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Apache Spark VS Dynatrace

Compare Apache Spark VS Dynatrace 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.

Dynatrace logo Dynatrace

Cloud-based quality testing, performance monitoring and analytics for mobile apps and websites. Get started with Keynote today!
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • Dynatrace Landing page
    Landing page //
    2023-01-14

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.

Dynatrace features and specs

  • Comprehensive Monitoring
    Dynatrace provides end-to-end visibility into your entire technology stack, from infrastructure and applications to user experiences. This comprehensive monitoring allows for a holistic view of performance and helps in identifying and resolving issues quickly.
  • AI-Powered Insights
    The platform leverages artificial intelligence to deliver precise, context-aware insights. Its AI engine, Davis, automatically detects anomalies, identifies root causes, and provides actionable recommendations, reducing the mean time to resolution (MTTR).
  • Automatic Dependency Detection
    Dynatrace automatically discovers applications and their dependencies, mapping out detailed service flows without requiring manual configuration. This feature is particularly beneficial in dynamic and complex environments.
  • Scalability and Flexibility
    Dynatrace is designed to scale seamlessly with your infrastructure, whether you're operating in a small, medium, or large enterprise environment. It supports a broad range of technologies and can integrate with various third-party tools.
  • Real User Monitoring (RUM)
    The platform offers robust real user monitoring capabilities, which track real user interactions with your applications in real-time. This helps in understanding user behavior, performance impact, and areas for improvement.

Possible disadvantages of Dynatrace

  • Cost
    Dynatrace tends to be on the pricier side compared to some other monitoring solutions. The cost can be a significant factor, especially for smaller organizations with limited budgets.
  • Learning Curve
    While Dynatrace offers a very powerful set of tools, they can be complex to use and require some time to learn. New users may need considerable training to utilize the platform effectively.
  • Resource Intensive
    Dynatrace can be resource-intensive, requiring a substantial amount of system resources to collect and analyze large volumes of data. This could potentially impact the performance of monitored infrastructure in some cases.
  • Customization Limitations
    While Dynatrace provides extensive monitoring capabilities out-of-the-box, some users may find its customization options limited compared to other platforms that offer more tailor-made solutions.
  • Dependency on Internet Connectivity
    For its full capabilities, Dynatrace requires a consistent internet connection, which could be seen as a downside for organizations with limited or unstable internet access.

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

Dynatrace videos

+ Add

Dynatrace Demo - 5 minute getting started overview

More videos:

  • Review - How Dynatrace Works
  • Review - Dynatrace Year 2016 In Review

Category Popularity

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

Apache Spark

Dynatrace

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 Dynatrace

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

Dynatrace Reviews

Top 10 Grafana Alternatives in 2024
Dynatrace is a unified observability and security platform with amazing application management capabilities.
Source: middleware.io
Top 11 Grafana Alternatives & Competitors [2024]
Dynatrace is a comprehensive observability and application performance management (APM) platform designed for monitoring that can be used as a Grafana alternative. It offers a wide range of features and capabilities to monitor, diagnose, and optimize application performance in complex, dynamic environments.
Source: signoz.io
10 Best Grafana Alternatives [2023 Comparison]
Dynatrace is great for big businesses looking for enterprise-level monitoring. It’s great for providing essential business metrics across numerous digital platforms, and even implements casual AI to help automate complex workflows.
Source: sematext.com
5 Best DevSecOps Tools in 2023
There are many platforms that can be utilized for monitoring and alerting. Some examples are New Relic, Datadog, AWS CloudWatch, Sentry, Dynatrace, and others. Again, these providers each have pros and cons related to pricing, offering, ad vendor lock-in. So research the options to see what may possibly be best for a given situation.
Source: devopsauthority.tech
The Top 10 Website Session Recording Tools for 2022
The Dynatrace session recording software allows you to capture every contact a customer has with your website. Dynatrace has a session replay interface that offers perceptions into the actions of your customers. With the support of these insights, you can produce flawless user experiences while also unifying business and IT. You can easily discover, troubleshoot, and fix...
Source: siterecording.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 / 19 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 / 21 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

Dynatrace mentions (0)

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

What are some alternatives?

When comparing Apache Spark and Dynatrace, 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.

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.

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

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 Hive - Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.

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

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