Software Alternatives, Accelerators & Startups

neo4j VS Google Cloud Dataflow

Compare neo4j VS Google Cloud Dataflow and see what are their differences

neo4j logo neo4j

Meet Neo4j: The graph database platform powering today's mission-critical enterprise applications, including artificial intelligence, fraud detection and recommendations.

Google Cloud Dataflow logo Google Cloud Dataflow

Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
  • neo4j Landing page
    Landing page //
    2023-05-09
  • Google Cloud Dataflow Landing page
    Landing page //
    2023-10-03

neo4j

Website
neo4j.com
$ Details
Release Date
2007 January
Startup details
Country
United States
State
California
City
San Mateo
Founder(s)
Emil Eifrem
Employees
500 - 999

neo4j features and specs

  • Graph DB

Google Cloud Dataflow features and specs

  • Scalability
    Google Cloud Dataflow can automatically scale up or down depending on your data processing needs, handling massive datasets with ease.
  • Fully Managed
    Dataflow is a fully managed service, which means you don't have to worry about managing the underlying infrastructure.
  • Unified Programming Model
    It provides a single programming model for both batch and streaming data processing using Apache Beam, simplifying the development process.
  • Integration
    Seamlessly integrates with other Google Cloud services like BigQuery, Cloud Storage, and Bigtable.
  • Real-time Analytics
    Supports real-time data processing, enabling quicker insights and facilitating faster decision-making.
  • Cost Efficiency
    Pay-as-you-go pricing model ensures you only pay for resources you actually use, which can be cost-effective.
  • Global Availability
    Cloud Dataflow is available globally, which allows for regionalized data processing.
  • Fault Tolerance
    Built-in fault tolerance mechanisms help ensure uninterrupted data processing.

Possible disadvantages of Google Cloud Dataflow

  • Steep Learning Curve
    The complexity of using Apache Beam and understanding its model can be challenging for beginners.
  • Debugging Difficulties
    Debugging data processing pipelines can be complex and time-consuming, especially for large-scale data flows.
  • Cost Management
    While it can be cost-efficient, the costs can rise quickly if not monitored properly, particularly with real-time data processing.
  • Vendor Lock-in
    Using Google Cloud Dataflow can lead to vendor lock-in, making it challenging to migrate to another cloud provider.
  • Limited Support for Non-Google Services
    While it integrates well within Google Cloud, support for non-Google services may not be as robust.
  • Latency
    There can be some latency in data processing, especially when dealing with high volumes of data.
  • Complexity in Pipeline Design
    Designing pipelines to be efficient and cost-effective can be complex, requiring significant expertise.

neo4j videos

All about GRAND Stack: GraphQL, React, Apollo, and Neo4j

More videos:

  • Review - Kevin Van Gundy | Building a Recommendation Engine with Neo4j and Python

Google Cloud Dataflow videos

Introduction to Google Cloud Dataflow - Course Introduction

More videos:

  • Review - Serverless data processing with Google Cloud Dataflow (Google Cloud Next '17)
  • Review - Apache Beam and Google Cloud Dataflow

Category Popularity

0-100% (relative to neo4j and Google Cloud Dataflow)
Databases
100 100%
0% 0
Big Data
0 0%
100% 100
Graph Databases
100 100%
0% 0
Data Dashboard
0 0%
100% 100

User comments

Share your experience with using neo4j and Google Cloud Dataflow. For example, how are they different and which one is better?
Log in or Post with

Reviews

These are some of the external sources and on-site user reviews we've used to compare neo4j and Google Cloud Dataflow

neo4j Reviews

Top 15 Free Graph Databases
Neo4j is an open-source graph database, implemented in Java described as embedded, disk-based, fully transactional Java persistence engine that stores data structured in graphs rather than in tables. Neo4j Community Edition
ArangoDB vs Neo4j - What you can't do with Neo4j
Multi-Model: Neo4j is a single-model graph database. It does not support any other data models. If your application requires a document or key/value store, you would have to use a second database technology to support it. Being multi-model, ArangoDB allows you to not only use one database for everything,but run ad hoc queries on data stored in different models.

Google Cloud Dataflow Reviews

Top 8 Apache Airflow Alternatives in 2024
Google Cloud Dataflow is highly focused on real-time streaming data and batch data processing from web resources, IoT devices, etc. Data gets cleansed and filtered as Dataflow implements Apache Beam to simplify large-scale data processing. Such prepared data is ready for analysis for Google BigQuery or other analytics tools for prediction, personalization, and other purposes.
Source: blog.skyvia.com

Social recommendations and mentions

Based on our record, neo4j should be more popular than Google Cloud Dataflow. It has been mentiond 34 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

neo4j mentions (34)

  • 6 retrieval augmented generation (RAG) techniques you should know
    The key difference lies in the retrieval mechanism. Vector databases focus on semantic similarity by comparing numerical embeddings, while graph databases emphasize relations between entities. Two solutions for graph databases are Neptune from Amazon and Neo4j. In a case where you need a solution that can accommodate both vector and graph, Weaviate fits the bill. - Source: dev.to / 22 days ago
  • LLM to extract and auto generate knowledge graph - step by step, in ~100 lines of python
    Neo4j is a leading graph database that is easy to use and powerful for knowledge graphs. - Source: dev.to / 23 days ago
  • 10 Ways AI Can Speed Up your Mobile App Development
    Neo4j is one of the most popular graph databases. It offers powerful querying capabilities through its Cypher query language. - Source: dev.to / 3 months ago
  • Databases in 2024: A Year in Review
    Great heads up. I wonder about graph databases. He mentioned and both include the graph use case and I wonder how they compare to . - Source: Hacker News / 5 months ago
  • Installing Neo4j In Ubuntu
    The first blog in this series is to install neo4j - desktop version and few plugins which would help us to build an application. I am using Ubuntu 22.04.4 LTS. - Source: dev.to / 9 months ago
View more

Google Cloud Dataflow mentions (14)

  • How do you implement CDC in your organization
    Imo if you are using the cloud and not doing anything particularly fancy the native tooling is good enough. For AWS that is DMS (for RDBMS) and Kinesis/Lamba (for streams). Google has Data Fusion and Dataflow . Azure hasData Factory if you are unfortunate enough to have to use SQL Server or Azure. Imo the vendored tools and open source tools are more useful when you need to ingest data from SaaS platforms, and... Source: over 2 years ago
  • Here’s a playlist of 7 hours of music I use to focus when I’m coding/developing. Post yours as well if you also have one!
    This sub is for Apache Beam and Google Cloud Dataflow as the sidebar suggests. Source: over 2 years ago
  • How are view/listen counts rolled up on something like Spotify/YouTube?
    I am pretty sure they are using pub/sub with probably a Dataflow pipeline to process all that data. Source: over 2 years ago
  • Best way to export several GCP datasets to AWS?
    You can run a Dataflow job that copies the data directly from BQ into S3, though you'll have to run a job per table. This can be somewhat expensive to do. Source: over 2 years ago
  • Why we don’t use Spark
    It was clear we needed something that was built specifically for our big-data SaaS requirements. Dataflow was our first idea, as the service is fully managed, highly scalable, fairly reliable and has a unified model for streaming & batch workloads. Sadly, the cost of this service was quite large. Secondly, at that moment in time, the service only accepted Java implementations, of which we had little knowledge... - Source: dev.to / about 3 years ago
View more

What are some alternatives?

When comparing neo4j and Google Cloud Dataflow, you can also consider the following products

ArangoDB - A distributed open-source database with a flexible data model for documents, graphs, and key-values.

Google BigQuery - A fully managed data warehouse for large-scale data analytics.

Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.

Amazon EMR - Amazon Elastic MapReduce is a web service that makes it easy to quickly process vast amounts of data.

OrientDB - OrientDB - The World's First Distributed Multi-Model NoSQL Database with a Graph Database Engine.

Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.‎What is Apache Spark?