Software Alternatives, Accelerators & Startups

Draxlr VS Google Cloud Dataflow

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

Draxlr logo Draxlr

Turn SQL Data into Decisions. Build professional dashboards and data visualizations without technical expertise. Easily embed analytics anywhere, receive automated alerts, and discover AI-powered insights all through a straightforward interface.
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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.
  • Draxlr Dashboard
    Dashboard //
    2025-01-17
  • Draxlr
    Image date //
    2025-01-17
  • Draxlr
    Image date //
    2025-01-17

Draxlr is a tool to analyze and monitor your data. It can help you get answers from your database, without writing code. These answers and insights can be shared with your team and customers. You can build graphs, charts, and dashboards and share them as links, images, or embed them on your website and app. Not only that you can set up monitoring on your data, so if any data changes you can be alerted via Slack and Email.

  • Google Cloud Dataflow Landing page
    Landing page //
    2023-10-03

Draxlr features and specs

  • Dashboards and Visualizations
  • Slack Notifications
  • Email notifications
  • Query Builder
  • Embedded Analytics
  • Data Export

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.

Draxlr videos

Get answers from your database data.

More videos:

  • Review - Draxlr lietime deal | Appsumo lifetime deal #bestsowftware
  • Review - Draxlr | Get more from your database for less with code-free data query tools #shorts

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 Draxlr and Google Cloud Dataflow)
Data Dashboard
36 36%
64% 64
Big Data
0 0%
100% 100
Data Visualization
100 100%
0% 0
No Code
100 100%
0% 0

User comments

Share your experience with using Draxlr and Google Cloud Dataflow. For example, how are they different and which one is better?
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Reviews

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

Draxlr Reviews

Explore 6 Metabase Alternatives for Data Visualization and Analysis
Draxlr is an intuitive Metabase alternative, blending a robust no-code query builder with AI-powered SQL generation for both non-technical and advanced users. It seamlessly integrates with various databases and provides real-time alerts through Slack, email, and more. With features like embeddable dashboards, granular team access, customizable visualizations, and live data...
Source: www.draxlr.com
Explore 7 Tableau Alternatives for Data Visualization and Analysis
Draxlr is a no-code data visualization tool that simplifies creating dashboards and setting up alerts for SQL databases like PostgreSQL, MySQL, MS SQL, and more. It features an intuitive query builder for filtering, sorting, joining, summarizing, and grouping data without coding. Draxlr also supports advanced visualizations, embedded dashboards, and AI-driven insights, as...
Source: www.draxlr.com
5 best Looker alternatives
Draxlr: Draxlr is a modern self-service BI tool with AI integration capabilities that is built to ensure everyone in the team can easily find answers in raw data, and build actionable dashboards. Since it is one of the new tools, it can lack community support but is compensated by great customer support.
Source: www.draxlr.com
5 best dashboard building tools for SQL data in 2024
Draxlr is a modern self-serve business intelligence tool for growing businesses. Seamlessly connecting with multiple SQL databases, it transforms raw SQL data into polished dashboards effortlessly within minutes, eliminating the need for coding skills. Empowering users to effortlessly visualize and interpret data, Draxlr is tailored for modern business insights.
Source: www.draxlr.com

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, Google Cloud Dataflow seems to be a lot more popular than Draxlr. While we know about 14 links to Google Cloud Dataflow, we've tracked only 1 mention of Draxlr. 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.

Draxlr mentions (1)

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
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What are some alternatives?

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

HistogramMaker.net - Create a Histogram for free with easy to use tools and download the Histogram as jpg, png or svg file. Customize Histogram according to your choice.

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

Microsoft Power BI - BI visualization and reporting for desktop, web or mobile

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

Canva - Canva is a graphic-design platform with a drag-and-drop interface to create print or visual content while providing templates, images, and fonts. Canva makes graphic design more straightforward and accessible regardless of skill level.

Qubole - Qubole delivers a self-service platform for big aata analytics built on Amazon, Microsoft and Google Clouds.