Apache Flink vs Google Cloud Data Fusion

Overview

Apache Flink

Stacks534

Followers879

Votes38

GitHub Stars25.4K

Forks13.7K

Google Cloud Data Fusion

Stacks25

Followers156

Votes1

Apache Flink vs Google Cloud Data Fusion: What are the differences?

Introduction: Apache Flink and Google Cloud Data Fusion are two popular tools used for big data processing and analytics. While both serve similar purposes, they have key differences that make them unique in their own ways.

Deployment Environment: Apache Flink is an open-source stream processing framework that can be deployed on various platforms, including on-premises or on cloud providers such as AWS, Azure, and Google Cloud Platform. On the other hand, Google Cloud Data Fusion is a fully-managed ETL and data integration service that runs on Google Cloud Platform, offering a more streamlined and integrated deployment experience.
Programming Model: Apache Flink provides a flexible and powerful API for building complex data processing pipelines in Java, Scala, or Python. It supports both batch and stream processing with its unified programming model. In contrast, Google Cloud Data Fusion offers a visual interface for building data pipelines using a drag-and-drop approach without the need for writing code, making it more user-friendly for non-technical users.
Integration with Ecosystem: Apache Flink integrates well with other big data tools and frameworks such as Apache Kafka, Apache Hadoop, and Apache Cassandra, providing a wide range of options for data ingestion and processing. Google Cloud Data Fusion, being a Google Cloud service, seamlessly integrates with other GCP services like BigQuery, Cloud Storage, and Pub/Sub, offering a more cohesive environment for data processing workflows.
Scalability and Performance: Apache Flink is known for its high throughput, low latency, and fault-tolerance features, making it suitable for processing large volumes of data in real-time. It can scale horizontally to handle massive workloads effectively. On the other hand, Google Cloud Data Fusion offers scalability benefits by automatically scaling resources based on workload demands, providing a more managed approach to scalability without the need for manual tuning.
Pricing Model: Apache Flink is an open-source framework, making it free to use without any licensing costs. However, users need to manage infrastructure and deployment costs when running Flink applications on cloud platforms. Google Cloud Data Fusion follows a usage-based pricing model, where users pay for the resources consumed and the services used, providing a more predictable cost structure for data integration and processing tasks.
Community Support and Updates: Apache Flink has a large and active community of contributors and users, regularly releasing updates and new features to enhance the framework's capabilities. On the other hand, Google Cloud Data Fusion is a managed service with updates and maintenance handled by Google Cloud Platform, offering a more hands-off approach for users who prefer seamless updates and support from the service provider.

In Summary, Apache Flink and Google Cloud Data Fusion differ in deployment environment, programming model, integration with ecosystem, scalability and performance, pricing model, and community support and updates, catering to different use cases and preferences in the big data analytics space.

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Advice on Apache Flink, Google Cloud Data Fusion

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

Apache Flink	Google Cloud Data Fusion
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.	A fully managed, cloud-native data integration service that helps users efficiently build and manage ETL/ELT data pipelines. With a graphical interface and a broad open-source library of preconfigured connectors and transformations, and more.
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	Code-free self-service; Collaborative data engineering; GCP-native; Enterprise-grade security; Integration metadata and lineage; Seamless operations; Comprehensive integration toolkit; Hybrid enablement
Statistics
GitHub Stars 25.4K	GitHub Stars -
GitHub Forks 13.7K	GitHub Forks -
Stacks 534	Stacks 25
Followers 879	Followers 156
Votes 38	Votes 1
Pros & Cons
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	Pros 1 Lower total cost of pipeline ownership
Integrations
YARN Hadoop Hadoop HBase Kafka	Google Cloud Storage Google BigQuery

What are some alternatives to Apache Flink, Google Cloud Data Fusion?

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

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.

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.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Related Comparisons

Apache Flink vs Google Cloud Data Fusion: What are the differences?

Deployment Environment: Apache Flink is an open-source stream processing framework that can be deployed on various platforms, including on-premises or on cloud providers such as AWS, Azure, and Google Cloud Platform. On the other hand, Google Cloud Data Fusion is a fully-managed ETL and data integration service that runs on Google Cloud Platform, offering a more streamlined and integrated deployment experience.
Programming Model: Apache Flink provides a flexible and powerful API for building complex data processing pipelines in Java, Scala, or Python. It supports both batch and stream processing with its unified programming model. In contrast, Google Cloud Data Fusion offers a visual interface for building data pipelines using a drag-and-drop approach without the need for writing code, making it more user-friendly for non-technical users.
Integration with Ecosystem: Apache Flink integrates well with other big data tools and frameworks such as Apache Kafka, Apache Hadoop, and Apache Cassandra, providing a wide range of options for data ingestion and processing. Google Cloud Data Fusion, being a Google Cloud service, seamlessly integrates with other GCP services like BigQuery, Cloud Storage, and Pub/Sub, offering a more cohesive environment for data processing workflows.
Scalability and Performance: Apache Flink is known for its high throughput, low latency, and fault-tolerance features, making it suitable for processing large volumes of data in real-time. It can scale horizontally to handle massive workloads effectively. On the other hand, Google Cloud Data Fusion offers scalability benefits by automatically scaling resources based on workload demands, providing a more managed approach to scalability without the need for manual tuning.
Pricing Model: Apache Flink is an open-source framework, making it free to use without any licensing costs. However, users need to manage infrastructure and deployment costs when running Flink applications on cloud platforms. Google Cloud Data Fusion follows a usage-based pricing model, where users pay for the resources consumed and the services used, providing a more predictable cost structure for data integration and processing tasks.
Community Support and Updates: Apache Flink has a large and active community of contributors and users, regularly releasing updates and new features to enhance the framework's capabilities. On the other hand, Google Cloud Data Fusion is a managed service with updates and maintenance handled by Google Cloud Platform, offering a more hands-off approach for users who prefer seamless updates and support from the service provider.

Apache Flink vs Google Cloud Data Fusion

Overview