Amazon RDS for Aurora vs Apache Spark

Overview

Amazon Aurora

Stacks807

Followers745

Votes55

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Amazon RDS for Aurora vs Apache Spark: What are the differences?

Introduction

Amazon RDS for Aurora and Apache Spark are two popular technologies used in data processing and analytics. While both offer solutions for handling large-scale data, there are several key differences between them.

Data Storage: Amazon RDS for Aurora uses a distributed, fault-tolerant storage system that replicates data across multiple Availability Zones for high durability and availability. On the other hand, Apache Spark does not have its own storage system but can integrate with various data storage systems like Hadoop Distributed File System (HDFS) or Amazon S3.
Processing Paradigm: Amazon RDS for Aurora is a managed relational database service, which means it follows a traditional query-based processing paradigm commonly used in SQL databases. In contrast, Apache Spark is a distributed computing system that utilizes in-memory processing and follows a more batch or streaming-oriented processing paradigm.
Scalability: Amazon RDS for Aurora provides automatic scaling capabilities, allowing it to handle a growing workload by adjusting the compute and storage resources. Apache Spark, on the other hand, is designed to scale horizontally by adding more worker nodes to the cluster, enabling it to handle large-scale data processing tasks.
Processing Speed: Due to its in-memory processing capabilities, Apache Spark can perform faster data processing operations compared to Amazon RDS for Aurora, which relies on disk-based storage. This makes Spark suitable for real-time or near-real-time processing scenarios where high-speed data analysis is required.
Data Processing Capabilities: Apache Spark offers a wide range of data processing capabilities, including batch processing, interactive queries, machine learning, and streaming analytics. Amazon RDS for Aurora primarily focuses on traditional SQL-based query processing, although it also supports some advanced analytic features like window functions and common table expressions.
Use Cases: Amazon RDS for Aurora is well-suited for applications that require a highly available and scalable relational database, such as e-commerce platforms or content management systems. Apache Spark, on the other hand, is commonly used in big data analytics, machine learning, and real-time data processing scenarios where speed and scalability are critical.

In summary, the key differences between Amazon RDS for Aurora and Apache Spark lie in their data storage, processing paradigms, scalability, processing speed, data processing capabilities, and use cases.

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Advice on Amazon Aurora, 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 Aurora	Apache Spark
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.	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.
High Throughput with Low Jitter;Push-button Compute Scaling;Storage Auto-scaling;Amazon Aurora Replicas;Instance Monitoring and Repair;Fault-tolerant and Self-healing Storage;Automatic, Continuous, Incremental Backups and Point-in-time Restore;Database Snapshots;Resource-level Permissions;Easy Migration;Monitoring and Metrics	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 807	Stacks 3.1K
Followers 745	Followers 3.5K
Votes 55	Votes 140
Pros & Cons
Pros 14 MySQL compatibility 12 Better performance 10 Easy read scalability 9 Speed 7 Low latency read replica Cons 2 Vendor locking 1 Rigid schema	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
Integrations
PostgreSQL MySQL	No integrations available

What are some alternatives to Amazon Aurora, Apache Spark?

Amazon RDS

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.

Presto

Distributed SQL Query Engine for Big Data

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 for Aurora vs Apache Spark: What are the differences?

Introduction

Data Storage: Amazon RDS for Aurora uses a distributed, fault-tolerant storage system that replicates data across multiple Availability Zones for high durability and availability. On the other hand, Apache Spark does not have its own storage system but can integrate with various data storage systems like Hadoop Distributed File System (HDFS) or Amazon S3.
Processing Paradigm: Amazon RDS for Aurora is a managed relational database service, which means it follows a traditional query-based processing paradigm commonly used in SQL databases. In contrast, Apache Spark is a distributed computing system that utilizes in-memory processing and follows a more batch or streaming-oriented processing paradigm.
Scalability: Amazon RDS for Aurora provides automatic scaling capabilities, allowing it to handle a growing workload by adjusting the compute and storage resources. Apache Spark, on the other hand, is designed to scale horizontally by adding more worker nodes to the cluster, enabling it to handle large-scale data processing tasks.
Processing Speed: Due to its in-memory processing capabilities, Apache Spark can perform faster data processing operations compared to Amazon RDS for Aurora, which relies on disk-based storage. This makes Spark suitable for real-time or near-real-time processing scenarios where high-speed data analysis is required.
Data Processing Capabilities: Apache Spark offers a wide range of data processing capabilities, including batch processing, interactive queries, machine learning, and streaming analytics. Amazon RDS for Aurora primarily focuses on traditional SQL-based query processing, although it also supports some advanced analytic features like window functions and common table expressions.
Use Cases: Amazon RDS for Aurora is well-suited for applications that require a highly available and scalable relational database, such as e-commerce platforms or content management systems. Apache Spark, on the other hand, is commonly used in big data analytics, machine learning, and real-time data processing scenarios where speed and scalability are critical.

Amazon RDS for Aurora vs Apache Spark

Overview

Amazon RDS for Aurora vs Apache Spark: What are the differences?