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Apache Flink VS Amazon Aurora

Compare Apache Flink VS Amazon Aurora and see what are their differences

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Apache Flink logo Apache Flink

Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

Amazon Aurora logo Amazon Aurora

MySQL and PostgreSQL-compatible relational database built for the cloud. Performance and availability of commercial-grade databases at 1/10th the cost.
  • Apache Flink Landing page
    Landing page //
    2023-10-03
  • Amazon Aurora Landing page
    Landing page //
    2023-03-17

Apache Flink features and specs

  • Real-time Stream Processing
    Apache Flink is designed for real-time data streaming, offering low-latency processing capabilities that are essential for applications requiring immediate data insights.
  • Event Time Processing
    Flink supports event time processing, which allows it to handle out-of-order events effectively and provide accurate results based on the time events actually occurred rather than when they were processed.
  • State Management
    Flink provides robust state management features, making it easier to maintain and query state across distributed nodes, which is crucial for managing long-running applications.
  • Fault Tolerance
    The framework includes built-in mechanisms for fault tolerance, such as consistent checkpoints and savepoints, ensuring high reliability and data consistency even in the case of failures.
  • Scalability
    Apache Flink is highly scalable, capable of handling both batch and stream processing workloads across a distributed cluster, making it suitable for large-scale data processing tasks.
  • Rich Ecosystem
    Flink has a rich set of APIs and integrations with other big data tools, such as Apache Kafka, Apache Hadoop, and Apache Cassandra, enhancing its versatility and ease of integration into existing data pipelines.

Possible disadvantages of Apache Flink

  • Complexity
    Flink’s advanced features and capabilities come with a steep learning curve, making it more challenging to set up and use compared to simpler stream processing frameworks.
  • Resource Intensive
    The framework can be resource-intensive, requiring substantial memory and CPU resources for optimal performance, which might be a concern for smaller setups or cost-sensitive environments.
  • Community Support
    While growing, the community around Apache Flink is not as large or mature as some other big data frameworks like Apache Spark, potentially limiting the availability of community-contributed resources and support.
  • Ecosystem Maturity
    Despite its integrations, the Flink ecosystem is still maturing, and certain tools and plugins may not be as developed or stable as those available for more established frameworks.
  • Operational Overhead
    Running and maintaining a Flink cluster can involve significant operational overhead, including monitoring, scaling, and troubleshooting, which might require a dedicated team or additional expertise.

Amazon Aurora features and specs

  • High Performance
    Amazon Aurora is designed to provide up to five times the throughput of standard MySQL and three times the throughput of standard PostgreSQL databases.
  • Scalability
    Aurora scales storage automatically, growing from 10GB up to 128TB with no downtime. This automatic scaling makes it ideal for applications with fluctuating workloads.
  • High Availability and Durability
    Aurora automatically replicates six copies of data across three availability zones and continuously backs up data to Amazon S3, ensuring durability.
  • Security
    Aurora offers multiple layers of security including network isolation using Amazon VPC, encryption at rest using keys that you create and control through AWS Key Management Service (KMS), and encryption of data in transit using SSL.
  • Fully Managed
    Aurora is fully managed by AWS, which automates time-consuming administrative tasks such as hardware provisioning, database setup, patching, and backups.
  • Compatibility
    Aurora is compatible with MySQL and PostgreSQL, making it easier to migrate existing applications to Aurora with minimal changes.

Possible disadvantages of Amazon Aurora

  • Cost
    Aurora can be more expensive than traditional RDS instances, particularly for workloads that do not fully utilize its high performance and scalability features.
  • Complexity
    The numerous features and configurations can make Aurora complex to manage and tune, especially for those who are not familiar with AWS services.
  • Vendor Lock-in
    Adopting Aurora ties you into the AWS ecosystem, which can make it difficult to migrate to other cloud providers or on-premises systems.
  • Cold Start Latency
    Aurora Serverless can experience latency during cold starts, which can be problematic for applications requiring instant scalability.
  • Limited to AWS Environment
    Aurora is only available within the AWS environment, which can be limiting if your infrastructure spans multiple cloud providers.

Analysis of Apache Flink

Overall verdict

  • Yes, Apache Flink is considered a good distributed stream processing framework.

Why this product is good

  • Rich api
    Flink offers a rich set of APIs for various levels of abstraction, catering to different needs of developers.
  • Scalability
    Flink provides excellent horizontal scalability, making it suitable for handling large data streams and high-throughput applications.
  • Fault tolerance
    Flink's checkpointing mechanism ensures fault-tolerance, maintaining data state consistency even after failures.
  • Ease of integration
    Flink integrates well with other big data tools and ecosystems, facilitating broader data architecture designs.
  • Real-time processing
    It excels at processing data in real-time, allowing for immediate insights and action on streaming data.
  • Community and support
    Being a part of the Apache Software Foundation, Flink benefits from a large community and comprehensive documentation.
  • Complex event processing
    It supports complex event processing, which is essential for many real-time applications.

Recommended for

  • real-time analytics
  • stream data processing
  • complex event processing
  • machine learning in streaming applications
  • applications requiring high-throughput and low-latency processing
  • companies looking for robust fault-tolerance in distributed systems

Analysis of Amazon Aurora

Overall verdict

  • Amazon Aurora is generally regarded as an excellent database service for businesses that require robust performance and high availability. It strikes a balance between cost-effectiveness and advanced database features, making it suitable for a wide range of applications.

Why this product is good

  • Amazon Aurora is considered a good choice for many applications due to its high performance, scalability, and compatibility with popular database systems like MySQL and PostgreSQL. It offers features like automated backups, quick failover, and replication capabilities. Aurora is designed to be fault-tolerant and highly available, providing a fully managed solution that relieves users from the operational burden associated with on-premise database management.

Recommended for

    Amazon Aurora is recommended for organizations that need reliable, scalable, and high-performance databases. It is well-suited for web and mobile applications, e-commerce platforms, real-time analytics, and other use cases requiring high availability and fault tolerance. It's ideal for businesses looking to modernize their database infrastructure and take advantage of cloud-native capabilities.

Apache Flink videos

+ Add

GOTO 2019 • Introduction to Stateful Stream Processing with Apache Flink • Robert Metzger

More videos:

  • Tutorial - Apache Flink Tutorial | Flink vs Spark | Real Time Analytics Using Flink | Apache Flink Training
  • Tutorial - How to build a modern stream processor: The science behind Apache Flink - Stefan Richter

Amazon Aurora videos

+ Add

Introduction to Amazon Aurora - Relational Database Built for the Cloud - AWS

More videos:

  • Review - Amazon Aurora Global Database Deep Dive
  • Review - What's New in Amazon Aurora - AWS Online Tech Talks

Category Popularity

0-100% (relative to Apache Flink and Amazon Aurora)

Apache Flink

Amazon Aurora

Big Data
100 100%
Big Data
0% 0
Databases
27 27%
Databases
73% 73
Stream Processing
100 100%
Stream Processing
0% 0
Relational Databases
0 0%
Relational Databases
100% 100

User comments

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Social recommendations and mentions

Based on our record, Apache Flink should be more popular than Amazon Aurora. It has been mentiond 41 times since March 2021. 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 Flink mentions (41)

  • What is Apache Flink? Exploring Its Open Source Business Model, Funding, and Community
    Continuous Learning: Leverage online tutorials from the official Flink website and attend webinars for deeper insights. - Source: dev.to / 18 days ago
  • Is RisingWave the Next Apache Flink?
    Apache Flink, known initially as Stratosphere, is a distributed stream processing engine initiated by a group of researchers at TU Berlin. Since its initial release in May 2011, Flink has gained immense popularity in both academia and industry. And it is currently the most well-known streaming system globally (challenge me if you think I got it wrong!). - Source: dev.to / about 1 month ago
  • 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 / about 1 month ago
  • RisingWave Turns Four: Our Journey Beyond Democratizing Stream Processing
    The last decade saw the rise of open-source frameworks like Apache Flink, Spark Streaming, and Apache Samza. These offered more flexibility but still demanded significant engineering muscle to run effectively at scale. Companies using them often needed specialized stream processing engineers just to manage internal state, tune performance, and handle the day-to-day operational challenges. The barrier to entry... - Source: dev.to / about 1 month ago
  • Twitter's 600-Tweet Daily Limit Crisis: Soaring GCP Costs and the Open Source Fix Elon Musk Ignored
    Apache Flink: Flink is a unified streaming and batching platform developed under the Apache Foundation. It provides support for Java API and a SQL interface. Flink boasts a large ecosystem and can seamlessly integrate with various services, including Kafka, Pulsar, HDFS, Iceberg, Hudi, and other systems. - Source: dev.to / about 2 months ago
View more

Amazon Aurora mentions (23)

  • Building a RAG System for Video Content Search and Analysis
    Using Amazon Bedrock to invoke Amazon Titan Foundation Models for generating multimodal embeddings, Amazon Transcribe for converting speech to text, and Amazon Aurora postgreSQL for vector storage and similarity search, you can build an application that understands both visual and audio content, enabling natural language queries to find specific moments in videos. - Source: dev.to / about 2 months ago
  • Everyone Uses Postgres… But Why?
    Cloud deployment: PostgreSQL can be deployed in the cloud with AWS RDS, Amazon Aurora, Azure Database for PostgreSQL, or Cloud SQL for PostgreSQL. - Source: dev.to / 6 months ago
  • Announcing the public beta for dedicated clusters
    Today, our Postgres databases are Amazon Aurora instances. You can trust that your database will have the scalability, reliability and security that AWS is known for. With dedicated clusters you can configure both the Postgres engine version, cluster class and number of replicas for failover and query distribution. - Source: dev.to / 10 months ago
  • Vector database is not a separate database category
    As far as the big players are concerned, Google offers AlloyDB (https://cloud.google.com/alloydb) while Amazon offers Aurora (https://aws.amazon.com/rds/aurora/). - Source: Hacker News / over 1 year ago
  • Building realtime experiences with Amazon Aurora
    Aurora is a managed database service from Amazon compatible with MySQL and PostgreSQL. It allows for the use of existing MySQL code, tools, and applications and can offer increased performance for certain workloads compared to MySQL and PostgreSQL. - Source: dev.to / almost 2 years ago
View more

What are some alternatives?

When comparing Apache Flink and Amazon Aurora, you can also consider the following products

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

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

Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.

MySQL - The world's most popular open source database

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

Oracle DBaaS - See how Oracle Database 12c enables businesses to plug into the cloud and power the real-time enterprise.

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