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  1. Stackups
  2. Application & Data
  3. Relational Databases
  4. SQL Database As A Service
  5. Amazon RDS for Aurora vs Apache Flink

Amazon RDS for Aurora vs Apache Flink

OverviewDecisionsComparisonAlternatives

Overview

Amazon Aurora
Amazon Aurora
Stacks813
Followers745
Votes55
Apache Flink
Apache Flink
Stacks536
Followers879
Votes38
GitHub Stars25.4K
Forks13.7K

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

What is Amazon RDS for Aurora? MySQL and PostgreSQL compatible relational database with several times better performance. 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.

What is Apache Flink? Fast and reliable large-scale data processing engine. 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.

Amazon RDS for Aurora can be classified as a tool in the "SQL Database as a Service" category, while Apache Flink is grouped under "Big Data Tools".

Some of the features offered by Amazon RDS for Aurora are:

  • High Throughput with Low Jitter
  • Push-button Compute Scaling
  • Storage Auto-scaling

On the other hand, Apache Flink provides the following key features:

  • Hybrid batch/streaming runtime that supports batch processing and data streaming programs.
  • Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.
  • Flexible and expressive windowing semantics for data stream programs

"MySQL compatibility " is the primary reason why developers consider Amazon RDS for Aurora over the competitors, whereas "Unified batch and stream processing" was stated as the key factor in picking Apache Flink.

Apache Flink is an open source tool with 9.35K GitHub stars and 5K GitHub forks. Here's a link to Apache Flink's open source repository on GitHub.

According to the StackShare community, Amazon RDS for Aurora has a broader approval, being mentioned in 121 company stacks & 31 developers stacks; compared to Apache Flink, which is listed in 20 company stacks and 22 developer stacks.

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Advice on Amazon Aurora, Apache Flink

Nilesh
Nilesh

Technical Architect at Self Employed

Jul 8, 2020

Needs adviceonElasticsearchElasticsearchKafkaKafka

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.

577k views577k
Comments

Detailed Comparison

Amazon Aurora
Amazon Aurora
Apache Flink
Apache Flink

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.

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.

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
Hybrid batch/streaming runtime that supports batch processing and data streaming programs.;Custom memory management to guarantee efficient, adaptive, and highly robust switching between in-memory and data processing out-of-core algorithms.;Flexible and expressive windowing semantics for data stream programs;Built-in program optimizer that chooses the proper runtime operations for each program;Custom type analysis and serialization stack for high performance
Statistics
GitHub Stars
-
GitHub Stars
25.4K
GitHub Forks
-
GitHub Forks
13.7K
Stacks
813
Stacks
536
Followers
745
Followers
879
Votes
55
Votes
38
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
  • 16
    Unified batch and stream processing
  • 8
    Out-of-the box connector to kinesis,s3,hdfs
  • 8
    Easy to use streaming apis
  • 4
    Open Source
  • 2
    Low latency
Integrations
PostgreSQL
PostgreSQL
MySQL
MySQL
YARN Hadoop
YARN Hadoop
Hadoop
Hadoop
HBase
HBase
Kafka
Kafka

What are some alternatives to Amazon Aurora, Apache Flink?

Amazon RDS

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.

Apache Spark

Apache Spark

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.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

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

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.

lakeFS

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

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

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

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

Splunk

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

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