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  5. Azure Data Factory vs IBM App Connect

Azure Data Factory vs IBM App Connect

OverviewDecisionsComparisonAlternatives

Overview

Azure Data Factory
Azure Data Factory
Stacks253
Followers484
Votes0
GitHub Stars516
Forks610
IBM App Connect
IBM App Connect
Stacks6
Followers7
Votes0

Azure Data Factory vs IBM App Connect: What are the differences?

Introduction

When it comes to data integration and workflow orchestration in enterprises, Azure Data Factory and IBM App Connect are two prominent platforms that offer unique features and capabilities. Understanding the key differences between these two platforms is crucial for organizations looking to select the best tool for their data integration needs.

  1. Underlying Cloud Infrastructure: Azure Data Factory is a cloud-based data integration service that is fully managed by Microsoft Azure, providing seamless integration with other Azure services. On the other hand, IBM App Connect is part of IBM Cloud Integration platform, offering integration capabilities within IBM Cloud environment and with on-premises systems. The underlying cloud infrastructure of these platforms plays a significant role in terms of scalability, flexibility, and integration options.

  2. Workflow Orchestration Capabilities: Azure Data Factory is designed specifically for efficient data movement and transformation through a code-free visual interface, enabling users to set up complex data workflows with ease. In contrast, IBM App Connect caters to a broader range of integration scenarios, including application integration and business process automation, utilizing a wide array of pre-built connectors. The workflow orchestration capabilities differ in terms of the level of complexity and types of integrations supported.

  3. Pricing Model: Azure Data Factory follows a pay-as-you-go pricing model based on the number of activities and data volume processed, providing transparent cost estimation for users. On the contrary, IBM App Connect offers different pricing tiers based on the number of flows, monthly usage, and advanced features, which may require additional expenses. Understanding the pricing model of each platform is crucial for organizations to manage their integration costs effectively.

  4. Security and Compliance Features: Azure Data Factory integrates seamlessly with Azure Active Directory for secure user authentication and access control, ensuring data security and compliance with industry standards. IBM App Connect also emphasizes security with role-based access control and encryption capabilities, but the level of integration with external identity providers may vary. Assessing the security and compliance features of each platform is essential for organizations dealing with sensitive data and regulatory requirements.

  5. Ease of Integration with External Services: Azure Data Factory offers a wide range of connectors for integrating with various Azure services, third-party applications, and on-premises data sources, providing comprehensive connectivity options. IBM App Connect boasts a vast library of connectors for integrating with popular SaaS applications, databases, and enterprise systems, enhancing the interoperability of different systems. Evaluating the ease of integration with external services is crucial for organizations with diverse data sources and systems to connect.

  6. Customization and Extensibility Options: Azure Data Factory allows users to incorporate custom code and scripts using Azure Functions or Azure Databricks for advanced data processing and transformations, offering a high degree of customization. In comparison, IBM App Connect enables developers to create custom connectors and actions using APIs and templates, extending the platform's capabilities beyond the built-in features. The customization and extensibility options vary in terms of flexibility and developer control, depending on the platform's architecture and design principles.

In Summary, Azure Data Factory and IBM App Connect differ significantly in terms of underlying cloud infrastructure, workflow orchestration capabilities, pricing model, security features, ease of integration with external services, and customization options, making it essential for organizations to evaluate these differences when choosing a data integration platform.

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Advice on Azure Data Factory, IBM App Connect

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
IBM App Connect
IBM App Connect

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.

The powerful all-in-one tool for easily connecting apps, integrating data, building APIs and acting on events.

Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing
Integrate apps and data; Build APIs; Act on events
Statistics
GitHub Stars
516
GitHub Stars
-
GitHub Forks
610
GitHub Forks
-
Stacks
253
Stacks
6
Followers
484
Followers
7
Votes
0
Votes
0
Integrations
Octotree
Octotree
Java
Java
.NET
.NET
No integrations available

What are some alternatives to Azure Data Factory, IBM App Connect?

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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