Software Alternatives, Accelerators & Startups
Register | Login

Apache Spark VS Amazon RDS

Compare Apache Spark VS Amazon RDS and see what are their differences

Hive
Seamless project management and collaboration for your team. featured
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » 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.

Amazon RDS logo Amazon RDS

Easy to manage relational databases optimized for total cost of ownership.
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • Amazon RDS Landing page
    Landing page //
    2023-03-18

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.

Amazon RDS features and specs

  • Managed Service
    Amazon RDS takes care of routine database tasks such as backups, patch management, and scalability, reducing the operational burden on users.
  • Scalability
    Easily scale your database's compute and storage resources with a few clicks or automatically with Amazon RDS Auto Scaling.
  • High Availability
    Amazon RDS provides Multi-AZ deployments for disaster recovery and automated backups, ensuring high availability and durability.
  • Security
    Integrated with AWS Identity and Access Management (IAM), Amazon RDS offers encryption at rest and in transit, as well as network isolation using Amazon VPC.
  • Performance Monitoring
    Amazon RDS provides built-in performance monitoring tools such as Amazon CloudWatch for tracking key metrics and identifying issues.
  • Compatibility
    Supports multiple database engines including MySQL, PostgreSQL, MariaDB, Oracle, and SQL Server, offering flexibility based on your requirements.

Possible disadvantages of Amazon RDS

  • Cost
    The cost of using Amazon RDS can accumulate quickly, especially with high storage demands, high availability configurations, and extensive data transfer.
  • Limited Customization
    As a managed service, there are limits to the customization and fine-tuning compared to self-managed databases, which might not meet all specialized needs.
  • Vendor Lock-In
    Reliance on Amazon RDS ties you into the AWS ecosystem, making migration to another cloud provider or on-premise environment more challenging.
  • Performance Variability
    While generally reliable, users may sometimes experience variability in performance due to shared cloud infrastructure.
  • Configuration Restrictions
    Certain database configurations and features available in on-premise setups might not be supported or might have limited support in Amazon RDS.
  • Complexity in Hybrid Environments
    Integrating Amazon RDS with on-premise systems or other cloud providers can be complex and might require additional configuration and management.

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

Amazon RDS videos

+ Add

Amazon Relational Database Service (Amazon RDS)

More videos:

  • Review - Getting Started with Amazon RDS - Relational Database Service on AWS

Category Popularity

0-100% (relative to Apache Spark and Amazon RDS)

Apache Spark

Amazon RDS

Databases
51 51%
Databases
49% 49
Big Data
100 100%
Big Data
0% 0
NoSQL Databases
0 0%
NoSQL Databases
100% 100
Stream Processing
100 100%
Stream Processing
0% 0

User comments

Share your experience with using Apache Spark and Amazon RDS. For example, how are they different and which one is better?
Log in or Post with

Reviews

These are some of the external sources and on-site user reviews we've used to compare Apache Spark and Amazon RDS

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

Amazon RDS Reviews

We have no reviews of Amazon RDS yet.
Be the first one to post

Social recommendations and mentions

Amazon RDS might be a bit more popular than Apache Spark. We know about 72 links to it since March 2021 and only 70 links to Apache Spark. 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 / 27 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 / 29 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

Amazon RDS mentions (72)

View more

What are some alternatives?

When comparing Apache Spark and Amazon RDS, 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.

PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.

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

Microsoft SQL Server - Microsoft Azure is an open, flexible, enterprise-grade cloud computing platform. Move faster, do more, and save money with IaaS + PaaS. Try for FREE.

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

MariaDB - An enhanced, drop-in replacement for MySQL

Loading...