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ActiveBatch VS Google Cloud Dataflow

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

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ActiveBatch logo ActiveBatch

Orchestrate the entire tech stack with ActiveBatch Workload Automation & Job Scheduling. Build and manage workflows from one place.

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.
  • ActiveBatch Landing page
    Landing page //
    2020-12-04

Orchestrate your entire tech stack with ActiveBatch Workload Automation and Enterprise Job Scheduling. Build and centralize end-to-end workflows under a single pane of glass. Seamlessly manage systems, applications, and services across your organization. Eliminate manual workflows with ActiveBatch so you can focus on higher value activities that drive your company forward.

Limitless Endpoints: Use native integrations and our low-code REST API adapter to connect to any server, any application, any service.

Proactive Support Model: 24/7- US-based support and predictive diagnostics.

Low Code Drag-and-Drop GUI: Easily build reliable, customizable, end-to-end processes.

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

ActiveBatch

Website
advsyscon.com
Pricing URL
Official ActiveBatch Pricing
$ Details
paid Free Trial
Platforms
Cross Platform
Release Date
2000 January
Edit details

Google Cloud Dataflow

Website
cloud.google.com
Pricing URL
-
$ Details
-
Platforms
-
Release Date
-
Edit details

ActiveBatch features and specs

  • Comprehensive Automation Capabilities
    ActiveBatch offers a wide range of automation capabilities, allowing users to manage complex workflows across various systems and applications effectively.
  • Integration with Multiple Platforms
    ActiveBatch supports integration with numerous third-party applications, cloud services, and databases, facilitating seamless workflow automation across diverse environments.
  • User-Friendly Interface
    The platform provides an intuitive drag-and-drop interface that simplifies the creation and management of workflows, making it accessible for users without extensive technical knowledge.
  • Scalability
    ActiveBatch is designed to scale and accommodate growing business needs, providing robust performance for both small and large operations.
  • Extensive Library of Pre-Built Templates
    The solution comes with a wide range of pre-built job steps and templates, helping users accelerate deployment and reduce the complexity of workflow automation setup.

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.

Analysis of Google Cloud Dataflow

Overall verdict

  • Google Cloud Dataflow is a strong choice for users who need a flexible and scalable data processing solution. It is particularly well-suited for real-time and large-scale data processing tasks. However, the best choice ultimately depends on your specific requirements, including cost considerations, existing infrastructure, and technical skills.

Why this product is good

  • Google Cloud Dataflow is a fully managed service for stream and batch data processing. It is based on the Apache Beam model, allowing for a unified data processing approach. It is highly scalable, offers robust integration with other Google Cloud services, and provides powerful data processing capabilities. Its serverless nature means that users do not have to worry about infrastructure management, and it dynamically allocates resources based on the data processing needs.

Recommended for

  • Organizations that require real-time data processing.
  • Projects involving complex data transformations.
  • Users who already utilize Google Cloud Platform and need seamless integration with other Google services.
  • Developers and data engineers familiar with Apache Beam or those willing to learn.

ActiveBatch videos

+ Add

Redefine Your IT Automation Strategy with ActiveBatch

More videos:

  • Review - Demand More From Your IT Automation
  • Demo - ActiveBatch Self-Service Portal for Business Users

Google Cloud Dataflow videos

+ Add

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 ActiveBatch and Google Cloud Dataflow)

ActiveBatch

Google Cloud Dataflow

Workflow Automation
100 100%
Workflow Automation
0% 0
Big Data
0 0%
Big Data
100% 100
IT Automation
100 100%
IT Automation
0% 0
Data Dashboard
0 0%
Data Dashboard
100% 100

User comments

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Reviews

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

ActiveBatch Reviews

6 Best Power Automate Alternatives & Competitors in 2024
ActiveBatch is an all-in-one solution that gives organizations the power to centrally manage workload automation and job scheduling. By seamlessly bringing together different tools and applications, ActiveBatch offers a unified platform that gets rid of bottlenecks and failures while improving IT service levels.
Source: www.softwaretestingmaterial.com
Top 10 Control-M Alternatives in ’23
ActiveBatch is a versatile workload automation tool that can optimize and simplify manual and redundant tasks, enabling business process automation. ActiveBatch offers an intuitive drag-and-drop interface and a wide range of functionalities on a single platform for a centralized workload automation.
Source: research.aimultiple.com
9 Control-M Alternatives & Competitors In 2023
ActiveBatch offers a feature-rich workload automation tool. It’s a robust platform that provides security, auditing, and compliance as well as high availability of non-cluster failover. ActiveBatch’s Mobile Ops app makes it easy for field agents to stay connected with their workflows and processes while on the go.
Source: www.softwaretestingmaterial.com
The Top 5 BMC Control-M API Alternatives
ActiveBatch offers many features, including job scheduling, event-based triggers, file transfers, workload balancing, dependency tracking, notification and reporting capabilities, and support for various technologies and platforms. The software also includes a drag-and-drop visual interface and a pre-built library of job steps and templates, making it easier for users to...
Source: www.redwood.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 more popular. It has been mentiond 14 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.

ActiveBatch mentions (0)

We have not tracked any mentions of ActiveBatch yet. Tracking of ActiveBatch recommendations started around Mar 2021.

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 ActiveBatch and Google Cloud Dataflow, you can also consider the following products

Stonebranch - Stonebranch builds IT orchestration and automation solutions that transform business IT environments from simple IT task automation into sophisticated, real-time business service automation.

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

Control-M - Control‑M simplifies and automates diverse batch application workloads while reducing failure rates, improving SLAs, and accelerating application deployment.

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

JAMS Scheduler - Enterprise workload automation software supporting processes on Windows, Linux, UNIX, iSeries, SAP, Oracle, SQL, ERPs and more.

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

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