Azure Data Factory vs IBM InfoSphere DataStage comparison

Azure Data Factory is a managed cloud service built for extract-transform-load (ETL), extract-load-transform (ELT), and data integration projects. This is a digital integration tool as well as a cloud data warehouse that allows users to create, schedule, and manage data in the cloud or on premises. The use cases of the product include data engineering, operational data integration, analytics, ingesting data into data warehouses, and migrating on-premise SQL Server Integration Services (SSIS) packages to Azure.

The tool allows users to create data-driven workflows for initiating data movement and data transformation at scale. Data can be ingested from disparate data stores via pipelines. Companies can utilize this product to build complex ETL processes for transforming data visually with data flows. Azure Data Factory also offers services such as Azure HDInsight Hadoop, Azure Databricks, Azure Synapse Analytics, and Azure SQL Database. These services are created to facilitate data management and control for organizations, providing them with better visibility of their data for improved decision-making.

Azure Data Factory allows companies to create schedules for moving and transforming data into their pipelines. This can be done hourly, daily, weekly, or according to the specific needs of the organization. The steps through which the data-driven workflows work in Azure Data Factory are the following:

1. Connecting to required sources and collecting data. After connecting to the various sources where data is stored, the pipelines move the data to a centralized location for further processing.

2. Transforming and enriching the data. Once the data is moved to a centralized data store in the cloud, the pipelines transform it through services like HDInsight Hadoop, Azure Data Lake Analytics, Spark, and Machine Learning.

3. Delivering the transformed data to on-premise sources or keeping it in cloud storage sources for usage by different tools and applications.

Azure Data Factory Concepts

The solution consists of a series of interconnected systems that provide data integration and related services for users. The following concepts create the end product for users:

• Pipelines: A pipeline refers to the logical grouping of activities that performs a unit of work which together perform a task.
  
  
• Mapping data flows: Azure Data Factory lets its users create and manage graphs of data transformation logic for transforming any-sized data. The logic is executed on a Spark cluster, which does not have to be managed or maintained personally by the user.
  
  
• Linked services: The linked services in the tool define the connection to the data source. There are various services used for two main purposes - to represent a data store that the solution supports and to represent a compute resource that can host the execution of an activity.
  
  
• Integration runtime: The integration runtime in the tool provides the bridge between the activity and linked services needed for it.
  
  
• Triggers: There are various types of triggers in the solution, created for different types of events. They determine when a pipeline execution should be initiated.
  
  
• Pipeline runs: Pipeline runs are instantiated by passing the arguments to the parameters that are defined in pipelines, executing the pipelines' work.
  
  
• Control flow: Control flow in Azure Data Factory is an orchestration of pipeline activities.
  
  
• Connect and collect: This serves as the first step of the services that this tool offers. It connects all the required sources of data and processing in order to prepare the data for moving it to a centralized location for further processing. The step eliminates the need for companies to integrate expensive custom solutions for data movement. Through Copy Activity, Azure Blob storage, and Azure HDInsight Hadoop cluster, users can quickly initiate the first step of organizing their data.
  
  
• Transform and enrich: The collected data can be processed or transformed by using the mapping data flows of the product. Data transformation graphs can be executed on Spark without the need to understand its clusters or how programming works.
  
  
• CI/CD and publish: Through Azure DevOps and GitHub clients, the tool can receive full support for CI/CD for their data pipelines, which allows for the development and delivery of ETL processes before publishing the finished product.
  
  
• Monitor: When users have successfully built and deployed their data integration pipelines, the service offers them the option to monitor the scheduled activities and pipelines. This is done through Azure Monitor, API, PowerShell, and health panels on the Azure portal.

Azure Data Factory Benefits

Azure Data Factory offers clients many several benefits. Some of these include:

• An easy-to-use platform which is suitable for both beginner and expert users, as it offers code-free processes and built-in support.
  
  
• Pay-as-you-go option for clients to pay only for the services that they are using.
  
  
• Powerful tool with more than 90 built-in connectors, which allow companies to ingest on-premise and software as service (SaaS) data quickly.
  
  
• Provided autonomous ETL, which unlocks operational efficiencies and citizen integrators.
  
  
• The tool is designed to handle large volumes of data and provide users with better scalability and performance than classic ETL systems.
  
  
• Azure Data Factory allows users to easily migrate ETL workloads to the solution’s cloud.
  
  
• The solution offers great security for its users, as it provides the option for assigning specific permissions and roles within the organization.
  
  
• Azure Data Factory is highly automated, which allows users to orchestrate their data more efficiently.
  
  
• The platform is a combination of GUI and scripting-based interfaces, which gives users more freedom over data management.
  
  
• The tool provides organizations with the option to rely on Microsoft to fully manage the process. This eliminates the potential need of hiring a third-party expert.

Reviews from Real Users

According to Dan M., a Chief Strategist & CTO at a consultancy, Azure Data Factory is secure and reasonably priced.

A Senior Manager at a tech services company evaluates the tool as reasonably priced, scales well, good performance.

IBM InfoSphere DataStage is a high-quality data integration tool that aims to design, develop, and run jobs that move and transform data for organizations of different sizes. The product works by integrating data across multiple systems through a high-performance parallel framework. It supports extended metadata management, enterprise connectivity, and integration of all types of data.

The solution is the data integration component of IBM InfoSphere Information Server, providing a graphical framework for moving data from source systems to target systems. IBM InfoSphere DataStage can deliver data to data warehouses, data marts, operational data sources, and other enterprise applications. The tool works with various types of patterns - extract, transform and load (ETL), and extract, load, and transform (ELT). The scalability of the platform is achieved by using parallel processing and enterprise connectivity.

The solution has various versions, catering to different types of companies, which include the Server Edition, the Enterprise Edition, and the MVS Edition. Depending on which version a company has bought, different goals can be achieved. They include the following:

• Designing data flows to extract information from multiple sources, transform the data, and deliver it to target databases or applications.
  
  
• Delivery of relevant and accurate data through direct connections to enterprise applications.
  
  
• Reduction of development time and improvement of consistency through prebuilt functions.
  
  
• Utilization of InfoSphere Information Server tools for accelerating the project delivery cycle.

IBM InfoSphere DataStage can be deployed in various ways, including:

• As a service: The tool can be accessed from a subscription model, where its capabilities are a part of IBM DataStage on IBM Cloud Park for Data as a Service. This option offers full management on IBM Cloud.
  
  
• On premises or in any cloud: The two editions - IBM DataStage Enterprise and IBM DataStage Enterprise Plus - can run workloads on premises or in any cloud when added to IBM DataStage on IBM Cloud Pak for Data as a Service.
  
  
• On premises: The basic jobs of the tool can be run on premises using IBM DataStage.

IBM InfoSphere DataStage Features

The tool has various features through which users can integrate and utilize their data effectively. The components of IBM InfoSphere DataStage include:

• AI services: The tool offers services such as data science, event messaging, data warehousing, and data virtualization. It accelerates processes through artificial intelligence (AI) and offers a connection with IBM Cloud Paks - the cloud-native insight platform of the solution.
  
  
• Parallel engine: Through this feature, ETL performance can be optimized to process data at scale. This is achieved through parallel engine and load balancing, which maximizes throughput.
  
  
• Metadata support: This feature of the product uses the IBM Watson Knowledge Catalog to protect companies' sensitive data and monitor who can access it and at what levels.
  
  
• Automated delivery pipelines: IBM InfoSphere DataStage reduces costs by automating continuous integration and delivery of pipelines.
  
  
• Prebuilt connectors: The feature for prebuilt connectivity and stages allows users to move data between multiple cloud sources and data warehouses, including IBM native products.
  
  
• IBM DataStage Flow Designer: This feature offers assistance through machine learning design. The product offers its clients a user-friendly interface which facilitates the work process.
  
  
• IBM InfoSphere QualityStage: The tool provides a feature that automatically resolves data quality issues and increases the reliability of the delivered data.
  
  
• Automated failure detection: Through this feature, companies can reduce infrastructure management efforts, relying on the automated detection that the tool offers.
  
  
• Distributed data processing: Cloud runtimes can be executed remotely through this feature while maintaining its sovereignty and decreasing costs.

IBM InfoSphere DataStage Benefits

This solution offers many benefits for the companies that utilize it for data integration. Some of these benefits include:

• Increased speed of workload execution due to better balancing and a parallel engine.
  
  
• Reduction of data movement costs through integrations and seamless design of jobs.
  
  
• Modernization of data integration by extending the capabilities of companies' data.
  
  
• Delivery of reliable data through IBM Cloud Pak for Data.
  
  
• Utilization of a drag-and-drop interface which assists in the delivery of data without the need for code.
  
  
• Effective data manipulation allows data to be merged before being mapped and transformed.
  
  
• Creating easier access of users to their data by providing visual maps of the process and the delivered data.

Reviews from Real Users

A data/solution architect at a computer software company says the product is robust, easy to use, has a simple error logging mechanism, and works very well for huge volumes of data.

Tirthankar Roy Chowdhury, team leader at Tata Consultancy Services, feels the tool is user-friendly with a lot of functionalities, and doesn't require much coding because of its drag-and-drop features.