Apache Spark vs Google Cloud Data Fusion

Overview

Apache Spark

Stacks3.1K

Followers3.5K

Votes140

GitHub Stars42.2K

Forks28.9K

Google Cloud Data Fusion

Stacks25

Followers156

Votes1

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

Introduction

Apache Spark and Google Cloud Data Fusion are both popular technologies used for data processing and analytics. However, there are key differences between the two platforms that set them apart in terms of functionality and use cases.

Architecture: Apache Spark is a powerful open-source data processing framework that provides a distributed computing environment for big data analytics. It operates on the concept of resilient distributed datasets (RDDs) and provides various high-level APIs for processing structured and unstructured data. On the other hand, Google Cloud Data Fusion is a fully managed data integration service that enables users to build, deploy, and manage data pipelines for batch and streaming data processing. It offers a graphical user interface (GUI) for designing and executing data pipelines without writing code, leveraging Google Cloud Platform's infrastructure.
Ease of Use: Apache Spark requires coding in languages like Scala, Java, Python, or R to develop data processing and analytics applications. It provides a rich set of APIs, libraries, and tooling, making it highly customizable but requiring programming skills. In contrast, Google Cloud Data Fusion focuses on a no-code or low-code approach, allowing users to visually design data pipelines using a drag-and-drop interface. This makes it more accessible to users without strong programming backgrounds and enables faster development and deployment of data integration workflows.
Scalability and Performance: Apache Spark is known for its ability to handle massive volumes of data with its distributed computing capabilities. It can scale horizontally across a cluster of machines, parallelizing data processing tasks efficiently. Spark also provides in-memory processing capabilities that significantly improve performance for iterative algorithms and real-time streaming applications. Google Cloud Data Fusion, being a managed service on Google Cloud Platform, also offers scalability and high-performance data processing. It leverages the underlying infrastructure and managed services provided by Google for seamless scalability and reliable performance.
Integration with Ecosystem: Apache Spark has a vibrant ecosystem with a wide range of libraries and connectors that can be leveraged for various tasks. It integrates well with other big data tools and technologies like Hadoop, Hive, HBase, Kafka, and more. This enables seamless data integration and interoperability with existing data infrastructure and workflows. On the other hand, Google Cloud Data Fusion is tightly integrated with Google Cloud Platform services. It provides pre-built connectors to various Google Cloud services like BigQuery, Cloud Storage, Cloud Pub/Sub, and more, simplifying data ingestion and integration with Google's ecosystem.
Managed vs. Self-managed: Apache Spark requires manual setup, configuration, and management of the underlying infrastructure and resources for running Spark clusters. Organizations need to provision and manage their own infrastructure or leverage cloud services to host and run Spark. In contrast, Google Cloud Data Fusion is a fully managed service provided by Google Cloud Platform. It abstracts away the complexity of infrastructure management, automates resource provisioning, and ensures high availability and reliability of data pipelines. This makes it easier for organizations to focus on their data integration and processing tasks rather than managing the underlying infrastructure.
Costs and Pricing: Apache Spark is an open-source framework, meaning it is free to use and deploy on your own infrastructure or cloud services. However, organizations need to consider the costs associated with maintaining and scaling their Spark clusters. On the other hand, Google Cloud Data Fusion follows a pay-as-you-go pricing model. Users are billed based on factors like the number of active data integration pipelines, the amount of data processed, and any additional services used within the Google Cloud Platform ecosystem. Organizations can choose the pricing plan that best suits their requirements and optimize costs accordingly.

In summary, Apache Spark and Google Cloud Data Fusion differ in terms of architecture, ease of use, scalability, integration with ecosystems, deployment model, and costs. Spark provides a powerful, customizable, and programmable environment for big data processing, while Data Fusion offers a user-friendly, no-code approach for building and managing data pipelines on Google Cloud Platform.

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Advice on Apache Spark, 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 Spark	Google Cloud Data Fusion
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.	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.
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3	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 42.2K	GitHub Stars -
GitHub Forks 28.9K	GitHub Forks -
Stacks 3.1K	Stacks 25
Followers 3.5K	Followers 156
Votes 140	Votes 1
Pros & Cons
Pros 61 Open-source 48 Fast and Flexible 8 One platform for every big data problem 8 Great for distributed SQL like applications 6 Easy to install and to use Cons 4 Speed	Pros 1 Lower total cost of pipeline ownership
Integrations
No integrations available	Google Cloud Storage Google BigQuery

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

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.

Apache Flink

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.

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 Spark vs Google Cloud Data Fusion: What are the differences?

Introduction

Architecture: Apache Spark is a powerful open-source data processing framework that provides a distributed computing environment for big data analytics. It operates on the concept of resilient distributed datasets (RDDs) and provides various high-level APIs for processing structured and unstructured data. On the other hand, Google Cloud Data Fusion is a fully managed data integration service that enables users to build, deploy, and manage data pipelines for batch and streaming data processing. It offers a graphical user interface (GUI) for designing and executing data pipelines without writing code, leveraging Google Cloud Platform's infrastructure.
Ease of Use: Apache Spark requires coding in languages like Scala, Java, Python, or R to develop data processing and analytics applications. It provides a rich set of APIs, libraries, and tooling, making it highly customizable but requiring programming skills. In contrast, Google Cloud Data Fusion focuses on a no-code or low-code approach, allowing users to visually design data pipelines using a drag-and-drop interface. This makes it more accessible to users without strong programming backgrounds and enables faster development and deployment of data integration workflows.
Scalability and Performance: Apache Spark is known for its ability to handle massive volumes of data with its distributed computing capabilities. It can scale horizontally across a cluster of machines, parallelizing data processing tasks efficiently. Spark also provides in-memory processing capabilities that significantly improve performance for iterative algorithms and real-time streaming applications. Google Cloud Data Fusion, being a managed service on Google Cloud Platform, also offers scalability and high-performance data processing. It leverages the underlying infrastructure and managed services provided by Google for seamless scalability and reliable performance.
Integration with Ecosystem: Apache Spark has a vibrant ecosystem with a wide range of libraries and connectors that can be leveraged for various tasks. It integrates well with other big data tools and technologies like Hadoop, Hive, HBase, Kafka, and more. This enables seamless data integration and interoperability with existing data infrastructure and workflows. On the other hand, Google Cloud Data Fusion is tightly integrated with Google Cloud Platform services. It provides pre-built connectors to various Google Cloud services like BigQuery, Cloud Storage, Cloud Pub/Sub, and more, simplifying data ingestion and integration with Google's ecosystem.
Managed vs. Self-managed: Apache Spark requires manual setup, configuration, and management of the underlying infrastructure and resources for running Spark clusters. Organizations need to provision and manage their own infrastructure or leverage cloud services to host and run Spark. In contrast, Google Cloud Data Fusion is a fully managed service provided by Google Cloud Platform. It abstracts away the complexity of infrastructure management, automates resource provisioning, and ensures high availability and reliability of data pipelines. This makes it easier for organizations to focus on their data integration and processing tasks rather than managing the underlying infrastructure.
Costs and Pricing: Apache Spark is an open-source framework, meaning it is free to use and deploy on your own infrastructure or cloud services. However, organizations need to consider the costs associated with maintaining and scaling their Spark clusters. On the other hand, Google Cloud Data Fusion follows a pay-as-you-go pricing model. Users are billed based on factors like the number of active data integration pipelines, the amount of data processed, and any additional services used within the Google Cloud Platform ecosystem. Organizations can choose the pricing plan that best suits their requirements and optimize costs accordingly.

Apache Spark vs Google Cloud Data Fusion

Overview

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