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Sumo Logic VS Apache Spark

Compare Sumo Logic VS Apache Spark and see what are their differences

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Sumo Logic logo Sumo Logic

Sumo Logic is a secure, purpose-built cloud-based machine data analytics service that leverages big data for real-time IT insights

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.
  • Sumo Logic Landing page
    Landing page //
    2023-10-20
  • Apache Spark Landing page
    Landing page //
    2021-12-31

Sumo Logic features and specs

  • Scalability
    Sumo Logic is designed to handle large volumes of data, making it suitable for organizations of different sizes and industries. It can scale up or down based on your needs.
  • Real-time Analytics
    The platform provides real-time analysis of logs and metrics, allowing for immediate insights and faster decision-making.
  • Unified Platform
    Sumo Logic offers a single platform for application observability, security, and compliance, reducing the need for multiple tools and streamlining workflows.
  • Machine Learning Capabilities
    The platform includes advanced machine learning features for anomaly detection, predictive analytics, and root cause analysis, enhancing the ability to detect and troubleshoot issues.
  • Integrations
    Sumo Logic supports numerous integrations with other tools and platforms, including AWS, Azure, Google Cloud, and various DevOps, security, and observability tools.
  • Compliance and Security
    The platform offers robust security features and facilitates compliance with various industry standards, such as HIPAA, GDPR, and SOC 2.

Possible disadvantages of Sumo Logic

  • Cost
    Sumo Logic can be expensive, particularly for smaller organizations or those with budget constraints. The cost may increase significantly with higher data volumes.
  • Complexity
    The platform has a steep learning curve, especially for users who are new to log management and analytics tools. This could lead to a longer onboarding process.
  • Search Performance
    In some cases, users have reported slow search performance, especially when querying large datasets or during peak usage times.
  • Limited Customization
    While Sumo Logic offers a wide range of features, there are limitations in customizing dashboards and alerts to fit specific requirements fully.
  • Dependence on Internet Connectivity
    As a cloud-based solution, Sumo Logic requires a reliable internet connection. Any disruption in connectivity can impact access to the platform and its features.

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.

Analysis of Sumo Logic

Overall verdict

  • Overall, Sumo Logic is a strong solution for log management and analytics, particularly for organizations operating in cloud environments. Its comprehensive set of features and focus on security make it a reliable choice for businesses looking to gain deeper insights into their IT infrastructure.

Why this product is good

  • Sumo Logic is considered a good choice for many organizations due to its powerful cloud-native analytics capabilities. It provides real-time insights across various types of machine data and helps in monitoring, troubleshooting, and securing applications. Its scalability allows it to handle vast amounts of data efficiently, and it integrates seamlessly with a variety of cloud and on-premises solutions. Additionally, Sumo Logic offers advanced threat detection and operational intelligence, which are valuable for modern IT operations and security teams.

Recommended for

  • Organizations using cloud-native applications
  • Businesses needing real-time operational and security insights
  • Enterprises seeking scalable log management solutions
  • IT teams focused on proactive monitoring and troubleshooting
  • Security teams requiring advanced threat detection capabilities

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.

Sumo Logic videos

+ Add

Sumo Logic 2013 Year in Review

More videos:

  • Demo - Next Generation Log Management & Analytics - Demo of Sumo Logic

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

Category Popularity

0-100% (relative to Sumo Logic and Apache Spark)

Sumo Logic

Apache Spark

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

Sumo Logic Reviews

The 10 Best Nagios Alternatives in 2024 (Paid and Open-source)
Sumo Logic is yet another tempting Nagios alternative, especially appealing to large corporations, while also offering notable infrastructure monitoring capabilities. One standout feature of Sumo Logic is its utilization of cloud-based machine learning, which proves invaluable in efficiently managing vast amounts of data concurrently, making it particularly advantageous for...
Source: betterstack.com
10 Best Grafana Alternatives [2023 Comparison]
Sumo Logic is able to process big data, which means that it is aimed at companies that have a lot of data. In other words, Sumo Logic is aimed at big corporations with big budgets.
Source: sematext.com
11 Best Splunk Alternatives
Sumo Logic is a SaaS-based log management application that can monitor both on-premises and cloud-based services. The platform includes integrations for AWS, Microsoft Azure, Google Cloud, Kubernetes, and Docker, allowing it to work alongside your current tools and services.
Source: www.ittsystems.com
8 Dynatrace Alternatives to Consider in 2021
Sumo Logic is an APM platform that promises faster troubleshooting with integrated logs, metrics, and traces. It focuses on cloud operations and providing analytics to support developers. It has multi-cloud support with over 150 apps that you can integrate with your work. It promises security, scalability, reliability, and performance by ensuring that data is unlimited for...
Source: scoutapm.com
Top 5 NGINX Log Analyzer Tools – Driving Business Growth with Data
Sumo Logic offers an application to analyze NGINX server logs. In addition to analyzing NGINX server performance, the tool can monitor complex transactions and track usage patterns. It uses machine learning capabilities to efficiently analyze huge amounts of logs. The unified logging system enables developers to monitor and troubleshoot issues in real-time, allowing faster...
Source: www.dnsstuff.com

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

Social recommendations and mentions

Based on our record, Apache Spark seems to be a lot more popular than Sumo Logic. While we know about 70 links to Apache Spark, we've tracked only 2 mentions of Sumo Logic. 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.

Sumo Logic mentions (2)

  • Show HN: HyperTemplates, a pure-HTML templating system and static site generator
    Hello, my name is Caleb. I'm a product manager by trade, and have enjoyed working in/around the software industry over the past 15 years. I was most recently CEO & co-founder at Sensu (https://sensu.io), which was eventually acquired by Sumo Logic (https://sumologic.com), resulting in my "funemployment". I've met so many people over the course of my career who are interested in making websites – they even teach... - Source: Hacker News / 3 days ago
  • Roadmap for July
    He's coming with years of experience of having architected systems at Uber, Flock, Sumo Logic and was a founding engineer who helped design the cryptography primitives at Zeta. Someone of his caliber coming onboard means that we'll be able to ship nicer things faster. 🎉. Source: almost 4 years ago

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
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What are some alternatives?

When comparing Sumo Logic and Apache Spark, you can also consider the following products

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 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

LogicMonitor - LogicMonitor is the SaaS performance monitoring platform for the world's best IT teams. Deploy Fast, Monitor More, Improve Ops.

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

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