Azure Data Factory vs Hitachi Lumada Data Integration comparison

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Executive Summary

We performed a comparison between Azure Data Factory and Hitachi Lumada Data Integration based on real PeerSpot user reviews.

Find out in this report how the two Data Integration Tools solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI.
To learn more, read our detailed Azure Data Factory vs. Hitachi Lumada Data Integration Report (Updated: March 2023).
688,083 professionals have used our research since 2012.
Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"It's cloud-based, allowing multiple users to easily access the solution from the office or remote locations. I like that we can set up the security protocols for IP addresses, like allow lists. It's a pretty user-friendly product as well. The interface and build environment where you create pipelines are easy to use. It's straightforward to manage the digital transformation pipelines we build.""It is very modular. It works well. We've used Data Factory and then made calls to libraries outside of Data Factory to do things that it wasn't optimized to do, and it worked really well. It is obviously proprietary in regards to Microsoft created it, but it is pretty easy and direct to bring in outside capabilities into Data Factory.""I like the basic features like the data-based pipelines.""Data Factory's best feature is the ease of setting up pipelines for data and cloud integrations.""This solution has provided us with an easier, and more efficient way to carry out data migration tasks.""In terms of my personal experience, it works fine.""The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring.""The most important feature is that it can help you do the multi-threading concepts."

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"The way it has improved our product is by giving our users the ability to do ad hoc reports, which is very important to our users. We can do predictive analysis on trends coming in for contracts, which is what our product does. The product helps users decide which way to go based on the predictive analysis done by Pentaho. Pentaho is not doing predictions, but reporting on the predictions that our product is doing. This is a big part of our product.""The fact that it's a low-code solution is valuable. It's good for more junior people who may not be as experienced with programming.""Its drag-and-drop interface lets me and my team implement all the solutions that we need in our company very quickly. It's a very good tool for that.""It has a really friendly user interface, which is its main feature. The process of automating or combining SQL code with some databases and doing the automation is great and really convenient.""It's my understanding that the product can scale.""One of the valuable features is the ability to use PL/SQL statements inside the data transformations and jobs.""We use Lumada’s ability to develop and deploy data pipeline templates once and reuse them. This is very important. When the entire pipeline is automated, we do not have any issues in respect to deployment of code or with code working in one environment but not working in another environment. We have saved a lot of time and effort from that perspective because it is easy to build ETL pipelines.""The fact that it enables us to leverage metadata to automate data pipeline templates and reuse them is definitely one of the features that we like the best. The metadata injection is helpful because it reduces the need to create and maintain additional ETLs. If we didn't have that feature, we would have lots of duplicated ETLs that we would have to create and maintain. The data pipeline templates have definitely been helpful when looking at productivity and costs."

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Cons
"Data Factory could be improved in terms of data transformations by adding more metadata extractions.""DataStage is easier to learn than Data Factory because it's more visual. Data Factory has some drag-and-drop options, but it's not as intuitive as DataStage. It would be better if they added more drag-and-drop features. You can start using DataStage without knowing the code. You don't need to learn how the code works before using the solution.""Occasionally, there are problems within Microsoft itself that impacts the Data Factory and causes it to fail.""Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities. This is something that is just not fully covered. Additionally, the integration could improve for other tools, such as Azure Data Catalog.""I have not found any real shortcomings within the product.""We require Azure Data Factory to be able to connect to Google Analytics.""Lacks in-built streaming data processing.""Azure Data Factory should be cheaper to move data to a data center abroad for calamities in case of disasters."

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"The product needs more plugins.""The performance could be improved. If they could have analytics perform well on large volumes, that would be a big deal for our products.""I would like to see improvement when it comes to integrating structured data with text data or anything that is unstructured. Sometimes we get all kinds of different files that we need to integrate into the warehouse.""Some of the scheduling features about Lumada drive me buggy. The one issue that always drives me up the wall is when Daylight Savings Time changes. It doesn't take that into account elegantly. Every time it changes, I have to do something. It's not a big deal, but it's annoying.""I work with different databases. I would like to work with more connectors to new databases, e.g., DynamoDB and MariaDB, and new cloud solutions, e.g., AWS, Azure, and GCP. If they had these connectors, that would be great. They could improve by building new connectors. If you have native connections to different databases, then you can make instructions more efficient and in a more natural way. You don't have to write any scripts to use that connector.""I was not happy with the Pentaho Report Designer because of the way it was set up. There was a zone and, under it, another zone, and under that another one, and under that another one. There were a lot of levels and places inside the report, and it was a little bit complicated. You have to search all these different places using a mouse, clicking everywhere... each report is coded in a binary file... You cannot search with a text search tool...""If you're working with a larger data set, I'm not so sure it would be the best solution. The larger things got the slower it was.""The reporting definitely needs improvement. There are a lot of general, basic features that it doesn't have. A simple feature you would expect a reporting tool to have is the ability to search the repository for a report. It doesn't even have that capability. That's been a feature that we've been asking for since the beginning and it hasn't been implemented yet."

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Pricing and Cost Advice
  • "The licensing is a pay-as-you-go model, where you pay for what you consume."
  • "Our licensing fees are approximately 15,000 ($150 USD) per month."
  • "The licensing cost is included in the Synapse."
  • "It's not particularly expensive."
  • "Product is priced at the market standard."
  • "There's no licensing for Azure Data Factory, they have a consumption payment model. How often you are running the service and how long that service takes to run. The price can be approximately $500 to $1,000 per month but depends on the scaling."
  • "I don't see a cost; it appears to be included in general support."
  • "Pricing appears to be reasonable in my opinion."
  • More Azure Data Factory Pricing and Cost Advice →

  • "It does seem a bit expensive compared to the serverless product offering. Tools, such as Server Integration Services, are "almost" free with a database engine. It is comparable to products like Alteryx, which is also very expensive."
  • "I think Lumada's price is fair compared to some of the others, like BusinessObjects, which is was the other thing that I used at my previous job. BusinessObject's price was more reasonable before SAP acquired it. They jacked the price up significantly. Oracle's OBIEE tool was also prohibitively expensive."
  • "When we first started with it, it was much cheaper. It has gone up drastically, especially since Hitachi bought out Pentaho."
  • "The cost of these types of solutions are expensive. So, we really appreciate what we get for our money. Though, we don't think of the solution as a top-of-the-line solution or anything like that."
  • "The pricing has been pretty good. I'm used to using everything open-source or freeware-based. I understand that organizations need to make sure that the solutions are secure, and that's basically where I hit a roadblock in my current organization. They needed to ensure that we had a license and we had a secure way of accessing it so that no outside parties could get access to our data, but in terms of pricing, considering how much other teams are spending on cloud solutions or even their existing solutions, its price point is pretty good. At this time, there are no additional costs. We just have the licensing fees."
  • "There was a cost analysis done and Pentaho did favorably in terms of cost."
  • "If a company is looking for an ETL solution and wants to integrate it with their tech stack but doesn't want to spend a bunch of money, Pentaho is a good solution"
  • "You need to go through the paid version to have Hitachi Lumada specialized support. However, if you are using the free version, then you will have only the community support. You will depend on the releases from Hitachi to solve some problem or questions that you have, such as bug fixes. You will need to wait for the newest versions or releases to solve these types of problems."
  • More Hitachi Lumada Data Integration Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:AWS Glue and Azure Data factory for ELT best performance cloud services.
    Top Answer:Azure Data Factory is flexible, modular, and works well. In terms of cost, it is not too pricey. It offers the stability and reliability I am looking for, good scalability, and is easy to set up and… more »
    Top Answer:Azure Data Factory is a solid product offering many transformation functions; It has pre-load and post-load transformations, allowing users to apply transformations either in code by using Power… more »
    Top Answer:Hi Rajneesh, yes here is the feature comparison between the community and enterprise edition :… more »
    Top Answer: In my opinion, the reporting side of this tool needs serious improvements. In my previous company, we worked with Hitachi Lumada Data Integration and while it does a good job for what it’s worth, it… more »
    Top Answer:My company has used this product to transform data from databases, CSV files, and flat files. It really does a good job. We were most satisfied with the results in terms of how many people could use… more »
    Ranking
    1st
    Views
    40,005
    Comparisons
    32,089
    Reviews
    48
    Average Words per Review
    502
    Rating
    8.0
    6th
    Views
    6,004
    Comparisons
    3,376
    Reviews
    24
    Average Words per Review
    1,319
    Rating
    7.8
    Comparisons
    Also Known As
    Kettle, Pentaho Data Integration
    Learn More
    Overview

    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.

    Hitachi Lumada Data Integration is a top-raking data integration tool that aims to deliver accurate data from various sources to end users. This is a complete data integration platform that utilizes visual tools in the delivery of analytics-ready data. The product eliminates coding and complexity to ensure equal accessibility of its services to IT users as well as businesses that do not specialize in the field.

    The solution offers powerful data integration, which is achieved through:

    • Accelerated data onboarding
    • Flexible data self-service
    • Robust data flow orchestration

    Users of Hitachi Lumada Data Integration can collaborate to build, deploy, and monitor dataflows in order to streamline data delivery. The visual tools of the product reduce the time of operation and lower complexity, allowing even beginners to operate the platform seamlessly. The onboarding process is initiated through broad connectivity to a wide variety of data sources and applications.

    A drag-and-drop interface allows users to easily create data pipelines and ready-made templates to execute edge to cloud. The product provides users with the opportunity to blend data on premises or using cloud services, including Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP). The tool allows for a seamless switch between the native engine and Apache Spark, and operationalizes Python, Scala, and Weka machine-learning models.

    The tool offers features for extensive business analytics through:

    • Ad-hoc analysis
    • Flexible interface
    • Enterprise reporting

    Hitachi Lumada Data Integration offers its clients modern data architectures for data analytics. Through interactive visualizations and easy integration, users are able to increase data integrity for their organizations. The product offers a web-based drag-and-drop dashboard for a flexible experience, collaboration with other applications, and advanced multi tenancy. There is special enterprise reporting which consists of operational self-serving reporting, security with content permissions, and additional high-level protection, achieved through locking, and expirations.

    Hitachi Lumada Data Integration Features

    The tool offers its clients various features which can be used to achieve efficient data integration and further analysis. These features include:

    • Data access: The tool allows users to access data sources at the edge, core, and cloud. This reduces the time and complexity of the process while blending sources to deliver data in a format ready to be analyzed.

    • Machine learning: The solution offers a feature to orchestrate machine learning. R, Python, Scale, and Weka models are provided to users of this product.

    • Enterprise reporting: Hitachi Lumada Data Integration provides its clients with detailed visualized reporting. This feature is highly secured, which provides additional protection for clients' data.

    • Connect and move: This feature offers users the option to connect to sources on premises or in the cloud and move data of any size and format.

    • Flexibility: The product allows users broad connectivity and flexibility with no vendor lock-in to on-premise or cloud services.

    • Cluster to container: The tool offers the option to create scalable pipelines with Kubernetes clusters. This is possible across multiple clouds.

    • Dataflow studio: This feature allows users to build and manage data pipelines, view run metrics, analyze activities, and resume paused ones.

    • Authoring: The tool has an editor feature, which allows for the transformation of activities while the dataflow is in progress.

    Hitachi Lumada Data Integration Benefits

    The tool offers increased work productivity through efficient data integration. A number of the benefits include:

    • Ability to increase productivity in the work process due to effective automation.

    • Production deployment time can be sped up while saving costs for the company.

    • The no-code functionality improves pipeline quality in comparison to hand-coding data.

    • The tool offers high-quality reports which reduce implementation time.

    • Employees can save time and resources by manually embedding reporting and applications through this solution.

    • The utilization of this tool can increase business user adoption by improving data accuracy.

    Reviews from Real Users

    Philip R., a senior engineer at a comms service provider, says this product "Saves time and makes it easy for our mixed-skilled team to support the product".

    Ryan F., a senior data engineer at Burgiss, appreciates Hitachi Lumada Data Integration because low-code makes development faster than with Python.

    Offer
    Learn more about Azure Data Factory
    Learn more about Hitachi Lumada Data Integration
    Sample Customers
    Milliman, Pier 1 Imports, Rockwell Automation, Ziosk, Real Madrid
    66Controls, Providential Revenue Agency of Ro Negro, NOAA Information Systems, Swiss Real Estate Institute
    Top Industries
    REVIEWERS
    Computer Software Company35%
    Insurance Company8%
    Manufacturing Company8%
    Financial Services Firm8%
    VISITORS READING REVIEWS
    Computer Software Company17%
    Financial Services Firm12%
    Government7%
    Energy/Utilities Company6%
    REVIEWERS
    Healthcare Company19%
    Financial Services Firm19%
    Comms Service Provider11%
    Manufacturing Company11%
    VISITORS READING REVIEWS
    Computer Software Company17%
    Comms Service Provider13%
    Financial Services Firm13%
    Government7%
    Company Size
    REVIEWERS
    Small Business28%
    Midsize Enterprise20%
    Large Enterprise53%
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise13%
    Large Enterprise70%
    REVIEWERS
    Small Business27%
    Midsize Enterprise31%
    Large Enterprise42%
    VISITORS READING REVIEWS
    Small Business24%
    Midsize Enterprise10%
    Large Enterprise65%
    Buyer's Guide
    Azure Data Factory vs. Hitachi Lumada Data Integration
    March 2023
    Find out what your peers are saying about Azure Data Factory vs. Hitachi Lumada Data Integration and other solutions. Updated: March 2023.
    688,083 professionals have used our research since 2012.

    Azure Data Factory is ranked 1st in Data Integration Tools with 47 reviews while Hitachi Lumada Data Integration is ranked 6th in Data Integration Tools with 24 reviews. Azure Data Factory is rated 8.0, while Hitachi Lumada Data Integration is rated 7.8. The top reviewer of Azure Data Factory writes "The good, the bad and the lots of ugly". On the other hand, the top reviewer of Hitachi Lumada Data Integration writes "Saves time and makes it easy for our mixed-skilled team to support the product, but more guidance and better error messages are required in the UI". Azure Data Factory is most compared with Informatica PowerCenter, Microsoft Azure Synapse Analytics, Informatica Cloud Data Integration, Alteryx Designer and Snowflake, whereas Hitachi Lumada Data Integration is most compared with SSIS, Talend Open Studio, Informatica Enterprise Data Catalog, Oracle Data Integrator (ODI) and AWS Glue. See our Azure Data Factory vs. Hitachi Lumada Data Integration report.

    See our list of best Data Integration Tools vendors.

    We monitor all Data Integration Tools reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.