Amazon EMR vs Apache Flink

Overview

Amazon EMR

Stacks543

Followers682

Votes54

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

Amazon EMR vs Apache Flink: What are the differences?

Key Differences between Amazon EMR and Apache Flink

Amazon EMR and Apache Flink are both popular big data processing tools, but they have key differences that set them apart.

Data Processing Model: Amazon EMR is based on the Hadoop framework and is primarily geared towards batch processing. It supports traditional MapReduce jobs, Hive, and Pig for data processing. On the other hand, Apache Flink is a stream processing engine that is designed for low-latency and real-time processing. It supports both batch and stream processing, making it suitable for a wider range of use cases.
Fault Tolerance: Amazon EMR uses Hadoop Distributed File System (HDFS) for fault tolerance. It replicates data across multiple nodes to ensure reliability. In contrast, Apache Flink uses a distributed streaming dataflow engine with exactly-once processing semantics. It provides built-in mechanisms for fault tolerance and state management, making it more resilient to failures.
Event Time Processing: Apache Flink has built-in support for event time processing, which allows processing of events based on their actual occurrence time rather than arrival time. This is crucial for accurate analysis of time-sensitive data, such as IoT sensor readings or financial transactions. Amazon EMR does not have native support for event time processing, although it can be implemented using custom code.
Data Streaming Capabilities: While Amazon EMR can process streaming data using technologies like Spark Streaming or Storm, it is not a pure streaming processing engine like Apache Flink. Flink provides advanced features for event-driven stream processing, including event time handling, windowing, and complex event processing. It excels in use cases where real-time processing and near real-time insights are required.
Integration with Ecosystem: Amazon EMR seamlessly integrates with the entire AWS ecosystem, including services like S3 for storage, CloudWatch for monitoring, and IAM for access management. It also provides integrations with popular data processing frameworks like Apache Spark and Apache HBase. Apache Flink, on the other hand, has integrations with various storage systems and other big data tools but may require additional configuration and setup.
Development Paradigm: Amazon EMR allows developers to work with popular programming languages like Java, Python, and Scala, using frameworks like MapReduce, Hive, Pig, and Spark. Apache Flink, on the contrary, is a Java-based streaming engine that adopts a more unified and consistent programming model. Flink's APIs support both stream and batch processing and provide a higher level of abstraction for developers.

In summary, the key differences between Amazon EMR and Apache Flink lie in their data processing models (batch vs. stream processing), fault tolerance mechanisms, event time processing capabilities, integration with ecosystems, data streaming capabilities, and development paradigms.

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Advice on Amazon EMR, Apache Flink

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 EMR	Apache Flink
It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.	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.
Elastic- Amazon EMR enables you to quickly and easily provision as much capacity as you need and add or remove capacity at any time. Deploy multiple clusters or resize a running cluster;Low Cost- Amazon EMR is designed to reduce the cost of processing large amounts of data. Some of the features that make it low cost include low hourly pricing, Amazon EC2 Spot integration, Amazon EC2 Reserved Instance integration, elasticity, and Amazon S3 integration.;Flexible Data Stores- With Amazon EMR, you can leverage multiple data stores, including Amazon S3, the Hadoop Distributed File System (HDFS), and Amazon DynamoDB.;Hadoop Tools- EMR supports powerful and proven Hadoop tools such as Hive, Pig, and HBase.	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 543	Stacks 534
Followers 682	Followers 879
Votes 54	Votes 38
Pros & Cons
Pros 15 On demand processing power 12 Don't need to maintain Hadoop Cluster yourself 7 Hadoop Tools 6 Elastic 4 Backed by Amazon	Pros 16 Unified batch and stream processing 8 Easy to use streaming apis 8 Out-of-the box connector to kinesis,s3,hdfs 4 Open Source 2 Low latency
Integrations
No integrations available	YARN Hadoop Hadoop HBase Kafka

What are some alternatives to Amazon EMR, Apache Flink?

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

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.

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

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.

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.

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

Related Comparisons

Amazon EMR vs Apache Flink: What are the differences?

Key Differences between Amazon EMR and Apache Flink

Amazon EMR and Apache Flink are both popular big data processing tools, but they have key differences that set them apart.

Data Processing Model: Amazon EMR is based on the Hadoop framework and is primarily geared towards batch processing. It supports traditional MapReduce jobs, Hive, and Pig for data processing. On the other hand, Apache Flink is a stream processing engine that is designed for low-latency and real-time processing. It supports both batch and stream processing, making it suitable for a wider range of use cases.
Fault Tolerance: Amazon EMR uses Hadoop Distributed File System (HDFS) for fault tolerance. It replicates data across multiple nodes to ensure reliability. In contrast, Apache Flink uses a distributed streaming dataflow engine with exactly-once processing semantics. It provides built-in mechanisms for fault tolerance and state management, making it more resilient to failures.
Event Time Processing: Apache Flink has built-in support for event time processing, which allows processing of events based on their actual occurrence time rather than arrival time. This is crucial for accurate analysis of time-sensitive data, such as IoT sensor readings or financial transactions. Amazon EMR does not have native support for event time processing, although it can be implemented using custom code.
Data Streaming Capabilities: While Amazon EMR can process streaming data using technologies like Spark Streaming or Storm, it is not a pure streaming processing engine like Apache Flink. Flink provides advanced features for event-driven stream processing, including event time handling, windowing, and complex event processing. It excels in use cases where real-time processing and near real-time insights are required.
Integration with Ecosystem: Amazon EMR seamlessly integrates with the entire AWS ecosystem, including services like S3 for storage, CloudWatch for monitoring, and IAM for access management. It also provides integrations with popular data processing frameworks like Apache Spark and Apache HBase. Apache Flink, on the other hand, has integrations with various storage systems and other big data tools but may require additional configuration and setup.
Development Paradigm: Amazon EMR allows developers to work with popular programming languages like Java, Python, and Scala, using frameworks like MapReduce, Hive, Pig, and Spark. Apache Flink, on the contrary, is a Java-based streaming engine that adopts a more unified and consistent programming model. Flink's APIs support both stream and batch processing and provide a higher level of abstraction for developers.

Amazon EMR vs Apache Flink

Overview

Amazon EMR vs Apache Flink: What are the differences?