Apache Flink vs Cloudera Enterprise

Overview

Cloudera Enterprise

Stacks126

Followers172

Votes5

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

Apache Flink vs Cloudera Enterprise: What are the differences?

Introduction

Apache Flink and Cloudera Enterprise are both widely used big data processing frameworks offering various capabilities, but they differ in several key aspects.

Processing Model: Apache Flink uses a stream processing model, allowing continuous data processing in real-time, while Cloudera Enterprise primarily focuses on batch processing, processing data in discrete chunks.
Native Integration: Cloudera Enterprise provides seamless integration with various components of the Hadoop ecosystem, such as HDFS and YARN, while Apache Flink has its own ecosystem and doesn't rely on Hadoop infrastructure.
Support for Stateful Computation: Apache Flink natively supports stateful computations, making it easier to maintain and process application state across different computations, whereas Cloudera Enterprise requires additional tools for managing states.
Language Support: Apache Flink supports both Java and Scala for programming, offering developers flexibility in choosing their preferred language, while Cloudera Enterprise primarily supports Java, making it less versatile in terms of language options.
Optimization Techniques: Cloudera Enterprise offers advanced optimization techniques for batch processing, making it more suitable for large-scale batch data processing, while Apache Flink is optimized for real-time stream processing, resulting in better performance for streaming workloads.
Deployment Flexibility: Apache Flink allows deployment in various environments, including standalone, YARN, and Mesos, offering more flexibility in deployment options compared to Cloudera Enterprise, which is typically deployed on Hadoop clusters.

In Summary, Apache Flink and Cloudera Enterprise differ in their processing models, native integration, support for stateful computation, language support, optimization techniques, and deployment flexibility.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs

CLI (Node.js)

Manual

Advice on Cloudera Enterprise, 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.

576k views576k

Comments

Detailed Comparison

Cloudera Enterprise	Apache Flink
Cloudera Enterprise includes CDH, the world’s most popular open source Hadoop-based platform, as well as advanced system management and data management tools plus dedicated support and community advocacy from our world-class team of Hadoop developers and experts.	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.
Unified – one integrated system, bringing diverse users and application workloads to one pool of data on common infrastructure; no data movement required;Secure – perimeter security, authentication, granular authorization, and data protection;Governed – enterprise-grade data auditing, data lineage, and data discovery;Managed – native high-availability, fault-tolerance and self-healing storage, automated backup and disaster recovery, and advanced system and data management;Open – Apache-licensed open source to ensure your data and applications remain yours, and an open platform to connect with all of your existing investments in technology and skills	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 126	Stacks 534
Followers 172	Followers 879
Votes 5	Votes 38
Pros & Cons
Pros 1 Multicloud 1 Scalability 1 Hybrid cloud 1 Easily management 1 Cheeper	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
No integrations available	YARN Hadoop Hadoop HBase Kafka

What are some alternatives to Cloudera Enterprise, 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 EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

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.

Related Comparisons

Apache Flink vs Cloudera Enterprise: What are the differences?

Introduction

Apache Flink and Cloudera Enterprise are both widely used big data processing frameworks offering various capabilities, but they differ in several key aspects.

Processing Model: Apache Flink uses a stream processing model, allowing continuous data processing in real-time, while Cloudera Enterprise primarily focuses on batch processing, processing data in discrete chunks.
Native Integration: Cloudera Enterprise provides seamless integration with various components of the Hadoop ecosystem, such as HDFS and YARN, while Apache Flink has its own ecosystem and doesn't rely on Hadoop infrastructure.
Support for Stateful Computation: Apache Flink natively supports stateful computations, making it easier to maintain and process application state across different computations, whereas Cloudera Enterprise requires additional tools for managing states.
Language Support: Apache Flink supports both Java and Scala for programming, offering developers flexibility in choosing their preferred language, while Cloudera Enterprise primarily supports Java, making it less versatile in terms of language options.
Optimization Techniques: Cloudera Enterprise offers advanced optimization techniques for batch processing, making it more suitable for large-scale batch data processing, while Apache Flink is optimized for real-time stream processing, resulting in better performance for streaming workloads.
Deployment Flexibility: Apache Flink allows deployment in various environments, including standalone, YARN, and Mesos, offering more flexibility in deployment options compared to Cloudera Enterprise, which is typically deployed on Hadoop clusters.

Apache Flink vs Cloudera Enterprise

Overview

Apache Flink vs Cloudera Enterprise: What are the differences?