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Apache Spark VS LogicMonitor

Compare Apache Spark VS LogicMonitor and see what are their differences

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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.

LogicMonitor logo LogicMonitor

LogicMonitor is the SaaS performance monitoring platform for the world's best IT teams. Deploy Fast, Monitor More, Improve Ops.
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • LogicMonitor Landing page
    Landing page //
    2023-10-18

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.

LogicMonitor features and specs

  • Comprehensive Monitoring
    LogicMonitor provides end-to-end IT infrastructure monitoring, including servers, networks, applications, and cloud services, ensuring holistic visibility.
  • Scalability
    Designed to handle both small and large enterprises, LogicMonitor can scale effortlessly with the growth of an organization’s IT infrastructure.
  • Ease of Deployment
    The platform is known for its quick setup process, allowing businesses to deploy and gain insights rapidly without significant downtime.
  • Customizable Dashboards
    Offers highly customizable dashboards that allow users to visualize data and metrics in a manner that suits their specific needs.
  • Automated Discovery
    Automatically discovers devices and applications within the network, significantly reducing the manual effort required for setup.
  • Third-party Integrations
    Supports numerous third-party integrations, enhancing its capabilities and making it easier to fit into an existing tech ecosystem.
  • Real-time Alerting
    Features real-time alerting mechanisms that help IT teams respond swiftly to potential issues, minimizing downtime and disruption.
  • Cloud and Hybrid Environment Support
    Offers robust support for cloud-based and hybrid environments, making it suitable for modern infrastructures that leverage diverse technologies.

Possible disadvantages of LogicMonitor

  • Cost
    Potentially high costs can be prohibitive for smaller organizations, particularly when additional features or large-scale deployments are involved.
  • Complexity
    Although powerful, the platform can be complex and may require a steep learning curve for users not familiar with advanced monitoring tools.
  • Resource Intensive
    The software can be resource-intensive, potentially necessitating additional hardware or computing resources to operate efficiently.
  • Customization Limitations
    Despite offering customization, certain areas may feel limited, especially for organizations with highly specific monitoring needs.
  • Dependency on Internet Connectivity
    Being a SaaS solution, LogicMonitor requires a reliable internet connection; any disruptions in connectivity can impact monitoring capabilities.
  • Alert Spam
    Without proper configuration, users might experience alert fatigue due to an overwhelming number of notifications.
  • Support Limitations
    While support is generally strong, some users report delays or less than satisfactory resolutions during peak times or with complex issues.

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.

Analysis of LogicMonitor

Overall verdict

  • Yes, LogicMonitor is generally considered a good monitoring solution.

Why this product is good

  • LogicMonitor is praised for its comprehensive monitoring capabilities, ease of use, and scalability. It supports a wide range of technologies and offers robust reporting and alerting features. Users appreciate its cloud-based nature, which allows for quick deployment and minimal maintenance.

Recommended for

  • IT teams looking for a scalable, cloud-based monitoring solution.
  • Organizations seeking a tool with strong reporting and alerting capabilities.
  • Businesses that need to monitor a wide array of technologies and devices.

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

LogicMonitor videos

+ Add

An Introduction to LogicMonitor

More videos:

  • Review - Step Inside LogicMonitor [OUTDATED 2/4/20]
  • Review - Ted Baker Customer Fireside Chat | LogicMonitor Level Up Event June 2019

Category Popularity

0-100% (relative to Apache Spark and LogicMonitor)

Apache Spark

LogicMonitor

Databases
100 100%
Databases
0% 0
Monitoring Tools
0 0%
Monitoring Tools
100% 100
Big Data
100 100%
Big Data
0% 0
Log Management
0 0%
Log Management
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 Apache Spark and LogicMonitor

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

LogicMonitor Reviews

Top 10 PRTG Alternatives for Monitoring Networks and IT Infrastructure
If you’re not looking to be overwhelmed with network alerts, consider using LogicMonitor to automatically identify and take action for the most important alerts with 90% less noise.
Source: www.networkstraining.com
7 Best Containerization Software Solutions of 2022
LogicMonitor’s dynamic topology mapping feature provides an at-a-glance view of your entire infrastructure, including networks, servers, containers, and more.
Source: techgumb.com
8 Dynatrace Alternatives to Consider in 2021
LogicMonitor is a massive proponent of hybrid IT. It is extensible and secure infrastructure monitoring on a single platform. With over 2,000 integrations available, it is a powerful package with many features that support configuration monitoring and AIOps early warning systems.
Source: scoutapm.com
Best New Relic Alternatives for Application Performance Monitoring (Cloud & SaaS)
Their Auto-Discover feature scans your systems and pulls data and information into easy to read graphs and data points that help you manage and monitor pain points. LogicMonitor helps you be more proactive about monitoring mission critical systems and lets you look at historical data to plan for future growth and capacity planning.
Source: www.ittsystems.com
15 Best IT Monitoring Tools and Software
LogicMonitor offers monitoring templates and frequently expands to include new features that increase its functionality.
Source: blog.inedo.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

LogicMonitor mentions (0)

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

What are some alternatives?

When comparing Apache Spark and LogicMonitor, 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.

Dynatrace - Cloud-based quality testing, performance monitoring and analytics for mobile apps and websites. Get started with Keynote today!

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

Datadog - See metrics from all of your apps, tools & services in one place with Datadog's cloud monitoring as a service solution. Try it for free.

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

Zabbix - Track, record, alert and visualize performance and availability of IT resources

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