Software Alternatives, Accelerators & Startups
Register | Login

Apache Spark VS Control-M

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

Flowlu
All-in-one work management platform for team collaboration. featured
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Apache Spark logo Apache Spark

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

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 Spark Landing page
    Landing page //
    2021-12-31
  • Control-M Landing page
    Landing page //
    2023-07-12

Apache Spark features and specs

  • Speed
    Apache Spark processes data in-memory, significantly increasing the processing speed of data tasks compared to traditional disk-based engines.
  • Ease of Use
    Spark offers high-level APIs in Java, Scala, Python, and R, making it accessible to a broad range of developers and data scientists.
  • Advanced Analytics
    Spark supports advanced analytics, including machine learning, graph processing, and real-time streaming, which can be executed in the same application.
  • Scalability
    Spark can handle both small- and large-scale data processing tasks, scaling seamlessly from a single machine to thousands of servers.
  • Support for Various Data Sources
    Spark can integrate with a wide variety of data sources, including HDFS, Apache HBase, Apache Hive, Cassandra, and many others.
  • Active Community
    Spark has a vibrant and active community, providing a wealth of extensions, tools, and support options.

Possible disadvantages of Apache Spark

  • Memory Consumption
    Spark's in-memory processing can be resource-intensive, requiring substantial amounts of RAM, which can drive up costs for large-scale deployments.
  • Complexity in Configuration
    To optimize performance, Spark requires careful configuration and tuning, which can be complex and time-consuming.
  • Learning Curve
    Despite its ease of use, mastering the full range of Spark's features and best practices can take considerable time and effort.
  • Latency for Small Data
    For smaller datasets or low-latency requirements, Spark might not be the most efficient choice, as other technologies could offer better performance.
  • Integration Overhead
    Though Spark integrates with many systems, incorporating it into an existing data infrastructure can introduce additional overhead and complexity.
  • Community Support Variability
    While the community is active, the support and quality of third-party libraries and tools can be inconsistent, leading to potential challenges in implementation.

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 Spark

Overall verdict

  • Yes, Apache Spark is generally considered good, especially for organizations and individuals that require efficient and fast data processing capabilities. It is well-supported, frequently updated, and widely adopted in the industry, making it a reliable choice for big data solutions.

Why this product is good

  • Apache Spark is highly valued because it provides a fast and general-purpose cluster-computing framework for big data processing. It offers extensive libraries for SQL, streaming, machine learning, and graph processing, making it versatile for various data processing needs. Its in-memory computing capability boosts the processing speed significantly compared to traditional disk-based processing. Additionally, Spark integrates well with Hadoop and other big data tools, providing a seamless ecosystem for large-scale data analysis.

Recommended for

  • Data scientists and engineers working with large datasets.
  • Organizations leveraging machine learning and analytics for decision-making.
  • Businesses needing real-time data processing capabilities.
  • Developers looking to integrate with Hadoop ecosystems.
  • Teams requiring robust support for multiple data sources and formats.

Apache Spark videos

+ Add

Weekly Apache Spark live Code Review -- look at StringIndexer multi-col (Scala) & Python testing

More videos:

  • Review - What's New in Apache Spark 3.0.0
  • Review - Apache Spark for Data Engineering and Analysis - Overview

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 Spark and Control-M)

Apache Spark

Control-M

Databases
100 100%
Databases
0% 0
IT Automation
0 0%
IT Automation
100% 100
Big Data
100 100%
Big Data
0% 0
Monitoring Tools
0 0%
Monitoring Tools
100% 100

User comments

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

Apache Spark Reviews

15 data science tools to consider using in 2021
Apache Spark is an open source data processing and analytics engine that can handle large amounts of data -- upward of several petabytes, according to proponents. Spark's ability to rapidly process data has fueled significant growth in the use of the platform since it was created in 2009, helping to make the Spark project one of the largest open source communities among big...
Source: searchbusinessanalytics.techtarget.com
Top 15 Kafka Alternatives Popular In 2021
Apache Spark is a well-known, general-purpose, open-source analytics engine for large-scale, core data processing. It is known for its high-performance quality for data processing – batch and streaming with the help of its DAG scheduler, query optimizer, and engine. Data streams are processed in real-time and hence it is quite fast and efficient. Its machine learning...
Source: www.spec-india.com
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Spark is an open-source and flexible in-memory framework which serves as an alternative to map-reduce for handling batch, real-time analytics and data processing workloads. It provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning and graph processing. From its beginning in the AMPLab at...
Source: www.analyticsinsight.net

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 Spark seems to be more popular. It has been mentiond 70 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 Spark mentions (70)

  • 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
  • How to Reduce Big Data Analytics Costs by 90% with Karpenter and Spark
    Apache Spark powers large-scale data analytics and machine learning, but as workloads grow exponentially, traditional static resource allocation leads to 30–50% resource waste due to idle Executors and suboptimal instance selection. - Source: dev.to / about 2 months ago
  • Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
    One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / 3 months ago
  • The Application of Java Programming In Data Analysis and Artificial Intelligence
    [1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Pearson, 2020. [2] F. Chollet, Deep Learning with Python. Manning Publications, 2018. [3] C. C. Aggarwal, Data Mining: The Textbook. Springer, 2015. [4] J. Dean and S. Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008. [5] Apache Software Foundation, "Apache... - Source: dev.to / 3 months ago
  • Automating Enhanced Due Diligence in Regulated Applications
    If you're designing an event-based pipeline, you can use a data streaming tool like Kafka to process data as it's collected by the pipeline. For a setup that already has data stored, you can use tools like Apache Spark to batch process and clean it before moving ahead with the pipeline. - Source: dev.to / 4 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 Spark and Control-M, you can also consider the following products

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

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.

Hadoop - Open-source software for reliable, scalable, distributed computing

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.

Apache Storm - Apache Storm is a free and open source distributed realtime computation system.

SECDO - SECDO offers automated endpoint security and incident response solutions

Loading...