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Databricks VS Control-M

Compare Databricks VS Control-M and see what are their differences

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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. featured
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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Databricks logo Databricks

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

Control-M logo Control-M

Control‑M simplifies and automates diverse batch application workloads while reducing failure rates, improving SLAs, and accelerating application deployment.
  • Databricks Landing page
    Landing page //
    2023-09-14
  • Control-M Landing page
    Landing page //
    2023-07-12

Databricks features and specs

  • Unified Data Analytics Platform
    Databricks integrates various data processing and analytics tools, offering a unified environment for data engineering, machine learning, and business analytics. This integration can streamline workflows and reduce the complexity of data management.
  • Scalability
    Databricks leverages Apache Spark and other scalable technologies to handle large datasets and high computational workloads efficiently. This makes it suitable for enterprises with significant data processing needs.
  • Collaborative Environment
    The platform offers collaborative notebooks that allow data scientists, engineers, and analysts to work together in real-time. This enhances productivity and fosters better communication within teams.
  • Performance Optimization
    Databricks includes various performance optimization features such as caching, indexing, and query optimization, which can significantly speed up data processing tasks.
  • Support for Various Data Formats
    The platform supports a wide range of data formats and sources, including structured, semi-structured, and unstructured data, making it versatile and adaptable to different use cases.
  • Integration with Cloud Providers
    Databricks is designed to work seamlessly with major cloud providers like AWS, Azure, and Google Cloud, allowing users to easily integrate it into their existing cloud infrastructure.

Possible disadvantages of Databricks

  • Cost
    Databricks can be expensive, especially for large-scale deployments or high-frequency usage. It may not be the most cost-effective solution for smaller organizations or projects with limited budgets.
  • Complexity
    While powerful, Databricks can be complex to set up and manage, requiring specialized knowledge in Apache Spark and cloud infrastructure. This might lead to a steeper learning curve for new users.
  • Dependency on Cloud Providers
    Being heavily integrated with cloud providers, Databricks might face issues like vendor lock-in, where switching providers becomes difficult or costly.
  • Limited Offline Capabilities
    Databricks is primarily designed for cloud environments, which means offline or on-premise capabilities are limited, posing challenges for organizations with strict data governance policies.
  • Resource Management
    Efficiently managing and allocating resources can be challenging in Databricks, especially in large multi-user environments. Mismanagement of resources could lead to increased costs and reduced performance.

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.

Databricks videos

+ Add

Introduction to Databricks

More videos:

  • Tutorial - Azure Databricks Tutorial | Data transformations at scale
  • Review - Databricks - Data Movement and Query

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 Databricks and Control-M)

Databricks

Control-M

Data Dashboard
100 100%
Data Dashboard
0% 0
IT Automation
0 0%
IT Automation
100% 100
Big Data Analytics
100 100%
Big Data Analytics
0% 0
Monitoring Tools
0 0%
Monitoring Tools
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 Databricks and Control-M

Databricks Reviews

Jupyter Notebook & 10 Alternatives: Data Notebook Review [2023]
Databricks notebooks are a popular tool for developing code and presenting findings in data science and machine learning. Databricks Notebooks support real-time multilingual coauthoring, automatic versioning, and built-in data visualizations.
Source: lakefs.io
7 best Colab alternatives in 2023
Databricks is a platform built around Apache Spark, an open-source, distributed computing system. The Databricks Community Edition offers a collaborative workspace where users can create Jupyter notebooks. Although it doesn't offer free GPU resources, it's an excellent tool for distributed data processing and big data analytics.
Source: deepnote.com
Top 5 Cloud Data Warehouses in 2023
Jan 11, 2023 The 5 best cloud data warehouse solutions in 2023Google BigQuerySource: https://cloud.google.com/bigqueryBest for:Top features:Pros:Cons:Pricing:SnowflakeBest for:Top features:Pros:Cons:Pricing:Amazon RedshiftSource: https://aws.amazon.com/redshift/Best for:Top features:Pros:Cons:Pricing:FireboltSource: https://www.firebolt.io/Best for:Top...
Source: www.shipyardapp.com
Top 10 AWS ETL Tools and How to Choose the Best One | Visual Flow
Databricks is a simple, fast, and collaborative analytics platform based on Apache Spark with ETL capabilities. It accelerates innovation by bringing together data science and data science businesses. It is a fully managed open-source version of Apache Spark analytics with optimized connectors to storage platforms for the fastest data access.
Source: visual-flow.com
Top Big Data Tools For 2021
Now Azure Databricks achieves 50 times better performance thanks to a highly optimized version of Spark. Databricks also enables real-time co-authoring and automates versioning. Besides, it features runtimes optimized for machine learning that include many popular libraries, such as PyTorch, TensorFlow, Keras, etc.
Source: blog.bismart.com

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, Databricks seems to be more popular. It has been mentiond 18 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.

Databricks mentions (18)

  • Platform Engineering Abstraction: How to Scale IaC for Enterprise
    Vendors like Confluent, Snowflake, Databricks, and dbt are improving the developer experience with more automation and integrations, but they often operate independently. This fragmentation makes standardizing multi-directional integrations across identity and access management, data governance, security, and cost control even more challenging. Developing a standardized, secure, and scalable solution for... - Source: dev.to / 8 months ago
  • dolly-v2-12b
    Dolly-v2-12bis a 12 billion parameter causal language model created by Databricks that is derived from EleutherAI’s Pythia-12b and fine-tuned on a ~15K record instruction corpus generated by Databricks employees and released under a permissive license (CC-BY-SA). Source: about 2 years ago
  • Clickstream data analysis with Databricks and Redpanda
    Global organizations need a way to process the massive amounts of data they produce for real-time decision making. They often utilize event-streaming tools like Redpanda with stream-processing tools like Databricks for this purpose. - Source: dev.to / almost 3 years ago
  • DeWitt Clause, or Can You Benchmark %DATABASE% and Get Away With It
    Databricks, a data lakehouse company founded by the creators of Apache Spark, published a blog post claiming that it set a new data warehousing performance record in 100 TB TPC-DS benchmark. It was also mentioned that Databricks was 2.7x faster and 12x better in terms of price performance compared to Snowflake. - Source: dev.to / almost 3 years ago
  • A Quick Start to Databricks on AWS
    Go to Databricks and click the Try Databricks button. Fill in the form and Select AWS as your desired platform afterward. - Source: dev.to / about 3 years 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 Databricks and Control-M, you can also consider the following products

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

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.

Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.

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.

Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.

SECDO - SECDO offers automated endpoint security and incident response solutions

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