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Apache Spark VS Appian

Compare Apache Spark VS Appian and see what are their differences

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

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.

Appian logo Appian

See how Appian, leading provider of modern low-code and BPM software solutions, has helped transform the businesses of over 3.5 million users worldwide.
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • Appian Landing page
    Landing page //
    2023-10-20

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.

Appian features and specs

  • Low-Code Development
    Appian allows users to create applications with minimal hand-coding, catering to business analysts and developers alike. Its drag-and-drop interface simplifies development and accelerates time-to-market.
  • Process Automation
    Appian excels in automating complex business processes, improving operational efficiency, and reducing human error. Its powerful BPM tools streamline workflows effectively.
  • Integration Capabilities
    Appian provides strong integration capabilities with various third-party systems, databases, and cloud services. This ensures that applications can seamlessly communicate with existing enterprise systems.
  • Enterprise-Grade Security
    Appian offers robust security features, including role-based access control and data encryption, making it suitable for businesses with stringent security requirements.
  • Scalability
    As a cloud-native platform, Appian is highly scalable, supporting the needs of growing enterprises by easily handling increased loads and more complex applications.
  • User Experience
    The platform provides a user-friendly interface, both for developers building the applications and end-users interacting with them, enhancing overall user satisfaction.

Possible disadvantages of Appian

  • Cost
    Appian can be expensive, particularly for small to medium-sized businesses. Its pricing model might not be feasible for organizations operating on a limited budget.
  • Learning Curve
    Although it simplifies development, mastering Appian still requires a learning curve. Users need to invest time in training, which can slow down the initial development phase.
  • Complex Customization
    Highly tailored or very specific customizations can be challenging to implement within Appian. Some complicated functionalities may require extensive workarounds.
  • Limited Offline Functionality
    Appian's offline capabilities are limited, which can be a disadvantage for field services or users who need to access the application without a reliable internet connection.
  • Vendor Lock-In
    Due to its proprietary technology, organizations may face vendor lock-in, making it challenging to migrate applications or data to another platform if needed.
  • Performance Issues at Scale
    While Appian is scalable, some users report performance issues when running extremely large and complex applications, which can impact user experience and overall efficiency.

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.

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

Appian videos

+ Add

Appian CEO: Delivering โ€˜Mission-Criticalโ€™ Software | Mad Money | CNBC

More videos:

  • Review - This is Appian
  • Review - Appian Application Designer: Build Applications in Days, not Years

Category Popularity

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

Apache Spark

Appian

Databases
100 100%
Databases
0% 0
BPM
0 0%
BPM
100% 100
Big Data
100 100%
Big Data
0% 0
Workflow Automation
0 0%
Workflow Automation
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 Appian

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 โ€“ 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

Appian Reviews

Top 10 Microsoft Power Apps Alternatives and Competitors 2024
Strengths: A powerful low-code platform, Appian caters to large enterprises with complex business process automation needs. It offers robust workflow management capabilities, allowing you to automate intricate business processes with decision-making logic and case management features. Appian excels in industries like finance, insurance, and healthcare, where complex...
Source: medium.com
7 Best Business Process Management Tools (2023)
Appian is used by companies to automate their routine tasks, integrate with other enterprise tools, and create custom workflows. It offers a wide range of features such as mobile app development, chatbots, AI assistants, etc.
Source: theecmconsultant.com
11 Business Process Management (BPM) Software for SMBs
Experience the power of business workflow automation with Appian and accelerate your business governance, results, and efficiency. Appian simplifies your workflow design to empower users and pro developers to draw processes, such as a flowchart.
Source: geekflare.com
10 Best Low-Code Development Platforms in 2020
Verdict: Appian is the provider of the software development platform. The Appian low code development platform is a combination of intelligent automation and low-code development.
Source: www.softwaretestinghelp.com
Best Google App Maker alternatives in 2020
Appian excels when used to create form-based apps. The product does allow building custom UI components, but only by using Appianโ€™s in-product customization tools. Appianโ€™s reasoning for this is security and to ensure JS compliance across browsers and devices.
Source: retool.com

Social recommendations and mentions

Based on our record, Apache Spark seems to be a lot more popular than Appian. While we know about 80 links to Apache Spark, we've tracked only 7 mentions of Appian. 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 (80)

  • MLOps Lifecycle: Stages, Workflow, and Best Practices
    Feature transformations should be deterministic: The same input should produce the same output when the same feature definition and configuration are applied. This is what allows training, backtesting, and live inference to remain aligned. Tools such as Pandas, Spark, or feature platforms such as Feast can be used to implement that logic. - Source: dev.to / 3 months ago
  • 7 Free Tools for Data Pipeline Reconciliation and Cross-Source Validation
    Apache Spark provides distributed in-memory data processing and is the appropriate tool when the data set to be reconciled does not fit in a single machine's memory, or when parallelizing the comparison across a cluster would reduce runtime from hours to minutes. - Source: dev.to / 3 months ago
  • Why Apache IoTDB Is Written in Java: A Decade of Engineering Trade-offs
    When IoTDB was initiated in 2011, almost all influential distributed systems and databases were built in Java or on the JVMโ€”such as Hadoop, HBase, Spark (Scala on JVM), Cassandra, Kafka, and Flink. To integrate deeply with the big data ecosystem, choosing Java was a natural decision. - Source: dev.to / 5 months ago
  • I Scraped 47M+ Hacker News Items Into Parquet Files โ€“ Here's What I Discovered About HN's Hidden Data Patterns
    For handling even larger datasets or building production applications, Apache Spark provides excellent Parquet support with distributed processing capabilities. - Source: dev.to / 5 months ago
  • Show HN: Spark โ€“ Zero-config IoT deployment tool written in Rust
    You may want to consider renaming this project. The name "Spark" already refers to: A popular data analytics framework of the Apache Foundation: https://spark.apache.org/ A subset of the Ada programming language used for formal verification: https://learn.adacore.com/courses/intro-to-spark/chapters/01_Overview.html An Nvidia AI development system: https://www.nvidia.com/en-us/products/workstations/dgx-spark/. - Source: Hacker News / 7 months ago
View more

Appian mentions (7)

  • AI Coding Adoption at Enterprise Scale Is Harder Than Anyone Admits
    AI coding adoption at enterprise scale is hard because the real project is not installing a tool. It is redesigning trust, review, ownership, and delivery discipline around a new source of code generation. That's where platforms like Retool, ToolJet, Appian, etc. shine. - Source: dev.to / 6 months ago
  • Enterprise App Builders That Are Actually Enterprise-Ready (Top 5)
    You are process-heavy and regulated, and your app is basically a workflow engine: Appian. - Source: dev.to / 6 months ago
  • Low-Code tools & Frontend
    Does any of you use a low-code tool like Retool or Appian? If so, what is the most common use case? Source: over 3 years ago
  • Appian Associate Developer Certification help
    Look for use case inspiration in the Solutions area of appian.com and within the AppMarket. See if you can build proof of concepts of some of these. Source: almost 4 years ago
  • What is best way to create an online responsive database driven website to help manage my small orchard?
    There are low code database driven website creation systems out there at the moment e.g. OutSystems and Appian however they have very limited free trials (e.g. auto-disable after a few days of no use), and then the paid options are again too expensive. Although I will note that they seem to be great in terms of their usability and would be perfect for creating a simple interface without too much diving into code. Source: about 4 years ago
View more

What are some alternatives?

When comparing Apache Spark and Appian, 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.

Camunda - The Universal Process Orchestrator

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

Kintone - Build business apps and supercharge your company's productivity with kintone's all-in-one...

Apache Kafka - Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.

Bizagi - Bizagi is a Business Process Management (BPMS) solution for faster and flexible process automation. It's powerful yet intuitive BPM Suite is designed to make your business more agile.

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