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  5. Azure Data Factory vs Google Cloud Data Fusion

Azure Data Factory vs Google Cloud Data Fusion

OverviewDecisionsComparisonAlternatives

Overview

Azure Data Factory
Azure Data Factory
Stacks253
Followers484
Votes0
GitHub Stars516
Forks610
Google Cloud Data Fusion
Google Cloud Data Fusion
Stacks25
Followers156
Votes1

Azure Data Factory vs Google Cloud Data Fusion: What are the differences?

Azure Data Factory and Google Cloud Data Fusion are two popular cloud-based data integration services that provide capabilities for orchestrating and managing data workflows. Let's explore the key differences between them.

  1. Scalability: Azure Data Factory leverages the power of Azure's cloud infrastructure, allowing users to scale up or down based on their needs. It provides flexible options for data movement and transformation, enabling seamless integration with various data sources and destinations. On the other hand, Google Cloud Data Fusion offers built-in scalability and can handle large volumes of data with ease, thanks to Google's massive infrastructure. It offers a no-code visual interface for ETL (Extract, Transform, Load) processes, making it accessible to non-technical users.

  2. Integration with Native Services: Azure Data Factory is tightly integrated with other Azure services like Azure Databricks, Azure Synapse Analytics, and Azure Machine Learning. This integration allows users to build end-to-end data pipelines that span across various Azure services. Google Cloud Data Fusion, on the other hand, is designed to seamlessly integrate with Google Cloud Platform services such as BigQuery, Pub/Sub, and Dataproc. This integration enables users to take full advantage of Google Cloud's ecosystem for data processing and analytics.

  3. Ease of Use: Azure Data Factory provides a visual interface for designing data pipelines using a drag-and-drop approach. It also offers advanced data transformation capabilities through its mapping data flows feature. Google Cloud Data Fusion provides a code-free environment for designing and deploying data pipelines. It offers a wide range of connectors and transformations that can be easily configured through a visual interface, making it easy for users to build and manage complex data workflows.

  4. Pricing Model: Azure Data Factory follows a consumption-based pricing model, where users pay for the resources they consume, such as data movement, data transformation, and pipeline execution. The pricing is based on factors like the number of pipeline runs, data movement volume, and data transformation complexity. Google Cloud Data Fusion, on the other hand, follows a fixed pricing model based on the size and complexity of the data pipelines. Users are billed based on the number of pipeline nodes, data movement volume, and the usage of additional features like data transformation.

  5. Monitoring and Management: Azure Data Factory provides a rich set of monitoring and management features, including pipeline monitoring, alerts, and automatic retries. It integrates with Azure Monitor and Azure Log Analytics for collecting and analyzing pipeline metrics and logs. Google Cloud Data Fusion offers built-in monitoring capabilities that provide real-time insights into data pipelines, including metrics on data ingestion, transformation, and output. It also integrates with Google Cloud's monitoring and logging services for centralized management and monitoring.

  6. Ecosystem and Third-Party Integrations: Azure Data Factory benefits from being part of the wider Azure ecosystem, which includes a vast array of services and tools for data analytics, AI, and machine learning. It integrates seamlessly with services like Azure Data Lake Storage, Azure SQL Database, and Power BI. Google Cloud Data Fusion has a growing ecosystem of third-party connectors and integrations, allowing users to connect to various data sources and destinations. Additionally, it integrates with popular Google Cloud services like AutoML, Cloud Pub/Sub, and Bigtable.

In summary, Azure Data Factory provides strong integration with the Azure ecosystem and offers advanced data transformation capabilities, while Google Cloud Data Fusion excels in scalability and ease of use, with a focus on native integration with Google Cloud Platform services.

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Advice on Azure Data Factory, Google Cloud Data Fusion

Vamshi
Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceonPySparkPySparkAzure Data FactoryAzure Data FactoryDatabricksDatabricks

I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?

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Comments

Detailed Comparison

Azure Data Factory
Azure Data Factory
Google Cloud Data Fusion
Google Cloud Data Fusion

It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.

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.

Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing
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
516
GitHub Stars
-
GitHub Forks
610
GitHub Forks
-
Stacks
253
Stacks
25
Followers
484
Followers
156
Votes
0
Votes
1
Pros & Cons
No community feedback yet
Pros
  • 1
    Lower total cost of pipeline ownership
Integrations
Octotree
Octotree
Java
Java
.NET
.NET
Google Cloud Storage
Google Cloud Storage
Google BigQuery
Google BigQuery

What are some alternatives to Azure Data Factory, Google Cloud Data Fusion?

Apache Spark

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

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

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

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

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

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

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.

Apache Camel

Apache Camel

An open source Java framework that focuses on making integration easier and more accessible to developers.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

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.

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