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Apache Flink VS Control-M

Compare Apache Flink VS Control-M and see what are their differences

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Apache Flink logo Apache Flink

Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

Control-M logo Control-M

Control‑M simplifies and automates diverse batch application workloads while reducing failure rates, improving SLAs, and accelerating application deployment.
  • Apache Flink Landing page
    Landing page //
    2023-10-03
  • Control-M Landing page
    Landing page //
    2023-07-12

Apache Flink features and specs

  • Real-time Stream Processing
    Apache Flink is designed for real-time data streaming, offering low-latency processing capabilities that are essential for applications requiring immediate data insights.
  • Event Time Processing
    Flink supports event time processing, which allows it to handle out-of-order events effectively and provide accurate results based on the time events actually occurred rather than when they were processed.
  • State Management
    Flink provides robust state management features, making it easier to maintain and query state across distributed nodes, which is crucial for managing long-running applications.
  • Fault Tolerance
    The framework includes built-in mechanisms for fault tolerance, such as consistent checkpoints and savepoints, ensuring high reliability and data consistency even in the case of failures.
  • Scalability
    Apache Flink is highly scalable, capable of handling both batch and stream processing workloads across a distributed cluster, making it suitable for large-scale data processing tasks.
  • Rich Ecosystem
    Flink has a rich set of APIs and integrations with other big data tools, such as Apache Kafka, Apache Hadoop, and Apache Cassandra, enhancing its versatility and ease of integration into existing data pipelines.

Possible disadvantages of Apache Flink

  • Complexity
    Flink’s advanced features and capabilities come with a steep learning curve, making it more challenging to set up and use compared to simpler stream processing frameworks.
  • Resource Intensive
    The framework can be resource-intensive, requiring substantial memory and CPU resources for optimal performance, which might be a concern for smaller setups or cost-sensitive environments.
  • Community Support
    While growing, the community around Apache Flink is not as large or mature as some other big data frameworks like Apache Spark, potentially limiting the availability of community-contributed resources and support.
  • Ecosystem Maturity
    Despite its integrations, the Flink ecosystem is still maturing, and certain tools and plugins may not be as developed or stable as those available for more established frameworks.
  • Operational Overhead
    Running and maintaining a Flink cluster can involve significant operational overhead, including monitoring, scaling, and troubleshooting, which might require a dedicated team or additional expertise.

Control-M features and specs

  • Comprehensive Job Scheduling
    Control-M provides an extensive range of job scheduling capabilities, supporting various environments and platforms, which ensures that all workflows and batch jobs can be managed consistently and efficiently.
  • Ease of Use
    The user interface is intuitive and user-friendly, making it easier for both technical and non-technical users to manage job workflows without extensive training.
  • Scalability
    Control-M scales effortlessly, accommodating the needs of small businesses to large enterprises, without compromising on performance.
  • Integrations
    It seamlessly integrates with numerous applications and technologies, including cloud services, databases, ERP systems, and more, which makes it versatile across different IT landscapes.
  • Advanced Automation Features
    Provides advanced automation capabilities such as predictive analytics, machine learning, and DR capabilities that enhance efficiency and reduce manual intervention.
  • Robust Reporting
    Offers powerful reporting tools and dashboards that provide actionable insights and visibility into job performance and system health.

Possible disadvantages of Control-M

  • Cost
    The comprehensive features and enterprise-level capabilities come at a high cost, which may be prohibitive for smaller organizations.
  • Complexity in Initial Setup
    The initial installation and configuration can be complex and require significant investment in time and resources to set up properly.
  • Learning Curve
    Despite its user-friendly interface, the depth and breadth of features can result in a steep learning curve for new users, necessitating substantial training.
  • Resource Intensive
    Control-M can be resource-intensive, requiring considerable computing resources to run efficiently, which might be a constraint for organizations with limited IT infrastructure.
  • Dependency on Vendor Support
    While support is robust, the complexity of the system can sometimes necessitate frequent interaction with vendor support, which can be time-consuming.
  • Customization Challenges
    While the tool is highly configurable, extensive customization can become complicated and may require professional services or advanced knowledge.

Analysis of Apache Flink

Overall verdict

  • Yes, Apache Flink is considered a good distributed stream processing framework.

Why this product is good

  • Rich api
    Flink offers a rich set of APIs for various levels of abstraction, catering to different needs of developers.
  • Scalability
    Flink provides excellent horizontal scalability, making it suitable for handling large data streams and high-throughput applications.
  • Fault tolerance
    Flink's checkpointing mechanism ensures fault-tolerance, maintaining data state consistency even after failures.
  • Ease of integration
    Flink integrates well with other big data tools and ecosystems, facilitating broader data architecture designs.
  • Real-time processing
    It excels at processing data in real-time, allowing for immediate insights and action on streaming data.
  • Community and support
    Being a part of the Apache Software Foundation, Flink benefits from a large community and comprehensive documentation.
  • Complex event processing
    It supports complex event processing, which is essential for many real-time applications.

Recommended for

  • real-time analytics
  • stream data processing
  • complex event processing
  • machine learning in streaming applications
  • applications requiring high-throughput and low-latency processing
  • companies looking for robust fault-tolerance in distributed systems

Apache Flink videos

+ Add

GOTO 2019 • Introduction to Stateful Stream Processing with Apache Flink • Robert Metzger

More videos:

  • Tutorial - Apache Flink Tutorial | Flink vs Spark | Real Time Analytics Using Flink | Apache Flink Training
  • Tutorial - How to build a modern stream processor: The science behind Apache Flink - Stefan Richter

Control-M videos

+ Add

Control-M Version 8 Overview

More videos:

  • Review - Control-M Self Service Overview
  • Review - Connect With Control-M: Control-M/Server 9 High Availability

Category Popularity

0-100% (relative to Apache Flink and Control-M)

Apache Flink

Control-M

Big Data
100 100%
Big Data
0% 0
IT Automation
0 0%
IT Automation
100% 100
Stream Processing
100 100%
Stream Processing
0% 0
Monitoring Tools
0 0%
Monitoring Tools
100% 100

User comments

Share your experience with using Apache Flink and Control-M. 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 Apache Flink and Control-M

Apache Flink Reviews

We have no reviews of Apache Flink yet.
Be the first one to post

Control-M Reviews

Top 10 Control-M Alternatives in ’23
Job scheduling: On G2, the job scheduling feature receives the highest score with 9.4. However, Control-M alternatives, ActiveBatch and Redwood obtain higher scores for each category under functionality than Control-M (See Figure 5). Integrations/APIs: A user mentioned API and integration to other applications as a weak capability of the tool (Figure 1).
Source: research.aimultiple.com
9 Control-M Alternatives & Competitors In 2023
Verdict: Redwood platform offers better performance and visibility than the Control-M. This tool supports over 25 scripting languages and interfaces such as Python, R, and PowerShell with built-in syntax highlighting and parameter replacement. It also features advanced architecture and provides safe passage to businesses looking for Control-M alternatives through its...
Source: www.softwaretestingmaterial.com
The Top 5 BMC Control-M API Alternatives
Control-M Reports provide insights into job execution and performance. While the BMC Control-M interface provides robust reporting capabilities, there are also alternatives to generate reports using tools such as SQL and Hadoop. These tools can extract data from Control-M job logs and generate custom reports based on specific business requirements.
Source: www.redwood.com

Social recommendations and mentions

Based on our record, Apache Flink seems to be more popular. It has been mentiond 41 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.

Apache Flink mentions (41)

  • What is Apache Flink? Exploring Its Open Source Business Model, Funding, and Community
    Continuous Learning: Leverage online tutorials from the official Flink website and attend webinars for deeper insights. - Source: dev.to / about 1 month ago
  • Is RisingWave the Next Apache Flink?
    Apache Flink, known initially as Stratosphere, is a distributed stream processing engine initiated by a group of researchers at TU Berlin. Since its initial release in May 2011, Flink has gained immense popularity in both academia and industry. And it is currently the most well-known streaming system globally (challenge me if you think I got it wrong!). - Source: dev.to / about 2 months ago
  • Every Database Will Support Iceberg — Here's Why
    Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly. - Source: dev.to / about 2 months ago
  • RisingWave Turns Four: Our Journey Beyond Democratizing Stream Processing
    The last decade saw the rise of open-source frameworks like Apache Flink, Spark Streaming, and Apache Samza. These offered more flexibility but still demanded significant engineering muscle to run effectively at scale. Companies using them often needed specialized stream processing engineers just to manage internal state, tune performance, and handle the day-to-day operational challenges. The barrier to entry... - Source: dev.to / about 2 months ago
  • Twitter's 600-Tweet Daily Limit Crisis: Soaring GCP Costs and the Open Source Fix Elon Musk Ignored
    Apache Flink: Flink is a unified streaming and batching platform developed under the Apache Foundation. It provides support for Java API and a SQL interface. Flink boasts a large ecosystem and can seamlessly integrate with various services, including Kafka, Pulsar, HDFS, Iceberg, Hudi, and other systems. - Source: dev.to / 2 months ago
View more

Control-M mentions (0)

We have not tracked any mentions of Control-M yet. Tracking of Control-M recommendations started around Mar 2021.

What are some alternatives?

When comparing Apache Flink and Control-M, you can also consider the following products

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

ManageEngine RecoveryManager Plus - RecoveryManager Plus is one such enterprise backup solution which has the ability to easily backup and restores both the domain controllers and virtual machines.

Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.

Heroku Enterprise - Heroku Enterprise is a flexible IT management for developers that lets them build apps using their preferred languages and tools like Ruby, Java, Python and Node.

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

SECDO - SECDO offers automated endpoint security and incident response solutions

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