Azure Data Factory vs SnapLogic

Overview

SnapLogic

Stacks11

Followers18

Votes0

Azure Data Factory

Stacks254

Followers484

Votes0

GitHub Stars516

Forks610

Azure Data Factory vs SnapLogic: What are the differences?

Introduction

Azure Data Factory and SnapLogic are both popular integration platforms that offer data integration and data pipeline capabilities. However, there are key differences between the two that set them apart. In this article, we will explore these differences and understand the unique features and capabilities of each platform.

Orchestration and Workflow: Azure Data Factory provides a comprehensive orchestration and workflow engine that allows users to easily define and manage complex data integration workflows. It offers a graphical interface for building and orchestrating data pipelines, providing a visual representation of data transformations and data flow paths. On the other hand, SnapLogic also offers workflow capabilities, but it takes a more code-less approach where users can configure workflows using a drag-and-drop interface.
Connectivity and Data Source Support: Azure Data Factory offers rich connectivity options and supports a wide range of data sources, including various databases, data lakes, and cloud-based services. It provides built-in connectors for popular data sources such as Azure SQL Database, Azure Blob Storage, and more. SnapLogic also provides connectivity to various data sources, but it offers a larger number of pre-built connectors for different applications and platforms, making it easier to integrate with specific systems.
Transformation and Data Manipulation: Azure Data Factory includes capabilities for data transformation and manipulation, allowing users to perform tasks such as data cleansing, data conversion, and data enrichment. It provides a set of built-in data transformation activities that can be used within data pipelines. On the other hand, SnapLogic offers a more extensive set of pre-built data transformation functions and operators, enabling users to perform complex data transformations without writing custom code.
Real-time and Event-driven Integration: Azure Data Factory provides support for real-time and event-driven integration through its Event Grid integration. Users can trigger data pipelines based on events and perform real-time data ingestion, processing, and delivery. SnapLogic, on the other hand, also offers real-time integration capabilities but takes a more event-driven approach, allowing users to define event-based triggers and actions through its event-based architecture.
Data Governance and Security: Azure Data Factory provides robust security features and integrates with Azure Active Directory for authentication and authorization. It also offers advanced data governance capabilities, including data masking, encryption, and data classification. SnapLogic also provides security features such as user authentication and role-based access control. However, it may require additional configuration and customizations to achieve advanced data governance requirements.
Cost and Pricing Model: Azure Data Factory pricing is based on a consumption-based model, where users pay for the resources consumed during data integration activities. It offers different pricing tiers based on the scale and complexity of data integration needs. SnapLogic, on the other hand, follows a subscription-based pricing model. The cost is based on the number of users and the desired level of support, which can be more suitable for organizations with specific budget requirements.

In Summary, Azure Data Factory provides comprehensive orchestration capabilities, rich data source support, and advanced data governance features. SnapLogic offers a code-less workflow approach, extensive data transformation functions, and a flexible pricing model. Organizations can choose the platform that best aligns with their integration requirements, skillsets, and budget.

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 SnapLogic, Azure Data Factory

Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceon

PySpark

Azure Data Factory

Databricks

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?

269k views269k

Comments

Detailed Comparison

SnapLogic	Azure Data Factory
It provides data and application integration tools for connecting Cloud data sources, SaaS applications and on-premise business applications.	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.
Customer Experience/CRM; Cloud Data Warehousing; Finance & Accounting; Human Capital Management; Big Data	Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing
Statistics
GitHub Stars -	GitHub Stars 516
GitHub Forks -	GitHub Forks 610
Stacks 11	Stacks 254
Followers 18	Followers 484
Votes 0	Votes 0
Integrations
Microsoft Dynamics 365 Eloqua Oracle Tableau Snowflake Amazon Redshift	Octotree Java .NET

What are some alternatives to SnapLogic, Azure Data Factory?

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.

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.

Apache Camel

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

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.

Related Comparisons

Azure Data Factory vs SnapLogic: What are the differences?

Introduction

Orchestration and Workflow: Azure Data Factory provides a comprehensive orchestration and workflow engine that allows users to easily define and manage complex data integration workflows. It offers a graphical interface for building and orchestrating data pipelines, providing a visual representation of data transformations and data flow paths. On the other hand, SnapLogic also offers workflow capabilities, but it takes a more code-less approach where users can configure workflows using a drag-and-drop interface.
Connectivity and Data Source Support: Azure Data Factory offers rich connectivity options and supports a wide range of data sources, including various databases, data lakes, and cloud-based services. It provides built-in connectors for popular data sources such as Azure SQL Database, Azure Blob Storage, and more. SnapLogic also provides connectivity to various data sources, but it offers a larger number of pre-built connectors for different applications and platforms, making it easier to integrate with specific systems.
Transformation and Data Manipulation: Azure Data Factory includes capabilities for data transformation and manipulation, allowing users to perform tasks such as data cleansing, data conversion, and data enrichment. It provides a set of built-in data transformation activities that can be used within data pipelines. On the other hand, SnapLogic offers a more extensive set of pre-built data transformation functions and operators, enabling users to perform complex data transformations without writing custom code.
Real-time and Event-driven Integration: Azure Data Factory provides support for real-time and event-driven integration through its Event Grid integration. Users can trigger data pipelines based on events and perform real-time data ingestion, processing, and delivery. SnapLogic, on the other hand, also offers real-time integration capabilities but takes a more event-driven approach, allowing users to define event-based triggers and actions through its event-based architecture.
Data Governance and Security: Azure Data Factory provides robust security features and integrates with Azure Active Directory for authentication and authorization. It also offers advanced data governance capabilities, including data masking, encryption, and data classification. SnapLogic also provides security features such as user authentication and role-based access control. However, it may require additional configuration and customizations to achieve advanced data governance requirements.
Cost and Pricing Model: Azure Data Factory pricing is based on a consumption-based model, where users pay for the resources consumed during data integration activities. It offers different pricing tiers based on the scale and complexity of data integration needs. SnapLogic, on the other hand, follows a subscription-based pricing model. The cost is based on the number of users and the desired level of support, which can be more suitable for organizations with specific budget requirements.

Azure Data Factory vs SnapLogic

Overview

Azure Data Factory vs SnapLogic: What are the differences?