Google BigQuery vs Neo4j

Overview

Google BigQuery

Stacks1.8K

Followers1.5K

Votes152

Neo4j

Stacks1.2K

Followers1.4K

Votes351

GitHub Stars15.3K

Forks2.5K

Google BigQuery vs Neo4j: What are the differences?

Introduction

Google BigQuery and Neo4j are two popular technologies used for data storage and processing. While they both have their strengths, they also have key differences that set them apart from each other. In this article, we will explore these differences in detail.

1. Scalability and Performance:

Google BigQuery is a highly scalable data warehouse that can handle massive amounts of data and process queries at a high speed. It uses a distributed architecture to parallelize the execution of queries, allowing it to scale horizontally and handle large workloads efficiently. On the other hand, Neo4j is a graph database that excels in handling complex relationships and traversing large graphs. It is designed for highly connected data and provides high-performance, real-time graph processing capabilities.

2. Data Model:

Google BigQuery follows a tabular data model, similar to traditional relational databases. It stores data in tables with rows and columns, and queries are performed using SQL-like syntax. Neo4j, on the other hand, follows a graph data model. It represents data as nodes and relationships, allowing for flexible and expressive querying of complex relationships. Neo4j's graph model is well-suited for applications that require deep and complex analysis of relationships.

3. Querying Capabilities:

Google BigQuery supports ANSI SQL queries, which are widely supported and familiar to many users. It also provides advanced querying features such as window functions and nested queries. BigQuery is optimized for running analytical queries on large datasets and can handle complex aggregations and joins efficiently. Neo4j, as a graph database, provides a query language called Cypher, specifically designed for graph traversal and pattern matching. Cypher allows users to express complex graph queries in a concise and intuitive manner.

4. Use Cases:

Google BigQuery is commonly used for data warehousing, business intelligence, and analytics. It is well-suited for scenarios where structured and semi-structured data needs to be analyzed at scale. On the other hand, Neo4j is often used for applications that require querying and analyzing highly connected data, such as social networks, recommendation engines, and fraud detection systems. Its graph-based approach allows for efficient navigation and querying of complex relationships.

5. Data Integration:

Google BigQuery integrates well with other Google Cloud Platform services and supports data ingestion from various sources, including streaming data. It provides connectors for popular data integration tools and frameworks, making it easy to load data from different systems. Neo4j, on the other hand, provides integration capabilities with different programming languages and frameworks through its drivers and APIs. It also supports data import and export in various formats, allowing users to integrate with different data sources and workflows.

6. Data Consistency and Transactions:

Google BigQuery is built for eventual consistency rather than strong consistency. It uses a columnar storage format and is optimized for read-heavy workloads. While it supports ACID transactions within a single query, it does not offer multi-row ACID transactions across multiple queries. In contrast, Neo4j provides strong consistency guarantees and supports ACID transactions for both read and write operations. It ensures data integrity and allows for complex multi-transactional operations within the graph.

In Summary, Google BigQuery is a scalable data warehouse optimized for analytical queries on large datasets, using a tabular data model and SQL-like syntax. On the other hand, Neo4j is a graph database designed for efficient traversal and querying of highly connected data, using a graph data model and the Cypher query language. Both technologies have their strengths and are suitable for different use cases based on the nature of the data and the analytical requirements.

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Advice on Google BigQuery, Neo4j

Jaime

none at none

Aug 31, 2020

Needs advice

Hi, I want to create a social network for students, and I was wondering which of these three Oriented Graph DB's would you recommend. I plan to implement machine learning algorithms such as k-means and others to give recommendations and some basic data analyses; also, everything is going to be hosted in the cloud, so I expect the DB to be hosted there. I want the queries to be as fast as possible, and I like good tools to monitor my data. I would appreciate any recommendations or thoughts.

Context:

I released the MVP 6 months ago and got almost 600 users just from my university in Colombia, But now I want to expand it all over my country. I am expecting more or less 20000 users.

56.4k views56.4k

Comments

Julien

CTO at Hawk

Sep 19, 2020

Decided

Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.

Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.

BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.

BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.

Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.

BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.

We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution

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Comments

Detailed Comparison

Google BigQuery	Neo4j
Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.	Neo4j stores data in nodes connected by directed, typed relationships with properties on both, also known as a Property Graph. It is a high performance graph store with all the features expected of a mature and robust database, like a friendly query language and ACID transactions.
All behind the scenes- Your queries can execute asynchronously in the background, and can be polled for status.;Import data with ease- Bulk load your data using Google Cloud Storage or stream it in bursts of up to 1,000 rows per second.;Affordable big data- The first Terabyte of data processed each month is free.;The right interface- Separate interfaces for administration and developers will make sure that you have access to the tools you need.	intuitive, using a graph model for data representation;reliable, with full ACID transactions;durable and fast, using a custom disk-based, native storage engine;massively scalable, up to several billion nodes/relationships/properties;highly-available, when distributed across multiple machines;expressive, with a powerful, human readable graph query language;fast, with a powerful traversal framework for high-speed graph queries;embeddable, with a few small jars;simple, accessible by a convenient REST interface or an object-oriented Java API
Statistics
GitHub Stars -	GitHub Stars 15.3K
GitHub Forks -	GitHub Forks 2.5K
Stacks 1.8K	Stacks 1.2K
Followers 1.5K	Followers 1.4K
Votes 152	Votes 351
Pros & Cons
Pros 28 High Performance 25 Easy to use 22 Fully managed service 19 Cheap Pricing 16 Process hundreds of GB in seconds Cons 1 You can't unit test changes in BQ data 0 Sdas	Pros 69 Cypher – graph query language 61 Great graphdb 33 Open source 31 Rest api 27 High-Performance Native API Cons 9 Comparably slow 4 Can't store a vertex as JSON 1 Doesn't have a managed cloud service at low cost
Integrations
Xplenty Fluentd Looker Chartio Treasure Data	No integrations available

What are some alternatives to Google BigQuery, Neo4j?

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

Stitch

Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Dgraph

Dgraph's goal is to provide Google production level scale and throughput, with low enough latency to be serving real time user queries, over terabytes of structured data. Dgraph supports GraphQL-like query syntax, and responds in JSON and Protocol Buffers over GRPC and HTTP.

Dremio

Dremio—the data lake engine, operationalizes your data lake storage and speeds your analytics processes with a high-performance and high-efficiency query engine while also democratizing data access for data scientists and analysts.

RedisGraph

RedisGraph is a graph database developed from scratch on top of Redis, using the new Redis Modules API to extend Redis with new commands and capabilities. Its main features include: - Simple, fast indexing and querying - Data stored in RAM, using memory-efficient custom data structures - On disk persistence - Tabular result sets - Simple and popular graph query language (Cypher) - Data Filtering, Aggregation and ordering

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