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Apache Spark VS AppDynamics

Compare Apache Spark VS AppDynamics 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.

AppDynamics logo AppDynamics

Get real-time insight from your apps using Application Performance Management—how they’re being used, how they’re performing, where they need help.
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • AppDynamics Landing page
    Landing page //
    2023-10-10

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.

AppDynamics features and specs

  • Comprehensive Monitoring
    AppDynamics provides end-to-end visibility across applications, infrastructure, and user experience. This helps in identifying performance issues quickly and accurately.
  • Real-time Analytics
    AppDynamics offers real-time monitoring and analytics, which enables immediate detection of anomalies and potential problems before they impact end-users.
  • Ease of Integration
    AppDynamics integrates easily with various platforms, technologies, and third-party services, providing flexibility and extending its usability in diverse environments.
  • Automated Root Cause Analysis
    The platform's advanced algorithms and AI capabilities help in automatically determining the root causes of performance issues, reducing the mean time to resolution.
  • User-friendly Interface
    AppDynamics has an intuitive and user-friendly interface which makes it easier for IT teams to use without extensive training.

Possible disadvantages of AppDynamics

  • Cost
    AppDynamics can be expensive, making it less accessible for smaller organizations or startups with limited budgets.
  • Complexity
    Due to its extensive features and capabilities, the platform can be complex to set up and configure, requiring a significant time investment for initial deployment.
  • Resource Intensive
    The monitoring and analytics processes can be resource-intensive, potentially impacting system performance especially in environments with limited resources.
  • Steep Learning Curve
    Despite its user-friendly interface, mastering the full range of AppDynamics' features and capabilities can take time and necessitate detailed learning.
  • Possible Overhead
    Integrating and running AppDynamics can add additional overhead to the system, which might be an issue in performance-sensitive scenarios.

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

AppDynamics videos

+ Add

AppDynamics Acquired for $3.7 Billion | Crunch Report

More videos:

  • Review - AppDynamics CEO Talks Cisco Acquisition | Crunch Report
  • Review - Glassdoor Client Testimonial: AppDynamics

Category Popularity

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

Apache Spark

AppDynamics

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 AppDynamics

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

AppDynamics Reviews

Top 10 Grafana Alternatives in 2024
AppDynamics is an APM tool that enables users to monitor application performance, pinpoint root causes for performance issues, get complete visibility into application ecosystems, extract real-time data insights, and automatically optimize the application environment.
Source: middleware.io
Top 11 Grafana Alternatives & Competitors [2024]
AppDynamics is an enterprise Application Performance Management (APM) solution known for its comprehensive monitoring capabilities. It provides in-depth visibility into application performance and user experiences, offering code-level diagnostics, transaction tracing, and real-time insights.
Source: signoz.io
10 Best Grafana Alternatives [2023 Comparison]
visibility into the health and performance of their applications. As an excellent alternative to Grafana, AppDynamics is particularly renowned for its end-user monitoring (EUM) capabilities, ensuring users are well-informed about end-user errors, issues, crashes, and page-loading details. This enables businesses to tap into valuable insights, swiftly and effortlessly...
Source: sematext.com
10 Best Website Monitoring Services and Tools of 2022
AppDynamics is another website availability monitoring software that helps you detect anomalies and helps you run your business smoothly. The software allows you to track the visual revenue paths with the help of tracked customer or application experience in order to fix the ongoing website issues. Moreover, the tool allows you to monitor every click, swipe, and tap in order...
Source: www.hostingseekers.com
8 Dynatrace Alternatives to Consider in 2021
Cisco’s APM, AppDynamics, is a proactive performance monitoring platform that ensures success for its users, primarily businesses. They focus on observability of the software and application. AppDynamics uses AI-powered insights and focuses on visibility to support application improvement and business performance for their applications.
Source: scoutapm.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 / 20 days 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 / 22 days 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 / 2 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 / 2 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 / 3 months ago
View more

AppDynamics mentions (0)

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

What are some alternatives?

When comparing Apache Spark and AppDynamics, 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 Hive - Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.

NewRelic - New Relic is a Software Analytics company that makes sense of billions of metrics across millions of apps. We help the people who build modern software understand the stories their data is trying to tell them.

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