Amazon RDS vs Apache Spark

Overview

Amazon RDS

Stacks16.2K

Followers10.8K

Votes761

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Amazon RDS vs Apache Spark: What are the differences?

Scalability: Amazon RDS is a managed relational database service that provides different engine options like MySQL, PostgreSQL, SQL Server, etc. It offers automatic scaling capabilities to handle a growing workload efficiently. On the other hand, Apache Spark is a distributed data processing engine that is designed for big data workloads. It can scale horizontally by adding more worker nodes to the cluster, allowing it to handle large amounts of data and perform computations in parallel.
Data Processing Paradigm: Amazon RDS follows a traditional relational database model with support for SQL queries and transactions. It is optimized for OLTP workloads and ensures ACID compliance. In contrast, Apache Spark is built around the concept of Resilient Distributed Datasets (RDDs) and supports various data processing paradigms like batch processing, interactive querying, streaming, and machine learning. It enables complex data transformations and analytics operations on distributed datasets.
Managed Service vs. Framework: Amazon RDS is a fully managed service provided by AWS, where users can focus on application development without worrying about database administration tasks like backups, patching, and scaling. On the contrary, Apache Spark is an open-source distributed computing framework that users need to deploy and manage on their own infrastructure or cloud environment. Although cloud providers like Databricks offer managed Apache Spark services, users still have more control and flexibility compared to a fully managed database service like Amazon RDS.
Data Storage: Amazon RDS stores data in a structured format using a relational database management system, which enforces a schema and ensures data integrity through constraints and relationships. In contrast, Apache Spark supports various data sources, including structured and semi-structured data, and can work with both relational and non-relational data formats. It provides more flexibility in how data is stored, processed, and analyzed.
Real-time Processing: Amazon RDS is optimized for transactional workloads that require consistent and reliable data processing capabilities. While it supports some real-time features like read replicas for scalable read operations, it is not designed for real-time data processing or stream processing tasks. On the other hand, Apache Spark's streaming capabilities allow for real-time data processing and analysis, making it suitable for use cases that require low-latency insights from continuously streaming data sources.
Use Cases: Amazon RDS is well-suited for traditional OLTP applications, reporting, and analytics workloads that require a relational database with ACID compliance. It is a good fit for applications that demand high availability, durability, and consistent performance for structured data. Apache Spark, on the other hand, is ideal for big data processing tasks such as ETL (Extract, Transform, Load), data warehousing, data exploration, real-time analytics, and machine learning. It excels in handling large-scale data processing and analysis requirements where speed and scalability are essential.

In Summary, When choosing between Amazon RDS and Apache Spark, consider factors like data processing paradigm, scalability, managed service vs. framework, data storage requirements, real-time processing needs, and specific use cases to select the most suitable solution for your application.

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Advice on Amazon RDS, Apache Spark

Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceon

Elasticsearch

Kafka

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Detailed Comparison

Amazon RDS	Apache Spark
Amazon RDS gives you access to the capabilities of a familiar MySQL, Oracle or Microsoft SQL Server database engine. This means that the code, applications, and tools you already use today with your existing databases can be used with Amazon RDS. Amazon RDS automatically patches the database software and backs up your database, storing the backups for a user-defined retention period and enabling point-in-time recovery. You benefit from the flexibility of being able to scale the compute resources or storage capacity associated with your Database Instance (DB Instance) via a single API call.	Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Pre-configured Parameters;Monitoring and Metrics;Automatic Software Patching;Automated Backups;DB Snapshots;DB Event Notifications;Multi-Availability Zone (Multi-AZ) Deployments;Provisioned IOPS;Push-Button Scaling;Automatic Host Replacement;Replication;Isolation and Security	Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3
Statistics
GitHub Stars -	GitHub Stars 42.2K
GitHub Forks -	GitHub Forks 28.9K
Stacks 16.2K	Stacks 3.1K
Followers 10.8K	Followers 3.5K
Votes 761	Votes 140
Pros & Cons
Pros 165 Reliable failovers 156 Automated backups 130 Backed by amazon 92 Db snapshots 87 Multi-availability	Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed

What are some alternatives to Amazon RDS, Apache Spark?

Presto

Distributed SQL Query Engine for Big Data

Amazon Aurora

Amazon Aurora is a MySQL-compatible, relational database engine that combines the speed and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides up to five times better performance than MySQL at a price point one tenth that of a commercial database while delivering similar performance and availability.

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Google Cloud SQL

Run the same relational databases you know with their rich extension collections, configuration flags and developer ecosystem, but without the hassle of self management.

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

ClearDB

ClearDB uses a combination of advanced replication techniques, advanced cluster technology, and layered web services to provide you with a MySQL database that is "smarter" than usual.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Related Comparisons

Amazon RDS vs Apache Spark: What are the differences?

Scalability: Amazon RDS is a managed relational database service that provides different engine options like MySQL, PostgreSQL, SQL Server, etc. It offers automatic scaling capabilities to handle a growing workload efficiently. On the other hand, Apache Spark is a distributed data processing engine that is designed for big data workloads. It can scale horizontally by adding more worker nodes to the cluster, allowing it to handle large amounts of data and perform computations in parallel.
Data Processing Paradigm: Amazon RDS follows a traditional relational database model with support for SQL queries and transactions. It is optimized for OLTP workloads and ensures ACID compliance. In contrast, Apache Spark is built around the concept of Resilient Distributed Datasets (RDDs) and supports various data processing paradigms like batch processing, interactive querying, streaming, and machine learning. It enables complex data transformations and analytics operations on distributed datasets.
Managed Service vs. Framework: Amazon RDS is a fully managed service provided by AWS, where users can focus on application development without worrying about database administration tasks like backups, patching, and scaling. On the contrary, Apache Spark is an open-source distributed computing framework that users need to deploy and manage on their own infrastructure or cloud environment. Although cloud providers like Databricks offer managed Apache Spark services, users still have more control and flexibility compared to a fully managed database service like Amazon RDS.
Data Storage: Amazon RDS stores data in a structured format using a relational database management system, which enforces a schema and ensures data integrity through constraints and relationships. In contrast, Apache Spark supports various data sources, including structured and semi-structured data, and can work with both relational and non-relational data formats. It provides more flexibility in how data is stored, processed, and analyzed.
Real-time Processing: Amazon RDS is optimized for transactional workloads that require consistent and reliable data processing capabilities. While it supports some real-time features like read replicas for scalable read operations, it is not designed for real-time data processing or stream processing tasks. On the other hand, Apache Spark's streaming capabilities allow for real-time data processing and analysis, making it suitable for use cases that require low-latency insights from continuously streaming data sources.
Use Cases: Amazon RDS is well-suited for traditional OLTP applications, reporting, and analytics workloads that require a relational database with ACID compliance. It is a good fit for applications that demand high availability, durability, and consistent performance for structured data. Apache Spark, on the other hand, is ideal for big data processing tasks such as ETL (Extract, Transform, Load), data warehousing, data exploration, real-time analytics, and machine learning. It excels in handling large-scale data processing and analysis requirements where speed and scalability are essential.

Amazon RDS vs Apache Spark

Overview