides unmatched analytic processing of complex queries that require many joins, filters, and aggregation. Graph Lakehouse enables data scientists, data architects, and application developers to deliver supercharged analytic insights at massive scale to support vital real-time solutions for detecting fraud, ensuring compliance, optimizing supply chains, building enterprise knowledge bases, and more. In accordance with the RDF/SPARQL standard, Graph Lakehouse has robust multi-graph support.

Standards-Based Query Languages and Protocols

Graph Lakehouse adheres to the W3C RDF and SPARQL 1.1 standards and offers the standard SPARQL 1.1 and RDF Graph Store Protocol on HTTP/S for sending and receiving SPARQL queries between client applications and the database. Graph Lakehouse also supports the industry standard CSV and RDF load file formats. Developers and analysts do not need to learn a proprietary query language to work with Graph Lakehouse and can incorporate Graph Lakehouse into their existing infrastructure of products that support standard graph APIs, such as data preparation, graph transaction processing, visualization, business intelligence, and machine learning tools.

In addition to SPARQL, Graph Lakehouse provides Cypher query language support. Graph Lakehouse supports the Bolt protocol to provide a Cypher-based CLI from which users can directly execute Cypher statements. Other Cypher applications that use the Bolt protocol can also execute either Cypher or SPARQL queries against Graph Lakehouse data. For more information on Graph Lakehouse Cypher language support, see Cypher Query Language Reference.

Advanced Analytics

Graph Lakehouse extends the SPARQL 1.1 specification to add support for advanced analytics such as window aggregates and advanced grouping capabilities. Graph Lakehouse also supports conditional expressions, named queries and views, inferencing (RDFS+), labeled property graphs (using the W3C RDF-star proposed standard), and graph algorithms. In addition, Graph Lakehouse provides pre-built extension libraries that you can also use in the same way as other native, built-in analytic functions. For more information about using built-in analytics and extensions, see SPARQL Query Language Reference.

In addition to supporting all standard SPARQL functions, Graph Lakehouse includes a rich library of SQL and Microsoft Excel-like built-in functions as well as both C++ and Java APIs for creating user-defined or custom extension functions, aggregates, and services. For more information about the extensions, see Develop.

Flexible and Schema-less Data Loading

Loading data to Graph Lakehouse does not require maintenance of error-prone and time-consuming ETL pipelines, rigid schemas, or relational database models. And Graph Lakehouse’s virtually unlimited capacity and real-time performance enables users to load structured, unstructured, internal, or external data on-demand, bringing immediate access and analysis to everyone. For more information, see Load & Manage Data.

Graph Lakehouse provides a number of different data samples, tutorials, and notebooks to help you get started quickly using Graph Lakehouse and also familiarize you with the various operations you can perform. For more information, see Get Started.